ArticlePloS one2016
Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Formal verification confirms the role of p53 protein in cell fate decision mechanism.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- Crosstalk between Plk1, p53, cell cycle, and G2/M DNA damage checkpoint regulation in cancer: computational modeling and analysis.NPJ systems biology and applications · 2021Article
- Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model.BMC cancer · 2021Article
- Simulating ATO Mechanism and EGFR Signaling with Fuzzy Logic and Petri Net.Journal of biomedical physics & engineering · 2021Article
- Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach.Computational and structural biotechnology journal · 2021Article
- Nets-within-nets for modeling emergent patterns in ontogenetic processes.Computational and structural biotechnology journal · 2021Article
- Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells.Bioinformatics (Oxford, England) · 2020Article
- Order Through Disorder: The Characteristic Variability of Systems.Frontiers in cell and developmental biology · 2020Review
- Modeling biological systems with uncertain kinetic data using fuzzy continuous Petri nets.BMC systems biology · 2018Article
- Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease.NPJ systems biology and applications · 2018Article
- Analyzing the Behavior of Neuronal Pathways in Alzheimer's Disease Using Petri Net Modeling Approach.Frontiers in neuroinformatics · 2018Article
- Extended Robust Boolean Network of Budding Yeast Cell Cycle.Journal of medical signals and sensorsArticle
- Predicting Efficacy of 5-Fluorouracil Therapy via a Mathematical Model with Fuzzy Uncertain Parameters.Journal of medical signals and sensorsArticle
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3 authors.
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Abstract
Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.
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