國立清華大學 數學系

Abstract One important aspect in the systems biology is to gain understanding in the systems level with molecular basis. With molecular basis in the model, new treatment or genetic perturbation can be designed in order to change the systems behavior. In this talk, I’ll focus on the methodology and an application for simulating a biological system, particularly for systems with noises. Gene expression noise is ubiquitous in cells. One source of noises is that genes are expressed in bursts, as both mRNA and protein bursts were observed in the past. In order to simulate gene network dynamics with noise effects, I’ll talk about the Langevin equation formulism for gene production in bursts. The results in the application of this Langevin equation approach for the development of model organism C. elegans will also be discussed. In this work, we study the noise propagation in a regulatory network of C. elegans. In the development, the distal tip cell (DTC) migration is under a tight timing control, which is achieved by a genetic network composed of multiple feedforward pathways. Feedforward loop has the potential to filter the noise, but such noise-filtering is asymmetric, i.e. it works at either the “on” or the “off” states in the source. With multiple, interlinked feed forward loops, we show that the propagated noises are largely filtered regardless of the states in the source. Positive feedback loops are also helpful in maintaining the desirable activity of the target gene. We demonstrate that the incomplete penetrant phenotype observed in mutants can be attributed to both noises and dynamics of the system.