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                <h3 class="subhead subhead--dark">IGEM 2018</h3>
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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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<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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                    Modelling Overview
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                      Community Model
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                      Pathway Model
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                        Statistical Models
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                  <p><font size="3">The core of the model uses enzymatic turnover rates (Kcat) to simulate the relative rate (molecules per second) of conversion from substrate to product for each enzymatic reaction in the pathway. Each reaction is split into two parts, formation of the enzyme substrate complex (Substrate binding) and conversion of the complex to the reaction products (Catalysis). The formation of the substrate enzyme complex is reversible whilst the conversion of the complex to the reaction products is not. In the case of 4CL and CHS, for which more than one substrate is required for catalysis of the product, additional steps are coded as separate reversible reactions to emulate these circumstances. </font></p>
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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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                  <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">The model was built on Matlab using differential equations which employ mass action kinetics to describe the behaviour of reactants and products for each reaction step. Mass action kinetics describes the behaviour between a substrate and reactant as an equation where the velocity or rate of a chemical reaction is directly proportional to the concentration of the reactants. These kinetics are used for substrate binding in which substrate and enzyme diffuse towards each other to dimerise via the formation of a covalent bond, and catalysis in which the covalent bond breaks to produce two or more products (excluding the reverse reaction of substrate binding which represents failed substrate binding).</font></p>
 
  
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<p><font size="3">Mass Action kinetics are used due to the fact that we have not fully characterised all aspects of molecular biology with respect to enzyme mechanics. Mass action kinetics are also deterministic in contrast to the stochastic nature of biochemical systems. Deterministic models don’t account for the variety of outcomes that can occur when a low molecular count is used as the probability of collision changes under low molecular counts due to the fact that conversion become event dependant and not rate dependant (5).</font></p>
 
  
  
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<p><font size="3">Enzymatic rates were obtained from literature via the BRENDA enzyme database (6). Enzymatic rates from the origin species of each gene in the operon were used and are displayed in Table 1. Where more than one figure was available, the most cited value was used. The CHI enzyme in the operon originates from Gerbera sp. No rates were identified from this species so the only available rate from <i> Eubacterium rumulus</i> was used. </font></p>
 
  
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<p><font size="3">Table 1. KM constant for each substrate of the four enzymes coded for by the naringenin synthesis operon BBa_1497017 and the source literature used to identify each value. </font></p>
 
  
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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