research-article

Modelling and Verifying Dynamic Properties of Biological Neural Networks in Coq

Authors:

Abdorrahim Bahrami,

Elisabetta De Maria,

Amy FeltyAuthors Info & Claims

CSBio 2018: Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics

Article No.: 12, Pages 1 - 11

https://doi.org/10.1145/3291757.3291771

Published: 10 December 2018 Publication History

Abstract

Formal verification has become increasingly important because of the kinds of guarantees that it can provide for software systems. Verification of models of biological and medical systems is a promising application of formal verification. Human neural networks have recently been emulated and studied as a biological system. Some recent research has been done on modelling some crucial neuronal circuits and using model checking techniques to verify their temporal properties. In large case studies, model checkers often cannot prove the given property at the desired level of generality. In this paper, we provide a model using the Coq Proof Assistant and prove properties concerning the dynamic behavior of some basic neuronal structures. Understanding the behavior of these modules is crucial because they constitute the elementary building blocks of bigger neuronal circuits. By using a proof assistant, we guarantee that the properties are true for any input values, any length of input, and any amount of time. With such a model, there is the potential to detect inactive regions of the human brain and to treat mental disorders. Furthermore, our approach can be generalized to the verification of other kinds of networks, such as regulatory, metabolic, or environmental networks.

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Cited By

DE MARIA EDESPEYROUX JFELTY ALIÒ POLARTE CBAHRAMI ALhoussaine CRemy É(2023)Computational Logic for Biomedicine and NeurosciencesSymbolic Approaches to Modeling and Analysis of Biological Systems10.1002/9781394229086.ch6(187-234)Online publication date: 4-Aug-2023
https://doi.org/10.1002/9781394229086.ch6
Huang SDiep MJang KCherry EFenton FCleaveland RLindvall MMangharam RPorter A(2020)Towards Automated Comprehension and Alignment of Cardiac Models at the System Invariant LevelCSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics10.1145/3429210.3429225(18-28)Online publication date: 19-Nov-2020
https://dl.acm.org/doi/10.1145/3429210.3429225

Index Terms

Modelling and Verifying Dynamic Properties of Biological Neural Networks in Coq
1. Applied computing
  1. Life and medical sciences
    1. Systems biology
2. Theory of computation
  1. Logic
    1. Logic and verification

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cover image ACM Other conferences

CSBio 2018: Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics

December 2018

73 pages

ISBN:9781450365604

DOI:10.1145/3291757

Copyright © 2018 ACM.

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KMUTT: King Mongkut's University of Technology Thonburi
KU: Kasetsart University

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Association for Computing Machinery

New York, NY, United States

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Published: 10 December 2018

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CSBio 2018

CSBio 2018: The 9th International Conference on Computational Systems-Biology and Bioinformatics

December 10 - 13, 2018

Bangkok, Thailand

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CSBio 2018 Paper Acceptance Rate 12 of 19 submissions, 63%;

Overall Acceptance Rate 23 of 37 submissions, 62%

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Cited By

DE MARIA EDESPEYROUX JFELTY ALIÒ POLARTE CBAHRAMI ALhoussaine CRemy É(2023)Computational Logic for Biomedicine and NeurosciencesSymbolic Approaches to Modeling and Analysis of Biological Systems10.1002/9781394229086.ch6(187-234)Online publication date: 4-Aug-2023
https://doi.org/10.1002/9781394229086.ch6
Huang SDiep MJang KCherry EFenton FCleaveland RLindvall MMangharam RPorter A(2020)Towards Automated Comprehension and Alignment of Cardiac Models at the System Invariant LevelCSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics10.1145/3429210.3429225(18-28)Online publication date: 19-Nov-2020
https://dl.acm.org/doi/10.1145/3429210.3429225

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