Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training...

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Understanding complex biological systems requires accurate models derived from dynamic data.
• Parameter estimation and network structure identification are crucial inverse problems in biosystem modeling.
• Existing methods often struggle with noisy datasets and computational efficiency.

Purpose of the Study:

• To propose an efficient inverse problem approach, the embedded training functions in a genetic algorithm (ETFGA), for parameter estimation and structure identification in nonlinear dynamical biosystems.
• To demonstrate the ETFGA's capability in handling noisy data and achieving computational efficiency.
• To validate the ETFGA methodology using three distinct biochemical model systems.

Main Methods:

• Developed an efficient inverse problem approach by embedding training functions within a genetic algorithm methodology (ETFGA).
• Utilized multiple shooting and decomposition techniques as training functions to manage noisy datasets.
• Applied the ETFGA to S-system canonical models for parameter estimation, state, and structure identification.

Main Results:

• The ETFGA successfully estimated numerous parameters and identified network structures simultaneously in a gene regulatory system, outperforming other metaheuristic approaches.
• Demonstrated flexibility in incorporating partial system information for modeling cAMP oscillations, enhancing accuracy and predictive ability.
• Successfully estimated an alternate S-system model for Drosophila circadian oscillations with robust predictive capabilities using limited noisy data.

Conclusions:

• The ETFGA provides a superior and flexible approach for parameter estimation and structure identification in nonlinear dynamical biosystems.
• The methodology is robust in handling noisy and limited datasets, offering accurate predictions.
• ETFGA advances the study of complex biological networks and regulatory mechanisms.