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Maximum likelihood reconstruction of ancestral networks by integer linear programming.

Vaibhav Rajan1, Ziqi Zhang2, Carl Kingsford3

  • 1Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore 117417, Singapore.

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|November 2, 2020
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Summary
This summary is machine-generated.

We developed an Integer Linear Programming (ILP) method for reconstructing ancestral protein-protein interaction (PPI) networks using the Duplication-Mutation with Complementarity (DMC) model. This optimal solution outperforms existing heuristic methods in accuracy and biological relevance.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Understanding biological network evolution is key to deciphering bio-molecular processes.
  • Network growth models, like Duplication-Mutation with Complementarity (DMC), explain protein-protein interaction (PPI) evolution.
  • Existing ancestral network reconstruction methods often yield suboptimal results.

Purpose of the Study:

  • To develop an optimal Integer Linear Programming (ILP) solution for ancestral PPI network reconstruction using the DMC model.
  • To improve upon current heuristic-based methods for accuracy and efficiency.

Main Methods:

  • Formulated a novel Integer Linear Programming (ILP) approach for maximum likelihood ancestral PPI network reconstruction.
  • Proved the correctness of the ILP formulation for finding optimal solutions.
  • Utilized general-purpose ILP solvers to efficiently find optimal and near-optimal solutions.

Main Results:

  • The ILP method achieves higher likelihood reconstructions compared to previous approaches on synthetic data.
  • The method demonstrates robustness against noise and model mismatch.
  • Evaluations on real PPI networks (bZIP transcription factors, Commander complex) show superior likelihood and better agreement with biological evidence.

Conclusions:

  • The presented ILP method provides an optimal and accurate approach for ancestral PPI network reconstruction under the DMC model.
  • This method offers significant improvements over existing heuristic techniques.
  • The findings enhance our understanding of evolutionary network dynamics and have practical applications in systems biology.