Rigor of cell fate decision by variable p53 pulses and roles of cooperative gene expression by p53

Investigation of oscillation accumulation triggered genetic switch in gene regulatory networks

Genetic oscillator motifs and genetic switch motifs are blocks of biochemical reaction networks, which are involved in the regulation of rhythms, cell cycle progression, signal processing and cell fate decision. These motifs often interact to constitute complex signal processing systems. There widely exists the oscillation accumulation triggered mechanism in gene regulatory networks, i.e., an oscillator promotes the accumulation of a signaling protein over a threshold value, and activates a switch. The structure can be fund in some important biological function switches, such as apoptosis and DNA repair. We propose the structure as an oscillation accumulation triggered genetic switch (OATGS). Through mathematical modeling and analysis, results show the OATGS with features of robustness to noise and triggered mode. In addition, we show the existence of OATGS features and triggered manner in p53 gene regulatory networks, and explain some of the p53 regulation process, such as counting mechanism and pulse shape. We speculate that OATGS with oscillation accumulation triggered manner is a new important biological function switch.

Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network

When exact values of model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC) is a useful method. Biological experiments are often performed with cell population, and the results are represented by histograms. On another front, experiments sometimes indicate the existence of a specific bifurcation pattern. In this study, to deal with both type of such experimental results and information for parameter inference, we introduced functions to evaluate fitness to both type of experimental results, named quantitative and qualitative fitness measures respectively. We formulated Bayesian formula for those hybrid fitness measures (HFM), and implemented it to MCMC (MCMC-HFM). We tested MCMC-HFM first for a kinetic toy model with a positive feedback. Inferring kinetic parameters mainly related to the positive feedback, we found that MCMC-HFM reliably infer them with both qualitative and quantitative fitness measures. Then, we applied the MCMC-HFM to an apoptosis signal transduction network previously proposed. For kinetic parameters related to implicit positive feedbacks, which are important for bistability and irreversibility of the output, the MCMC-HFM reliably inferred these kinetic parameters. In particular, some kinetic parameters that have the experimental estimates were inferred without these data and the results were consistent with the experiments. Moreover, for some parameters, the mixed use of quantitative and qualitative fitness measures narrowed down the acceptable range of parameters. Taken together, our approach could reliably infer the kinetic parameters of the target systems.