Difference between revisions of "Team:Heidelberg/Model/Lagoon Contamination"

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     Modeling.|
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     Modeling|
     Mutation Rate Estimation|
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     Lagoon contamination|
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             <h1>Number of mutations and mutated sequences</h1>
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             <a href="https://2017.igem.org/Team:Heidelberg/Model/Contamination">
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<img padding="50" height="30" src="https://static.igem.org/mediawiki/2017/thumb/2/2e/T--Heidelberg--Team_Heidelberg_2017_modeling-logo.png/320px-T--Heidelberg--Team_Heidelberg_2017_modeling-logo.png">
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Interactive Contamination Model
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            To maintain a succesful continuous PACE experiment persisting contaminations of the lagoons with foreign bacteria or funghi, which would harm or interfere with the host <i>E. coli</i> strain have to be avoided. Therefore the question arose, whether a unwanted microorganism would propagate inside the lagoons in a way that it would persist and displace the host strain. Since a cancelled PACE run due to suspected contamination can be very costly an simulation of contamination growth would be vital to save precious resources. If the constant dilution rate of the lagoons is sufficiently high that the overall change in population size of the contamination is negative, a contamination of suspended cells would likey wash out and wouldn't be a lasting problem.
 +
            <h1>Theory</h1>
 +
            <b>Note:</b> contamination with viruses such as bacteriophages is not accounted for, because only exponential growth of the contamination is assumed in this model. This is plausible for bacteria and funghi since the lagoons are constantly diluted so that there is always fresh medium and space available.
  
             Directed evolution experiments are basically a search for a set of mutations. Consequently sequencing a few plaques from a PACE or PREDCEL experiment is regularly performed to monitor the current state of the experiments and to make sure mutagenesis plasmids work. To minimize the required time, consumed materials and costs, it is helpful to estimate the number of clones that are sequenced so that with a given probability at least one clone contains a mutation.
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             The change in concentration of the contamination can be described as
  
             To calculate this probability or the number of clones needed to reach it, we used a basic mutation model. The mutation rate is assumed to be the same for each sequence and each position of a sequence. Mutations that revert previous mutations are ignored since we ususally expected only a few mutations in hundreds of basepairs.
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             $$
 +
            \frac{\partial c_{X}(t)}{\partial t} = -\Phi_{L} \cdot c_{X}(t) + \frac{\ln{2} }{t_{X} } \cdot c_{X}(t)
 +
            $$
 +
           
 +
            The concentration of the contamination \(x_{X}\) is assumed to only depend on the time, since the constant dilution reduces effects such as spent resources or growth inhibition by waste products. The growth factor \(\frac{ln(2)}{t_{X} }\) is the factor by which the current concentration and its derivative are proportional.
 +
            There is the dilution term containing the factor \(\Phi_{L}\) that is the flow rate trough the lagoon in volumes per hour and the growth term of the contamination based on the contaminations generation time \(t_{X}\). Solving this equation yields
  
             Expected number of mutations in a single sequence \(p_{m}\):
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             $$
             $$p_{m} = \frac{N_{M} }{L_{S} } = N_{g} \cdot r_{M} = \frac{t \cdot \Phi_{L} }{2} \cdot r_{M}$$
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             c_{X}(t) = c_{X}(t_{0}) \cdot e^{\big(\frac{ln(2)}{t_{X} } - \Phi_{L}\big) \cdot t}
            \(N_{M}\) is the number of mutations, while \(L_{S}\) is the length of the sequence that can mutate free of seleciton and is sequenced. \(N_{g}\) is the number of generations that happened before the sequencing. According to <i>Esvelt et al.</i><x-ref>RN66</x-ref> the number of generations translates into half the flow rate \(\Phi_{L}\) in volumes per hour. The basic assumption is a steady state of phage replication and dilution which is reasonable for PACE. For PREDCEL experiments arbitrary numbers of generations can be set.
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            $$
 
              
 
              
             The expected share of sequences that shows at least one mutation in \(L_{S}\) basepairs, is the probability that \(L_{S}\) basepairs stay unchanged when \(p_{m}\) mutations per sequence length are expected:
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             Here \(c_{X}(t_{0})\) is introduced, which is the initial concentration of the contamination.
             $$p_{M} = \frac{N_{M} }{N_{S} } = 1 - p_{(N_{M}=0)} = 1 - (1-p_{m})^{L_{S} } $$
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             <br>
 +
            When working with the model it is more practical to calculate relative changes in the concentration:
 
              
 
