FLEXIBLE QUANTITATIVE SIMULATION IN CELL CYCLE CONTROL, ALGORITHMS AND IMPLEMENTATION

Abstract

The procedure of cell cycle is regulated by complex metabolic networks. There are two different quantitative approaches: stochastic and deterministic. Previously work emphasizes mostly on stochastic approach which is adaptive but computational inefficient. This goal of this dissertation is to extend the adaptive capacity of deterministic methods and to enrich its flexibility. This dissertation describes algorithms which can produce laboratory genomic controls (gene express promoting and prohibiting) and stochastic perturbation effects in deterministic simulation. Originally, the perturbation effects can only be produced in stochastic simulation. It also introduces the in-silico experiment platform made for cell cycle regulatory systems. At the end of this dissertation, some results of simulation on budding yeast cell cycle model are presented. The algorithm and experiment platform in this dissertation will refine the resolution of previous deterministic simulation techniques. The results of numerical experiment on the budding yeast model will help to better understand the inner mechanism of cell regulatory systems.