Team:Edinburgh/Modelling/Kinetic

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Latest revision as of 04:08, 30 October 2008

Introduction

This section aims to outline the model of the cellulase secretion pathway for cellulose degradation as constructed by the University of Edinburgh 2008 iGEM team. The computational model includes two operons, 14 species, and 15 reactions, and focusses on the switching on of genes for cellulose degradation by CRP-cAMP.

Kinetic Modelling

Through mathematical modelling of cellular processes, we aim to reflect biological function in sets of equations supplied with parameter values that best fit observed reality (Drillon, G. 2008). If the outcomes of modelled simulations closely correspond to experimental results, the model is valuable for understanding the biological mechanism. Thus through modelling we may generate new hypotheses at low cost, while also investigating and corroborating or ending doubts about previous hypotheses (Baralla, 2008). Modelling in preparation and in response to wetlab work can save money and time by focussing on more likely hypotheses.

A standard way of modelling (non-spatial) physical systems which has been successfully applied to simulate metabolic systems (Bakker et al., 1997) is the use of Ordinary Differential Equations (ODEs). The aim of using ODEs is to describe a biological phenomenon, so that the time dynamics of the system can be analyzed or the steady state values of the variables involved in the model can be calculated. The effects of transcriptional regulation as well as of the changes in concentration of metabolites via enzymes are thus simulated.

The Biological System

The system we sought to model is as follows: Reduction in glucose concentration leads to increased adenylate cyclase activity. This results in more AMP being converted to cAMP. cAMP binds to CRP. The resultant CRP-cAMP transcription factor activates the transcription of cellulase and cellubiase proteins (cenA, cex, and bglX) and comK via the cstA operator. ComK in turn binds to the comK operator, whereupon phiX174E, a lysis enzyme, is produced. Cell lysis releases the cellulases and bglX into the growth medium.

The Model

Modelling was accomplished using [http://www.copasi.org/tiki-index.php COPASI]. Each step of the pathway is expressed using an appropriate rate law and short-hand reaction equation.

Our survey of the literature yielded some kinetic information for the genes in our system, in vivo, as well as in E. coli. Parameter values are currently being fit.

Ordinary Differential Equations

ODEs of the kinetic model as generated by the COPASI modelling application.

References

Bakker, BM., Michelis, PAM., Opperdoes, FR, and Westerhoff, HV. (1997). Glycolysis in bloodstream from trypanasoma brucei can be understood in terms of the kinetics of the glycotic enzymes. J. Biol. Chem., 272:3207–3215.

Baralla, A. (2008). Modeling and parameter estimation of the sos response network in E. coli. Technical report, University of Trento.

Drillon, G.(2008). MSc Thesis: An Integrated Kinetic Model of Ammonia Assimilation Regulation in Escherichia coli. University of Edinburgh.

