Difference between revisions of "Team:Newcastle/Model"

 
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                        Microbial Community
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<p><font size="3">We used two different mathematical modelling approaches to describe and simulate different aspects of our project. We built an <a href="https://2018.igem.org/Team:Newcastle/Modelling/Community" class="black">agent-based model</a> to understand the behaviour of nitrogen fixing bacteria in response to the chemoattractant naringenin. We used Simbiotics to visualise stochastic simulations via real-time animations. These models guided experimental work and were then informed by data from our chemotaxis experiments and bacterial growth characterisation. Our model provided insight into the biofilm formation process, including biofilm thickness and number of cells of each nitrogen-fixing species present. We also built a <a href="https://2018.igem.org/Team:Newcastle/Naringenin_Pathway" class="black">kinetic model</a> describing metabolic flux through the naringenin biosynthetic pathway. Our model employed mass action kinetics to describe the behaviour of reactants and products for each step in the pathway. By coupling this information with models describing the rates of production and turnover of the four naringenin biosynthetic enzymes we developed an improved genetic design for our biosynthetic devices.<font></p>
  
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<p><font size="3">In addition to mathematical models, our team also utilised statistical models for optimising both competent cell buffers and a defined media for <i>E. coli</i> DH5α transformation efficiency and growth respectively. Statistically driven design of experiments (DoE) was performed using JMP Pro 12 statistical software (SAS Institute Inc., USA), allowing the maximum design space coverage with minimum experimental runs. The least squares and partial least squares models produced for transformation efficiency and defined media respectively allowed identification of significant factors and predicted optimal buffer and media compositions. Additionally, the JMP screening platform predicted significant interaction affects using the principle of effect sparsity.<font></p>
  
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                <h1 class="display-2">References & Attributions</h1>
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<p class="about-para"><font size="2"><strong>Attributions: Frank Eardley, Patrycja Ubysz, Matthew Burridge and Sam Went
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Latest revision as of 19:09, 17 October 2018

Alternative Roots/Notebook

Alternative Roots

Modelling Overview

Background Community Model Pathway Model Statistical Models

Background

We used two different mathematical modelling approaches to describe and simulate different aspects of our project. We built an agent-based model to understand the behaviour of nitrogen fixing bacteria in response to the chemoattractant naringenin. We used Simbiotics to visualise stochastic simulations via real-time animations. These models guided experimental work and were then informed by data from our chemotaxis experiments and bacterial growth characterisation. Our model provided insight into the biofilm formation process, including biofilm thickness and number of cells of each nitrogen-fixing species present. We also built a kinetic model describing metabolic flux through the naringenin biosynthetic pathway. Our model employed mass action kinetics to describe the behaviour of reactants and products for each step in the pathway. By coupling this information with models describing the rates of production and turnover of the four naringenin biosynthetic enzymes we developed an improved genetic design for our biosynthetic devices.

In addition to mathematical models, our team also utilised statistical models for optimising both competent cell buffers and a defined media for E. coli DH5α transformation efficiency and growth respectively. Statistically driven design of experiments (DoE) was performed using JMP Pro 12 statistical software (SAS Institute Inc., USA), allowing the maximum design space coverage with minimum experimental runs. The least squares and partial least squares models produced for transformation efficiency and defined media respectively allowed identification of significant factors and predicted optimal buffer and media compositions. Additionally, the JMP screening platform predicted significant interaction affects using the principle of effect sparsity.







References & Attributions

Attributions: Frank Eardley, Patrycja Ubysz, Matthew Burridge and Sam Went