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MUFINS: multi-formalism interaction network simulator.

Huihai Wu1, Axel von Kamp2, Vytautas Leoncikas1

  • 1School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.

NPJ Systems Biology and Applications
|July 21, 2017
PubMed
Summary
This summary is machine-generated.

MUFINS software integrates diverse biological models for multi-scale systems biology. This novel approach enables simultaneous simulation of gene regulation, signaling, and metabolism, advancing mechanistic understanding of cellular processes.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Network Modeling

Background:

  • Systems Biology has developed numerous modeling approaches for cellular networks.
  • Integrating models with different formalisms is crucial for addressing multi-scale biological system organization.
  • Existing frameworks often struggle to combine diverse network descriptions.

Purpose of the Study:

  • To present MUFINS (MUlti-Formalism Interaction Network Simulator), a software tool for multi-formalism simulation of biological interaction networks.
  • To extend the constraint-based modeling (CBM) framework to incorporate linear inhibition constraints for integrated network modeling.
  • To provide a user-friendly platform for researchers to model and simulate complex biological systems.

Main Methods:

  • Extension of constraint-based modeling (CBM) with linear inhibition constraints.
  • Integration of logical hypergraph models with Genome-Scale Metabolic Networks (GSMN).
  • Incorporation of Quasi-Steady State Petri Net approach for dynamic model integration with CBM.
  • Development of omics data analysis methods, including a variant of the iMAT approach.

Main Results:

  • Enabled linear modeling of networks describing gene regulation, signaling, and metabolism simultaneously at steady state.
  • Demonstrated successful integration of a regulatory network model with a mouse macrophage GSMN.
  • Validated predictions through experimental data, showcasing an iterative hypothesis generation and refinement cycle.
  • Integrated dynamic models of cortisol signaling and nutrient dynamics with GSMNs.
  • Recovered features of metabolic reprogramming in cancer by analyzing tumor transcriptomes using the iMAT variant.

Conclusions:

  • MUFINS provides a unique platform for multi-formalism simulation, advancing systems biology research.
  • The software facilitates the integration of diverse biological models, enabling comprehensive analysis of cellular functions.
  • MUFINS supports iterative model refinement through experimental validation and offers a user-friendly interface for broader accessibility.