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Cell behaviour as a dynamic attractor in the intracellular signalling system.
1Department of Computer and Information Science, New Jersey Institute of Technology, Newark 07102, USA. ajames@ncrl.njit.edu
Journal of Theoretical Biology
|March 2, 1999
Summary
This study models cell signaling networks using a genetic algorithm to evolve protein interactions. It suggests cell behavior changes may represent switches between signaling network attractors.
Area of Science:
- Systems Biology
- Computational Biology
- Biophysics
Background:
- Cellular signaling relies on complex interactions between protein kinases (PKs) and protein phosphatases (PPs).
- Understanding the intrinsic dynamics of these phosphorylation systems is crucial for deciphering cellular behavior.
- Existing models often lack the capacity to explore the evolutionary and emergent properties of signaling networks.
Purpose of the Study:
- To develop a computational model for investigating the global properties and intrinsic dynamics of intracellular phosphorylation systems.
- To explore how genetic algorithms can simulate the evolution of protein interactions within a cellular context.
- To test the hypothesis that cellular behavior changes correspond to transitions between attractor states in signaling networks.
Main Methods:
- A genetic algorithm (GA) was employed to evolve populations of simulated cells.
- Cells were ranked based on their ability to exhibit diverse dynamic behaviors from various initial conditions.
- Evolutionary mechanisms, including mutation and domain shuffling analogues, were incorporated into the GA.
Main Results:
- The GA successfully evolved simulated intracellular signaling networks with diverse dynamic properties.
- Analysis of simulated network dynamics revealed distinct 'behaviors' corresponding to different initial conditions.
- The model demonstrated the potential for emergent complexity in cellular signaling pathways.
Conclusions:
- The developed model provides a framework for studying the emergent dynamics of cell signaling networks.
- Cellular behavior diversification can arise from evolutionary processes acting on protein interaction networks.
- Changes in cell behavior may be interpreted as switches between attractor basins within the signaling network, offering insights into cellular decision-making.