Ina Biermayer project_administrator

Projects: LiSyM network, SMART-NAFLD, Forschungsnetzwerk LiSyM-Krebs, DEEP-HCC network, C-TIP-HCC network

Institutions: German Cancer Research Center (DKFZ), Division of Systems Biology of Signal Transduction, HITS gGmbH

Nachiket Vartak palproject_administratorasset_housekeeper

Projects: Molecular Steatosis - Imaging & Modeling (LiSyM-MSIM), LiSyM network, LiSyM-Krebs Partnering

Institutions: Leibniz-Institut für Arbeitsforschung (IfADo)

https://orcid.org/0000-0001-8813-4799
Lutz Brusch pal

Projects: LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI), LiSyM PALs, LiSyM network, LiSyM-Krebs Partnering, Forschungsnetzwerk LiSyM-Krebs, DEEP-HCC network

Institutions: Zentrum für Informationsdienste und Hochleistungsrechnen (ZIH), Technische Universität Dresden

https://orcid.org/0000-0003-0137-5106
IFNalpha-induced signaling in Huh7.5 cells

ODE model describes dynamics of IFNalpha-induced signaling in Huh7.5 cells for a time scale up to 32 hours after stimulation with IFNalpha. The model consists of an IFN receptor model, formation/degradation and cytoplasmic/nuclear shuttling of STAT1-homodimers, STAT1-STAT2-heterodimers and STAT1-STAT2-IRF9 (ISGF3) complexes. On top, formation of feedback proteins STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3 and IRF2 and corresponding influences on IFNalpha signaling dynamics was incorporated. The model ...

Creators: Jens Timmer, Ursula Klingmüller, Daniel Seehofer, Marcus Rosenblatt, Krishna Kumar Tiwari, Frédérique Kok

Submitter: Olga Krebs

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FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

Abstract (Expand)

Motivation Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyze and understand the processes determining tissue dynamics. These models usually depend on … parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as Approximate Bayesian Computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. Results Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. Availability FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit Supplementary data are available at https://doi.org/10.5281/zenodo.7646287

Authors: Emad Alamoudi, Yannik Schälte, Robert Müller, Jörn Starruß, Nils Bundgaard, Frederik Graw, Lutz Brusch, Jan Hasenauer

Date Published: 21st Feb 2023

Publication Type: Misc

FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes.

Abstract (Expand)

MOTIVATION: Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend … on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. RESULTS: Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AVAILABILITY AND IMPLEMENTATION: FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.

Authors: E. Alamoudi, Y. Schalte, R. Muller, J. Starruss, N. Bundgaard, F. Graw, L. Brusch, J. Hasenauer

Date Published: 1st Nov 2023

Publication Type: Journal

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