Simulation-based Methods for Optimal Sampling Design in Systems Biology
This addresses the challenge of suboptimal sampling design in systems biology, such as in virology and pharmacokinetics, by providing methods that do not rely on initial parameter estimates, though it is incremental as it builds on classical optimality criteria.
The study tackled the problem of optimally selecting sampling points for accurate parameter estimation in dynamical systems in systems biology, proposing two simulation-based methods (E-optimal-ranking and LSTM neural network) that outperformed random selection and classical E-optimal design in simulation studies.
In many areas of systems biology, including virology, pharmacokinetics, and population biology, dynamical systems are commonly used to describe biological processes. These systems can be characterized by estimating their parameters from sampled data. The key problem is how to optimally select sampling points to achieve accurate parameter estimation. Classical approaches often rely on Fisher information matrix-based criteria such as A-, D-, and E-optimality, which require an initial parameter estimate and may yield suboptimal results when the estimate is inaccurate. This study proposes two simulation-based methods for optimal sampling design that do not depend on initial parameter estimates. The first method, E-optimal-ranking (EOR), employs the E-optimal criterion, while the second utilizes a Long Short-Term Memory (LSTM) neural network. Simulation studies based on the Lotka-Volterra and three-compartment models demonstrate that the proposed methods outperform both random selection and classical E-optimal design.