              
             With this equation it is possible to calculate the number of sequences \(N_{S}\) that have to be sequenced in order to find a mutated one with a probability of \(p_{(N_{M} > 0)}\). The number of sequences \(N_{S}\) that need to be sequenced is the relation of the probability to find at least one mutated sequence \(P_{(N_{M}>0)}\) to the probability of a single sequence to be mutated \(P_{M}\).
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             $$\frac{c_{X}(t)}{c{X}(t_{0})}$$
  
             $$ N_{S} = \frac{p_{M} }{p_{(N_{M} > 0)} } $$
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            That simplifies the above equation to
 +
 
 +
             $$
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            c_{X}(t) = e^{\big(-\Phi_{L} + \frac{\ln{2} }{t_{X} }\big) \cdot t}
 +
            $$
 
              
 
              
             The probability to find at least one mutated sequence under the given conditions can be calculated using the complementary probability. It is the probability to find exactly zero mutated sequences.
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             Differentiation after \(t\) gives
  
             $$p_{(N_{M}>0)} = 1 - (1-p_{M})^{N_{S} }$$
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             $$
 
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            \frac{\partial c_{X}(t)}{\partial t} = \Big(\frac{ln(2)}{t_{X} } - \Phi_{L}\big) e^{\big(\frac{ln(2)}{t_{X} } - \Phi_{L}\Big) \cdot t}
            Using this equation leads to
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            $$
 
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            $$N_{S} = \frac{p_{M} }{1 - (1-p_{M})^{N_{S} } } $$
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            <b>The following statements can be made:</b>
 
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            <br>
             $$ \Leftrightarrow N_{S} = \frac{ln(1-p_{(N_{M}>0)})}{ln(1-p_{M})}$$
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            The contamination expands if the flow rate \(\Phi_{L}\) is lower than the growth factor,
 
+
             $$
             $$ \Leftrightarrow N_{S} = \frac{ln(1-p_{(N_{M}>0)})}{ln\Big((1 - p_{m})^{L_{S} } \Big)}$$
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            c_{X}(t) > 0, \quad if \quad \Phi_{L} < \frac{ln(2)}{t_{X} }  
 
+
            $$
             Set \(\Phi\) to zero to use the number of generations for the calculation. If \(\Phi_{L}\) and the number of generations are given, \(\Phi_{L}\) is used.
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            it remains constant if the flow rate is exactly the growth factor
            Consider \(L_{Sequence}\) as the number of basepairs that is expected to be mutated. If half of the sequence you are interested in, is highly conserved choose a lower \(L_{Sequence}\).
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             $$
 +
            c_{X}(t) = 0, \quad if \quad \Phi_{L} = \frac{ln(2)}{t_{X} }
 +
            $$  
 +
             and it diminishes, when the flow rate is higher than the growth factor.
 +
            $$
 +
            c_{X}(t) < 0, \quad if \quad \Phi_{L} > \frac{ln(2)}{t_{X } }
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            $$
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            <br>
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                 {{Heidelberg/templateus/Tablebox|
 
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                         Table 1: Additional Variables and Parameters used for the calculation of the number of mutated sequences|
 
                         Table 1: Additional Variables and Parameters used for the calculation of the number of mutated sequences|
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                                         <td>\(t \)</td>
 
                                         <td>\(t \)</td>
 
                                         <td>[h]</td>
 
                                         <td>[h]</td>
                                         <td>Total time in lagoon</td>
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                                         <td>Time</td>
 
                                     </tr>  
 
                                     </tr>  
 
                                     <tr>
 
                                     <tr>
                                         <td>\(p_{m} \)</td>
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                                         <td>\(c_{X} \)</td>
                                         <td>[bp/bp]</td>
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                                         <td>>relative units</td>
                                         <td>Expected number of mutations per sequence</td>
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                                         <td>Contamination concentration </td>
 
                                     </tr>   
 
                                     </tr>   
 
                                     <tr>
 
                                     <tr>
                                         <td>\(p_{M} \)</td>
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                                         <td>\(\Phi_{L}\)</td>
                                         <td>[bp/sequences]</td>
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                                         <td>[volumes/h]</td>
                                         <td>Expected number of mutations in all sequences</td>
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                                         <td>Flow rate of the lagoons</td>
 
                                     </tr>   
 
                                     </tr>   
 
                                     <tr>
 
                                     <tr>
                                         <td>\(N_{M} \)</td>
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                                         <td>\(t_{X}\)</td>
                                         <td>[bp]</td>
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                                         <td>[min]</td>
                                         <td>Number of mutated basepairs</td>
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                                         <td>Generation time of contamination, time in which it doubles</td>
 
