Network Classification Based on Reducibility With Respect to the Stability of Canalizing Power of Genes in a Gene Regulatory Network - A Boolean Network Modeling Perspective

BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction

Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.

Random periodic sequence of globally mean-square exponentially stable discrete-time stochastic genetic regulatory networks with discrete spatial diffusions

<abstract><p>This paper regards the dual effects of discrete-space and discrete-time in stochastic genetic regulatory networks via exponential Euler difference and central finite difference. Firstly, the global exponential stability of such discrete networks is investigated by using discrete constant variation formulation. In particular, the optimal exponential convergence rate is explored by solving a nonlinear optimization problem under nonlinear constraints, and an implementable computer algorithm for computing the optimal exponential convergence rate is given. Secondly, random periodic sequence for such discrete networks is investigated based on the theory of semi-flow and metric dynamical systems. The researching findings show that the spatial diffusions with nonnegative intensive coefficients have no influence on global mean square boundedness and stability, random periodicity of the networks. This paper is pioneering in considering discrete spatial diffusions, which provides a research basis for future research on genetic regulatory networks.</p></abstract>