An Automatic Sparse Model Estimation Method Guided by Constraints That Encode System Properties
| Autor: | Zoltan A. Tuza, Guy-Bart Stan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Signal processing State variable Mathematical optimization Estimation theory 020208 electrical & electronic engineering 02 engineering and technology Bayesian inference Field (computer science) 020901 industrial engineering & automation Bounded function Convex optimization 0202 electrical engineering electronic engineering information engineering State (computer science) |
| Zdroj: | ECC |
| Popis: | Finding efficient methods to estimate model parameters or build models from time-series data is a central quest in Systems and Synthetic Biology. To aid this search, existing parameter estimation methods were adapted from other fields, or new ones were developed. In this paper, we use the Sparse Bayesian Learning (SBL) framework, which was first developed in the field of signal processing, and implement it as an iterative convex optimization problem. We extend the existing SBL framework to accommodate constraints that enforce certain system properties, such as nonnegative state variables or bounded state trajectories. These properties are vital parts of a dynamical model in biology and chemistry but are often overlooked in the parameter estimation literature. As a result of this work, the extended framework can automatically build “proper” dynamical models from time-series data. Finally, the examples show that such framework complemented with appropriate constraints can aid the model building process. |
| Databáze: | OpenAIRE |
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