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Published on: April 12, 2019
Parameter sensitivity analysis of stochastic models: application to catalytic reaction networks.
Chiara Damiani1, Alessandro Filisetti, Alex Graudenzi
1COSBI The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068 Rovereto (TN), Italy. chiara.damiani@unimib.it
Computational Biology and Chemistry
|December 19, 2012
Summary
This study introduces a numerical method for sensitivity analysis in stochastic models. It identifies key parameters influencing protocell evolution, finding that resource uptake rate is crucial for early life development.
Area of Science:
- Computational Biology
- Systems Chemistry
- Astrobiology
Background:
- Stochastic computational models are vital for understanding complex biological systems.
- Parametric sensitivity analysis is crucial for identifying influential parameters, especially with incomplete data.
- Protocells represent a key step in abiogenesis, requiring understanding of their environmental interactions.
Purpose of the Study:
- To develop a general numerical methodology for parametric sensitivity analysis of stochastic computational models.
- To apply this methodology to a protocell model to identify critical kinetic rates affecting material retention and molecular generation.
- To gain insights into evolutionary advantages related to resource acquisition in early life forms.
Main Methods:
- Development of a general numerical methodology for parametric sensitivity analysis.
- Application of the methodology to a computational model of a protocell with a catalytic reaction network and semi-permeable membrane.
- Analysis of sensitivity to variations in model parameters, focusing on kinetic rates.
Main Results:
- The protocell's ability to retain material from its environment showed low sensitivity to model parameter variations.
- A specific kinetic rate significantly influenced the generation of molecular species within the protocell.
- This influential rate was dependent on the specific reaction network within the protocell model.
Conclusions:
- The proposed sensitivity analysis method effectively identifies key parameters in stochastic models.
- Faster uptake of environmental resources may confer a significant evolutionary advantage for protocell development.
- This research provides foundational insights for identifying structures conducive to viable early life evolution.
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