The New Special Issue "Nonequilibrium Statistical Mechanics and Stochastic Processes of Complex Reaction Networks" is Open for Submission!
Entropy MDPI
Entropy is an international and interdisciplinary peer-reviewed open access journal of entropy and information studies.
Guest Editors: Dr. Schuyler B. Nicholson, Dr. Eun-jin Kim
Submit to the Special Issue: https://www.mdpi.com/journal/entropy/special_issues/X8Z8570RM7
Submission deadline: 31 October 2025
Special Issue Information:
Biological and chemical processes are mediated by the transformation and interactions between chemical species, events that are inherently stochastic. Despite being stochastic, our world's deterministic order and predictability manifest from the noise. Even when the set of possible species and rates can be collected into a chemical reaction network (CRN), descriptions at the level of molecule counts are often incomplete. This is especially true in the low molecular copy number regime where stochastic fluctuations dominate. Challenges in understanding the stochastic regime and transition towards the macro-scale can be attributed to the curse of dimensionality, insufficient information about contributing reactions, and vast time scales associated with rare events. Different approximation techniques and diagnostic measures have been developed to overcome these issues.
Information and uncertainty have long been seen as useful diagnostic measures. Ideas connecting entropy change to chemical spontaneity return to Gibbs and De Donder in the 1800s. Since then, it has become appreciated that information measures are not just diagnostic but integral to understanding and predicting chemical and biological evolutions and constraining quantities such as heat flux or chemical work. The advent of stochastic thermodynamics, modern approaches to machine learning, and methods for tackling high-dimensional processes means new tools exist to understand how information intertwines and influences far from equilibrium evolutions. This Special Issue will focus on works that address these questions regarding how information influences chemical and biological processes.
Possible questions could be: (1) What insights can be learned about chemical outcomes (yield, efficiency, prediction) from information processing? (2) What can we learn about non-chemical systems by applying CRNs and statistical mechanics? (3) How do thermodynamic concepts translate from micro to macro scales?