Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks

Magdevska, Lidija (author), Mraz, Miha (author), Zimic, Nikolaj (author), Moškon, Miha (author)

Background: Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models. Results: We propose a method to enlarge the validation datasets on which a fuzzy dynamic model of a cellular network can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and two models of the mammalian circadian clock. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational model, while keeping errors of predictions of accurate models small. Conclusions: With the improvement of validation methods, fuzzy models are becoming more accurate and are thus likely to gain new applications. This field of research is promising not only because fuzzy models can cope with uncertainty, but also because their run time is short compared to conventional modelling methods that are nowadays used in systems biology.

• Language: English
• Keywords: fuzzy logic, modelling and simulation, data-driven modelling, cellular networks, computational biology, model validation, dynamic modelling, MAPK signalling pathway, circadian clock
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science ; FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2018
• Number of pages: 7 str.
• Numbering: Vol. 19, art. 333
• PID: 20.500.12556/RUL-125563
• UDC: 004.94:57
• ISSN on article: 1471-2105
• DOI: 10.1186/s12859-018-2366-0
• COBISS.SI-ID: 1537917379
• Publication date in RUL: 25.03.2021

Record is a part of a journal

• Title: BMC bioinformatics
• Publisher: Springer Nature
• ISSN: 1471-2105
• COBISS.SI-ID: 2433556

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 25.03.2021

Secondary language

• Language: Slovenian
• Keywords: mehka logika, modeliranje in simulacija, podatkovno vodeno modeliranje, celična omrežja, računska biologija