ArticleBMC systems biology2018
Modeling biological systems with uncertain kinetic data using fuzzy continuous Petri nets.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Incremental modelling and analysis of biological systems with fuzzy hybrid Petri nets.Briefings in bioinformatics · 2024Article
- Automatic construction of Petri net models for computational simulations of molecular interaction network.NPJ systems biology and applications · 2024Article
- Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms.Biology · 2024Review
- Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model.BMC cancer · 2021Article
- Predicting Efficacy of 5-Fluorouracil Therapy via a Mathematical Model with Fuzzy Uncertain Parameters.Journal of medical signals and sensorsArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUncertainties exist in many biological systems, which can be classified as random uncertainties and fuzzy uncertainties. The former can usually be dealt with using stochastic methods, while the latter have to be handled with such approaches as fuzzy methods.
resultsIn this paper, we focus on a special type of biological systems that can be described using ordinary differential equations or continuous Petri nets (CPNs), but some kinetic parameters are missing or inaccurate. For this, we propose a class of fuzzy continuous Petri nets (FCPNs) by combining CPNs and fuzzy logics. We also present and implement a simulation algorithm for FCPNs, and illustrate our method with the heat shock response system.
conclusionsThis approach can be used to model biological systems where some kinetic parameters are not available or their values vary due to some environmental factors.
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