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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Michaelis-Menten dynamics in protein subnetworks.

Katy J Rubin1, Peter Sollich1

  • 1Department of Mathematics, King's College London, Strand, London WC2R 2LS, United Kingdom.

The Journal of Chemical Physics
|May 9, 2016
PubMed
Summary

This study extends subnetwork dynamics modeling to include Michaelis-Menten kinetics, offering a more accurate and efficient way to analyze complex biochemical reaction networks with memory effects.

Area of Science:

  • Biochemical Engineering
  • Systems Biology
  • Computational Chemistry

Background:

  • Understanding complex systems requires modeling subnetwork dynamics.
  • Previous projection methods calculated memory functions for unary and binary reactions.
  • Michaelis-Menten kinetics are common in biochemical networks, e.g., enzymatic reactions.

Purpose of the Study:

  • Extend projection methods for subnetwork dynamics to include Michaelis-Menten kinetics.
  • Broaden the applicability of subnetwork dynamics modeling.
  • Develop a procedure for analyzing networks with Michaelis-Menten reactions.

Main Methods:

  • Construct a larger network explicitly representing enzymes and enzyme complexes.
  • Derive projected equations for the extended network.

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  • Take the limit of fast enzyme reactions to recover Michaelis-Menten kinetics in closed form.

    • Main Results:

    • Successfully extended the projection approach to networks with Michaelis-Menten kinetics.
    • Developed a closed-form procedure for enzyme elimination in subnetwork dynamics.
    • Numerical tests confirm enhanced accuracy and computational efficiency compared to explicit enzyme representation.

    Conclusions:

    • The extended projection method accurately describes subnetwork dynamics with Michaelis-Menten kinetics.
    • The closed-form enzyme elimination offers a computationally efficient approach.
    • This work significantly broadens the applicability of subnetwork dynamics analysis for biochemical networks.