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CS 2 Towards Human Cell Simulation cHiPSet – CS 2 Final Report

This report discusses the challenges and progress made in the CS2 project on Whole (Human) Cell Modeling and Simulation. The report emphasizes the need to combine multiple modeling and simulation methods to account for missing parameters and intrinsic stochasticity. The goal of the project is to develop a comprehensive computational model for human cells using a combination of phenomenological and mechanistic approaches. The report outlines the various tasks and methodologies used, including parameter estimation, model reduction, and integration of deterministic and stochastic simulation techniques. The report also highlights ongoing work in automating data collection and inference of fuzzy rule-based models.

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CS 2 Towards Human Cell Simulation cHiPSet – CS 2 Final Report

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  1. CS2TowardsHuman Cell Simulation cHiPSet – CS2FinalReport Marco S. Nobile Vilnius, March28-29, 2019

  2. Whole (Human) Cell Modeling and Simulation Multiple scales = Multiple methods for modelling and simulation must be combined Missing parameters (e.g., kinetic parameters) Intrinsic stochasticity (e.g., signaling, gene regulation) Synchronization and communication between the simulation methods is necessary Massive amount of reactions andchemical species →massive calculations Attempted by Karr et al.using model of bacterium M. genitalium(see scheme) 521 genes: enough for all basic processes Never attempted on human cells Goal of the project Figure taken from: Karret al., A whole-cell computational model predicts phenotype from genotype, Cell 2012

  3. Combine phenomenological modelling/simulation techniques (e.g., Boolean networks, Fuzzy Rule-Based Models) with more detailed mechanistic approaches (e.g., differential equations, Markov chains) Intensive computations (e.g., ODEs) will be offloaded on multiple GPUs Fuzzy RB model defined by hand or (possibly?) automatically inferred from data The various algorithms will run in a distributed fashion using MPI (master node collects partial results) Stochastic simulation is one bottleneck of the method: we need a clever strategy to improve performances We need to estimate a huge amount of parameters We can automate the collection of biological data using Information Retrieval Our approach

  4. Progress of the CS Task A4 Metabolic modeling reduction and simulation Task A2 Integration of deterministic GPU-poweredsimulation Task A1 Accelerated stochasticbiochemicalsimulation Task A3 Automatic FRBM model inference Task Z1 Application to programmedcelldeath Task B1 Integration of stochastic simulator and FRBM Task C1 Estimation of missing parameters Task D1 Integration of allmodeling approaches

  5. ParameterEstimation can be statedas a minimizationproblem(i.e., minimize the «difference» betweensimulation and experimental data) We are currentlytesting DISH (DIstance Based Parameter Adaptation for Succes-History based Differential Evolution), a variant of DifferentialEvolution Among the state-of-the-art of evolutionaryalgorithms for global optimization Preliminary tests: sixbiochemical models with 50 reactions (=missingparameters) In silicogenerated target dynamic target data Preliminary results are notsatisfying– wewillrepeat the tests on more complex systems (>50 parameters) Estimation of missing parameters

  6. Two «regimes» thatcommunicate by means of a sharedinterface In ourtests, a FRBM wascombined with a reaction-based modelsimulatedusingGillespie’sStochasticSimulationAlgorithm (SSA) Bothmodelingapproachesrequire a massive domain expertise In the preliminarytests the method wasable to correctlyreproduce complex phenomenausingonly a smallerfraction of computationaleffort Spolaoret al., IEEE TFS (under revision) Hybridization of mechanistic and fuzzy rule-base models

  7. We are planning to model with Markovian Agents the concurrent reactions firing in the biochemicalsystema Would be helpful to stronglyimprove the performances of the stochasticsimulation Represents a simplification with respect to pure SSA See Bobbio et al., «Markovian Agent Models: A Dynamic Population of InterdendentMarkovian Agents», 2016 Work in progress (no results) Accelerated stochasticbiochemicalsimulation

  8. A book chapter in forthcoming vol. 11400 of Lecture Notes in Computer Science waswritten by all group members Topics: challenges of wholecellsimulation, HPC and Big Data in life sciences, hybrid and multi-formalismmodelingapproaches, model reduction, kineticparametersestimation, inference of fuzzy models Book chapter

  9. We plan to exploit automatic model reduction to improve performances (and possibly reduce the number of missingparameters to be estimated) Provide additional data for automaticinference of FRBM Test estimationalgorithms (DISH and FST-PSO in particular) on higherdimensional models Test Chaos-driven PSO and multi-objective(e.g., NSGA-II) ApplyMarkovian Agents to multi-scale tissuesimulation Next steps

  10. Daniela Besozzi Paolo Cazzaniga Luis Correia Mauro Iacono Joanna Kolodziej Giancarlo Mauri Ivan Merelli Marco S. Nobile CS members Zuzana KomínkováOplatková Sabri Pllana Roman Senkerik Simone Spolaor NataljaStojanovic Esko Turunen AlešZamuda

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