Week5: Simulations of Protein: Cellular process and common tools in whole cell modeling

1.Whole-Cell Modeling: An Overview

image source https://ccsb.scripps.edu/gallery/mycoplasma_model/

Definition & Goal:
Whole‐cell modeling (WCM) seeks to computationally simulate all the biochemical and biophysical processes within a living cell—from metabolism and gene expression to DNA replication and cell division—in a unified framework. The ultimate goal is to predict phenotype from genotype and provide a “virtual cell” that can be used to explore how cells grow, divide, and respond to perturbations.

Key Features:

• Integration Across Scales: WCM spans multiple time and length scales, capturing fast biochemical reactions alongside slower cellular processes.
• Hybrid Approaches: Most models combine deterministic methods (e.g., ordinary differential equations for metabolism) with stochastic simulations (e.g., for transcription or translation) and spatial diffusion (via PDEs or Brownian dynamics).
• Data-Driven Modeling: These models are grounded in diverse experimental data—from cryo-electron tomography (for spatial cell architecture) to omics and proteomics (for enzyme and gene expression levels).

2.Common Tools and Frameworks in Whole-Cell Modeling

Several software platforms and computational frameworks have been developed to aid in building and simulating whole‐cell models. Some of the widely used ones include:

Certainly! Here are some widely used tools and frameworks in whole-cell modeling, along with their respective links:

• Lattice Microbes:
  Often employed for simulating reaction-diffusion systems at the molecular level in a spatially resolved manner.
  https://scs.illinois.edu/schulten/lm/

• Virtual Cell (VCell):
  An open-source platform that converts biological schematics into systems of differential equations. It supports both spatial and compartmental simulations and is used for modeling reaction kinetics, transport, and diffusion.
  https://vcell.org/

• COPASI:
  A GUI-based tool for deterministic and stochastic simulation of biochemical networks, parameter estimation, and metabolic control analysis.
  http://copasi.org/

• libRoadRunner:
  A high-performance library designed for SBML-based simulations, useful for dynamic and steady-state analysis.
  https://libroadrunner.org/

• Vivarium & PhysiCell:
  Modern frameworks that enable hybrid multiscale simulations by coupling different submodels (e.g., reaction-diffusion, mechanical interactions) within a cell or tissue.

  • Vivarium: https://vivarium-collective.github.io/
  • PhysiCell: http://physicell.org/

These tools provide a range of capabilities for modeling and simulating complex biological systems, each with its unique features and strengths.

These tools leverage various numerical methods (ODE/PDE solvers, stochastic algorithms, Brownian dynamics) and allow researchers to integrate heterogeneous experimental data into a cohesive computational model.