Post on 14-Dec-2015
Simulation of Prokaryotic Genetic Circuits
Jonny Wells and Jimmy Bai
Overview
Organization of Genetic Regulatory Circuits
Simulations of Cellular Regulation
Modelling
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)
Regulatory feedback
Where output influences input signalsAutoregulatory feedback loopsIn E.coli, over 100 σ70 promoters
68% autoregulating13% autoactivating
Specialized enzymes often under regulatory control
Genetic Cascade
Regulatory mechanisms
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
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
Developmental Switches
Different physiological states require switching mechanisms
Cell-density-dependent gene expressionQuorum-sensing
Higher density = higher pheromone conc.Lysis/Lysogeny determination
Modelling
Promoter control ModelsStochastic processes in regulatory
kineticsModelling macromolecular complexesUncertainty in intracellular environment
and reaction rates
Promoter control Models
Boolean threshold logic paradigmGeneralised threshold model
Modelled as positive and negative feedback loops
Assumptions of Boolean network
Limitation of Boolean Network
Poor approximation
Other control mechanism
Besides promoter activation control Termination sites activation control Many posttranscriptional regulations Many protein-mediated controls
Proteolysis Phosphorylation Methylation
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
Stochastic Process
•Random burst of numbers of protein
•Timing uncertainty
•Stronger promoter
•Higher gene dosage
•Lower signal threshold
Stochastic Process
Different Activation time due to variation of the concentration
Modelling macromolecular complex
Realistic modelling -->central challengeGenetic network mechanism is more
complicatedDynamic behaviour