Name: procedure for adaptive laboratory evolution of microorganisms using a chemostat
Uploaded: 2016-09-20T08:00:00.000+00:00
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Description: Watch this Scientific Journal Video about Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat at JoVE.com

<p class='jove_content'>This study was financially supported by the Korean Ministry of Science, ICT and Future Planning (Intelligent Synthetic Biology Center program 2012M3A6A8054887). P. Kim was supported by a fellowship from the Catholic University of Korea (2015).</p>

<p></p>
<p class="jove_title">1. Equipment Preparation</p>
<ol>
	<li>Obtain a chemostat jar (150-250 ml) or an Erlenmeyer flask (250 ml) containing an inlet port and an outlet port. Connect the ports with silicon tubing allowing for flow rates of 10-100 ml/hr. Optionally, use an air vent, an air outlet port, and temperature-controlled water inlet and outlet ports.</li>
	<li>Obtain a device suitable for the chemostat jar that provides for agitation and temperature control (or use a rotary shaking incubator).</li>
	<li>Obtain two peristaltic pumps in order to deliver fresh medium and collect the culture.</li>
	<li>Obtain a reservoir jar (10-20 L) containing a medium outlet port and an air inlet port.</li>
	<li>Obtain silicon tubing suitable for the dilution rate (<em>i.e.</em>, ID 0.8 mm, flow range 0.06-36 ml/min; L/S 13 tubing).</li>
</ol>
<p class="jove_title">2. Medium Preparation and Sterilization</p>
<ol>
	<li>Initial Medium
	<ol>
		<li>Dissolve 0.3 g glucose, 0.08 g NH<sub>4</sub>Cl, 0.05 g NaCl, 0.75 g Na<sub>2</sub>HPO<sub>4</sub>&#183;2H<sub>2</sub>O, and 0.3 g KH<sub>2</sub>PO<sub>4</sub> in 90 ml distilled water (D.W.) in a chemostat jar.</li>
		<li>Seal the chemostat jar along with the tubing using clamps. Do not seal the air vent.</li>
		<li>Sterilize the chemostat jar in an autoclave at 121 &#176;C for 15 min. After sterilization, store the chemostat jar at room temperature.</li>
		<li>Dissolve 0.02 g MgSO<sub>4</sub>&#183;7H<sub>2</sub>O, 0.01 g CaCl<sub>2</sub>, and 0.1 mg thiamine in 10 ml D.W. (solution A).</li>
		<li>Filter solution A using a syringe and a pre-sterilized syringe filter (a 0.45 &#181;m pore filter).</li>
		<li>Add the solution A filtrates to the chemostat jar.</li>
	</ol>
	</li>
	<li>Stress Medium
	<ol>
		<li>Dissolve 30 g glucose, 8 g NH<sub>4</sub>Cl, 5 g NaCl, 75 g Na<sub>2</sub>HPO<sub>4</sub>&#183;2H<sub>2</sub>O, 30 g KH<sub>2</sub>PO<sub>4</sub>, and 300 g disodium succinate hexahydrate (Na<sub>2</sub>&#183;succinate&#183;6H<sub>2</sub>O; the stressor used in this experiment) in 9.9 L D.W. in a reservoir jar.</li>
		<li>Seal the reservoir jar along with the tubing using clamps. Do not seal the air vent.</li>
		<li>Sterilize the reservoir jar in an autoclave at 121 &#176;C for 15 min. After sterilization, store the jar at room temperature.</li>
		<li>Dissolve 2 g MgSO<sub>4</sub>&#183;7H<sub>2</sub>O, 1 g CaCl<sub>2</sub>, and 10 mg thiamine in 100 ml D.W. (solution A).</li>
		<li>Filter solution A with a syringe and a pre-sterilized syringe filter (a 0.45 &#181;m pore filter).</li>
		<li>Add the solution A filtrates to the reservoir jar.</li>
		<li>Aseptically connect the sterilized silicon tubing to the reservoir jar and attach the peristaltic pumps.</li>
	</ol>
	</li>
	<li>High-stress Medium
	<ol>
		<li>Prepare the medium as in section 2.2, but with a greater concentration of stressor (<em>i.e.</em>, 3-5 g/L higher in the succinate adaptation).<br />
		Note: This protocol is for adaptation to a stress that can be delivered via the medium. In the case of physical stressors such as temperature, agitation, or illumination, the cultivation should be designed accordingly.</li>
	</ol>
	</li>
</ol>
<p class="jove_title">3. Initial Cultivation</p>
<ol>
	<li>Inoculate a single colony of wild-type <em>E. coli</em> in a 15 ml test tube containing 4 ml of initial medium.</li>
	<li>Incubate the test tube in a shaking incubator for 12 hr at 37 &#176;C and 220 rpm.</li>
	<li>Aseptically transfer 1 ml of preculture to the chemostat jar.</li>
	<li>Incubate the chemostat jar, providing for aeration (air 50 ml/min) and agitation (200 rpm), at 37 &#176;C for 6 hr.</li>
</ol>
<p class="jove_title">4. Stress Adaptation</p>
<ol>
	<li>Aseptically connect the end of the silicon tubing from the pumps to the chemostat jar.</li>
	<li>Start the outlet pump (10 ml/hr or higher) and collect culture.<br />
	Note: The culture should be in the exponential phase, typically 4-8 hr after initial cultivation.</li>
	<li>Check the optical density (600 nm) of the culture from the outlet tubing.</li>
	<li>Start the inlet pump (10 ml/hr, corresponding to a rate of dilution of 0.1 hr<sup>-1</sup>).</li>
	<li>Check the optical density of the culture at 600 nm from the outlet tubing every 24 hr.</li>
	<li>Operate the chemostat for 96 hr (9.6-fold turnover) or more. If the optical density is stable, exchange the reservoir containing the high-stress medium. If the optical density is lower than 0.2, stop the feeding inlet pump for 6 hr. Restart the inlet pump and check that the optical density is over 0.2.</li>
	<li>Gradually increase the concentration of the stressor by changing to a reservoir containing a higher stressor concentration.</li>
	<li>Take samples of the adapted culture whenever it reaches a milestone (<em>e.g.</em>, a strain adapted to 100 g/L succinate stress), and store for further genomic analysis.</li>
	<li>For sample storage, mix the culture sample (0.5 ml) with a sterilized 80% glycerol solution (0.5 ml) and store it at -80 &#176;C.<br />
	Note: If the microorganism acquires an ability to degrade the stressor during the ALE process, the stressor concentration in the fermentation jar is not the same as that in the fresh reservoir.</li>
</ol>
<p class="jove_title">5. Single-colony Isolation of the Stress-adapted Strain</p>
<ol>
	<li>Prepare agar plate medium (1.6% agar) containing the same stressor and at the same concentration of medium.</li>
	<li>Plate the outlet culture (0.1 ml) from the chemostat, and incubate at 37 &#176;C for 16 hr.</li>
	<li>Pick single colonies from the plate using a sterile toothpick and inoculate them in 15 ml test tubes containing the same stressor and at the same medium concentration as in the chemostat, and incubate for 6 hr.</li>
	<li>Transfer 1 ml of culture broth into a 250 ml Erlenmeyer flask containing 50 ml of medium. Harvest 0.5 ml of the culture broth every 1 hr, and measure the O.D. at 600 nm. Compare the growth rate of the adapted strain to that of the wild-type strain given the stressor.</li>
</ol>

<ol>
	<li>Rando, O. J., Verstrepen, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Timescales+of+genetic+and+epigenetic+inheritance.">Timescales of genetic and epigenetic inheritance.</a> <em style='font-style: italic'>Cell</em>. <b>128</b>, 655-668 (2007).</li><li>Kim, H. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Short-term+differential+adaptation+to+anaerobic+stress+via+genomic+mutations+by+Escherichia+coli+strains+K-12+and+B+lacking+alcohol+dehydrogenase.">Short-term differential adaptation to anaerobic stress via genomic mutations by Escherichia coli strains K-12 and B lacking alcohol dehydrogenase.</a> <em style='font-style: italic'>Front Microbiol</em>. <b>5</b>, 476 (2014).</li><li>Mendizabal, I., Keller, T. E., Zeng, J., Yi, S. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epigenetics+and+evolution.">Epigenetics and evolution.</a> <em style='font-style: italic'>Integr Comp Biol</em>. <b>54</b>, 31-42 (2014).</li><li>Lee, J. Y., Seo, J., Kim, E. S., Lee, H. S., Kim, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptive+evolution+of+Corynebacterium+glutamicum+resistant+to+oxidative+stress+and+its+global+gene+expression+profiling.">Adaptive evolution of Corynebacterium glutamicum resistant to oxidative stress and its global gene expression profiling.</a> <em style='font-style: italic'>Biotechnol Lett</em>. <b>35</b>, 709-717 (2013).</li><li>Lee, J. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Artificial+oxidative+stress-tolerant+Corynebacterium+glutamicum.">Artificial oxidative stress-tolerant Corynebacterium glutamicum.</a> <em style='font-style: italic'>AMB Express</em>. <b>4</b>, 15 (2014).</li><li>Narang, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+steady+states+of+microbial+growth+on+mixtures+of+substitutable+substrates+in+a+chemostat.">The steady states of microbial growth on mixtures of substitutable substrates in a chemostat.</a> <em style='font-style: italic'>J Theor Biol</em>. <b>190</b>, 241-261 (1998).</li><li>Kwon, Y. D., Kim, S., Lee, S. Y., Kim, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-term+continuous+adaptation+of+Escherichia+coli+to+high+succinate+stress+and+transcriptome+analysis+of+the+tolerant+strain.">Long-term continuous adaptation of Escherichia coli to high succinate stress and transcriptome analysis of the tolerant strain.</a> <em style='font-style: italic'>J Biosci Bioeng</em>. <b>111</b>, 26-30 (2011).</li><li>Barrick, J. E., Lenski, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+dynamics+during+experimental+evolution.">Genome dynamics during experimental evolution.</a> <em style='font-style: italic'>Nat Rev Genet</em>. <b>14</b>, 827-839 (2013).</li><li>Li, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Sequence+Alignment/Map+format+and+SAMtools.">The Sequence Alignment/Map format and SAMtools.</a> <em style='font-style: italic'>Bioinformatics</em>. <b>25</b>, 2078-2079 (2009).</li><li>McKenna, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Genome+Analysis+Toolkit:+a+MapReduce+framework+for+analyzing+next-generation+DNA+sequencing+data.">The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.</a> <em style='font-style: italic'>Genome Res</em>. <b>20</b>, 1297-1303 (2010).</li><li>Deatherage, D. E., Barrick, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+mutations+in+laboratory-evolved+microbes+from+next-generation+sequencing+data+using+breseq.">Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq.</a> <em style='font-style: italic'>Methods Mol Biol</em>. <b>1151</b>, 165-188 (2014).</li></ol>

