Difference between revisions of "Team:Yale/modeling"

Line 83: Line 83:
 
       <h3>Forecasting Negative Results</h3>
 
       <h3>Forecasting Negative Results</h3>
 
       <div class="readable">
 
       <div class="readable">
−
         <p>Biofilm formation on surfaces is an issue in the medical field, naval industry, and other areas. We developed an anti-fouling peptide with two modular components: a mussel adhesion protein (MAP) anchor and LL-37, an antimicrobial peptide. MAPs can selectively attach to metal and organic surfaces via L-3,5-dihydroxyphenylalanine (L-DOPA), a nonstandard amino acid that was incorporated using a genetically recoded organism (GRO). Because this peptide is toxic to the GRO in which it is produced, we designed a better controlled inducible system that limits basal expression. This was achieved through a novel T7 riboregulation system that controls expression at both the transcriptional and translational levels.</p>
+
         <p>Our modelling team developed a PCR error rate estimation program. Originally, our modelling team developed a model based primarily off of binomial distribution. This model had similar results to the error rate calculator hosted by Thermofisher.  While not completely  correlated, the results were consistently close enough that it is likely these two models had the same underlying basis. The problem with applying simple binomial distribution to PCR reactions is that the event of copying the DNA is not independent.  If an error occurs in an early replication it is propagated through subsequent replications and thus this must be accounted for within the calculations.</p>
−
         <p>Biofilm formation on surfaces is an issue in the medical field, naval industry, and other areas. We developed an anti-fouling peptide with two modular components: a mussel adhesion protein (MAP) anchor and LL-37, an antimicrobial peptide. MAPs can selectively attach to metal and organic surfaces via L-3,5-dihydroxyphenylalanine (L-DOPA), a nonstandard amino acid that was incorporated using a genetically recoded organism (GRO). Because this peptide is toxic to the GRO in which it is produced, we designed a better controlled inducible system that limits basal expression. This was achieved through a novel T7 riboregulation system that controls expression at both the transcriptional and translational levels.</p>
+
 
−
         <p>Biofilm formation on surfaces is an issue in the medical field, naval industry, and other areas. We developed an anti-fouling peptide with two modular components: a mussel adhesion protein (MAP) anchor and LL-37, an antimicrobial peptide. MAPs can selectively attach to metal and organic surfaces via L-3,5-dihydroxyphenylalanine (L-DOPA), a nonstandard amino acid that was incorporated using a genetically recoded organism (GRO). Because this peptide is toxic to the GRO in which it is produced, we designed a better controlled inducible system that limits basal expression. This was achieved through a novel T7 riboregulation system that controls expression at both the transcriptional and translational levels.</p>
+
         <p>Original samples of PCR fragments stay in the mixture after they are used as templates in the initial round of replication.  Thus, the generation of a PCR fragment matters when replication is occurring. Therefore, while a third cycle fragment’s replicated product may have a [1-(0.99)]^3 chance of having a single mutation, there will still be first generation fragments whose replication products will only have a [1-0.99] chance of being mutated.  Therefore, without taking generations into account, one develops a more pessimistic model than may be accurate. Due to this, we called our original model the Pessimistic Model and the newer model, which we found in literature, the New Model. There are a few other factors that need to be taken into account, such as the chance that a strand of DNA is even replicated at all during a cycle.  These concerns are also accounted for in the New Model.
 +
</p>
 +
 
 +
<h2 id="doubletime">New Model</h2>
 +
<h3>Optimism and accounting for sequence history</h3>
 +
         <p>H Sharifian 2010 presents a PCR model based on the Galton-Watson process, where an ancestor particle produces a randomly-distributed number of progeny, which repeat this cycle. This branching process can be used to model errors in replication in PCR.
 +
</p>
 +
 
 +
<p>
 +
This model is based on a previous model by Sun 1995 [1]. We begin with S copies of identical single-stranded DNA fragments. Every cycle, each sequence generates a copy with probability lambda (we assume lambda = 1). With each cycle, PCR errors may occur. We formulate the error rate per base as mu, and the length of the sequence in question as G. Then, the error rate per sequence is mu*G. We also take into account the fact that later generations of PCR templates have a higher probability of containing errors.  
 +
</p>
 +
 
 +
<p>
 +
The following formula gives the probability of a randomly selected sequence containing m mutations. The parameter k represents the sequence generation, and n represents the number of PCR cycles.  
 +
<center><img src = https://static.igem.org/mediawiki/2015/5/54/Yale2015modelequation.png></img></center>
 +
</p>
 +
 
 +
 
 
       </div>
 
       </div>
 
        
 
        

Revision as of 01:13, 18 September 2015


<!DOCTYPE html> Yale iGem 2015: Modeling

Developing a Framework for the Genetic Manipulation of Non-Model and Environmentally Significant Microbes

Overview

Methods

Results

Modeling

Parts

Modeling

Even NonModels need some testing.

