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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Cellular behavior exhibits significant variability despite genetic identity, stemming from inherent molecular randomness.
  • Research investigates this using microscopic kinetic models and systems-level enzyme variability analysis.

Purpose of the Study:

  • To review Lattice Microbes software for reaction-diffusion simulations and population flux balance analysis (FBA) for metabolic phenotypes.
  • To present advances in Lattice Microbes for larger organism simulations.
  • To explore cell competition and cooperation using integrated methodologies.

Main Methods:

  • GPU-accelerated stochastic simulation (Lattice Microbes) for reaction-diffusion processes.
  • Population Flux Balance Analysis (FBA) to model metabolic phenotypes.
  • Hybrid approach integrating FBA with spatially resolved kinetic simulations.

Main Results:

  • Lattice Microbes extended for simulating larger organisms and colonies.
  • Population FBA of *Escherichia coli* predicted metabolic pathway variability due to protein expression heterogeneity.
  • Early work demonstrates hybrid method for studying cellular interactions in colonies.

Conclusions:

  • Stochasticity in biomolecular processes drives cellular heterogeneity.
  • Computational tools like Lattice Microbes and FBA are crucial for understanding cellular behavior and metabolism.
  • Integrated modeling approaches offer new insights into microbial community dynamics.