<p class='jove_content'>The authors have nothing to disclose.</p>

<p class="jove_content">Microorganisms are capable of adapting to almost all environments because of their rapid growth rate and genetic diversity. Adaptive laboratory evolution enables microorganisms to evolve under designed conditions, which provides a way of selecting individual organisms harboring spontaneous mutations that are beneficial under the given conditions.</p>
<p class="jove_content">The chemostat technique is more robust for achieving artificially driven evolution than transfer techniques for the following reasons: (a) a steady environment&#160;&#8212; because transfer techniques are based on batch cultures either on solid or in liquid medium, the environment of the cells varies during batch culture while the environmental stress of the chemostat is steady; (b) a larger population and a continuous selection&#160;&#8212; the larger population of the chemostat provides more genetic diversity than the smaller population of transfer techniques. Continuous selection of the chemostat technique is a faster way to achieve evolution than intermittent selection of transfer techniques. It is critical that the chemostat culture not be contaminated by other microorganisms during the ALE procedure, and aseptic conditions are required throughout chemostat culture. The ALE procedure can be modified by changes to stressors, such as oxidative stress, osmotic stress, and toxic product stress. There are a few technical limitations to ALE by chemostat culture. Evolution takes place by chance; therefore, different researchers may not be able to obtain the same evolutionary result, although the consequences of mutations might be related. Another limitation is the wash-out of chemostat culture when the concentration of stressor is increased too quickly. If the biomass is decreased to a certain amount (<em>i.e.</em>, O.D. = 0.2 in this procedure) after the concentration of stressor is increased, administration of the stressor should be halted for at least a few generations to give the strain time to adapt.</p>
<p class="jove_content">Recent developments in genome sequencing technology have helped the understanding of mutations that develop in response to certain stressful conditions. For example, a DNA mutation alters a particular systemic effect that is beneficial to the given environment (<em>i.e.</em>, tolerance to a toxic stressor). Thus, ALE may be used to study gene function or network regulation, or as a raw material for &#39;accelerated evolution&#39;. ALE studies are also useful in simulating the development of antibiotic-resistant bacteria, indicating the mechanisms by which dangerous drug-resistant bacterial strains arise. As the trajectory of evolution is engraved on the genome, uncovering the genetic alterations that lead to the desired phenotypes (such as stress tolerance) is the next obstacle once evolved strains are obtained<sup>8</sup>. Among the currently available next-generation sequencing technologies, the Illumina and Ion Torrent platforms are suitable for the detection of nucleotide substitutions or small indels that occur in the evolved strains, and both are affordable. Because variant detection usually involves short-read mapping of the reference sequences, the availability of the genome sequence of the ancestral strain is crucial. Even if the genome sequence of the founder strain is available from public databases, it is often desirable to sequence it simultaneously with the evolved one, because there might be some additional differences in the actual starting strain. Protocols for mutation identification are easily available via the web or a literature search<sup>9,10</sup>. For example, BRESEQ is a specially-designed pipeline for genomic analysis of laboratory-evolved microbes using next-generation sequencing data<sup>11</sup>. Comparisons of the evolved strains at certain milestones (for example, in this representative data, wild type, DST50, DST100, and DST160) can provide the sequence of the evolved strains and give insight into the consequences of a given stressor. Therefore, knowledge and applications of ALE experiments, coupled with system biology and the identification of systemic perturbation variables, are expected in the near future.</p>

<p class="jove_content">Microorganisms can survive and adapt to diverse environments. Under severe stress, adaptation can occur via acquisition of beneficial phenotypes by random genomic mutations and subsequent positive selection<sup>1-3</sup>. Therefore, microbial cells can adapt by changing metabolic or regulatory networks for optimal growth, which is termed &#34;adaptive evolution&#34;. Recent important microbial tendencies, such as outbreaks of superbugs and the occurrence of robust microbial strains, are very closely related to adaptive evolution under stressful conditions. Under defined laboratory conditions, we are able to study the mechanisms of molecular evolution and even control the direction of microbial evolution for various applications. Unlike multicellular organisms, single-celled organisms are well suited to adaptive laboratory evolution (ALE) for the following reasons: they regenerate quickly, they maintain large populations, and it is easy to create and maintain homogeneous environments. Combined with recent advances in DNA sequencing techniques and high-throughput technologies, ALE allows for the direct observation of genomic changes that lead to systemic regulatory changes. Mutational dynamics and a diversity of the population are also observable. Genetic engineering strategies can be determined from the analysis of ALE strains<sup>4,5</sup>.</p>
<p class="jove_content">Chemostat culture is a method used to obtain steady-state cells and increase productivity in fermentation processes<sup>6</sup>. Fresh medium is added and culture broth is harvested during the process (the latter includes medium and biomass). Long-term chemostat culture, however, changes the steady-state productivity of the culture and brings about the accumulation of spontaneous mutations and selection during culture (<strong>Figure 1a</strong>). Under various selection pressures (stressors), the accumulation of mutations is enhanced. A gradual increase of stress in a long-term chemostat provides for a continuous selection of mutations that work against the given stressors, such as temperature, pH, osmotic pressure, nutrient starvation, oxidation, toxic end products, <em>etc.</em> Colony transfer from a solid medium and serial transfer from a liquid medium (repeated batch culture) also allow researchers to obtain evolved microorganisms (<strong>Figure 1b </strong>and<strong> 1c</strong>). Although chemostat culture requires complicated methods, the pool of diversity (number of replications and population size) is higher than that obtained by colony transfer and serial transfer techniques. The stable stress exposure to individual cells and decreased variation in the cellular state during chemostat culture (steady state) are other benefits of ALE compared to batch culture-based techniques. Stress-induced ALE of <em>Escherichia coli</em> subjected to high succinate conditions is introduced in this article.</p>
<p class="jove_content"><img alt="Figure 1" src="/files/ftp_upload/54446/54446fig1.jpg" /><br />
<strong>Figure 1: Methods of adaptive laboratory evolution.</strong> (<strong>A</strong>) Chemostat; (<strong>B</strong>) serial transfer; (<strong>C</strong>) colony transfer. The top figures illustrate the concept of the methods for ALE, and the bottom figures illustrate the number of cells that grew during ALE. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54446/54446fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>

<table><tbody><tr><td>Mini-chemostat fermentor</td><td>Biotron Inc.</td><td>-</td><td>manufactured by special order</td></tr><tr><td>silicon tubing</td><td>Cole-Parmer</td><td>Masterflex L/S 13</td><td>tubing size can be varied depending on the dilution rate and the size of fermentor jar.</td></tr><tr><td>reservoir jar</td><td>Bellco</td><td>Media storage bottle</td><td>20 L</td></tr><tr><td>chemicals</td><td>Sigma-Aldrich</td><td>-</td><td>reagent grade</td></tr><tr><td>glucose</td><td>Sigma-Aldrich</td><td>G5767</td><td>ACS reagent</td></tr><tr><td>NH&lt;sub&gt;4&lt;/sub&gt;Cl</td><td>Sigma-Aldrich</td><td>A9434</td><td>for molecular biology, suitable for cell culture, &amp;ge;99.5%</td></tr><tr><td>NaCl</td><td>Sigma-Aldrich</td><td>746398</td><td>ACS reagent, &amp;ge;99%</td></tr><tr><td>Na&lt;sub&gt;2&lt;/sub&gt;HPO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;2H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Sigma-Aldrich</td><td>4272</td><td>98.5-101%</td></tr><tr><td>KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma-Aldrich</td><td>795488</td><td>ACS reagent, &amp;ge;99%</td></tr><tr><td>MgSO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;7H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Sigma-Aldrich</td><td>230391</td><td>ACS reagent, &amp;ge;98%</td></tr><tr><td>CaCl&lt;sub&gt;2&lt;/sub&gt;</td><td>Sigma-Aldrich</td><td>793639</td><td>ACS reagent, &amp;ge;96%</td></tr><tr><td>thiamine&amp;middot;HCl</td><td>Sigma-Aldrich</td><td>T4625</td><td>reagent grade, &amp;ge;99%</td></tr><tr><td>Na&lt;sub&gt;2&lt;/sub&gt;&amp;middot;succinate&amp;middot;6H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Sigma-Aldrich</td><td>S2378</td><td>ReagentPlus, &amp;ge;99%</td></tr></tbody></table>

procedure for adaptive laboratory evolution of microorganisms using a chemostat

<p class='jove_content'>Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) refers to the experimental situation in which evolution is observed using living organisms under controlled conditions and stressors; organisms are thereby artificially forced to make evolutionary changes. Microorganisms are subject to a variety of stressors in the environment and are capable of regulating certain stress-inducible proteins to increase their chances of survival. Naturally occurring spontaneous mutations bring about changes in a microorganism's genome that affect its chances of survival. Long-term exposure to chemostat culture provokes an accumulation of spontaneous mutations and renders the most adaptable strain dominant. Compared to the colony transfer and serial transfer methods, chemostat culture entails the highest number of cell divisions and, therefore, the highest number of diverse populations. Although chemostat culture for ALE requires more complicated culture devices, it is less labor intensive once the operation begins. Comparative genomic and transcriptome analyses of the adapted strain provide evolutionary clues as to how the stressors contribute to mutations that overcome the stress. The goal of the current paper is to bring about accelerated evolution of microorganisms under controlled laboratory conditions.</p>

<p class="jove_content" fo:keep-together.within-page="1">For high-succinate stress adaptation, the wild-type <em>E. coli</em> W3110 strain was cultured in a chemostat at D = 0.1 hr<sup>-1</sup> for 270 days (<strong>Figure 2</strong>).</p>
<p class="jove_content"><img alt="Figure 2" src="/files/ftp_upload/54446/54446fig2.jpg" /><br />
<strong>Figure 2: High-succinate stress adaptation of <em>E. coli</em> W3110 using chemostat culture. </strong>Thin arrows indicate the times at which the concentration of stressor was increased, and bold arrows indicate the times at which cultures were preserved. Numbers with arrows indicate the concentrations of the stressor, disodium succinate hexahydrate. The change in biomass during ALE and the concentrations of stressor are represented. The figure was modified from <em>J. Biosci. Bioeng</em><sup>7</sup>. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54446/54446fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<p class="jove_content" fo:keep-together.within-page="1">An initial stressor concentration of 30 g/L (disodium succinate hexahydrate) was gradually increased whenever the chemostat culture reached a steady state, until the concentration of stressor was 160 g/L. The adapted <em>E. coli</em> DST160 strain (tolerant to 160 g/L of disodium succinate stress) was acquired after 270 days. The DST160 strain exhibited uninhibited growth under high-succinate stress (160 g/L stressor), while the ancestor strain (W3110) showed nearly no growth (<strong>Figure 3</strong>).</p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 3" src="/files/ftp_upload/54446/54446fig3.jpg" /><br />
<strong>Figure 3: Growth curves of the wild-type (W3110, white circle) and adapted strain (DST160, black circle) under high-succinate stress. </strong>The figure shows that the adapted strain grew without delay under high-succinate stressed conditions while the wild type did not. The error bars indicate standard deviation of three independent experiments. The figure was modified from <em>J. Biosci. Bioeng</em><sup>7</sup>. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54446/54446fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>

Watch this Scientific Journal Video about Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat at JoVE.com

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat | Genetics | Video

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

<p class="jove_content">Here, we present a protocol to obtain adaptive laboratory evolution of microorganisms under conditions using chemostat culture. Also, genomic analysis of the evolved strain is discussed.</p>

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) ...

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14.2K Views.  Korea Research Institute of Bioscience and Biotechnology (KRIBB).  Hier presenteren we een protocol laboratorium adaptieve evolutie van micro-organismen onder omstandigheden met behulp chemostaat cultuur te verkrijgen. Ook is genomische analyse van het vrijkomende stam besproken. 

