Simulation of Prokaryotic Genetic Circuits Jonny Wells and Jimmy Bai.

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Simulation of Prokaryotic Genetic Circuits Jonny Wells and Jimmy Bai

Transcript of Simulation of Prokaryotic Genetic Circuits Jonny Wells and Jimmy Bai.

Page 1: Simulation of Prokaryotic Genetic Circuits Jonny Wells and Jimmy Bai.

Simulation of Prokaryotic Genetic Circuits

Jonny Wells and Jimmy Bai

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Overview

Organization of Genetic Regulatory Circuits

Simulations of Cellular Regulation

Modelling

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Regulatory circuits

Hierarchical organizationRegulons – control groups of operonsGlobal regulons – multiple pathway

regulation (e.g. σ32)Often neglected in simulationsHowever is needed in some circumstances

(e.g. 2 σ factors competing)

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Regulatory feedback

Where output influences input signalsAutoregulatory feedback loopsIn E.coli, over 100 σ70 promoters

68% autoregulating13% autoactivating

Specialized enzymes often under regulatory control

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Genetic Cascade

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Regulatory mechanisms

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Intergrating environmental signals

Eg. Chemotactic responsesAttractant or repellent molecules bind

directly to specialized receptors leading to phosphorylation cascadePulses of agents matched with behavioural

changes Mutants shown to have altered enzymatic

activity

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Cell cycle models

Genetic regulation coupling to cell cycleModelling of biochemical reactions that

support oscillationsp34activation, p34/cyclin interactions and

cyclin degradation suggestedHowever shown to be far more elaborate

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Developmental Switches

Different physiological states require switching mechanisms

Cell-density-dependent gene expressionQuorum-sensing

Higher density = higher pheromone conc.Lysis/Lysogeny determination

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Modelling

Promoter control ModelsStochastic processes in regulatory

kineticsModelling macromolecular complexesUncertainty in intracellular environment

and reaction rates

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Promoter control Models

Boolean threshold logic paradigmGeneralised threshold model

Modelled as positive and negative feedback loops

Assumptions of Boolean network

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Limitation of Boolean Network

Poor approximation

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Other control mechanism

Besides promoter activation control Termination sites activation control Many posttranscriptional regulations Many protein-mediated controls

Proteolysis Phosphorylation Methylation

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Stochastic Process

Model macroscopic kinetics of chemical reactions using ordinary differential equation

Difficult to achieve in genetic reaction due to low concentration and slow reaction rates

Gillespie algorithm – calculating the probabilistic outcome of each discrete chemical event

State vector

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Stochastic Process

•Random burst of numbers of protein

•Timing uncertainty

•Stronger promoter

•Higher gene dosage

•Lower signal threshold

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Stochastic Process

Different Activation time due to variation of the concentration

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Modelling macromolecular complex

Realistic modelling -->central challengeGenetic network mechanism is more

complicatedDynamic behaviour