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Related Experiment Video

Updated: Jun 6, 2026

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
11:06

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

Cloud computing and validation of expandable in silico livers.

Glen E P Ropella1, C Anthony Hunt

  • 1Tempus Dictum, Inc., Portland, OR 97202, USA.

BMC Systems Biology
|December 7, 2010
PubMed
Summary
This summary is machine-generated.

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In Silico Livers (ISLs) were validated on the Amazon EC2 cloud platform, demonstrating scientific equivalence with local cluster experiments. This advancement facilitates broader access to complex biomedical modeling and simulation.

Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Computational Biology and Bioinformatics
  • In Silico Modeling and Simulation

Background:

  • In Silico Livers (ISLs) are computational models used to study xenobiotic disposition and hepatic responses.
  • Enhancements to ISLs include discrete time metabolism, biliary elimination, and bolus dosing features.
  • Re-validation experiments required scaling beyond local cluster capabilities, necessitating the use of cloud computing.

Purpose of the Study:

  • To re-validate enhanced In Silico Livers (ISLs) using the Amazon EC2 cloud platform.
  • To demonstrate the efficacy of scaling ISL simulations across multiple cloud nodes.
  • To assess the scientific equivalence between ISL validation experiments conducted on local clusters versus the cloud.

Main Methods:

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  • The local cluster technology was replicated on the Amazon EC2 cloud platform.
  • Synthetic modeling protocols were employed for parameterization.
  • ISL outflow profiles were compared using diltiazem across various experiment sample sizes (49, 70, 84, and 152 simulated lobules).

    • Main Results:

    • Successful parameterization was achieved using synthetic modeling protocols.
    • Experimental indistinguishability was demonstrated for ISL outflow profiles with 84 or more simulated lobules on both platforms.
    • The results showed scientific equivalence between local cluster and cloud-based ISL validation experiments.

    Conclusions:

    • In Silico Liver (ISL) disposition simulations demonstrate in silico-to-wet-lab phenotype similarity across distinct experimental contexts.
    • Cloud technology adoption, supported by evidence of scientific equivalency, lowers the barrier for accessing and sharing multiscale biomedical models.
    • This advancement provides tools for scaling simulations with greater detail without additional hardware investment, facilitating wider research application.