Video: Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Recrystallization

<p><strong>Recrystallization</strong></p>
<p>Often, the desired product of a chemical reaction is part of a more complex reaction mixture, which can be composed of the solvent, starting materials, and impurities. Learning how to purify organic compounds properly is a valuable technique in organic chemistry. Recrystallization harnesses the differences in solubility of the desired compound and the impurity in the solvent to purify the desired product as a solid. There are three standard methods of purification: distillation, extraction, and recrystallization.</p>
<p><strong>Solubility</strong></p>
<p>The solubility of a substance is the maximum amount that dissolves in a fixed volume of a given solvent at a given temperature. Different solutes have different solubilities and dissolve in different solvents. Solutes may have defining characteristics that lend themselves to be exploited for recrystallization. Compounds exhibit one of the following behaviors in a solvent. First, the compound can be insoluble or have very low solubility in the solvent at all temperatures. Second, the compound can be soluble in the solvent at higher temperatures. Third, the compound can be soluble in the solvent at all temperatures.</p>
<p>A major factor in determining whether a solute dissolves in a solvent and forms a solution is the strength and type of intermolecular forces between the solute and solvent. The general rule of thumb is &#8220;like dissolves like,&#8221; meaning that substances with similar types of intermolecular forces dissolve in each other. For example, polar substances such as table salt (NaCl), dissolve well in polar water.</p>
<p>Another key factor that improves solubility is temperature. For many substances, solubility greatly increases at higher temperatures. This is due to the fact that the increased kinetic energy at higher temperatures breaks the solute intermolecular forces that keep molecules together. This is seen in everyday life. For example, we know that table salt (NaCl) dissolves well in water; however, more dissolves at higher temperatures than at lower temperatures.</p>
<p>Qualitatively, a solution is considered unsaturated if the maximum amount of dissolved solute has not yet been reached. When the maximum possible solute has dissolved, the solution is saturated. A supersaturated solution contains more dissolved solute than the maximum possible amount under typical conditions.</p>
<h4><strong>Recrystallization </strong></h4>
<p>Recrystallization takes advantage of the differences in solubility between the desired product and the contaminants at high temperatures. The first step of recrystallization is to dissolve the product mixture in a minimal volume of heated solvent that still results in a saturated &#8212; but not supersaturated &#8212; solution. Then, the solution is cooled to room temperature, decreasing the solubility of both the desired compound and the impurity.</p>
<p>As the solution cools, crystallization of the pure component begins, while the still soluble impurities do not. This occurs when the component of interest is in a significantly higher concentration than the impurity. First, in the nucleation phase, the solvent initiates the random agglomeration of the solute molecules, forming the first crystal called a seed or nucleus. Next, in the particle growth or crystallization phase, more molecules are added to the seed, forming a crystal. The crystal contains the pure compound, while the impurity remains in the solvent.</p>
<p>Nucleation proceeds faster than particle growth in a supersaturated solution. With more seeds, each crystal is smaller. Thus, if the solution is saturated, rather than supersaturated, fewer seeds form, resulting in larger crystals. Heating the solution to a higher temperature before cooling to room temperature enables the dissolution of a higher concentration of solute, decreasing supersaturation. Additionally, rapid cooling results in quick nucleation, forming many small crystals and trapping the impurity inside of the crystals. Slow cooling is preferred to achieve fewer, larger crystals.</p>
<p>Once the solution has cooled to room temperature, and the crystals have formed, the solution is filtered using vacuum filtration. Then, the crystals are allowed to dry. The percent recovery is calculated by dividing the mass of the recovered product by the mass of the crude product.</p>
<p style="text-align: center;"><img src="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/58/58_Concepts_1.jpg" /></p>
<p>The recovery is rarely 100%, as the solubility of the compound at low temperatures governs how much of the compound is crystallized.</p>
<h4><strong>Selecting a Solvent</strong></h4>
<p>For crystallization to be effective, the optimal solvent must be used. The desired product should have low solubility in the selected solvent at room temperature but high solubility in the solvent at a higher temperature. Ideally, the impurities should be soluble in the solvent at all temperatures. Thus, when the mixture is added to the solvent at a high temperature, the desired product and impurities dissolve readily.</p>
<p>As the solution is cooled, the solubility of the desired product decreases and crystallization begins to occur, forming purified product. Occasionally, impurities may remain insoluble in the solvent of choice, even at high temperatures. Hot gravity filtration of the solution that contains dissolved product can remove the solid impurities. The product can then be recrystallized by cooling the sample.</p>
<p>Ideally, the solvent to be used should be able to boil at a temperature well below the melting point of the desired product. The solvent should also be inert and not react with the desired purified product.</p>
<h4><strong>References</strong></h4>
<ol>
	<li style="font-size:17px">Kotz, J.C., Treichel Jr, P.M., Townsend, J.R. (2012). <em>Chemistry and Chemical Reactivity.</em> Belmont, CA: Brooks/Cole, Cengage Learning.</li>
	<li style="font-size:17px">Silberberg, M.S. (2009) <em>Chemistry: The Molecular Nature of Matter and Change.</em> Boston, MA: McGraw Hill.</li>
	<li style="font-size:17px">Harris, D.C. (2015). <em>Quantitative Chemical Analysis. </em>New York, NY: W.H. Freeman and Company.</li>
</ol>

Recrystallization of Acetanilide and trans-Cinnamic Acid - Concept

Recrystallization
Often, the desired product of a chemical reaction is part of a more complex reaction mixture, which can be composed of the solvent, ...

JoVE Lab Manual

Chemistry

Scientific Method

<p>The scientific method is used to solve problems and explain phenomena. The development of the scientific method coincided with changes in philosophy underpinning scientific discovery, radically transforming the views of society about nature. During the European Renaissance, individuals such as Francis Bacon, Galileo, and Isaac Newton formalized the concept of the scientific method and put it into practice. Although the scientific method has been revised since its early conceptions, much of the framework and philosophy remains in practice today.</p>
<h4>Step 1: The Observation and Question</h4>
<p>Prior to investigation, a scientist must define the question to be addressed. This crucial first step in the scientific process involves observing some natural phenomena of interest. This observation should then lead to a number of questions about the phenomena. This stage frequently requires background research necessary to understand the subject matter and past work on similar ideas. Reviewing and evaluating previous research allows scientists to refine their questions to more accurately address gaps in scientific knowledge. Defining a research question and understanding relevant prior research will influence how the scientific method is applied, making it an important first step in the research process.</p>
<p>An everyday example: You are trying to get to school or work and your car won&#8217;t start. The thought process that most people go through in that situation clearly mirrors the official scientific method (after you are finished getting upset). First, you make an observation: my car won&#8217;t start! The question that follows: why isn&#8217;t it working?</p>
<h4>Step 2: The Hypothesis</h4>
<p>The next step is making a hypothesis, based on prior knowledge. A hypothesis is an &#8220;uncertain explanation&#8221; or an unproven conjecture that seeks to explain some phenomenon based on knowledge obtained while executing subsequent experiments or observations. Generally, scientists develop multiple hypotheses to address their questions and test them systematically.</p>
<p>All hypotheses must meet certain criteria for the scientific process to work. First, a hypothesis must be testable and falsifiable. This aspect of the hypothesis is critical and of much greater importance than the hypothesis being correct. A testable hypothesis is one that generates testable predictions, addressed through observations or experiments. A falsifiable hypothesis is one that, through observation of conflicting outcomes, can be proven wrong. This allows investigators to gain more confidence over time, not by accumulating evidence showing that a hypothesis is correct, but rather by showing that situations that could establish its falsity do not occur.</p>
<p>Hypotheses come in two forms: null hypotheses and alternative hypotheses. The null hypothesis is tested against the alternative hypothesis and reflects that there will be no observed change in the experiment. The alternative hypothesis is generally the one described in the previous two paragraphs, also referred to as the experimental hypothesis. The alternative hypothesis is the predicted outcome of the experiment. If the null hypothesis is rejected, then this builds evidence for the alternative hypothesis.</p>
<p>An everyday example: Maybe it is freezing outside and therefore it is fairly likely that your car battery is dead. Maybe you know you were low on gas the night before and therefore it is likely that the tank is empty.</p>
<h4>Step 3: Experimentation and Data Collection</h4>
<p>Either way, the next step is to make more observations or to conduct experiments leading to conclusions. Following the formulation of hypotheses, scientists plan and conduct experiments to test their hypotheses. These experiments provide data that will either support or falsify the hypothesis. Data can be collected from quantitative or qualitative observations. Qualitative information refers to observations that can be made simply using one&#39;s senses, be that through sight, sound, taste, smell, or touch. In contrast, quantitative observations are ones in which precise measurements of some type are used to investigate one&#39;s hypothesis.</p>
<p>An experiment is a procedure designed to determine whether observations of the real world agree with or refute the derived predictions in the hypothesis. If evidence from an experiment supports a hypothesis, that gives the hypothesis more credibility. This does not indicate that the hypothesis is true, as future experiments may reveal new information about the original hypothesis. Experimental design is another critical step in the scientific method and can have a great effect on the results and conclusions one draws from an experiment. Careful thought and time should be devoted to experimental design and minimizing possible errors. The experiment should be designed so that every variable or factor that could influence the outcome of the experiment be under control of the researcher. Two types of variables are used to describe the conditions in an experiment: the independent and the dependent, or response, variable. The independent variable is directly manipulated or controlled by the scientist and is generally what one predicts will affect the dependent variable. The dependent, or response, variable thus depends on the value of the independent variable. Experiments are generally designed so that one specific factor is manipulated in the experiment in order to illuminate cause and effect relationships.</p>
<p>An everyday example: Does the car still have all of its parts? Is this the right key? What does the gas gauge say? Does a jump start help?</p>
<p>Another important aspect in experimental design is the role of the control treatment, which represents a non-manipulated treatment condition. The control treatment is kept in the same conditions as the experimental treatment, but the experimental manipulation is not applied to the control. For example, if a researcher were testing the effects of soil salinity on plant growth, the soil in the control treatment would have no added salt. The control provides a baseline of &#8220;normal&#8221; conditions with which to compare the experimental treatments.</p>
<p>Experimental design should also incorporate replicates of each treatment. Repeatability of experimental results is an important part of the scientific method that ensures the validity and accuracy of data. It is quite difficult to control all aspects of an experiment so there is inherent variation in results that cannot be controlled for even under the most carefully designed and controlled experiments. Having replicates enables an investigator to estimate this inherent variation in results. Precise recording and measurement of data is also of great importance for ensuring the accuracy of results and the conclusions one draws from the results.</p>
<h4>Step 4: Results and Data Analysis</h4>
<p>The next step in the scientific method involves determining what the results from the experiment mean. Scientists compare the predictions of their null hypothesis to that of their alternative hypothesis to determine if they are able to reject the null hypothesis. Rejecting the null hypothesis means that there is a significant probability that values of the dependent variable in the control versus experimental treatments are not equal to each other. If significant differences exist, then one can reject the null hypothesis and accept the alternative hypothesis. Conversely, the investigator may fail to reject the null hypothesis, meaning the treatment has no effect on the results. Before scientists can make any claims about their null hypothesis from their experimental data or observations, statistical tests are required to ensure the validity of the data and further interpretation of the data. Statistical tests allow researchers to determine if there are genuine differences between the control and experimental treatments. From there, they can create figures and tables to illustrate their findings.</p>
<h4>Step 5: Conclusions</h4>
<p>The last portion of the scientific method involves providing explanations of the results and the conclusions that can be logically drawn from the results. Generally, this step of the scientific process also requires one to revisit scientific literature and compare their results with other experiments or observations on related topics. This allows researchers to put their experiment in a more general context and elaborate on the significance of particular results. Additionally, it allows them to explain how their work fits into a larger context in their discipline.</p>
<p>The scientific process does not stop here! The scientific process works through time as knowledge on topics in science accumulate and drive our understanding of particular mechanisms or processes explaining natural phenomena. If we fail to reject our null hypothesis, then it becomes necessary to revisit the initial stages of the scientific method and try to reformulate our questions and understand why an anticipated result was not attained.</p>
<h4>Application of the Scientific Method</h4>
<p>The only difference between the use of this method in every-day life and in the laboratory is that scientists carefully document their work, from observation to hypothesis to experiment, and finally conclusions and peer review. In addition, unlike problem solving outside the lab, the scientific method in the lab includes controlled conditions and variables.</p>
<p>Let&#8217;s investigate the scientific method using an example from the lab. It is known that plant growth is affected by microbes, such as bacteria and fungi, living in their soil. It is possible to figure out what microbes have which effects by potting plants in completely sterile soil, then adding in microbes one at a time, or in different combinations and measuring the growth of the plant. Now let&#8217;s fit this into the terms used to describe the scientific method:</p>
<p><strong>Observation and Question</strong>: There are microbes in the soil&#8230;do these affect plant growth?</p>
<p>HYPOTHESES:</p>
<p>Experimental: One particular microbe of interest will cause the plants to grow more slowly.</p>
<p>Null: The presence or absence of microbes will have no effect on plant growth</p>
<p><strong>Experiment</strong>: set up groups of plants in 1) sterile soil, 2) soil with the microbe added in, and 3) natural soil. Measure the growth of the plants over time, using a ruler.</p>
<p><strong>Conclusion</strong>: if the plants in group 2 grow more slowly than the other two, the hypothesis is supported. This needs to be backed up with statistical analysis from many plants to be considered significant. An experiment like this is not legitimate with just one plant per group.</p>
<p>Group 1 is a<strong> control</strong> which shows the plants can grow in the sterile soil. Group 3 is a control that shows the plants can grow under normal conditions. Group 2 is the experimental group. It would be possible to add different amounts of the microbe, or different microbes, to introduce more variables. The main point is that the researcher has something to which to compare the experimental group- the control group. If the experiment included only group 2 and the researcher determined that the plants &#8220;looked sick,&#8221; that would be a matter of opinion. The only way to make that observation scientific is to have healthy plants to measure. The type or amount of microbe used is the <strong>independent variable</strong>, because the researcher has control over it. The size of the plant at the end of the experiment is the <strong>dependent or response variable</strong> because it is the result.</p>
<p>Ultimately, work like this is published in scientific journals so that other researchers can read about the methods used and conclusions drawn. Publications like this are subject to peer-review, which means that an article won&#8217;t be published in a journal until other researchers have checked it out and agree it is well-done. As a community of scientists, general concepts are developed based on observed patterns in the experiments that individual scientists conduct. This results in the development of a scientific <strong>theory</strong>. This term means that there is a consensus among researchers that a particular concept or process exists. It is important to note that the word theory does not mean the same thing as hypothesis. Once scientists label a concept with this term, it is considered to be true, considering all currently available data. Of course, if a large body of experimentation demonstrates information to the contrary, theories can be modified.</p>

