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Integrating biosystem models using waveform relaxation.
Linzhong Li1, Robert M Seymour, Stephen Baigent
1Institute for Energy Technology, Kjeller, Norway.
EURASIP Journal on Bioinformatics & Systems Biology
|January 7, 2009
Summary
The waveform relaxation (WR) method efficiently computes complex biological models by integrating submodels. This parallel computation approach handles diverse model couplings and time scales for systems biology.
Area of Science:
- Systems Biology
- Computational Biology
- Mathematical Modelling
Background:
- Integrating component models into larger composite models is a key challenge in systems biology.
- Coupling of submodels can be unidirectional or bidirectional with variable strengths, complicating integration.
- Efficient and systematic methods are needed for computing complex, linked biological systems.
Purpose of the Study:
- To adapt the waveform relaxation (WR) method for parallel computation of ordinary differential equations (ODEs).
- To present WR as a general methodology for computing systems of linked submodels in systems biology.
- To demonstrate the flexibility of WR in handling multitime-scale computation and model heterogeneity.
Main Methods:
- Adaptation of the waveform relaxation (WR) method for parallel computation of ODEs.
- Application of WR to four distinct test cases involving coupled harmonic oscillators and calcium oscillations.
- Testing WR on single-cell and multicellular models, including complex behaviors like bursting and chaotic dynamics.
Main Results:
- The WR method successfully computed systems of linked submodels across diverse scenarios.
- Demonstrated WR's capability in handling both deterministic and stochastic simulations of calcium oscillations.
- Validated WR's effectiveness for multitime-scale computations and heterogeneous model components.
Conclusions:
- The waveform relaxation method offers a flexible approach for computing complex biological systems.
- WR enables the capture of global solutions independent of individual component solution techniques.
- This methodology facilitates parallel computation and integration of diverse submodels in systems biology.
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