Estimation of kinetic rates of MAP kinase activation from experimental data

Mathematical model is an important tool in systems biology to study the dynamics of biological systems inside the cell. One of the significant challenges in systems biology is the lack of kinetic rates that should be measured in experiments or estimated from experimental data. This work addresses this issue by using a genetic algorithm to estimate reaction rates related to the phosphorylation and dephosphorylation of MAP kinase (ERK) in the mitogen-activated protein (MAP) kinase pathway from biological measurements. In addition, we discuss the robustness of the mathematical model with regards to the variation of kinetic rates together with external noise due to environmental fluctuations. This has been proposed as an additional criterion to choose the estimate from the candidate parameter sets that are obtained from different implementations of the genetic algorithm

Stochastic neural network models for gene regulatory networks

Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in population of cells. The discussion suggest that stochastic neural network models can give better description of gene regulatory networks and provide criteria for measuring the reasonableness o mathematical models

Parallel implementation of stochastic simulation for large-scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes

Effective simulation techniques for biological systems

In this paper we give an overview of some very recent work on the stochastic simulation of systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge-Kutta methods and the Balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and discuss how novel computing implementations can enhance the performance of these simulations

Phase Transitions for the Brusselator Model

Dynamic phase transitions of the Brusselator model is carefully analyzed, leading to a rigorous characterization of the types and structure of the phase transitions of the model from basic homogeneous states. The study is based on the dynamic transition theory developed recently by the authors

The Implementation of Flipped Classroom in Efl Class: a Taiwan Case Study

This article reports on a case study designed to examine the implementation of flipped classroom in the EFL classroom in Taiwan.  In addition, students' perception of flipped classroom was also investigated. Sixty-one senior high school students participated in this study; data were gathered from students' English midterm exam score and questionnaire. The data then were quantitatively analyzed by using T-test and descriptive statistics. The results show that students' English proficiency in flipped classroom was not significantly different with students in traditional classroom. However, the results reveal that students' perception of flipped classroom were generally favorable. Students' contended that flipped classroom enhanced their motivation in learning English, as they liked the self-pace through the course and they stated that flipped classroom gave them more class time to practice English. The results presented here may facilitate improvements in the implementation of flipped classroom in EFL class. Furthermore, suggestions for further research are also presented

A new model for the double well potential

A new model for the double well potential is presented in the paper. In the new potential, the exchanging rate could be easily calculated by the perturbation method in supersymmetric quantum mechanics. It gives good results whether the barrier is high or sallow. The new model have many merits and may be used in the double well problem.Comment: 3pages, 3figure