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Mathematical Modeling in Systems Biology, 2nd Edition

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

Deadline for manuscript submissions: 9 December 2025 | Viewed by 737

Special Issue Editor


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Guest Editor
Academy of Integrated Science, Division of Systems Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Interests: systems biology; mathematical modeling; cell cycle; gene regulatory networks; cell death mechanism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Systems biology is a rapidly growing field, yet many biological systems remain only qualitatively described. To better understand these systems, we use mathematical modeling and develop computational methods and tools that reveal how individual components interact to produce complex dynamic behavior. Without these computational techniques and mathematical frameworks, the overall behavior of complex biological systems often cannot be intuitively understood.

This Special Issue is dedicated to publishing research that applies computational and mathematical approaches to the study of biological systems. We welcome all types of contributions, including but not limited to mathematical, theoretical, analytical, and computational studies of system-level properties in biology; dynamic models of biological networks; control theory applications; systems-oriented approaches; and big data analysis methods and tools. Studies utilizing deterministic or stochastic modeling are also encouraged.

Topics of interest include (but are not limited to) the following:

  • Mathematical modeling;
  • Biological systems;
  • Computational methods;
  • Network dynamics;
  • Systems-oriented approaches;
  • Data analysis tools;
  • Regulatory networks;
  • Complex biological mechanisms;
  • Mechanistic approaches;
  • AI/ML modeling and approaches.

We especially welcome submissions focused on gene–protein interaction networks, metabolic pathways, cell signaling and communication, population dynamics, infectious disease modeling, and ecological systems.

Dr. Pavel Kraikivski
Guest Editor

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

  • mathematical modeling
  • biological systems
  • computational methods
  • network dynamics
  • systems-oriented approaches
  • data analysis tools
  • regulatory networks
  • complex biological mechanisms
  • mechanistic approaches

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Published Papers (1 paper)

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Research

20 pages, 974 KB  
Article
Circuit Design in Biology and Machine Learning. II. Anomaly Detection
by Steven A. Frank
Entropy 2025, 27(9), 896; https://doi.org/10.3390/e27090896 - 24 Aug 2025
Viewed by 529
Abstract
Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limited attention [...] Read more.
Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limited attention in analyses of cellular and physiological circuits. This study builds on machine learning techniques—such as dimensionality reduction, boosted decision trees, and anomaly classification—to develop a conceptual framework for biological circuits. One problem is that machine learning circuits tend to be unrealistically large for use by cellular and physiological systems. I therefore focus on minimal circuits inspired by machine learning concepts, reduced to the cellular scale. Through illustrative models, I demonstrate that small circuits can provide useful classification of anomalies. The analysis also shows how principles from machine learning—such as temporal and atemporal anomaly detection, multivariate signal integration, and hierarchical decision-making cascades—can inform hypotheses about the design and evolution of cellular circuits. This interdisciplinary approach enhances our understanding of cellular circuits and highlights the universal nature of computational strategies across biological and artificial systems. Full article
(This article belongs to the Special Issue Mathematical Modeling in Systems Biology, 2nd Edition)
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