Network growth models and genetic regulatory networks
D V Foster1, S A Kauffman, J E S Socolar
1Physics Department and Center for Nonlinear and Complex Systems, Duke University, Durham, North Carolina 27514, USA.
Summary
This study models genetic regulatory network evolution using graph growth algorithms. A specific initial seed is crucial for accurately reproducing yeast gene networks and transcription factor scaling.
Area of Science:
- Computational Biology
- Systems Biology
- Network Science
Background:
- Genetic regulatory networks (GRNs) are complex systems governing gene expression.
- Understanding GRN evolution is key to deciphering cellular function.
- Existing models may not fully capture the dynamics of network growth and adaptation.
Purpose of the Study:
- To develop and analyze graph growth algorithms as models for GRN evolution.
- To investigate the impact of node/link duplication and innovation on network structure.
- To identify conditions under which these algorithms can reproduce empirical biological data.
Main Methods:
- Simulation of directed graph growth algorithms.
- Incorporation of partial node and link duplication.
- Modeling of new link innovation with differential input/output link survival probabilities.
- Analysis of in-degree distributions and scaling relationships.
Main Results:
- Observed counterintuitive trends, such as in-degree distribution broadening when input link retention probability decreases.
- Demonstrated that the growth algorithm can reproduce yeast gene degree distributions.
- Showed that accurate reproduction requires a specific initial seed for the algorithm.
Conclusions:
- The proposed growth algorithms offer a plausible mechanism for GRN evolution.
- Parameter choices, particularly link retention probabilities, significantly influence network topology.
- A specific initialization (seed) is critical for the model's biological relevance, especially for yeast GRNs.
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