A synergic simulation-optimization approach for analyzing biomolecular dynamics in living organisms

Area of Science:

• Biophysics
• Computational Biology
• Biochemistry

Background:

• Fluorescence microscopy is crucial for studying biomolecular dynamics in vivo.
• Current analysis methods may not fully capture complex physiological information.
• Understanding protein-substrate interactions requires advanced modeling techniques.

Purpose of the Study:

• To develop and implement a synergic simulation-optimization approach for studying protein-substrate dynamics and binding kinetics.
• To compare the efficacy of full space-time series versus averaged time series in fluorescence microscopy.
• To refine parameterization strategies for biological mass transfer processes.

Main Methods:

• Developed a simulation-optimization approach solving nonlinear partial differential equations.
• Employed finite difference discretization with an incomplete Cholesky preconditioner and adaptive time-stepping.
• Validated the model with analytical and reference solutions, simulating GFP-GR dynamics in mouse cancer cells.

Main Results:

• The simulation-optimization approach successfully generated full biomolecular/drug space-time series.
• Analysis indicated that bleach spot-averaged time series are inefficient for extracting physiological information.
• Identified limitations in using zero-gradient stopping rules for inverse problem parameterization.

Conclusions:

• Full space-time series are recommended for accurately studying biomacromolecule and drug dynamics in vivo.
• Current fluorescence microscopy analysis protocols may be suboptimal for detailed physiological insights.
• Multi-criteria stopping rules are more effective for quantifying model parameters in biological mass transfer studies.