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 article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

Mathematical Models in Biology began as a set of lecture notes for a course taught at Brown
University. It has since evolved through several years of classroom testing at Brown and …

“But although, as a matter of history, statistical mechanics owes its origin to investigations in
thermodynamics, it seems eminently worthy of an independent development, both on …

Since the first edition of Stochastic Modelling for Systems Biology, there have been many
interesting developments in the use of" likelihood-free" methods of Bayesian inference for …

Membrane computing is a vibrant and fast-growing research area of natural computation
which covers the study of computing models, called membrane systems or P systems …

Numerous methods have been developed for inferring gene regulatory networks from
expression data, however, both their absolute and comparative performance remain poorly …

During the last 100 years, cell signaling has evolved into a common mechanism for most
physiological processes across systems. Although the majority of cell signaling principles …

Genome sequences are now available that enable us to determine the biological
components that make up a cell or an organism. The discipline of systems biology examines …

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 …