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Molecular simulations of cellular processes.

Fabio Trovato¹, Giordano Fumagalli²

¹Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany. fabiotrovato@gmail.com.

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|November 30, 2017

Summary

This summary is machine-generated.

Computer simulations now mimic cellular environments, aiding study of macromolecule diffusion and genetic material processes. Challenges remain in developing accurate cell-scale models for complex biological events.

Keywords:

Coarse-graining Diffusion in the cytoplasm Facilitated diffusion Genetic material Hydrodynamic interactions Integrative modeling Macromolecular crowding Molecular dynamics Nuclear bodies Soft interactions Stochastic processes Sub-diffusion

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Area of Science:

Biophysics
Computational Biology
Cellular Biology

Background:

Simulating biological processes within cell-mimicking environments is increasingly feasible.
Existing molecular models vary in accuracy and often simplify complexity to study slow processes.

Purpose of the Study:

To review insights from computer simulations of macromolecule diffusion, nuclear body formation, and genetic material processes in cellular compartments.
To discuss challenges in developing cell-scale simulation models for long timescales and non-equilibrium events.
To highlight the importance of balancing structural simplification with energetic description in model development.

Main Methods:

Review of computer simulation studies on macromolecule diffusion, nuclear body formation, and genetic material dynamics.
Discussion of model development strategies for cell-scale simulations.
Integration of experimental and computational methods for enhanced biological simulations.

Main Results:

Computer simulations provide valuable insights into macromolecule diffusion and processes involving genetic material within cellular spaces.
Significant challenges exist in creating models that accurately capture cell-cycle-long dynamics and non-equilibrium events like protein folding and aggregation.
The selection of appropriate structural simplifications and energetic descriptions is crucial for effective modeling.

Conclusions:

Advancements in computer simulations offer powerful tools for understanding complex biological processes at the cellular level.
Future progress requires innovative modeling approaches that integrate experimental data and efficient algorithms.
Developing accurate, long-timescale cell models is essential for elucidating dynamic biological events.