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

In order to understand the functioning of organisms on the molecular level, we need to know
which genes are expressed, when and where in the organism, and to which extent. The …

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 …

Motivation: Our goal is to construct a model for genetic regulatory networks such that the
model class:(i) incorporates rule-based dependencies between genes;(ii) allows the …

The controllability and observability of Boolean control networks are investigated. After a
brief review on converting a logic dynamics to a discrete-time linear dynamics with a …

A new matrix product, called semi-tensor product of matrices, is reviewed. Using it, a matrix
expression of logic is proposed, where a logical variable is expressed as a vector, a logical …

The aim of this book is to outline the recent development of Markov chain models and their
applications in queueing systems, manufacturing systems, remanufacturing systems …

This advanced textbook is tailored for an introductory course in Systems Biology and is well-
suited for biologists as well as engineers and computer scientists. It comes with student …

Motivation: Network inference algorithms are powerful computational tools for identifying
putative causal interactions among variables from observational data. Bayesian network …

Mathematical and computational modeling of genetic regulatory networks promises to
uncover the fundamental principles governing biological systems in an integrative and …