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Towards Human Cell Simulation
University of Milano-Bicocca, Italy;SYSBIO.IT Centre for Systems Biology, Italy.
Politecnico di Milano, Italy.
Università degli Studi della Campania “Luigi Vanvitelli”, Italy.
Tomas Bata University in Zlin, Czech Republic.
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2019 (English)In: High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet / [ed] Joanna Kołodziej, Horacio González-Vélez, Springer, 2019, p. 221-249Chapter in book (Refereed)
Abstract [en]

The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

Place, publisher, year, edition, pages
Springer, 2019. p. 221-249
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11400
Keywords [en]
Agent-based simulation, Big data, Biochemical simulation, Computational intelligence, Constraint-based modeling, Fuzzy logic, High-performance computing, Model reduction, Multi-scale modeling, Parameter estimation, Reaction-based modeling, Systems biology
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:lnu:diva-81345DOI: 10.1007/978-3-030-16272-6_8Scopus ID: 2-s2.0-85063786885ISBN: 978-3-030-16271-9 (print)ISBN: 978-3-030-16272-6 (electronic)OAI: oai:DiVA.org:lnu-81345DiVA, id: diva2:1299425
Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-08-29Bibliographically approved

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Pllana, Sabri

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