Computational Biology: A Statistical Mechanics Perspective

Dear Colleagues,

Computational biology is an interdisciplinary field of investigation that combines the principles of physics and the methods of mathematical analysis to investigate the rules governing living systems. At a fundamental level, this discipline is concerned with basic questions such as the following: How can we predict an organism's phenotype from its genotype? What is the relation between the sequence of a gene and the biochemical function of the protein it encodes for? How do living systems maintain complex spatiotemporal structures on vastly different scales? How do molecules collectively determine the fate of a cell during development?

Continuous advancements in technology together with fast-growing computational capabilities are providing unprecedented access to the aforementioned questions, which are becoming increasingly amenable to empirical analysis and thus modeling. Nevertheless, any quantitative theory faces a challenge: biological processes are stochastic in nature and result from the interplay between an enormous number of elementary units. Statistical mechanics appears as the natural methodological choice to tackle these issues thanks to its ability to relate microscopic interactions to the emergent collective behavior of a system. Mapping biological questions onto problems of statistical mechanics opens the way to a host of mathematical techniques that are optimally suited to describe complex systems.

This Special Issue collects recent results drawn from diverse active research areas of computational biology, such as the structure and conformational propensities of biomolecules, molecular evolution, modeling of neural systems, properties of metabolic or gene regulatory networks, and dynamics of microbial communities. The goal is to promote the cross-fertilization of ideas and convergence of approaches. An emphasis is placed on newly developed machine learning approaches to solve direct or inverse problems in statistical mechanics and on the out-of-equilibrium statistical physics of biological active matter.

Possible topics include, but are not limited to the following:

• Potts models for protein sequence covariation
• Machine learning for importance sampling of biomolecular systems
• Restricted Boltzmann machine, variational autoencoders, and deep neural networks as generative models of protein sequence families
• Intrinsic dimension and dimensionality reduction protein sequences and structures
• Neural-network based potential for biomolecular simulations
• Pattern formation in active fluids of biomolecules
• Liquid–liquid phase separations and biomolecular condensates in cellular environments

Prof. Vincenzo Carnevale
Dr. Antonio Suma
Guest Editors

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• enhanced sampling
• machine learning
• variational autoencoders
• restricted Boltzmann machine
• active matter
• molecular evolution
• Potts models
• intrinsically disordered proteins
• molecular simulations
• liquid–liquid phase separation