Information Flow and Entropy Production in Biomolecular Networks

Dear Colleagues,

Information theory was introduced in 1948 by Shannon as a mathematical formalization of communication. Biologists realized that many biological processes can be described in terms of information flow and rapidly adopted Shannon's information theory. For instance, the central dogma of molecular biology can be formulated as information flow from genes to proteins. In biological signaling, information comes from the environment, is encoded by cell surface receptors, and is transmitted via biomolecular networks to transcription factors and effector genes.

In order to process information, cells use biochemical processes that are subject to constraints. Physical energetic and thermodynamic constraints are well known: in order to write or delete information, energy has to be dissipated. A theoretical limit for the energy per processed bit of information was calculated by Szilard and Landauer, but biological devices function far above this limit, which suggests that other constraints may exist. A different approach to information is the algorithmic theory of information was introduced in the 1960s by Kolmogorov and Chaitin. This theory deals with the relation between complexity and information. Contrary to Shannon's theory, algorithmic theory of information was less utilized in biology. It deserves more attention, especially in relation to emerging complexity in cellular decision making during embryonic development, or in adaptation to environmental changes.

As an alternative to the concept of information, entropy production and related energy dissipation are also central to phenomenological thermodynamics. It is of great interest to study them for open systems as pioneered by von Bertalanffy and Prigogine and to find principles of optimality for biological processes that allow cells to operate with a limited amount of loss of energy. Applications are widespread from understanding the organization of living systems to planning of biotechnological processes.

The main topics of this Special Issue include, but are not limited to, the following:

_ The coding, recognition, and prediction of the environment;

_ Channel information capacity and network bottlenecks;

_ Optimal information flow in biomolecular networks;

_ Redundancy and error-correction;

_ Energetic, thermodynamic, and other constraints in information processing;

_ Informational aspects of cell and tissue-level decision making and adaptation;

_ Optimality principles for entropy production;

_ Emerging complexity in biomolecular networks.

Prof. Dr. Edda Klipp
Prof. Dr. Ovidiu Radulescu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.