                                     </tr>   
 
                                     </tr>   
                                    <tr>
 
                                        <td>\(L_{S} \)</td>
 
                                        <td>[bp]</td>
 
                                        <td>Length of sequence that is considered</td>
 
                                    </tr>
 
 
                                      
 
                                      
                                    <tr>
 
                                        <td>\(N_{g} \)</td>
 
                                        <td>[generations]</td>
 
                                        <td>Number of generations</td>
 
                                    </tr>
 
                                    <tr>
 
                                        <td>\(r_{M} \)</td>
 
                                        <td>\([\frac{1}{bp \cdot generation}]\)</td>
 
                                        <td></td>
 
                                    </tr>
 
                                    <tr>
 
                                        <td>\(\Phi_{L} \)</td>
 
                                        <td>[Vol/h]</td>
 
                                        <td></td>
 
                                    </tr>
 
                                    <tr>
 
                                        <td>\(N_{S} \)</td>
 
                                        <td>[sequences]</td>
 
                                        <td>Number of sequences</td>
 
                                    </tr>
 
                                    <tr>
 
                                        <td>\(p_{(N_{M} > 0)} \)</td>
 
                                        <td></td>
 
                                        <td>Probability to find at least one mutated sequence in a pool of sequences</td>
 
                                    </tr>
 
                                    <tr>
 
                                        <td>\(p_{(N_{M} = 0)} \)</td>
 
                                        <td></td>
 
                                        <td>Probability to find no mutated sequences in a pool of sequences</td>
 
                                    </tr>
 
 
                                 </tbody>
 
                                 </tbody>
 
                             </table>
 
                             </table>
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                        }}|{{#tag:html|
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                    List of all paramters and variables used in the analytical solution of this model and for calculations with the <a href="https://2017.igem.org/Team:Heidelberg/Model/Contamination">interactive webtool</a>. When possible values are given.}}
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                    <h1>Practice</h1>
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                    All PACE experiments we planned used lagoon flow rates \(\Phi_{L}\) between \(0.8\) and \(1.5\) volumes per hour. We assumed that the contaminations have generation times greater than \(20\) minutes.
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                    {{Heidelberg/templateus/Imagebox|
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                        https://static.igem.org/mediawiki/2017/c/cd/T--Heidelberg--2017_contaminations_used.png|
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                        Fig: 1 Contamination growth depending on lagoon flow rate and contamination generation time |
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                        {{#tag:html|
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                        The first derivative of the exponential growth of contaminations was calculated for a range of parameters that spans all experiments planned and performed by iGEM Heidelberg. Exact parameters used with the <a href="https://2017.igem.org/Team:Heidelberg/Model/Contamination">interactive webtool for this model</a>: lower contamination generation time: 20 min, Upper contamination generation time: 90 min, Lower flow rate: 0.5 volumes/hour, Upper flow rate 2.0 volumes/hour, Resolution: 200.
 
                         }}|
 
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                         List of all additional paramters and variables used in the numeric solution of this model. When possible values are given.
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                         pos = left
 
                     }}
 
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                    <h1>Conclusion</h1>
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                    Contaminations that have a generation time below 40 min are theoretically able to grow in the lagoons. That does not apply to most funghi even if there are some with generation times below one hour<x-ref>trinci1969</x-ref>, so the main concern remaining is bacteria. The host <i>E. coli</i> for PACE experiments carry two or three plasmids with different resistance genes, to which the corresponding antibiotics are added to the medium. The combination of multiple antibiotics makes bacterial contamination highly unlikely, since the contaminating bacteria would need a defined set of resistance genes in order to grow in one of the lagoons used.
 +
                    <br>
 +
                    Since bacteriophages are not covered by this model, they remain as the main contamination risk.
 
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            <a href="https://2017.igem.org/Team:Heidelberg/Model/Contamination">
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<img padding="50" height="30" src="https://static.igem.org/mediawiki/2017/thumb/2/2e/T--Heidelberg--Team_Heidelberg_2017_modeling-logo.png/320px-T--Heidelberg--Team_Heidelberg_2017_modeling-logo.png">
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Interactive Contamination Model
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Latest revision as of 00:36, 16 December 2017


Modeling
Lagoon contamination
To maintain a succesful continuous PACE experiment persisting contaminations of the lagoons with foreign bacteria or funghi, which would harm or interfere with the host E. coli strain have to be avoided. Therefore the question arose, whether a unwanted microorganism would propagate inside the lagoons in a way that it would persist and displace the host strain. Since a cancelled PACE run due to suspected contamination can be very costly an simulation of contamination growth would be vital to save precious resources. If the constant dilution rate of the lagoons is sufficiently high that the overall change in population size of the contamination is negative, a contamination of suspended cells would likey wash out and wouldn't be a lasting problem.

