Manuscript Topics

Recent technological and experimental advancements have allowed to collect large amounts of data in a wide variety of biological systems. Consequently, there is a clear need for rigorous mathematical techniques to analyze and extract useful insights from this data, which are often noisy, incomplete, censored/truncated, and high-dimensional. For example, epidemic data, which are mostly reported in the form of an epidemic curve, are almost always incomplete, and (left/right or interval) censored. Another challenge comes from noisy observations of biological aggregation across multiple scales.

The classical approach to modelling biological systems has been to use deterministic ordinary/partial differential equations (ODEs/PDEs). This is usually justified because the ODEs/PDEs can be seen as the mean-field (large population/volume) limit of the corresponding stochastic models. However, in many practical situations, for instance, for the purpose of parameter inference based on experimental data or for the purpose of quasi-steady-state approximations, the use of deterministic equations is often ad hoc and has been shown to be even problematic. Stochastic methods are often a natural choice in those situations and offer a more principled approach to handling various sources of uncertainties.

The language of stochastic chemical reaction networks has been used to model biological systems across multiple scales (from molecular to ecological), such as enzyme-kinetic reactions, transcription/translation, epidemic processes, predator-prey systems.

This special issue will focus on some of the recent advances in stochastic chemical reaction networks theory. Besides methodological contributions, a special focus will be on diverse and novel applications of the stochastic methods to systems biology, biochemistry, infectious disease epidemiology and public health, and ecology.

Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/