Spatiotemporal modeling of microbial metabolism

Area of Science:

• Microbial Systems Biology
• Computational Biology
• Biochemical Engineering

Background:

• Microbial environments often exhibit spatial and temporal variations.
• Existing metabolic modeling approaches (steady-state and dynamic Flux Balance Analysis) lack spatiotemporal capabilities.
• Spatiotemporal metabolic models are crucial for understanding complex microbial systems but have been underdeveloped.

Purpose of the Study:

• To develop a general methodology for spatiotemporal metabolic modeling.
• To integrate genome-scale metabolic reconstructions with fundamental transport equations.
• To provide a computational framework for simulating microbial systems in dynamic environments.

Main Methods:

• Combined genome-scale metabolic reconstructions with transport equations for convective and diffusional processes.
• Employed spatial discretization of partial differential equations.
• Utilized numerical integration of ordinary differential equations with embedded linear programs via DFBAlab (MATLAB).

Main Results:

• Successfully developed and solved spatiotemporal metabolic models for a bubble column reactor (Clostridium ljungdahlii) and a chronic wound biofilm (Pseudomonas aeruginosa).
• Demonstrated the efficiency and robustness of the computational framework for complex models (e.g., 900 ODEs/600 LPs).
• The methodology effectively handles spatiotemporal variations in microbial environments.

Conclusions:

• Established a new paradigm for formulating and solving genome-scale metabolic models with spatiotemporal variations.
• The approach has wide applicability to diverse natural and engineered microbial systems.
• Provides a robust computational solution for previously challenging spatiotemporal modeling problems.