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Entropy
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Information Theory and Biology: Seeking General Principles
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Information Theory and Biology: Seeking General Principles

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (31 December 2022)

Special Issue Editors

Prof. Dr. Alexandre Ferreira Ramos

E-Mail Website
Guest Editor
School of Arts, Sciences and Humanities, University of São Paulo, São Paulo 01246-000, SP, Brazil
Interests: regulation of stochastic gene expression; stochastic processes; lie symmetries in biology; graph theory; entropy and information theory; carcinogenesis; trascriptional control in fruit fly embryos
Prof. Dr. Gábor Balázsi

E-Mail Website
Guest Editor
The Louis & Beatrice Laufer Center for Physical & Quantitative Biology, Rm 115C, Laufer Center, Z-5252, Stony Brook University, Stony Brook, NY 11794, USA
Interests: stochastic gene expression; synthetic and systems biology; evolution, gene regulatory networks

Special Issue Information

Dear colleagues,

The massive amount of quantitative data acquired using recently developed experimental techniques for investigating molecular, cellular, tissue, organismic and population level processes, sets the field of quantitative biology for fruitful theoretical elaborations on biological phenomena. These phenomena are characterized by a plethora of interactions resulting in distinguishable dynamical behaviors, with experiments being necessary but not sufficient for revealing the subtleties of their mechanisms. Hence, the theoretical machinery developed in the context of physics, mathematics, statistics, engineering, and computer science can be the basis for providing new insights into the function and organization of living systems. Such interdisciplinary methods are key for understanding how the interaction among several biological components gives rise to dynamical networks capable of operating reliably despite unavoidable random fluctuations. The development of an organism is a striking example of how these networks cope with randomness and generate precise spatio-temporal patterns while adaptation during evolution exemplifies noise exploitation by living systems to deal with ever changing environments. These two examples, and the occurrence of diseases such as cancer characterized by still functional but increased randomness, indicate that biological systems have robust mechanisms to deal with noise and to generate reliable dynamical behaviors. Furthermore, they help to understand how noise effects are either canalized or amplified through the multiple hierarchical levels of biological organization. Information Theory provides a highly suitable toolset for investigating how fluctuating components of a system can generate a reliable behavior in the context of information transmission while helping with biological interpretation. In this call for a Special Issue, we invite the community to submit original research or review articles on the use of information theoretical methods for investigating, discovering, or synthesizing general principles that increase our understanding of biological phenomena. The presentation of experimental results for which subsequent analysis can motivate the development of the field of information theory and the dialogue among disciplines is also encouraged. Information theoretical approaches to Covid-19 related questions will also be considered.

Dr. Alexandre Ferreira Ramos
Prof. Gábor Balázsi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Keywords

  • Information theory
  • Entropy
  • Stochastic processes
  • Robustness, heterogeneity and adaptation in biological processes
  • Developmental biology
  • Carcinogenesis
  • Evolution
  • Gene networks
  • Signaling pathways
  • Biological networks

Published Papers (2 papers)

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Research

14 pages, 325 KiB  
Article
Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks
by Taylor Firman, Jonathan Huihui, Austin R. Clark and Kingshuk Ghosh
Entropy 2021, 23(3), 357; https://doi.org/10.3390/e23030357 - 17 Mar 2021
Cited by 1 | Viewed by 2262
Abstract
Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of [...] Read more.
Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of the circuit must be inferred. One potentially useful avenue is to harness hidden information from single-cell stochastic gene expression time trajectories measured for long periods of time—recorded at frequent intervals—over multiple cells. This raises the feasibility vs. accuracy dilemma while deciding between different models of mining these stochastic trajectories. We demonstrate that inference based on the Maximum Caliber (MaxCal) principle is the method of choice by critically evaluating its computational efficiency and accuracy against two other typical modeling approaches: (i) a detailed model (DM) with explicit consideration of multiple molecules including protein-promoter interaction, and (ii) a coarse-grain model (CGM) using Hill type functions to model feedback. MaxCal provides a reasonably accurate model while being significantly more computationally efficient than DM and CGM. Furthermore, MaxCal requires minimal assumptions since it is a top-down approach and allows systematic model improvement by including constraints of higher order, in contrast to traditional bottom-up approaches that require more parameters or ad hoc assumptions. Thus, based on efficiency, accuracy, and ability to build minimal models, we propose MaxCal as a superior alternative to traditional approaches (DM, CGM) when inferring underlying details of gene circuits with feedback from limited data. Full article
(This article belongs to the Special Issue Information Theory and Biology: Seeking General Principles)
17 pages, 342 KiB  
Article
The Free Energy Principle: Good Science and Questionable Philosophy in a Grand Unifying Theory
by Javier Sánchez-Cañizares
Entropy 2021, 23(2), 238; https://doi.org/10.3390/e23020238 - 19 Feb 2021
Cited by 8 | Viewed by 4266
Abstract
The Free Energy Principle (FEP) is currently one of the most promising frameworks with which to address a unified explanation of life-related phenomena. With powerful formalism that embeds a small set of assumptions, it purports to deal with complex adaptive dynamics ranging from [...] Read more.
The Free Energy Principle (FEP) is currently one of the most promising frameworks with which to address a unified explanation of life-related phenomena. With powerful formalism that embeds a small set of assumptions, it purports to deal with complex adaptive dynamics ranging from barely unicellular organisms to complex cultural manifestations. The FEP has received increased attention in disciplines that study life, including some critique regarding its overall explanatory power and its true potential as a grand unifying theory (GUT). Recently, FEP theorists presented a contribution with the main tenets of their framework, together with possible philosophical interpretations, which lean towards so-called Markovian Monism (MM). The present paper assumes some of the abovementioned critiques, rejects the arguments advanced to invalidate the FEP’s potential to be a GUT, and overcomes criticism thereof by reviewing FEP theorists’ newly minted metaphysical commitment, namely MM. Specifically, it shows that this philosophical interpretation of the FEP argues circularly and only delivers what it initially assumes, i.e., a dual information geometry that allegedly explains epistemic access to the world based on prior dual assumptions. The origin of this circularity can be traced back to a physical description contingent on relative system-environment separation. However, the FEP itself is not committed to MM, and as a scientific theory it delivers more than what it assumes, serving as a heuristic unification principle that provides epistemic advancement for the life sciences. Full article
(This article belongs to the Special Issue Information Theory and Biology: Seeking General Principles)
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