Gomez-Cabrero2011_Atherogenesis

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This model is from the article:
Workflow for generating competing hypothesis from models with parameter uncertainty.
David Gomez-Cabrero, Albert Compte and Jesper Tegner Interface Focus 6 June 2011 vol. 1 no. 3 438-449; doi: 10.1098/rsfs.2011.0015
Abstract:
Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters.

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SBML (L2V4)
Related Publication
  • Workflow for generating competing hypothesis from models with parameter uncertainty.
  • Gomez-Cabrero D, Compte A, Tegner J
  • Interface focus , 6/ 2011 , Volume 1 , pages: 438-449
  • Department of Medicine, Karolinska Institutet , Unit of Computational Medicine, Centre for Molecular Medicine , Solna, Stockholm , Sweden.
  • Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters.
Contributors
David Gomez-Cabrero

Metadata information

is
BioModels Database MODEL1002160000
hasTaxon
Taxonomy Homo sapiens
isVersionOf
Gene Ontology response to cholesterol
Experimental Factor Ontology 0003914
hasProperty
Mathematical Modelling Ontology Ordinary differential equation model
isDescribedBy
Curation status
Non-curated
Original model(s)
Atherosclerosis Model
  • Model originally submitted by : David Gomez-Cabrero
  • Submitted: Feb 16, 2010 5:38:08 PM
  • Last Modified: Feb 16, 2012 2:30:03 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Feb 16, 2012 2:30:03 PM
    • Submitted by: David Gomez-Cabrero
    • With comment: Current version of Gomez-Cabrero2011_Atherogenesis
  • Version: 1 public model Download this version
    • Submitted on: Feb 16, 2010 5:38:08 PM
    • Submitted by: David Gomez-Cabrero
    • With comment: Original import of ATHERO_MODEL