Scientific Method: Steps and Applications - Concept

The scientific method is used to solve problems and explain phenomena. The development of the scientific method coincided with changes in philosophy ...

Fundamentals

Stoichiometry, Product Yield, and Limiting Reactants

<p>Source: Smaa Koraym at Johns Hopkins University, MD, USA</p>
<ol class="note-items">
	<li data-note-item="1" data-parent-collapse="">Preparation of 25% v/v Ethylenediamine in Deionized Water<button class="expand">Expand</button>
	<p><em>Here, we show the laboratory preparation for 10 students working in pairs, with some excess. Please adjust quantities as needed.</em></p>
	<ul class="lab-items">
		<li data-sub-note-item="1-2">
		<div class="lab-step">To set up for this lab experiment, wear the appropriate personal protective equipment, including a lab coat, chemical splash goggles, and gloves. <strong>Note:</strong> Ethylenediamine is toxic, caustic, flammable, and corrosive. This solution must be prepared and used in a fume hood.</div>
		</li>
		<li data-sub-note-item="1-3">
		<div class="lab-step">Prepare 200 mL of a 25% v/v solution of ethylenediamine in deionized water. Pour about 50 mL into a 250-mL beaker.</div>
		</li>
		<li data-sub-note-item="1-4">
		<div class="lab-step">Place a powder funnel in a 50-mL graduated cylinder and measure 50 mL of ethylenediamine.</div>
		</li>
		<li data-sub-note-item="1-5">
		<div class="lab-step">Transfer the ethylenediamine to a 200-mL volumetric flask. Add deionized water to the flask until the solution level reaches the line.</div>
		</li>
		<li data-sub-note-item="1-6">
		<div class="lab-step">Add a magnetic stir bar to the flask, then place it on a stir plate, and mix the solution for approximately one to two minutes until it is homogeneous. Turn off the stir plate when finished.</div>
		</li>
		<li data-sub-note-item="1-7">
		<div class="lab-step">Label a brown glass dispensing bottle &#8216;25% v/v ethylenediamine&#8217;. Remove the dispensing pump and use a funnel to transfer the ethylenediamine solution to the bottle.</div>
		</li>
		<li data-sub-note-item="1-8">
		<div class="lab-step">Reconnect the glass dispensing pump with a 20 mL capacity to the bottle.</div>
		</li>
		<li data-sub-note-item="1-9">
		<div class="lab-step">Retrieve the stir bar with a magnetic wand and set both on a paper towel to absorb the excess liquid. Store the bottle in a flammables cabinet until it is needed.</div>
		</li>
		<li data-sub-note-item="1-10">
		<div class="lab-step">When finished, thoroughly clean the volumetric flask, graduated cylinder, funnel, and stir bar, using your lab standard practices. Store the ethylenediamine in its approved storage location.</div>
		</li>
	</ul>
	</li>
	<li data-note-item="2" data-parent-collapse="">Preparation of the Laboratory<button class="expand">Expand</button>
	<ul class="lab-items">
		<li data-sub-note-item="2-1">
		<div class="lab-step">Fill out labels for organic, aqueous, and solid nickel waste containers according to the waste disposal procedures of your institution. Place the labeled containers in an approved waste collection area.</div>
		</li>
		<li data-sub-note-item="2-2">
		<div class="lab-step">Ensure that the following glassware and equipment are available at each workstation (we suggest that students work in pairs):
		<table align="center" style="border-collapse: collapse; font-size: 17px; line-height: 24px;">
			<tbody>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;2&#160;&#160;&#160;&#160;10-mL graduated cylinders</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;100-mL beaker</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;400-mL beaker</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;600-mL beaker</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;800-mL beaker</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;500-mL filter flask</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;Glass stirring rod</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;B&#252;chner funnel (40-mL/83-mm)</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;Filter adapter (size 4)</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;Neoprene stopper (size 15)</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;Rubber policeman</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;1&#160;&#160;&#160;&#160;Roll of lab tape with pen/marker</td>
				</tr>
				<tr>
					<td style="border: 1px solid; padding: 0px; ">&#160;Silicone tubing</td>
				</tr>
			</tbody>
		</table>
		</div>
		</li>
		<li data-sub-note-item="2-3">
		<div class="lab-step">Set out packages of circular filter paper and disposable 1-mL plastic pipettes so that each workstation has access to them.</div>
		</li>
		<li data-sub-note-item="2-4">
		<div class="lab-step">Place at least 3 L of ice in an insulated cooler with a scoop and set out paper towels as needed.</div>
		</li>
		<li data-sub-note-item="2-5">
		<div class="lab-step">Obtain a container of nickel chloride hexahydrate and set it by the analytical balances. Verify that the balances have a sufficient supply of weighing boats, clean spatulas, and laboratory wipes.</div>
		</li>
		<li data-sub-note-item="2-6">
		<div class="lab-step">Fill five 100-mL polyethylene bottles with acetone. Place a squeeze bottle of deionized water and a bottle of acetone at each workstation.</div>
		</li>
		<li data-sub-note-item="2-7">
		<div class="lab-step">Label five tall-form beakers with &#8216;25% v/v ethylenediamine&#8217;. Bring the labeled beakers and five watch glasses to the hood with the ethylenediamine solution.</div>
		</li>
		<li data-sub-note-item="2-8">
		<div class="lab-step">When a student group is ready, use the glass pump to dispense 20 mL of ethylenediamine into a labeled beaker. Cover the beaker and carefully hand it to the students.</div>
		</li>
	</ul>
	</li>
</ol>

Source: Smaa Koraym at Johns Hopkins University, MD, USA

	Preparation of 25% v/v Ethylenediamine in Deionized WaterExpand
	Here, we show the laboratory ...

Lab Manual Sub Articles

Beer's Law

Beer-Lambert Law and Equilibrium Constant Determination - Procedure

Source: Smaa Koraym at Johns Hopkins University, MD, USA

	Lab Setup for Beer&#8217;s LawExpand
	In this lab, you will combine aqueous solutions of FeCl3 ...

