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Being able to predict how a novel gene regulatory network (GRN) will behave is crucial. Before one commits the time and resources to actually constructing such a network out of BioBricks (or other pieces of genetic material) it is important to know if it is stable, if the time-scales that it operates on are relevant and if it will produce the responses that one is looking for. Many approaches exist to model GRNs, the most popular being coupled ordinary differential equations (ODEs), stochastic networks and graphical models. For the levels of expression considered in this project the coupled ODE approach is the most valid, since it assumes that quantities of repressors, promoters, proteins etc. are high enough to be represented as being well mixed and continuous. | Being able to predict how a novel gene regulatory network (GRN) will behave is crucial. Before one commits the time and resources to actually constructing such a network out of BioBricks (or other pieces of genetic material) it is important to know if it is stable, if the time-scales that it operates on are relevant and if it will produce the responses that one is looking for. Many approaches exist to model GRNs, the most popular being coupled ordinary differential equations (ODEs), stochastic networks and graphical models. For the levels of expression considered in this project the coupled ODE approach is the most valid, since it assumes that quantities of repressors, promoters, proteins etc. are high enough to be represented as being well mixed and continuous. | ||
Revision as of 17:50, 27 October 2010
iGEM 2010
Modelling Strategy
Modelling has a key role in synthetic biology. Synthetic biologists use mathematical models to predict how new genetic networks will act, which is crucial when designing a BioBrick construct. It is also important to be able to predict how groups of bacteria will interact with one another and their environment. The primary function of our modelling team is to use computational simulations to assist and inform the work of our biologists.
BSim Modelling Framework
BSim is BCCS Bristol's award winning modelling package. It is an agent-based modelling framework written in Java. BSim's agents operate on the level of individual bacteria, vesicles and particles. The internal state of bacteria can be modelled with ODE's which are solved numerically by BSim. We hope that BSim can be used as a 'virtual microscope', a tool that all biologists can use to investigate almost any microbiological system, from the level of GRN's to multicelluar interaction to environmental interaction.
Modelling the GRN
Being able to predict how a novel gene regulatory network (GRN) will behave is crucial. Before one commits the time and resources to actually constructing such a network out of BioBricks (or other pieces of genetic material) it is important to know if it is stable, if the time-scales that it operates on are relevant and if it will produce the responses that one is looking for. Many approaches exist to model GRNs, the most popular being coupled ordinary differential equations (ODEs), stochastic networks and graphical models. For the levels of expression considered in this project the coupled ODE approach is the most valid, since it assumes that quantities of repressors, promoters, proteins etc. are high enough to be represented as being well mixed and continuous.
