Team:SydneyUni Australia/Modelling Intro

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Modelling is story-telling. Mathematicians and poets both try to condeense experience into simple, beautiful truths.  
Modelling is story-telling. Mathematicians and poets both try to condeense experience into simple, beautiful truths.  
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<div align="justify">A pharmacokinetic model was constructed in order to determine the intracellular concentration of the metabolites as a function of time and determine the rate at which DCA is removed from solution. The synthetic metabolic pathway involves four introduced enzymes which converts the substrate 1,2-dichloroethane (DCA) into the end product glycolate through 3 metabolic intermediates. The concentration of each metabolic species over time was modelled through a system of ordinary differential equations (ODE) where Michaelis-Menton (MM) equations were used to model the kinetics of each enzyme of our constructed metabolic pathway. </div>
 
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As described in other sections of our wiki,
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There were 2 motives in mind when concocting our model:
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1. At what rate will our engineered cells (our DCA degraders) remove DCA from solution given the DCA and cell concentration.
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2. At what intracellular concentration does the cytotoxic chloracetaldehyde (metabolic intermediate) reach?
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3. Of our 2 possible pathways (monooxygenase and non-monooxygenase), which has the highest rate of DCA removal? And which keeps chloroacetaldehyde at the lowest possible concentration?
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So, a pharmacokinetic model was constructed in order to gauge how the intracellular concentrations of each of our metabolites change over time.
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The rate at which our metabolite concentration changes was modelled by 2 additive Michaelis-Menton equations: one describing the rate at which the metabolite is formed (acting as a product) and the other described the rate at which the metabolite is removed/used (acting as a substrate).
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By linking all metabolites together by classifying them simultaneously as a product and substrate (bar the initial substrate and final product), a system of ordinary differential equations (ODE) is born.
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Revision as of 12:09, 10 October 2013

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Introduction

So why bother with modelling? Generally, a model of a natural/physical system allows humans to probe the inner workings of that system. They are used to quantitatively and/or qualititatively understand the mechanisms by which the system exists. Mathematics is a language which connects the human brain to the world in which it exists in. A model is the screen which translates nature's languange into one which we can interprate and try to begin to understand. By designing a model based on truths, we try to learn what nature has to say. Modelling is story-telling. Mathematicians and poets both try to condeense experience into simple, beautiful truths.


As described in other sections of our wiki,

There were 2 motives in mind when concocting our model:

1. At what rate will our engineered cells (our DCA degraders) remove DCA from solution given the DCA and cell concentration.

2. At what intracellular concentration does the cytotoxic chloracetaldehyde (metabolic intermediate) reach?

3. Of our 2 possible pathways (monooxygenase and non-monooxygenase), which has the highest rate of DCA removal? And which keeps chloroacetaldehyde at the lowest possible concentration?

So, a pharmacokinetic model was constructed in order to gauge how the intracellular concentrations of each of our metabolites change over time. The rate at which our metabolite concentration changes was modelled by 2 additive Michaelis-Menton equations: one describing the rate at which the metabolite is formed (acting as a product) and the other described the rate at which the metabolite is removed/used (acting as a substrate). By linking all metabolites together by classifying them simultaneously as a product and substrate (bar the initial substrate and final product), a system of ordinary differential equations (ODE) is born.

With thanks to:



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