Concentration Dependence

<p>Source: Smaa Koraym at Johns Hopkins University, MD, USA</p>
<ol class="note-items">
	<li data-note-item="1" data-parent-collapse="">Concentration Dependence<button class="expand">Expand</button>
	<p>In this experiment, you will combine varying concentrations of aqueous HCl and sodium thiosulfate to make solid sulfur, which rapidly gathers into opaque yellow particles at a certain sulfur concentration. Since the reaction solution starts clear and colorless, you can easily tell when it reaches that concentration.</p>
	<p>You&#39;ll use the same test tube and total volume each time, so it will take the same amount of sulfur to make each solution completely opaque. Thus, you&#39;ll measure the reaction progress by timing how long that takes. You&#39;ll then use that data to estimate the reaction orders of the individual reactants and of the overall reaction.</p>
	<p>Before starting the lab, make a table listing the reactant concentrations, the time to solution opacity, and the solution temperature for the trials.</p>
	<h4><strong>Table 1: Reaction progress</strong></h4>
	<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
		<tbody>
			<tr>
				<td><strong>Trial</strong></td>
				<td><strong>[Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub>] (M)</strong></td>
				<td><strong>[HCl] (M)</strong></td>
				<td><strong>Time (s)</strong></td>
				<td><strong>Temp (&#176;C)</strong></td>
			</tr>
			<tr>
				<td style="text-align: center;">1</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">2</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">3</td>
				<td style="text-align: center;">0.2 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">4</td>
				<td style="text-align: center;">0.15 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">5</td>
				<td style="text-align: center;">0.05 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">6</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">6.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">7</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">4.5 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">8</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">1.5 M</td>
				<td></td>
				<td></td>
			</tr>
		</tbody>
	</table>
	<p><a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/50/Table_1._Reaction_progress.xlsx" target="_blank">Click Here to download Table 1</a></p>
	<p>We want to make sure that the reactions are all taking place at room temperature. You&#39;ll perform two benchmark trials, three trials with different sodium thiosulfate concentrations, and three trials with different HCl concentrations. You will vary the concentrations by diluting stock solutions of sodium thiosulfate and HCl, as shown in the following tables.</p>
	<h4><strong>Table 2: Sodium thiosulfate dilutions</strong></h4>
	<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
		<tbody>
			<tr>
				<td style="text-align: center;"><strong>Target Concentration</strong></td>
				<td style="text-align: center;"><strong>Volume of 0.2 M sodium thiosulfate</strong></td>
				<td style="text-align: center;"><strong>Volume of DI water</strong></td>
			</tr>
			<tr>
				<td style="text-align: center;">0.05 M</td>
				<td style="text-align: center;">5 mL</td>
				<td style="text-align: center;">15 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">0.10 M</td>
				<td style="text-align: center;">10 mL</td>
				<td style="text-align: center;">10 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">0.15 M</td>
				<td style="text-align: center;">15 mL</td>
				<td style="text-align: center;">5 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">0.20 M</td>
				<td style="text-align: center;">20 mL</td>
				<td style="text-align: center;">0 mL</td>
			</tr>
		</tbody>
	</table>
	<p><a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/50/Table_2._Sodium_thiosulfate_dilutions.xlsx" target="_blank">Click Here to download Table 2</a></p>
	<h4><strong>Table 3: HCl Dilutions</strong></h4>
	<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
		<tbody>
			<tr>
				<td style="text-align: center;"><strong>Target Concentration</strong></td>
				<td style="text-align: center;"><strong>Volume of 6.0 M HCl</strong></td>
				<td style="text-align: center;"><strong>Volume of DI water</strong></td>
			</tr>
			<tr>
				<td style="text-align: center;">1.5 M</td>
				<td style="text-align: center;">2.5 mL</td>
				<td style="text-align: center;">7.5 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">3.0 M</td>
				<td style="text-align: center;">5.0 mL</td>
				<td style="text-align: center;">5.0 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">4.5 M</td>
				<td style="text-align: center;">7.5 mL</td>
				<td style="text-align: center;">2.5 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">6.0 M</td>
				<td style="text-align: center;">10 mL</td>
				<td style="text-align: center;">0 mL</td>
			</tr>
		</tbody>
	</table>
	<p><a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/50/Table_3._HCl_Dilutions.xlsx" target="_blank">Click Here to download Table 3</a></p>
	<p>Remember to use caution when handling HCl, which is toxic and highly corrosive. Sulfur dioxide, which is a gaseous product of this reaction, is also toxic. You will leave the reaction waste in the fume hood overnight to let the sulfur dioxide escape harmlessly.</p>
	<ul class="lab-items">
		<li data-sub-note-item="1-6">
		<div class="lab-step">First, put on a lab coat, splash-proof safety glasses, and nitrile gloves.</div>
		</li>
		<li data-sub-note-item="1-7">
		<div class="lab-step">Fill a plastic wash bottle with deionized water and get a test tube marked with an X.</div>
		</li>
		<li data-sub-note-item="1-8">
		<div class="lab-step">Clamp the test tube over the center of a small stir plate. The label must be on the opposite side from you. Make sure that you can clearly see the X. Place a small magnetic stir bar in the test tube.</div>
		</li>
		<li data-sub-note-item="1-9">
		<div class="lab-step">Use a thermometer clamp to secure a glass thermometer in the lower third of the tube, keeping it clear of the X and the stir bar.</div>
		</li>
		<li data-sub-note-item="1-10">
		<div class="lab-step">Cut a sheet of about 20 squares of plastic paraffin film from a roll and bring the sheet back to your hood.</div>
		</li>
		<li data-sub-note-item="1-11">
		<div class="lab-step">Lay paper towels in the fume hood as a clean surface for labware to be reused.</div>
		</li>
		<li data-sub-note-item="1-12">
		<div class="lab-step">Label a 600-mL beaker as &#8216;waste&#8217; for the reaction, a 100-mL beaker as &#8216;0.2 M sodium thiosulfate&#8217;, and a 50-mL beaker as &#8216;6 M HCl&#8217;.</div>
		</li>
		<li data-sub-note-item="1-13">
		<div class="lab-step">Fill the 100-mL beaker with 0.2 M sodium thiosulfate from the stock bottle in the dispensing hood.</div>
		</li>
		<li data-sub-note-item="1-14">
		<div class="lab-step">After that, bring the 50-mL beaker and a watch glass to the acid-dispensing hood to obtain 50 mL of 6 M HCl. Cover the filled beaker with the watch glass and bring your portion of HCl back to your hood.</div>
		</li>
		<li data-sub-note-item="1-15">
		<div class="lab-step">Prepare 20 mL of 0.1 M sodium thiosulfate for the first benchmark trial. Use a 10-mL volumetric pipette to measure 10 mL of 0.2 M sodium thiosulfate and dispense it into a 20-mL volumetric flask. Fill the 20-mL volumetric flask with deionized water to the mark.</div>
		</li>
		<li data-sub-note-item="1-16">
		<div class="lab-step">Then, cut a square of plastic paraffin film, seal the flask, and invert it several times to thoroughly mix the solution.</div>
		</li>
		<li data-sub-note-item="1-17">
		<div class="lab-step">Remove the film and pour the solution into the test tube.</div>
		</li>
		<li data-sub-note-item="1-18">
		<div class="lab-step">Prepare 10 mL of 3 M HCl. Pipette 5 mL of 6 M HCl into a 10-mL graduated cylinder. Add deionized water to fill the graduated cylinder to the 10-mL line.</div>
		</li>
		<li data-sub-note-item="1-19">
		<div class="lab-step">Seal the top of the graduated cylinder with plastic paraffin film and thoroughly mix the HCl solution by inverting it several times.</div>
		</li>
		<li data-sub-note-item="1-20">
		<div class="lab-step">Now, start the stir motor and get a reset stopwatch. Confirm that you can see the X through the solution in the test tube.</div>
		</li>
		<li data-sub-note-item="1-21">
		<div class="lab-step">Simultaneously start the stopwatch and pour the 3 M HCl into the test tube.</div>
		</li>
		<li data-sub-note-item="1-22">
		<div class="lab-step">Watch the X through the solution, which will start becoming cloudy when enough sulfur has been produced to saturate it. Stop the stopwatch exactly when the solution becomes so opaque that the X disappears.</div>
		</li>
		<li data-sub-note-item="1-23">
		<div class="lab-step">Record the time in your lab notebook for benchmark trial 1 and then reset the stopwatch. Also, record the temperature of the solution.</div>
		</li>
		<li data-sub-note-item="1-24">
		<div class="lab-step">Now, turn off the stir motor, remove the thermometer, and rinse it with deionized water into the waste beaker.</div>
		</li>
		<li data-sub-note-item="1-25">
		<div class="lab-step">Then, empty the test tube into the waste beaker, retrieve the stir bar with long forceps, and rinse the test tube and stir bar with deionized water.</div>
		</li>
		<li data-sub-note-item="1-26">
		<div class="lab-step">Thoroughly clean the test tube with a test tube brush. Briefly rinse the volumetric flask and graduated cylinder with deionized water as well. Dry everything with paper towels.</div>
		</li>
		<li data-sub-note-item="1-27">
		<div class="lab-step">Repeat the trial in the exact same way for the second benchmark time. If the time is not within 3 - 5 s of the first trial, repeat the benchmark trial until you have two consistent trials.</div>
		</li>
		<li data-sub-note-item="1-28">
		<div class="lab-step">Follow the same overall procedure for each subsequent trial, changing the dilutions as indicated in your table and cleaning your glassware between each trial. Use a 5-mL volumetric pipette as needed when measuring the sodium thiosulfate stock solution in trials four and five.</div>
		</li>
		<li data-sub-note-item="1-29">
		<div class="lab-step">Once you have performed all eight trials, rinse your glassware and tools one last time and neutralize the reaction waste with baking soda. Use caution because sulfur dioxide is one of the reaction products.</div>
		</li>
		<li data-sub-note-item="1-30">
		<div class="lab-step">Transfer the neutralized reaction waste to the designated waste container. Then, pour any remaining sodium thiosulfate solution into its labeled waste container and rinse the beaker with deionized water.</div>
		</li>
		<li data-sub-note-item="1-31">
		<div class="lab-step">Neutralize any remaining HCl with baking soda before flushing it down the drain with tap water.</div>
		</li>
		<li data-sub-note-item="1-32">
		<div class="lab-step">Finally, clean and put away your labware according to your standard practices and throw out any trash remaining in your hood. Your instructor will dispose of the reaction waste later.</div>
		</li>
	</ul>
	</li>
	<li data-note-item="2" data-parent-collapse="">Results<button class="expand">Expand</button>
	<ul class="lab-items">
		<li data-sub-note-item="2-1">
		<div class="lab-step">Now, estimate the reaction order of sodium thiosulfate and HCl. Calculate the starting concentration of sodium thiosulfate in the reaction mixture for trials 1 through 5. Looking at the concentration of sodium thiosulfate and the reaction times, we see that as the concentration doubles, the time to opacity is halved.
		<h4><strong>Table 4: Estimating reaction order sodium thiosulfate</strong></h4>
		<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
			<tbody>
				<tr>
					<td style="text-align: center;"><strong>Trial</strong></td>
					<td style="text-align: center;"><strong>[Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub>] added</strong></td>
					<td style="text-align: center;"><strong>Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub> volume (mL)</strong></td>
					<td style="text-align: center;"><strong>Total volume (mL)</strong></td>
					<td style="text-align: center;"><strong>[Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub>] in mixture </strong></td>
					<td style="text-align: center;"><strong>Time (s)</strong></td>
					<td style="text-align: center;"><strong>Inverse time (s<sup>-1</sup>)</strong></td>
				</tr>
				<tr>
					<td style="text-align: center;">1, 2</td>
					<td style="text-align: center;">0.1</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">3</td>
					<td style="text-align: center;">0.2</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">4</td>
					<td style="text-align: center;">0.15</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">5</td>
					<td style="text-align: center;">0.05</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
			</tbody>
		</table>
		<a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/50/Table_4._Estimating_reaction_order_sodium_thiosulfate.xlsx" target="_blank">Click Here to download Table 4</a></div>
		</li>
		<li data-sub-note-item="2-2">
		<div class="lab-step">Remember that each trial used the same volume, so it took the same amount of sulfur to make each solution opaque. Thus, the reaction is twice as fast when the sodium thiosulfate concentration is doubled.</div>
		</li>
		<li data-sub-note-item="2-3">
		<div class="lab-step">When the concentration increases by a factor of 4, the reaction rate also increases by a factor of 4. This one-to-one relationship suggests that the reaction was first order for sodium thiosulfate. If so, we would expect a plot of rate versus sodium thiosulfate concentration to be linear.</div>
		</li>
		<li data-sub-note-item="2-4">
		<div class="lab-step">We didn&#39;t measure how much sulfur it took to turn the solution opaque, but we assume that it was the same for each trial. This lets us use the inverse of time to opacity as a stand-in for the rate. The slope won&#39;t be equal to k, but that&#39;s OK for this lab.</div>
		</li>
		<li data-sub-note-item="2-5">
		<div class="lab-step">Plot inverse reaction time as a function of sodium thiosulfate concentration. This confirms the linear relationship between sulfur production and thiosulfate concentration. Thus, the reaction seems to be first order with respect to thiosulfate.</div>
		</li>
		<li data-sub-note-item="2-6">
		<div class="lab-step">Use the same method to estimate the reaction order of HCl. First, calculate the starting concentration of HCl in the benchmark trials and in trials 6 through 8.
		<h4><strong>Table 5: Estimating reaction order HCl</strong></h4>
		<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
			<tbody>
				<tr>
					<td style="text-align: center;"><strong>Trial</strong></td>
					<td style="text-align: center;"><strong>[HCl] added</strong></td>
					<td style="text-align: center;"><strong>HCl volume (mL) </strong></td>
					<td style="text-align: center;"><strong>Total volume (mL) </strong></td>
					<td style="text-align: center;"><strong>[HCl] in mixture </strong></td>
					<td style="text-align: center;"><strong>Time (s)</strong></td>
				</tr>
				<tr>
					<td style="text-align: center;">1, 2</td>
					<td style="text-align: center;">0.1</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">3</td>
					<td style="text-align: center;">0.2</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">4</td>
					<td style="text-align: center;">0.15</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">5</td>
					<td style="text-align: center;">0.05</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
			</tbody>
		</table>
		<a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/50/Table_5._Estimating_reaction_order_HCl.xlsx" target="_blank">Click Here to download Table 5</a></div>
		</li>
		<li data-sub-note-item="2-7">
		<div class="lab-step">Then, look at the concentration of HCl and the reaction times for these trials. The reaction times are nearly identical, so the HCl concentration has no effect on the reaction rate. This means that HCl seems to be zeroth order for this reaction.</div>
		</li>
		<li data-sub-note-item="2-8">
		<div class="lab-step">Lastly, add up the reaction orders of the reactants to get the overall reaction order.</div>
		</li>
	</ul>
	</li>
</ol>

Concentration Dependence and Reaction Order - Procedure

Source: Smaa Koraym at Johns Hopkins University, MD, USA

	Concentration DependenceExpand
	In this experiment, you will combine varying concentrations of ...

