Simulating biological processes: stochastic physics from whole cells to colonies.

Area of Science:

• Biological Physics
• Cellular Biology
• Computational Biology

Background:

• The living cell is a crowded, heterogeneous environment where biomolecular concentrations and activities are influenced by random forces.
• This inherent randomness paradoxically gives rise to precisely timed and coordinated biological functions, leading to complex life forms and behaviors.
• Stochastic modeling has emerged as a significant subdiscipline within biological physics to address this paradox.

Purpose of the Study:

• To review major advances in understanding stochasticity in biology.
• To provide historical context and outline experimental results that motivated stochastic modeling.
• To discuss simulation methods, their applicability, and parameterization challenges.

Main Methods:

• Review of historical experimental findings.
• Rigorous treatment of stochastic simulation methods for biological models.
• Comparison and contrast of simulation method applicability and parameterization.

Main Results:

• Stochasticity impacts key cellular functions including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism.
• Coupling of these functions into a comprehensive model of a 'minimal cell' is explored.
• Integration of mesoscopic stochastic methods with atomic-scale molecular modeling is anticipated.

Conclusions:

• Stochastic modeling is crucial for understanding biological complexity arising from randomness.
• Accurate parameterization and selection of simulation methods are vital for reliable results.
• Future research will likely integrate multi-scale modeling approaches to capture life's processes across various scales.