PCR Mutation Predictor

Introduction to Modeling

PCR errors are an important problem for all genetic engineering projects, and particularly relevant towards testing MAGE in a new organism. Reliably tracking the mutations created by a MAGE process should take consideration of the errors generated during PCR, given factors such as sequence length, polymerase type, and number of cycles. We developed an interface to calculate the expected share of mutated sequences given this set of user-defined parameters.

View PCR Error Model on GitHub

Biofilm formation on surfaces is an issue in the medical field, naval industry, and other areas. We developed an anti-fouling peptide with two modular components: a mussel adhesion protein (MAP) anchor and LL-37, an antimicrobial peptide. MAPs can selectively attach to metal and organic surfaces via L-3,5-dihydroxyphenylalanine (L-DOPA), a nonstandard amino acid that was incorporated using a genetically recoded organism (GRO). Because this peptide is toxic to the GRO in which it is produced, we designed a better controlled inducible system that limits basal expression. This was achieved through a novel T7 riboregulation system that controls expression at both the transcriptional and translational levels.

Biofilm formation on surfaces is an issue in the medical field, naval industry, and other areas. We developed an anti-fouling peptide with two modular components: a mussel adhesion protein (MAP) anchor and LL-37, an antimicrobial peptide. MAPs can selectively attach to metal and organic surfaces via L-3,5-dihydroxyphenylalanine (L-DOPA), a nonstandard amino acid that was incorporated using a genetically recoded organism (GRO). Because this peptide is toxic to the GRO in which it is produced, we designed a better controlled inducible system that limits basal expression. This was achieved through a novel T7 riboregulation system that controls expression at both the transcriptional and translational levels.

PCR Mutation Predictor

Forecasting Negative Results

Our modelling team developed a PCR error rate estimation program. Originally, our modelling team developed a model based primarily off of binomial distribution. This model had similar results to the error rate calculator hosted by Thermofisher. While not completely correlated, the results were consistently close enough that it is likely these two models had the same underlying basis. The problem with applying simple binomial distribution to PCR reactions is that the event of copying the DNA is not independent. If an error occurs in an early replication it is propagated through subsequent replications and thus this must be accounted for within the calculations.

Original samples of PCR fragments stay in the mixture after they are used as templates in the initial round of replication. Thus, the generation of a PCR fragment matters when replication is occurring. Therefore, while a third cycle fragment’s replicated product may have a [1-(0.99)]^3 chance of having a single mutation, there will still be first generation fragments whose replication products will only have a [1-0.99] chance of being mutated. Therefore, without taking generations into account, one develops a more pessimistic model than may be accurate. Due to this, we called our original model the Pessimistic Model and the newer model, which we found in literature, the New Model. There are a few other factors that need to be taken into account, such as the chance that a strand of DNA is even replicated at all during a cycle. These concerns are also accounted for in the New Model.

New Model

Optimism and accounting for sequence history

H Sharifian 2010 presents a PCR model based on the Galton-Watson process, where an ancestor particle produces a randomly-distributed number of progeny, which repeat this cycle. This branching process can be used to model errors in replication in PCR.

This model is based on a previous model by Sun 1995 [1]. We begin with S copies of identical single-stranded DNA fragments. Every cycle, each sequence generates a copy with probability lambda (we assume lambda = 1). With each cycle, PCR errors may occur. We formulate the error rate per base as mu, and the length of the sequence in question as G. Then, the error rate per sequence is mu*G. We also take into account the fact that later generations of PCR templates have a higher probability of containing errors.

The following formula gives the probability of a randomly selected sequence containing m mutations. The parameter k represents the sequence generation, and n represents the number of PCR cycles.

Yale iGem 2015

Main Campus:

Yale Department of Molecular, Cellular & Developmental Biology

Attn: Farren Isaacs/iGEM

219 Prospect St

PO Box 208103

New Haven, CT 06520

Tel. +1(203) 432-3783

E-mail: igem.yale@gmail.com

© Yale iGEM 2015