Enzyme Activity

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Investigating the Effect of pH and Temperature on Peroxidase Activity
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">To make the extraction buffer for the peroxidase enzyme, mix equal volumes of 0.1 M sodium phosphate monobasic and 0.1 molar sodium phosphate dibasic solutions to achieve a final volume of 500 mL.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Test the resulting solution using a pH meter. The mixture should be between a pH of 6.8 and 7.2.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">To extract the turnip peroxidase, use a knife to remove the outer layer of a turnip and then cut the turnip into approximately two cm cubes.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Place 30 g of turnip cubes into a blender with 300 mL extraction buffer, and blend the turnip cubes at high speed three times for about 15 s each time.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Filter the homogenized mixture through three layers of cheesecloth, then strain the resulting solution through one coffee filter.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Store the filtrate in a 500 mL bottle, and immediately place the bottle in the refrigerator.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Next, to make the enzyme substrate solutions, first add 15 mL 3% H<sub>2</sub>O<sub>2</sub> to 435 mL distilled water to dilute the H<sub>2</sub>O<sub>2</sub> to a 0.1% concentration.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">To prepare the color-changing reactant, dissolve 0.2 mL concentrated guaiacol in 100 mL of isopropyl rubbing alcohol, and store this guaiacol solution in the refrigerator.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Then, to make solutions for the different pH conditions, add 100 mL distilled water to six 250 mL beakers, and place the buffer capsule contents with pH of 3, 4, 5, 6, 7, and 8 into separate beakers.
</div></li></ul></li></ol>

Enzyme Activity: Effect of pH and Temperature on Peroxidase Activity - Prep

Investigating the Effect of pH and Temperature on Peroxidase Activity
ExpandTo make the extraction buffer for the peroxidase enzyme, mix equal volumes of ...

Cellular Processes

Hardy-Weinberg and Genetic Drift

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Class Simulation
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">Begin by opening a new spreadsheet file. Following the Hardy-Weinberg equation where p is the frequency of a dominant allele A in a population, and q is defined as the frequency of a recessive allele B, input frequency p of allele A into cell B2, and frequency q of allele B into cell B3.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Assign the value 0.5 to cell C2.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Following the 1 &#8211; p = q equation, enter the formula &#8220;= 1 &#8211; C2&#8221; into cell C3 to calculate the frequency q of allele B. NOTE: Cells C2 and C3 will represent the gene pool used in the next few steps.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Label cells E2 and F2 &#8220;Allele 1&#8221; and &#8220;Allele 2&#8221; respectively, then enter the RAND code for random formula into cell E3. =IF (RAND()</div></li><li data-sub-note-item="1-5"><div class="lab-step">Select cell E3 and drag the bottom right corner of the cell down to E27 to duplicate the formula into 25 cells, creating an allele for the other offspring, and enter the same formula that was used in cell E3 into cell F3.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Add a third column of data starting with the description &#8220;Genotype&#8221; in cell G2, and add the CONCATENATE function to cell G3 to combine the two randomly generated alleles and to create a genotype.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Drag this formula down for 25 cells and label cells H2, I2, and J2 AA, AB, and BB respectively.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Then, input one IF function as shown here into cells H3, I3, and J3. Drag the formulas down for 25 rows per column. The formulas will return a 1 if the genotype in this row matches the genotype in the header row, and a 0 if the genotype in this row is one of the other two genotypes. H3: = IF (G3=&#8221;AA&#8221;,1,0) I3: =IF(OR(G3=&#8221;AB,G3=&#8221;BA&#8221;),1,0) J3: =IF(G3=&#8221;BB&#8221;,1,0)
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Then, label cell D28 as &#8220;SUM&#8221;, add the formula to cell H28, and drag the formula to cells I28 and J28 to get the total number of each genotype.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Next, label cells H30 and J30 &#8220;A&#8221; and &#8220;B&#8221; respectively and label cell D31 &#8220;Number of alleles&#8221;.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">Add code to cells H31 and J31 to obtain the total number of alleles in the simulated generation, which is two times the frequency of the respective homozygous genotype, plus the frequency of the heterozygous genotype.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Then, label cell E32 &#8220;Next gen allele frequency&#8221; and add code to cells H32 and J32 to obtain the ratio of alleles in the next generation.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Using the gene frequencies generated in the mathematical model you just built, adjust the gene pool values in cells C2 and C3 to those from H32 and J32, and run 10 more generations, updating the gene pool with the resulting allele frequency each time.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Record the ratio of the two variables at each time point in the spreadsheet on the table, and create a line graph to see how a small population changes over time. Hypotheses: The experimental hypothesis in this study is that smaller populations will deviate more from the Hardy-Weinberg expected frequencies of the alleles than will larger populations, because smaller populations are more susceptible to genetic drift. The null hypothesis is that the allele frequencies within the populations will not differ from the Hardy-Weinberg equilibrium equation expected frequencies, meaning that the allele frequencies will be the same from generation to generation.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">Set the frequency of gene A to 0.3 and gene B to 0.7, and run the model 10 more times, recording the ratio of the genes in the next generation in the table after each run.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Then, drag the formulas for the cells further down to alter the model to include 100 zygotes and run the model another 10 times.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">After the last run, save all of the working data to a personal storage device and exit the spreadsheet program.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Simulation for Hardy-Weinberg and Genetic Drift
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">To begin, find a partner and then collect one bag from the instructor.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Verify that the bag contains beads of two different colors and an even number of each color. 
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Designate one color as allele capital A and the other as small a. Hypotheses: The experimental hypothesis for this simulation is that allele frequencies will not change over the 10 generations of the experiment, and Hardy-Weinberg equilibrium will be observed. The null hypothesis is that equilibrium will not be seen in the population.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Note the initial frequency in Table 1 next to generation zero. Using the Hardy-Weinberg equation, calculate the initial genotype frequencies of the population and note this in Table 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/4/Lab_4_Table_A.xlsx" target="__blank" style="display:block;margin:20px;">Click Here to download Table A</a>
</div></li><li data-sub-note-item="2-5"><div class="lab-step">Then, discuss with your partner about the expected final allele and genotype frequencies after 10 generations of Hardy-Weinberg equilibrium. Note this next to prediction in Table 1.
</div></li><li data-sub-note-item="2-6"><div class="lab-step">Draw a pair of beads from the bag. This represents the genotype of one individual in the next generation.
</div></li><li data-sub-note-item="2-7"><div class="lab-step">Place a tally mark in the appropriate column next to Generation 1.
</div></li><li data-sub-note-item="2-8"><div class="lab-step">Now, keeping each pair of beads out of the bag every time, repeat the drawing 20 times to fill out the generation one row of Table 1.
</div></li><li data-sub-note-item="2-9"><div class="lab-step">Using the tallies, calculate the allele frequency for that generation, remembering to count both alleles in each genotype. Make a note of the new allele frequency, and then adjust the 100 beads to reflect the population after Generation 1 by adding and removing beads as necessary.
</div></li><li data-sub-note-item="2-10"><div class="lab-step">Repeat the drawing of pairs from this adjusted population for another generation of 20 picks, recording all of the observations in Table 1.
</div></li><li data-sub-note-item="2-11"><div class="lab-step">After calculating the allele frequency for this generation, adjust the population again to set up a third generation draw.
</div></li><li data-sub-note-item="2-12"><div class="lab-step">Keep drawing, calculating allele frequencies, and adjusting the starting population until 10 generations have been recorded in total.
</div></li></ul></li><li data-note-item="3" data-parent-collapse="">Violations of Hardy-Weinberg 
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="3-1"><div class="lab-step">To test the effect of different physical and ecological scenarios on the Hardy-Weinberg equilibrium, first draw a slip of paper to determine which Hardy-Weinberg assumption you will be testing.
</div></li><li data-sub-note-item="3-2"><div class="lab-step">Make a prediction of what you think might happen in your selected scenario before beginning the experiment, and record this on the board.
</div></li><li data-sub-note-item="3-3"><div class="lab-step">If you selected the mutation scenario, select 5 alleles at random from the set and replace them with 5 of a third color, allele capital B.
</div></li><li data-sub-note-item="3-4"><div class="lab-step">As before, perform the simulation by pulling pairs of beads from the bag for a generation of 20 picks, recording the results.  <a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/4/Lab_4_Table_B.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table B</a>
</div></li><li data-sub-note-item="3-5"><div class="lab-step">After each generation, make adjustments to have the starting population match the new allele frequency as before.
</div></li><li data-sub-note-item="3-6"><div class="lab-step">Then, replace 5 random alleles again with the new color, and simulate the new generation for 20 picks.
</div></li><li data-sub-note-item="3-7"><div class="lab-step">If you picked the non-random mating test, draw 1 allele at a time instead of 2 and then pair it with another of the same color pulled from the bag.
</div></li><li data-sub-note-item="3-8"><div class="lab-step">As with the previous simulation, adjust the population to match the new allele frequencies at the end of the previous generation, and carry out the simulation for 10 generations in total.
</div></li><li data-sub-note-item="3-9"><div class="lab-step">For the gene flow condition, begin by removing 10 alleles from the population and replacing them with 10 randomly selected alleles from another set of 100 beads.
</div></li><li data-sub-note-item="3-10"><div class="lab-step">Adjust the allele frequency and repeat the gene flow of removing 10 beads and adding 10 beads from another bag at the beginning of each generation.
</div></li><li data-sub-note-item="3-11"><div class="lab-step">If you drew small population size, simply start each generation with 60 alleles instead of 100.
</div></li><li data-sub-note-item="3-12"><div class="lab-step">As usual, adjust allele frequencies each generation and record 10 generations. In the selection condition, draw from the original bag of beads but do not count any homozygous recessive pairs that are drawn when calculating the allele frequency at the end of each generation.
</div></li></ul></li><li data-note-item="4" data-parent-collapse="">Simulation of Genetic Drift
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="4-1"><div class="lab-step">Make your predictions before running the simulations and record them on the board.
</div></li><li data-sub-note-item="4-2"><div class="lab-step">If you have the small population size test, run the standard Hardy-Weinberg simulation as before, but instead start each generation with 30 alleles and not 100.
</div></li><li data-sub-note-item="4-3"><div class="lab-step">If you drew the founder effect with two alleles test, start generation 1 by drawing only 5 pairs of beads from the bag.
</div></li><li data-sub-note-item="4-4"><div class="lab-step">Then, creating the gene pool for generation 2, include only 50 beads at the allele frequency determined in generation 1. When you create the gene pool for generation 3 and all subsequent generations, include 100 beads.
</div></li><li data-sub-note-item="4-5"><div class="lab-step">For a founder effect with 10 alleles, you will need to collect a bag containing random amounts of 10 different colored beads.
</div></li><li data-sub-note-item="4-6"><div class="lab-step">Designate an allele name for each of the new alleles, and then calculate their initial allele frequency.
</div></li><li data-sub-note-item="4-7"><div class="lab-step">Now, run the experiment in the same way as the founder effect with two alleles, by drawing just 5 pairs randomly for generation 1 and then creating the second generation with 50 alleles, and third and subsequent generations with 100. <a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/4/Lab_4_Table_C.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table C</a>
</div></li><li data-sub-note-item="4-8"><div class="lab-step">If you have the natural disaster with 2 alleles, you will need to collect a paper plate from your instructor.
</div></li><li data-sub-note-item="4-9"><div class="lab-step">Draw a line down the middle of the plate. Before generation 1, pour out all of the beads from a standard 100 bead setup onto the plate, and mix them randomly, spreading them evenly.
</div></li><li data-sub-note-item="4-10"><div class="lab-step">Choose one side of the plate and take all of the beads from that side. You should draw from these to create generation 1.
</div></li><li data-sub-note-item="4-11"><div class="lab-step">For generation 2 and beyond, adjust the population and use a total of 100 beads.
</div></li><li data-sub-note-item="4-12"><div class="lab-step">Finally, if you selected the natural disaster with 10 alleles test, you will need to collect a bag containing 10 different colored beads.
</div></li><li data-sub-note-item="4-13"><div class="lab-step">Assign a name to each new allele, and then place them on the plate and select half. Draw from these 50 beads for generation 1, and then calculate the allele frequencies to make a population of 100 in each subsequent round.
</div></li></ul></li><li data-note-item="5" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="5-1"><div class="lab-step">Using the entered functions, calculate the number of each allele by multiplying the number of the homozygous genotypes by 2 and adding the number of the heterozygous genotypes.
</div></li><li data-sub-note-item="5-2"><div class="lab-step">The frequency can then be calculated by dividing the number of the allele in question by the total number of alleles. Enter the results into the table.
</div></li><li data-sub-note-item="5-3"><div class="lab-step">Next, use the Hardy-Weinberg equation to calculate the expected genotype frequency of the next generation, and compare the results of the simulation with the expected frequency, using the table as a guide.
</div></li><li data-sub-note-item="5-4"><div class="lab-step">Plot the changes of the population gene frequencies over time that you recorded in the table. Note any changes in allele frequencies over time and if any alleles decrease in frequency to the point that they disappear from the population. 
</div></li><li data-sub-note-item="5-5"><div class="lab-step">Plot the changes in the populations of the 25 and 100 zygotes with initial gene frequencies of 0.5 for each allele. Compare the results of the smaller population and the larger populations.  NOTE: The observed allele frequencies are expected to differ slightly from the Hardy-Weinberg expected values as a result of chance from the RAND function.
</div></li><li data-sub-note-item="5-6"><div class="lab-step">Using separate lines for each allele, graph the allele frequencies found for the original Hardy-Weinberg experiment over 10 generations.
</div></li><li data-sub-note-item="5-7"><div class="lab-step">Compare the results across the class, and see if your results agree.
</div></li><li data-sub-note-item="5-8"><div class="lab-step">Next, again using one line per allele, graph the results of your second experiment testing violations of the Hardy-Weinberg equilibrium, and share your finding with the class. Compare your actual results with your prediction. 
</div></li><li data-sub-note-item="5-9"><div class="lab-step">Finally, graph the results of your genetic drift simulation scenario in the same manner.
</div></li><li data-sub-note-item="5-10"><div class="lab-step">Again, present your results to the group and hypothesize why your results did or did not match your prediction. Compare your results to those of your classmates&#8217;. 
</div></li></ul></li></ol>