@@ Line 1: / Line 1: @@
 <div id="header">{{Template:Team:Edinburgh/Templates/Header}}</div>
 ==Introduction==
-This section aims to outline the model formulation of the secretion pathway
+This section aims to outline the model of the cellulase secretion pathway for cellulose degradation as constructed by the University of
-for cellulose degradation enzymes as constructed by the University of
+Edinburgh 2008 iGEM team. The computational model includes two operons, 14 species, and 15 reactions, and
-Edinburgh 2008 iGEM team.
+focusses on the switching on of genes for cellulose degradation by CRP-cAMP.
-The pathway includes ...,
-The computational model includes two operons, 14 species, and 15 reactions, and
-focusses on the switching on of genes for cellulose degradation by CRP.
 ==Kinetic Modelling==
@@ Line 17: / Line 12: @@
 Through mathematical modelling of cellular processes, we aim to reflect
 biological function in sets of equations supplied with parameter values
-that best fit observed reality (Drillon, G. 2008). If the outcomes of
+that best fit observed reality (Drillon, G. 2008). If the outcomes of modelled simulations closely correspond to experimental results, the model is valuable for understanding the biological mechanism. Thus through modelling we may generate new hypotheses at low cost, while also investigating and corroborating or ending doubts about previous hypotheses (Baralla, 2008). Modelling in
-model-based simulations closely correspond to experimental results, this indicates that the
-underlying biological mechanism may be understood. Through the process
-of modelling, we may generate new hypotheses, as well as investigating
-doubts on previous suggestions, or corroborating them [Baralla, 2008]. Modelling in
 preparation and in response to wetlab work can save money and time by
 focussing on more likely hypotheses.
 A standard way of modelling (non-spatial) physical systems which has been
-successfully applied to simulate metabolic systems [Bakker et al., 1997] is the use of
+successfully applied to simulate metabolic systems (Bakker et al., 1997) is the use of
-Ordinary Differential Equations (ODEs). The aim "of using a set of ODEs is to describe a biological phenomenon
+Ordinary Differential Equations (ODEs). The aim of using ODEs is to describe a biological phenomenon, so that the time dynamics of the system can be  analyzed or the steady state values of the variables involved in the model can be calculated. The effects of transcriptional regulation as well as of the changes in concentration of metabolites via enzymes are thus simulated.
-in order to analyze the time dynamics of the system or to calculate the steady state values of the variables
-involved in the model."and includes the effects of transcriptional regulation as well as of the changes in concentration of
-metabolites via enzymes.
-"The process of modelling tries to identify the variables and determine
+==The Biological System==
-the relationships among them. The simulation (the execution of the model), allows to get
-results from the model. In case of differential equations models, the simulation
-refers simply to a numerical integration finding a solution to the set of equations.
-The goal of using a set of ODEs is to describe a biological phenomenon
-(like the nitrogen assimilation) in order to analyze the time dynamics
-of the system or to calculate the steady state values of the variables
-involved in the The goal of using a set of ODEs is to describe a biological phenomenon
-(like the nitrogen assimilation) in order to analyze the time dynamics
-of the system or to calculate the steady state values of the variables
-involved in the model."model."
-include the effects of gene regulation as well as the concentration
+[[Image:Edinburgh-CellLysisModelling.jpg]]
-changes of metabolites via enzymes.
+The system we sought to model is as follows:
+Reduction in glucose concentration leads to increased adenylate cyclase activity. This results in more AMP being converted to cAMP.
+cAMP binds to CRP. The resultant CRP-cAMP transcription factor activates the transcription of cellulase and cellubiase proteins (cenA, cex, and bglX) and comK via the cstA operator. ComK in turn binds to the comK operator, whereupon phiX174E, a lysis enzyme, is produced. Cell lysis releases the cellulases and bglX into the growth medium.
-==The Biological System==
-The section we're most interested in modelling is the cellulase
+==The Model==
-production leading to cell lysis pathway.
-[[Image:Edinburgh-CellLysisModelling.jpg]]
+Modelling was accomplished using [http://www.copasi.org/tiki-index.php COPASI]. Each step of the pathway is expressed using an appropriate rate law and short-hand reaction equation.
-The system we sought to model is as follows:
+[[Image:Edinburgh-ModelTable.jpg]]
-Reduction in glucose concentration (due to?) leads to increased
-adenylate cyclase activity. This results in more AMP being converted to cAMP.
-cAMP binds to CRP. The resultant CRP-cAMP transcription factor binds
-to the *cstA* operator, causing the transcription of cellulase proteins
-(cenA, cenB, cenC, and cex) and *comK*. ComK binds to the *comK*
-operator, whereupon *phiX174E*, a lysis enzyme, is produced. Cell lyses
-releases the cellulases into the growth medium.
+Our survey of the literature yielded some kinetic information for the genes in our system, in vivo, as well as in E. coli. Parameter values are currently being fit.
-We chose to model the lysis pathway for the cellulose degradation
+==Ordinary Differential Equations==
-enzymes, since the activation of the associated genes may be the most
-interesting focus for modelling in our system of interest.
-cellobiose and beta-glucosidase... hydrolyses the cellobiose to glucose'
+ODEs of the kinetic model as generated by the COPASI modelling application.
-(O'Neill et al, Gene 44:325-330).
-In the absence of experimental measurements, we have to fit likely
+[[Image:Edinburgh-ModelODEs.jpg]]
-numbers...
-Our survey of the literature yielded some kinetic information for the
-genes in our system, in vivo, as well as in E. coli.
-==The Model==
+==References==
+* Bakker, BM., Michelis, PAM., Opperdoes, FR, and Westerhoff, HV. (1997). Glycolysis in bloodstream from trypanasoma brucei can be understood in terms of the kinetics of the glycotic enzymes. ''J. Biol. Chem.'', '''272''':3207–3215.
-[[Image:Edinburgh-ModelTable.jpg]]
+* Baralla, A. (2008). ''Modeling and parameter estimation of the sos response network in E. coli''. Technical report, University of Trento.
-ODEs
+* Drillon, G.(2008). MSc Thesis: ''An Integrated Kinetic Model of Ammonia Assimilation Regulation in Escherichia coli''. University of Edinburgh.