Towards human cell simulation

Simone Spolaor, Marco Gribaudo, Mauro Iacono, Tomas Kadavy, Zuzana Komínková Oplatková, Giancarlo Mauri, Sabri Pllana, Roman Senkerik, Natalija Stojanovic, Esko Turunen, Adam Viktorin, Salvatore Vitabile, Aleš Zamuda, Marco S. Nobile

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationHigh-performance modelling and simulation for big data applications
Subtitle of host publicationselected results of the COST action IC1406 cHiPSet
EditorsJoanna Kołodziej , Horacio González-Vélez
PublisherSpringer
Pages221-249
Number of pages29
ISBN (Electronic)978-3-030-16272-6
ISBN (Print)978-3-030-16271-9
DOIs
Publication statusPublished - 31 Mar 2019
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11400

Keywords

  • 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

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