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                <h3 class="subhead subhead--dark">IGEM 2018</h3>
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                <h1 class="display-1 display-1--light">Model</h1>
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                <h1 class="display-2">Rationale and Aim</h1>
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<p><font size="3">We have chosen to use Pseudomonas fluorescens (DSM 25356) as a chassis organism due to its rapid and abundant colonisation abilities within the rhizosphere. To ensure P. fluorescens can colonise effectively, the metabolic load and resource drain of our system on the cell must be kept to a minimum in order to conserve natural homeostasis and minimise waste. As our system utilises enzymes found in flavonoid production, our naringenin synthesis pathway will be most taxing on resources used to maintain the natural homeostasis of flavonoid pathways. Some of these resources (ATP, CoA and Malonyl CoA, for example) are included but not restricted to flavonoid production. If we are to program a cell to produce an amount of naringenin per unit time, we need to consider what range of output works best in terms of maintaining the cells homeostasis.</font></p>
 
  
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<p><font size="3">On top of maintaining the cells homeostasis, sufficient naringenin needs to be produced by the pathway to attract the desired nitrogen fixing bacteria. The results of our chemotaxis experiments and previous research have shown higher concentrations of naringenin to inhibit growth of multiple soil microbes (1). It is therefore important that production by our system is stable in variable environmental conditions such that there are no detrimental effects on the rhizosphere community. By creating an enzymatic model of the pathway, we aimed to alter the operon design in order to minimise resource drain and stabilise naringenin production as a means of increasing system optimisation and robustness. </font></p>
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                <h3>Alternative Roots</h3>
  
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<p><font size="3">The chemotaxis modelling and wetware element to our project characterise the production rate of naringenin required for chemotaxis to occur. When the production rates of naringenin by our endophytic chassis have been characterised, the pathway model will give us the tools to alter the operon in order to optimise production.</font></p>
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                <h1>
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                    Modelling Overview
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                        Background
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                      Community Model
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                      Pathway Model
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                        Statistical Models
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                 <h1 class="display-2"><font color="white">Background</font></h1>
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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">Naringenin, our chemoattract of choice, is synthesised from L-tyrosine via the enzymatic action of tyrosine ammonia lyase (TAL), 4-coumarayl ligase (4CL), chalcone synthase (CHS) and chalcone isomerase (CHI) (Figure1). These four enzymes are contained within the iGEM part BBa_1497017. Within this part all four enzymes are under the control of a single strong T7 promoter (BBa_I712074), with each enzyme having the same strong RBS (BBa_B0034). This system gives equal expression of each of the four enzymes. Previous research has identified that changing relative expression of each enzyme in the pathway improved naringenin production (2). We intended to optimise our system by using a model to explore how we could redesign the operon in order to maximise flux through the pathway whilst minimising resource consumption and waste. </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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<li>Providing cities with fresh produce all year round.</li>
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<li>Reducing the Carbon footprint of crop production due to reduced food millage.</li>
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<li>No agricultural run-off.</li>
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<li>Limited need for pesticides and herbicides.</li>
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<li>Safer crops as there is less risk of contamination.</li>
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<li>Reduced spoilage because of shorter transportation times and reduced handling.</li>
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<li>Less agricultural pollution.</li></ul>
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                    <p style="font-size:100%"><br>With developing technologies in the field of sustainable energy, it could one day be possible to engineer contained growth systems that are self-sustaining in regards to its energy usage. By carefully controlling the parameters within these environments, we are able to emulate perfect surroundings that allow the crops to grow to their full potential, maximising yield.</p>
 
  
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                    <p style="font-size:100%">Our project plans to use genetically modified bacteria, which means we will be working with GMO’s, but what are GMO’s? - “Genetically modified organisms (GMOs) can be defined as organisms (i.e. plants, animals or microorganisms) in which the genetic material (DNA) has been altered in a way that does not occur naturally by mating and/or natural recombination.”[2]</p>
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                    <p style="font-size:100%">Integrations of GMO’s into the natural environment pose many concerns to both science and ecological communities. Introducing gm crops into the wild holds the potential to introduce engineered genes into foreign species. The effects of GMO release are widely unidentified, this is the main area of concern as there so many unknowns.</p>
 
  
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                    <p style="font-size:100%">The use of GM bacteria means that we have to take precautions when integrating it into the real world. We have identified the ways to ensure systems are enclosed and risk of GM run-off is minimised on our  <a href="https://2018.igem.org/Team:Newcastle/Safety">Safety Page</a>.</p>
 
  
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<p class="about-para"><font size="2">1. Despommier D (2011) The vertical farm: Controlled environment agriculture carried out in tall buildings would create greater food safety and security for large urban populations. J fur Verbraucherschutz und Leb 6(2):233–236.<font></p>
 
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<p class="about-para"><font size="2">2.World Health Organization. (2018). Q&A: genetically modified food. [online] Available at: http://www.who.int/foodsafety/areas_work/food-technology/faq-genetically-modified-food/en/ [Accessed 13 Sep. 2018]..
 
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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