(306a) Determination of An Accurate Reduction Method for Stochastic Chemical Kinetic Models with Applications In Synthetic Biological Design

Production of new biological devices within synthetic biology has been primarily driven by experimental research in recent years. Forward engineering of these biological devices, however, will require the prediction of function and thus may be driven by computational modeling.

The main benefit of simulation to predict biological function is in screening potential gene networks. As genetic devices are combined to produce complex outputs, the number of potential gene configurations will increase rapidly. Experimentally constructing each configuration would be time consuming and expensive. Biological simulations may aide in screening these potential gene networks to determine promising configurations.

Unfortunately, the simulation of complex gene networks is challenging and often inaccurate. A fundamental source of inaccuracies is the inability to determine many of the kinetic parameters in the chemical reaction network. As models become more complex the number of unknown kinetic parameters will increase dramatically. One solution to such a problem is to reduce the number of unknown kinetic parameters by combining reactions using steady-state assumptions and the use of complex rate laws.

The present work focuses on a general reduction method for biological systems and the issues surrounding model reduction in stochastic simulations. Numerous reduction methods exist, but only for limiting cases (e.g. a stochastic quasi-steady state approximation requires a near zero molecule count for components to be removed from the simulation). The present work differs from previously developed reduction methods by exploring the consequences of reducing models outside of these limiting cases. The method takes in a complex chemical kinetic model which functions far from the thermodynamic limit (i.e. stochasticity is important). Sets of reactions within the model are then reduced to single kinetic rate laws (e.g. transcription with repression can be represented by a Hill-type kinetic rate law). By utilizing complex rate laws a conceptually simpler model is produced, and by accounting for the consequences of such approximations accuracy can be maintained.