BioSystems

BioSystems encourages theoretical, computational, and experimental articles that link biology, evolutionary concepts, and the information sciences. The journal is dedicated to publishing research on self-organizing information systems—with the goal of obtaining a better understanding of the origins of biochemical, genetic, epigenetic, physiological, cognitive, linguistic, sociocultural, and biological organization and evolution.

The scope of the journal encompasses the fundamental nature of biological information and (self)-organization. This includes quantum phenomena in information transfer, natural computing, biological coding systems, biological complexity, theoretical biology, artificial life, computational modeling of complex biological systems, evolutionary models of computation, application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.

The journal does not publish purely medical, computational, or ecological research, unless it is clearly linked to the basic and conceptual aspects of biological organization.

The editors encourage articles that deal, in particular, with the following topics:

• Biological computation

• Molecular recognition

• Physical foundations of biology

• Quantum phenomena in biological systems

• Cellular control

• Neuromolecular computing

• Biological coding systems

• Molecular computing processes

• Self-organizing and self-replicating systems

• Origin of the genetic code

• Origins and evolution of genomes

• Stochastic evolutionary algorithms

• Origins and evolution of mind and language

• Ecological evolutionary developmental biology

• Reticulate evolution (symbiosis, symbiogenesis, lateral gene transfer)

• Simulation of genetic and ecological systems

• Applications (neural nets, machine learning, robotics)

• History and philosophy of science

In addition, the editors encourage the following types of papers for submission:

• Papers that extract novel biological insights from multidimensional data, using AI-driven language models

• Biological hypothesis papers producing new insights based on a body of pre-existing empirical research

• Perspectives papers intended to stimulate scientific discussions and provide guidelines for future directions