Petri Nets in Systems Biology: Methodology and Applications

Author:   Rza Bashirov
Publisher:   Springer Nature Switzerland AG
ISBN:  

9783032200402


Pages:   113
Publication Date:   23 May 2026
Format:   Hardback
Availability:   Not yet available   Availability explained
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Petri Nets in Systems Biology: Methodology and Applications


Overview

Petri Nets in Systems Biology presents a rigorous and coherent framework for modeling and analyzing complex biological systems. Positioned at the intersection of computer science, biomedicine, bioinformatics, and target-based drug discovery, the book demonstrates how Petri nets naturally capture key characteristics of biological processes, including concurrency, synchronization, causality, stochastic, and fuzzy behavior. The book introduces fundamental Petri net concepts and progressively extends them to address core problems in systems biology, such as the modeling of metabolic networks, gene regulatory systems, signal transduction pathways, and cellular control mechanisms. Both qualitative and quantitative analysis techniques are covered, including invariant analysis, reachability, structural properties, stochastic modeling, and hybrid extensions. A strong emphasis is placed on modularity, compositional modeling, and biological interpretability, enabling readers to construct scalable and reusable models that remain closely aligned with underlying biological knowledge. Through carefully selected running examples and case studies, the book illustrates how Petri net technologies can be used to simulate system dynamics, analyze regulatory structure, and gain insight into emergent behaviors of complex biological networks. Intended for researchers, graduate students, and advanced practitioners, Petri Nets in Systems Biology serves as both an accessible introduction and a comprehensive reference. It provides the theoretical foundations and practical tools necessary for applying Petri nets to contemporary challenges in computational and systems biology.

Full Product Details

Author:   Rza Bashirov
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032200402


ISBN 10:   3032200407
Pages:   113
Publication Date:   23 May 2026
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

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Author Information

Rza Bashirov is a distinguished scholar in computational modeling, serving as a Professor of Computer Science at Eastern Mediterranean University in Famagusta, North Cyprus. His academic journey includes a Ph.D. in Computer Science from Moscow State University and an M.S. in Applied Mathematics from Azerbaijan State University. Dr. Bashirov’s research spans bioinformatics, Petri nets, and modeling in systems biology, with a particular focus on Petri net–based approaches for complex biochemical systems. Over the course of his career, he has published numerous peer-reviewed articles and book chapters on the application of Petri nets with extensions, and invariant-based methods to biological signaling pathways, disease modeling, and therapeutic strategy development. His work has advanced understanding in areas ranging from epidemic spreading and gene regulation to pathway analysis and drug combination prediction. In addition to his research contributions, Dr. Bashirov has held significant academic leadership roles, including Dean of the Faculty of Arts and Sciences and Founding Acting Head of the Department of Biological Sciences at Eastern Mediterranean University. He also serves on the editorial board of the Edelweiss Applied Science and Technology Journal and has been actively engaged in scholarly service through editorial duties and peer review. Dr. Bashirov’s work continues to bridge rigorous computational formalisms and real-world biological applications, contributing to the development of scalable, interpretable models in systems biology.

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