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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Path-based approach to random walks on networks characterizes how proteins evolve new functions
Michael Manhart1, Alexandre V Morozov
1Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, USA.
Physical Review Letters
|September 10, 2013
Summary
We developed a novel path-based method for analyzing continuous-time random walks on complex networks. This approach reveals distinct protein adaptation regimes based on binding and folding energetics, offering insights into evolutionary dynamics.
Area of Science:
- Computational Biology
- Biophysics
- Network Science
Background:
- Continuous-time random walks (CTRWs) are fundamental to modeling transport and dynamics on networks.
- Understanding protein evolution requires models that link function, stability, and adaptation.
- Existing methods may not efficiently capture the statistical properties of complex stochastic processes.
Purpose of the Study:
- To develop a path-based computational framework for analyzing CTRWs on networks with weighted edges.
- To apply this framework to model protein evolutionary adaptation and function evolution.
- To identify distinct regimes of protein adaptation based on biophysical properties.
Main Methods:
- Developed a path-based approach for CTRWs on arbitrarily weighted networks.
- Designed an efficient numerical algorithm to compute statistical properties of stochastic paths.
- Constructed a biophysical model for protein function evolution and thermodynamic stability.
- Applied the methodology to analyze evolutionary adaptation dynamics.
Main Results:
- Demonstrated the approach on reaction rate problems.
- Successfully reproduced key features of directed evolution experiments.
- Identified two distinct regimes of protein adaptation.
- Linked adaptation regimes to protein binding and folding energetics.
Conclusions:
- The path-based CTRW approach provides an efficient tool for analyzing complex network dynamics.
- Protein adaptation is governed by distinct regimes influenced by energetic landscapes.
- This framework advances our understanding of evolutionary processes in proteins.
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