Theory

Note: contamination with viruses such as bacteriophages is not accounted for, because only exponential growth of the contamination is assumed in this model. This is plausible for bacteria and funghi since the lagoons are constantly diluted so that there is always fresh medium and space available. The change in concentration of the contamination can be described as $$ \frac{\partial c_{X}(t)}{\partial t} = -\Phi_{L} \cdot c_{X}(t) + \frac{\ln{2} }{t_{X} } \cdot c_{X}(t) $$ The concentration of the contamination \(x_{X}\) is assumed to only depend on the time, since the constant dilution reduces effects such as spent resources or growth inhibition by waste products. The growth factor \(\frac{ln(2)}{t_{X} }\) is the factor by which the current concentration and its derivative are proportional. There is the dilution term containing the factor \(\Phi_{L}\) that is the flow rate trough the lagoon in volumes per hour and the growth term of the contamination based on the contaminations generation time \(t_{X}\). Solving this equation yields $$ c_{X}(t) = c_{X}(t_{0}) \cdot e^{\big(\frac{ln(2)}{t_{X} } - \Phi_{L}\big) \cdot t} $$ Here \(c_{X}(t_{0})\) is introduced, which is the initial concentration of the contamination.
When working with the model it is more practical to calculate relative changes in the concentration: $$\frac{c_{X}(t)}{c{X}(t_{0})}$$ That simplifies the above equation to $$ c_{X}(t) = e^{\big(-\Phi_{L} + \frac{\ln{2} }{t_{X} }\big) \cdot t} $$ Differentiation after \(t\) gives $$ \frac{\partial c_{X}(t)}{\partial t} = \Big(\frac{ln(2)}{t_{X} } - \Phi_{L}\big) e^{\big(\frac{ln(2)}{t_{X} } - \Phi_{L}\Big) \cdot t} $$ The following statements can be made:
The contamination expands if the flow rate \(\Phi_{L}\) is lower than the growth factor, $$ c_{X}(t) > 0, \quad if \quad \Phi_{L} < \frac{ln(2)}{t_{X} } $$ it remains constant if the flow rate is exactly the growth factor $$ c_{X}(t) = 0, \quad if \quad \Phi_{L} = \frac{ln(2)}{t_{X} } $$ and it diminishes, when the flow rate is higher than the growth factor. $$ c_{X}(t) < 0, \quad if \quad \Phi_{L} > \frac{ln(2)}{t_{X } } $$

Table 1: Additional Variables and Parameters used for the calculation of the number of mutated sequences List of all paramters and variables used in the analytical solution of this model and for calculations with the interactive webtool. When possible values are given.

Symbol Value and Unit Explanation
\(t \) [h] Time
\(c_{X} \) >relative units Contamination concentration
\(\Phi_{L}\) [volumes/h] Flow rate of the lagoons
\(t_{X}\) [min] Generation time of contamination, time in which it doubles

Practice

All PACE experiments we planned used lagoon flow rates \(\Phi_{L}\) between \(0.8\) and \(1.5\) volumes per hour. We assumed that the contaminations have generation times greater than \(20\) minutes.
Fig: 1 Contamination growth depending on lagoon flow rate and contamination generation time
The first derivative of the exponential growth of contaminations was calculated for a range of parameters that spans all experiments planned and performed by iGEM Heidelberg. Exact parameters used with the interactive webtool for this model: lower contamination generation time: 20 min, Upper contamination generation time: 90 min, Lower flow rate: 0.5 volumes/hour, Upper flow rate 2.0 volumes/hour, Resolution: 200.

Conclusion

Contaminations that have a generation time below 40 min are theoretically able to grow in the lagoons. That does not apply to most funghi even if there are some with generation times below one hourtrinci1969, so the main concern remaining is bacteria. The host E. coli for PACE experiments carry two or three plasmids with different resistance genes, to which the corresponding antibiotics are added to the medium. The combination of multiple antibiotics makes bacterial contamination highly unlikely, since the contaminating bacteria would need a defined set of resistance genes in order to grow in one of the lagoons used.
Since bacteriophages are not covered by this model, they remain as the main contamination risk.

References