Hardy-Weinberg Equilibrium Principle and Genetic Drift - Procedure

Class Simulation
ExpandBegin by opening a new spreadsheet file. Following the Hardy-Weinberg equation where p is the frequency of a dominant allele A in a ...

Population Growth

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Exponential Population Growth Model
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">NOTE: In these experiments you will use computer software to simulate different types of growth models. HYPOTHESES: The experimental hypothesis might be that if we increase either R, the reproductive rate or the initial population size, NT, that this will increase the population size after 10 generations in an exponential growth model compared to unmanipulated control populations. The null hypothesis may be that populations of different R and initial population size will have the same population size as control groups after 10 generations.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">To build an exponential population growth model, first open a new workbook in Excel.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Save the file to a secure location and name it &#8220;exponential growth&#8221;.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Label cell A1 &#8220;generation&#8221;, and cell B1 &#8220;population size&#8221;.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Type the number zero into cell A2. This row will represent the initial generation.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">In cell A3 type this formula: = 1 + A2
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Now hit enter.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Click on cell A3 and drag the small box in the bottom right corner to cell A12 for 10 generations.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Type the number two in cell B2. This will represent the initial population size in the model.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Label cell E1 &#8220;growth rate&#8221;, and in cell E2 type the number 0.05. This value represents the proportion of new offspring produced by each individual for each generation, or R in the exponential growth model.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">NOTE: In the classic exponential growth equation, T is the generation, and NT is the number of organisms in the current generation. NT+1 will be is the number of individuals in the next generation.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Enter the exponential growth equation in cell B3 as the formula shown here: = B2 + (1 + E2) * B2
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Now, hit enter.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Click cell B3 and drag the small square in the bottom right corner down to cell B12 to simulate 10 generations of exponential growth.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">To observe the results, select all data, along with the headers, in columns A and B.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Click &#8220;insert&#8221; and select &#8220;scatter plot with smooth lines and markers&#8221; from the toolbar. A scatter plot should appear in the sheet. TIP: If it does not, check to see if a new sheet was created in the workbook.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">Label the Y axis &#8220;population size&#8221; and the X axis &#8220;generation&#8221;. Title the chart &#8220;exponential growth of a population&#8221;.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Observe how the population increases in a classic exponential growth pattern.
</div></li><li data-sub-note-item="1-19"><div class="lab-step">Next, create Table 1 labeling the &#8220;growth rate R&#8221; as the header of column A, and &#8220;population size after 10 generations&#8221; as the second column.
</div></li><li data-sub-note-item="1-20"><div class="lab-step">Record the population size at generation 10 for the growth rate of 0.05, the rate we just simulated.
</div></li><li data-sub-note-item="1-21"><div class="lab-step">Now, set the growth rate in cell E2 to 0.1, and repeat the steps to generate a new scatter plot.
</div></li><li data-sub-note-item="1-22"><div class="lab-step">Record the population size at generation 10.
</div></li><li data-sub-note-item="1-23"><div class="lab-step">Continue to alter the growth rate using the values in table one, noticing how the Y axis changes to accommodate the increasing values.
</div></li><li data-sub-note-item="1-24"><div class="lab-step">After completing Table 1, set the growth rate back to 0.05 and change the initial population size in cell B2 to 100 individuals.
</div></li><li data-sub-note-item="1-25"><div class="lab-step">Record the population size at generation 10 in a new table, Table 2.
</div></li><li data-sub-note-item="1-26"><div class="lab-step">Proceed to change the initial population size using the values in Table 2. Once the table is complete, save the Excel sheet and close it.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Logistic Population Growth Model
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">HYPOTHESES: In a logistic growth model, the experimental hypothesis could be that populations will approach and meet the carrying capacity, and any populations with sizes greater than the carrying capacity will decrease in the subsequent generation. The null hypothesis might be that population growth will not follow a logistic S-shaped curve in the logistic models, and that generations with population sizes greater than the carrying capacity will continue to increase or stay the same in the next generation.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">To build a logistic population growth model, open a new spreadsheet in Excel.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Save the file as &#8220;logistic growth&#8221; in the same folder as before.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Then label cell A1 &#8220;generation&#8221; and B1 &#8220;population size&#8221;.
</div></li><li data-sub-note-item="2-5"><div class="lab-step">Type the number zero in cell A2. This row will represent the initial generation.
</div></li><li data-sub-note-item="2-6"><div class="lab-step">In cell A3 type the formula: = 1 + A2
</div></li><li data-sub-note-item="2-7"><div class="lab-step">Now, hit enter.
</div></li><li data-sub-note-item="2-8"><div class="lab-step">Now, click on cell A3 and drag the small box in the bottom right corner to cell A12 to number up to the 10th generation.
</div></li><li data-sub-note-item="2-9"><div class="lab-step">Type the number two in cell B2 to represent the initial population size.
</div></li><li data-sub-note-item="2-10"><div class="lab-step">Label cell E1 as &#8220;max growth rate&#8221; and cell F1 as &#8220;carrying capacity&#8221;.
</div></li><li data-sub-note-item="2-11"><div class="lab-step">Finally, set the max growth rate in cell E2 to 0.05. This value will represent the maximum growth rate the population may achieve &#8211; &#8220;R max&#8221; in the discrete logistic equation. NOTE: In the classic logistic growth equation the term K represents carrying capacity. As the population size of the current generation or NT, approaches the carrying capacity, the growth of the population begins to slow.
</div></li><li data-sub-note-item="2-12"><div class="lab-step">Now set the carrying capacity in cell F2 to 50.
</div></li><li data-sub-note-item="2-13"><div class="lab-step">Enter the logistic growth equation in cell B3 as the formula below: = B2 + ((1 + E$2) * B2) * ((F$2 - B2) /F$2)
</div></li><li data-sub-note-item="2-14"><div class="lab-step">Now hit enter.
</div></li><li data-sub-note-item="2-15"><div class="lab-step">Drag the formula down to cell B12 to add it to all 10 generations.
</div></li><li data-sub-note-item="2-16"><div class="lab-step">To observe the results of this exercise, highlight all data and headers in columns A and B, then click &#8220;insert&#8221; and select &#8220;scatter plot with smooth lines and markers&#8221;.
</div></li><li data-sub-note-item="2-17"><div class="lab-step">Label the Y axis population size and the X axis generation.
</div></li><li data-sub-note-item="2-18"><div class="lab-step">Title the chart as logistic growth of a population.
</div></li><li data-sub-note-item="2-19"><div class="lab-step">Observe the growth of the population as it nears carrying capacity and record the generation that the population meets or exceeds carrying capacity in a new table.
</div></li><li data-sub-note-item="2-20"><div class="lab-step">Now set the max growth rate in cell E2 to 1.
</div></li><li data-sub-note-item="2-21"><div class="lab-step">Notice how the population responds when it rises over carrying capacity.
</div></li><li data-sub-note-item="2-22"><div class="lab-step">Continue to set the max growth rate to the values in Table 3 and record the generation at which the population rises two or above carrying capacity.
</div></li><li data-sub-note-item="2-23"><div class="lab-step">Now set the max growth rate back to 0.05 and set the carrying capacity to 1,000.
</div></li><li data-sub-note-item="2-24"><div class="lab-step">Notice how the graph changes as the growth approaches the carrying capacity.
</div></li><li data-sub-note-item="2-25"><div class="lab-step">Drag the formulas in A12 and B12 down to A14 and B14.
</div></li><li data-sub-note-item="2-26"><div class="lab-step">Adjust the plot to include these new data points to show when the population reaches carrying capacity.
</div></li><li data-sub-note-item="2-27"><div class="lab-step">Save the Excel sheet and close it.
</div></li></ul></li><li data-note-item="3" data-parent-collapse="">Predator-Prey Model
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="3-1"><div class="lab-step">Hypotheses: In our third simulation of predator-prey logistics, the experimental hypothesis could be that predator populations will increase as prey populations increase and that the two populations will experience growth and decline dependent on the population of the other species. The null hypothesis may be that the two populations will show patterns of growth unrelated to the other species&#39; population size.
</div></li><li data-sub-note-item="3-2"><div class="lab-step">To build a predator-prey model, open a new spreadsheet in Excel.
</div></li><li data-sub-note-item="3-3"><div class="lab-step">Save the file in the same folder as the other two models and name it &#8220;predator-prey model&#8221;.
</div></li><li data-sub-note-item="3-4"><div class="lab-step">Label cell A1 &#8220;time&#8221;, cell B1 &#8220;prey&#8221; and cell C1 &#8220;predator&#8221;.
</div></li><li data-sub-note-item="3-5"><div class="lab-step">In cell A2 type zero and hit enter.
</div></li><li data-sub-note-item="3-6"><div class="lab-step">In cell A3 type the formula below: = 1 + A2
</div></li><li data-sub-note-item="3-7"><div class="lab-step">Now hit enter.
</div></li><li data-sub-note-item="3-8"><div class="lab-step">Drag the formula down to cell A802 as the model will require many time points to visualize changes of both populations.
</div></li><li data-sub-note-item="3-9"><div class="lab-step">Now type 100 into cell B2 to represent the initial prey population and type 25 into cell C2 as the initial predator population.
</div></li><li data-sub-note-item="3-10"><div class="lab-step">Label cell E1 &#8220;prey growth rate&#8221;, cell F1 &#8220;consumption of prey by predator&#8221;, cell G1 &#8220;conversion efficiency&#8221; and cell H1 &#8220;starvation of predator&#8221;.
</div></li><li data-sub-note-item="3-11"><div class="lab-step">Type the number 0.02 in cell E2. This value represents the growth rate of the prey species or R in the prey population model. NOTE: Here the prey population is represented as VT and the predator population as CT. The value A is the per capita attack rate of the predator species on the prey. In the predator population model, variables are introduced for predators produced per prey consumed, F or conversion efficiency as well as for the starvation rate of the predators noted as Q.
</div></li><li data-sub-note-item="3-12"><div class="lab-step">Type 0.0005 in cell F2 for the attack rate in both the prey population and the predator population model.
</div></li><li data-sub-note-item="3-13"><div class="lab-step">Then type 0.8 in cell G2 to represent the conversion efficiency, and 0.05 in cell H2 for the starvation rate in the predator population equation.
</div></li><li data-sub-note-item="3-14"><div class="lab-step">Now type the prey growth equation in cell B3 as the formula below: =B2+(E$2*B2)-(F$2*C2*B2)
</div></li><li data-sub-note-item="3-15"><div class="lab-step">Hit enter.
</div></li><li data-sub-note-item="3-16"><div class="lab-step">Then type the prey growth equation in cell C3 as the displayed formula: =C2 + (F$2*G$2*B2*C2)-(H$2*C2)
</div></li><li data-sub-note-item="3-17"><div class="lab-step">Hit enter.
</div></li><li data-sub-note-item="3-18"><div class="lab-step">Hold down the shift keys and click to select both cells B3 and C3.
</div></li><li data-sub-note-item="3-19"><div class="lab-step">Click the small box at the bottom right corner of cell B3 and drag the two formulas down to row 802 to simulate the predator and prey populations over 800 generations.
</div></li><li data-sub-note-item="3-20"><div class="lab-step">To visualize the data, select all of the data and headers in columns A, B and C.
</div></li><li data-sub-note-item="3-21"><div class="lab-step">Click &#8220;insert&#8221; and select &#8220;scatter plot with smoothed lines&#8221;.
</div></li><li data-sub-note-item="3-22"><div class="lab-step">A chart showing the population change of the predator and the prey populations should appear.
</div></li><li data-sub-note-item="3-23"><div class="lab-step">Label the Y axis of the plot population size and the X axis time.
</div></li><li data-sub-note-item="3-24"><div class="lab-step">Title the plot &#8220;predator-prey interactions&#8221;.
</div></li><li data-sub-note-item="3-25"><div class="lab-step">Observe the interactions between the predator and prey populations.
</div></li><li data-sub-note-item="3-26"><div class="lab-step">Set the initial predator population to zero and observe what happens in the plot.
</div></li><li data-sub-note-item="3-27"><div class="lab-step">Now set the initial predator population to 120 and observe what happens to the predator population when there are more predators than prey.
</div></li><li data-sub-note-item="3-28"><div class="lab-step">Finally set the initial prey population to zero and observe what happens to the population of the predators.
</div></li><li data-sub-note-item="3-29"><div class="lab-step">Experiment with other values to see what other interactions can be created.
</div></li><li data-sub-note-item="3-30"><div class="lab-step">When finished, save the workbook and close it.
</div></li></ul></li><li data-note-item="4" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="4-1"><div class="lab-step">In the exponential growth model built in the first activity, observe the shape of the resulting population growth curve, and consider whether you think this type of growth could be sustained in nature.
</div></li><li data-sub-note-item="4-2"><div class="lab-step">Note how the growth rate R affected the population size after 10 generations in the exponential growth model.
</div></li><li data-sub-note-item="4-3"><div class="lab-step">Also consider how the initial population size affected the population size after 10 generations in the exponential growth model.
</div></li><li data-sub-note-item="4-4"><div class="lab-step">Now, consider the logistic growth model, and observe the shape of this graph, noting how it differs from the exponential growth model.
</div></li><li data-sub-note-item="4-5"><div class="lab-step">Then, note how the populations behave as they approach the carrying capacity in your logistic growth model.
</div></li><li data-sub-note-item="4-6"><div class="lab-step">Decide if you think that the maximum per capita growth rate, R max, influenced how quickly populations reached carrying capacity in the logistic growth model.
</div></li><li data-sub-note-item="4-7"><div class="lab-step">Also, consider what happens to population size when populations are over carrying capacity in the logistic growth model, or what might happen if the carrying capacity is lowered.
</div></li><li data-sub-note-item="4-8"><div class="lab-step">In the predator-prey model, note whether the prey population responded to increasing predator population. Conversely, consider how the predators then respond to decreasing prey populations.
</div></li></ul></li></ol>

