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Derivation of Continuum Models from An Agent-based Cancer Model: Optimization and Sensitivity Analysis
Dimitrios Voulgarelis1,2, Ajoy Velayudhan3, Frank Smith2
1Centre for Mathematics, Physics and Engineering in the Life Sciences and Experimental Biology, UCL, Physics Building, Gower Pl, London, WC1E 6BT, United Kingdom.
Current Pharmaceutical Biotechnology
|March 30, 2018
Summary
Equation-based models complement agent-based models for biological systems analysis. This study develops differential equations models for tumor cell reprogramming, enabling efficient treatment optimization and sensitivity analysis.
Area of Science:
- Computational Biology
- Mathematical Modeling
- Systems Biology
Background:
- Agent-based models (ABMs) excel at simulating complex biological systems but require significant computational resources.
- Equation-based models (EBMs) offer faster analysis for complex systems.
- Bridging ABMs and EBMs is crucial for efficient biological system analysis.
Purpose of the Study:
- To develop ordinary differential equations (ODEs) and stochastic differential equations (SDEs) models mirroring an existing ABM of tumor cell reprogramming.
- To apply these EBMs for optimizing cancer treatment strategies.
- To perform dosage sensitivity analysis for identified treatments.
Main Methods:
- Formulation of ODE and SDE models based on an established ABM.
- Parameter space exploration to identify conditions where EBMs closely approximate ABM behavior.
- Application of EBMs for treatment optimization and sensitivity analysis.
Main Results:
- Achieved close agreement between EBMs and ABMs for specific parameter ranges.
- Demonstrated the utility of EBMs for treatment optimization and dosage sensitivity analysis.
- Highlighted the complementary strengths of ABMs and EBMs.
Conclusions:
- EBMs can effectively capture the behavior of complex biological systems, like tumor cell reprogramming, when calibrated with ABM data.
- A hybrid approach, leveraging the strengths of both ABMs and EBMs, is advantageous for biological system analysis and therapeutic development.
- This work supports the efficient exploration of treatment strategies and dosage sensitivities in cancer research.
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