Rapid progress in the development of next-generation sequencing (NGS) technologies in
recent years has provided many valuable insights into complex biological systems, ranging …

Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

In this article, we initiate the strong structural controllability of Boolean networks (BNs), in
order to cope with the difficulty of identifying intricate nodal dynamics. The derived …

Boolean network models are one of the simplest models to study complex dynamic behavior
in biological systems. They can be applied to unravel the mechanisms regulating the …

This article studies the optimal control of probabilistic Boolean control networks (PBCNs)
with the infinite horizon average cost criterion. By resorting to the semitensor product (STP) …

Behaviours of complex biomolecular systems are often irreducible to the elementary
properties of their individual components. Explanatory and predictive mathematical models …

This book unifies and extends latent variable models, including multilevel or generalized
linear mixed models, longitudinal or panel models, item response or factor models, latent …

In this paper, Hamilton's (1988, 1989) Markov-switching model is extended to a general
state-space model. This paper also complements Shumway and Stoffer's (1991) dynamic …

Background Co-expression measures are often used to define networks among genes.
Mutual information (MI) is often used as a generalized correlation measure. It is not clear …

Mathematical modeling of biological processes provides deep insights into complex cellular
systems. While quantitative and continuous models such as differential equations have been …