Theoretical biology in the era of big data and artificial intelligence

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Guest Editors
Prof. Roberto Cazzolla Gatti, Ph.D.
Biological Institute, Tomsk State University, Russia; Department of Forestry and Natural Resources (FNR), Purdue University, USA

Dr. Alfonso Monaco, Ph.D.
Istituto Nazionale di Fisica Nucleare (Bari); Department of Physics, University of Bari, Italy

Dr. Nicola Amoroso, Ph.D.
Department of Physics, University of Bari, Italy

Manuscript Topics
Theoretical biology employs theoretical analysis, models, and abstractions to investigate the principles that govern the structure, development, and behavior of living beings and ecosystems. It has both theoretical and practical applications in biological, biomedical and biotechnology research. To study systems in a holistic manner, their behavior should be better simulated, and hence properties can be predicted that might not be evident with an atomistic approach. Theoretical biology has contributed to the development of new techniques, including complex networks, big data analytics and artificial intelligence.

Research in theoretical biology is characterized by highly complex, nonlinear, and supercomplex mechanisms, as it is being increasingly recognized that the result of such interactions may only be understood through a combination of mathematical, logical, evolutionary, ecological, physical/chemical, molecular and computational models. Biological elements interact between each other constructing complex network systems. The progress of theoretical biology also affects our lives through biomedicine and the economy. However, we currently have a limited understanding of the emergence of biological functions from systems. We do not know how and why biological systems generate well-designed biological functions including homeostatic regulations, pattern formations or adaptive behaviors. Therefore, there is an urgent need to understand the mechanisms and the principles of the emergence of such biological functions. The biology is now facing a large change in its methodology. In addition to the classical experimental and empirical methods, theoretical and computational methods including machine learning and advanced computer science are becoming important. Such theoretical methods in biology are developing under the expectation to decipher huge amounts of experimental information and to give an integrative understanding of complex biological systems.

Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is April 30th, 2020.

Instructions for authors
Please submit your manuscript to online submission system

Open Access Journals
Open Access Journals