Population Growth: Exponential and Logistic Growth and Predator-Prey Model - Procedure

Exponential Population Growth Model
ExpandNOTE: In these experiments you will use computer software to simulate different types of growth models. ...

Ecology

Bacterial Transformation

<ol class="note-items"><li data-note-item="1" data-parent-collapse=""><strong>Bacterial Transformation</strong>
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">First, ensure that each student or student group has a filled ice bucket.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Then, set up a tube rack for each work station.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Into the tube racks, place two empty, sterile 1.5 mL tubes, and then two one mL aliquots of SOC media.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Then, place the plasmid tube into the ice bucket and add two tubes of competent E. coli cells, allowing them to thaw if frozen.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Set out two LB agar plates per group and two LB agar ampicillin plates per group.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Ensure the students have the appropriate pipettes.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">And finally, set a water bath to 42 &#176;C and a temperature-controlled shaker to 37 &#176;C.
</div></li></ul></li></ol>

Bacterial Transformation using Plasmids - Prep

Bacterial Transformation
ExpandFirst, ensure that each student or student group has a filled ice bucket.
Then, set up a tube rack for each work station.
...

Natural Selection

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Simulating Natural Selection
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">To begin, cut the white, black, and gray pipe cleaners into quarters, which should produce a total of 100 pieces that are 3 inches long.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Then, cut the green pipe cleaners in the quantities and lengths specified in the video, taking care to minimize waste. This approximates a normal distribution of lengths and should result in an additional 100 pieces total. NOTE: One 12-inch pipe cleaner should yield multiple lengths, for example, a 5-inch piece and a 7-inch piece. Use this advantage to reduce waste.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Now, cut one sheet of black and one sheet of white construction paper into approximately 3-inch strips measured along the long edge of the paper.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Tape these strips down to cover the base of a dissecting tray to create an alternating pattern of black and white strips. NOTE: In the first simulation, you will use the white, gray, and black pipe cleaner segments against a black and white background to simulate different selection pressures due to predation on a population. You will select your prey using two different strategies, eyes-open and eyes-closed. For each of these strategies, you will perform four rounds consisting of 25 selections, alternating between two partners. After each round you will restore the population back to its original size using the proportions of phenotypes present after selection.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">To begin the first scenario, count out 12 white, 12 black, and 26 gray pipe cleaner segments with your partner. Hypotheses: Here the experimental hypothesis might be that different predation strategies, such as having the eyes open or closed, will result in distinct selection pressures on the populations. Further, different phenotypes may be positively selected in the alternate scenarios. The null hypothesis might be that different predation strategies will have no effect on the populations or phenotypes.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Mix the pipe cleaner segments together as best as possible. Then, sprinkle them into the papered tray. NOTE: Ideally, the pipe cleaners will be spread out. However, this may not occur due to their self-adhering nature.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Once the segments are in the tray, separate them as best as possible without moving them too far from where they landed. As few segments as possible should be touching.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Have the first partner close their eyes. When given an audible signal by the second partner, the first should open their eyes and grab the first pipe cleaner they see from the tray as quickly as possible.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Set the removed pipe cleaner to the side.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Alternating partners, repeat this process a total of 25 times (steps 8 &#8211; 9).
</div></li><li data-sub-note-item="1-11"><div class="lab-step">Count the remaining pipe cleaners by color and record this in the data collection table for Scenario 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/23/Lab_23_Tables_1_and_2.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Tables 1 and 2</a>
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Then, restore the population to 50 by adding one pipe cleaner of the same color for each of the remaining pipe cleaners. Hold these to the side until they are all gathered.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Then, sprinkle the pipe cleaners into the tray as previously described (steps 6-7). Repeat this process for three more rounds consisting of 25 selections each and record the counts for each of the rounds in the data collection table for Scenario 1.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Remove all pipe cleaners from the tray after the fourth round.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">For Scenario 2, again count out 12 white, 12 black, and 26 gray pipe cleaner segments and sprinkle them randomly into the tray.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Follow the same protocol as in Scenario 1. However, when given the signal, the grabbing partner should keep their eyes closed. To aid in accuracy, it may help to position the hand near to the tray before grabbing.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">Repeat this process 25 times, switching partner roles, and record the remaining pipe cleaner amounts in the data collection table for Scenario 2.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Then, restore the population to 50 based on that count. Do this for all four rounds. NOTE: In the second simulation, you will be using the eyes-open and eyes-closed predation strategies as previously described to select pipe cleaners of various lengths.
</div></li><li data-sub-note-item="1-19"><div class="lab-step">For Scenario 3, gather all 100 of the pipe cleaners that have been cut to varying lengths to represent different phenotypes.
</div></li><li data-sub-note-item="1-20"><div class="lab-step">One partner should hold all the pipe cleaners upright and loosely in one hand resting on the tabletop, such that the bottom end of each pipe cleaner rests against the tabletop.
</div></li><li data-sub-note-item="1-21"><div class="lab-step">Now, the partner that is not holding the pipe cleaners should close their eyes. When given an audible signal, the partner should open their eyes and grab the first pipe cleaner they see from their partner&#39;s hand.
</div></li><li data-sub-note-item="1-22"><div class="lab-step">Repeat this process 50 times (step 21). However, the partners should not switch roles during the round this time. The partner holding the pipe cleaners should always be the one giving the signal and never grabbing from the bunch.
</div></li><li data-sub-note-item="1-23"><div class="lab-step">After the first round consisting of 50 selections, count the remaining pipe cleaners by length. Then, record this information in the data collection table for scenario three. <a href="https://www-jove-com.remotexs.ntu.edu.sg/CDNSource/lm/labs/23/Lab_23_Tables_3_and_4.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Tables 3 and 4</a>
</div></li><li data-sub-note-item="1-24"><div class="lab-step">Add one pipe cleaner of the same length as each of the remaining ones to restore the population to 100. This will likely require more pipe cleaners than were originally cut. Try to use scraps from the class to reduce waste.
</div></li><li data-sub-note-item="1-25"><div class="lab-step">Repeat this for three more rounds, alternating which partner holds or grabs each round.
</div></li><li data-sub-note-item="1-26"><div class="lab-step">To perform Scenario 4, gather the varied-length pipe cleaners in their original proportions.
</div></li><li data-sub-note-item="1-27"><div class="lab-step">Now repeat the grabbing process, as described in Scenario 3. However, only remove 30 pipe cleaners instead of 50.
</div></li><li data-sub-note-item="1-28"><div class="lab-step">Divide the count at the end of each round by two for each phenotype 4 inches or below, rounding fractions down.
</div></li><li data-sub-note-item="1-29"><div class="lab-step">Then, record these values in the data recording table for Scenario 4.
</div></li><li data-sub-note-item="1-30"><div class="lab-step">Use the relative proportions of the phenotypes in the final population to restore the population to 100. When restoring the population to 100, it may be necessary to calculate relative proportions of phenotypes.
</div></li><li data-sub-note-item="1-31"><div class="lab-step">Repeat this for three more rounds, again alternating holding and grabbing the pipe cleaners.
</div></li><li data-sub-note-item="1-32"><div class="lab-step">For each scenario calculate the survival for each phenotype, that is the proportion of the phenotype surviving.
</div></li><li data-sub-note-item="1-33"><div class="lab-step">Then, calculate the relative fitness, or w, between each round. As reproductive rate does not vary in this simulation, this should always be considered equal, or 1.
</div></li><li data-sub-note-item="1-34"><div class="lab-step">Also, calculate the selection coefficients for each phenotype.
</div></li><li data-sub-note-item="1-35"><div class="lab-step">Now, use the relative frequencies of each phenotype to create histograms with appropriately labeled axes for each scenario showing the population before any selection took place and then alongside these in a different color after four rounds of selection.
</div></li><li data-sub-note-item="1-36"><div class="lab-step">Identify the type of selection taking place in each scenario and label the graphs. 
</div></li><li data-sub-note-item="1-37"><div class="lab-step">Develop a hypothesis to explain your observations. 
</div></li></ul></li></ol>

Natural Selection: Calculating Fitness and Selection - Procedure

Simulating Natural Selection
ExpandTo begin, cut the white, black, and gray pipe cleaners into quarters, which should produce a total of 100 pieces that ...

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