BioDynaMo is a biological processes simulator developed by an international community of researchers and software engineers working closely with neuroscientists. The authors have taken part in the development of the physical engine, and they are currently working on gene expression, i.e. the process by which the heritable information in a gene - the sequence of DNA base pairs - is made into a functional gene product, such as protein or RNA. Typically, gene regulatory models employ either statistical or analytical approaches, being the former already well understood and broadly used. In this paper, we utilize analytical approaches representing the regulatory networks by means of differential equations, such as Euler and Runge-Kutta methods. The two solutions are implemented in the BioDynaMo project and are compared for accuracy and performance.
@cite_25: We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as a continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We conduct Monte Carlo experiments to examine the effectiviness of the proposed method. We also demonstrate the proposed method through the analysis of the Saccharomyces cerevisiae gene expression data.
The research @cite_25 introduces a new statistical gene network estimation method based on the dynamic Bayesian network and nonparametric regression model with advantages over Bayesian and Boolean networks. For example, it can detect nonlinear dependencies. This article can be taken into consideration in further work if the statistical approach will be implemented in BioDynaMo.