Parameter Estimation in Computational Biology by Approximate Bayesian Computation coupled with Sensitivity Analysis
This work addresses the problem of efficient parameter estimation for researchers in computational biology, though it appears incremental as it builds on existing ABC methods by integrating sensitivity analysis.
The paper tackles parameter estimation in systems biology models from noisy observations by combining Approximate Bayesian Computation (ABC) with sensitivity analysis to identify sloppy and stiff parameters, reducing computational complexity while maintaining accuracy in probing posterior distributions.
We address the problem of parameter estimation in models of systems biology from noisy observations. The models we consider are characterized by simultaneous deterministic nonlinear differential equations whose parameters are either taken from in vitro experiments, or are hand-tuned during the model development process to reproduces observations from the system. We consider the family of algorithms coming under the Bayesian formulation of Approximate Bayesian Computation (ABC), and show that sensitivity analysis could be deployed to quantify the relative roles of different parameters in the system. Parameters to which a system is relatively less sensitive (known as sloppy parameters) need not be estimated to high precision, while the values of parameters that are more critical (stiff parameters) need to be determined with care. A tradeoff between computational complexity and the accuracy with which the posterior distribution may be probed is an important characteristic of this class of algorithms.