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

1 Citation (Scopus)

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

Fingerprint

simulation
estimation method
simulator
phenotype
parameterization
infrastructure
kinetics
prediction
modeling
rate
biochemical process
parameter estimation

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

Cite this

Spolaor, S., Gribaudo, M., Iacono, M., Kadavy, T., Komínková Oplatková, Z., Mauri, G., ... Nobile, M. S. (2019). Towards human cell simulation. In J. Kołodziej , & H. González-Vélez (Eds.), High-performance modelling and simulation for big data applications: selected results of the COST action IC1406 cHiPSet (pp. 221-249). (Lecture Notes in Computer Science; Vol. 11400). Springer. https://doi.org/10.1007/978-3-030-16272-6_8
Spolaor, Simone ; Gribaudo, Marco ; Iacono, Mauro ; Kadavy, Tomas ; Komínková Oplatková, Zuzana ; Mauri, Giancarlo ; Pllana, Sabri ; Senkerik, Roman ; Stojanovic, Natalija ; Turunen, Esko ; Viktorin, Adam ; Vitabile, Salvatore ; Zamuda, Aleš ; Nobile, Marco S. / Towards human cell simulation. High-performance modelling and simulation for big data applications: selected results of the COST action IC1406 cHiPSet. editor / Joanna Kołodziej ; Horacio González-Vélez. Springer, 2019. pp. 221-249 (Lecture Notes in Computer Science).
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Spolaor, S, Gribaudo, M, Iacono, M, Kadavy, T, Komínková Oplatková, Z, Mauri, G, Pllana, S, Senkerik, R, Stojanovic, N, Turunen, E, Viktorin, A, Vitabile, S, Zamuda, A & Nobile, MS 2019, Towards human cell simulation. in J Kołodziej & H González-Vélez (eds), High-performance modelling and simulation for big data applications: selected results of the COST action IC1406 cHiPSet. Lecture Notes in Computer Science, vol. 11400, Springer, pp. 221-249. https://doi.org/10.1007/978-3-030-16272-6_8

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

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. 221-249 (Lecture Notes in Computer Science; Vol. 11400).

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

TY - CHAP

T1 - Towards human cell simulation

AU - Spolaor, Simone

AU - Gribaudo, Marco

AU - Iacono, Mauro

AU - Kadavy, Tomas

AU - Komínková Oplatková, Zuzana

AU - Mauri, Giancarlo

AU - Pllana, Sabri

AU - Senkerik, Roman

AU - Stojanovic, Natalija

AU - Turunen, Esko

AU - Viktorin, Adam

AU - Vitabile, Salvatore

AU - Zamuda, Aleš

AU - Nobile, Marco S.

PY - 2019/3/31

Y1 - 2019/3/31

N2 - 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.

AB - 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.

KW - Agent-based simulation

KW - Big data

KW - Biochemical simulation

KW - Computational intelligence

KW - Constraint-based modeling

KW - Fuzzy logic

KW - High-performance computing

KW - Model reduction

KW - Multi-scale modeling

KW - Parameter estimation

KW - Reaction-based modeling

KW - Systems biology

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SN - 978-3-030-16271-9

T3 - Lecture Notes in Computer Science

SP - 221

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BT - High-performance modelling and simulation for big data applications

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Spolaor S, Gribaudo M, Iacono M, Kadavy T, Komínková Oplatková Z, Mauri G et al. Towards human cell simulation. In Kołodziej J, González-Vélez H, editors, High-performance modelling and simulation for big data applications: selected results of the COST action IC1406 cHiPSet. Springer. 2019. p. 221-249. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-16272-6_8