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Emerging whole-cell modeling principles and methods.

Arthur P Goldberg1, Balázs Szigeti1, Yin Hoon Chew1

  • 1Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

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Whole-cell computational models predict cellular phenotypes by integrating genomic and molecular data. Despite challenges, advancements in technology and modeling are enabling the creation of these comprehensive biological simulations.

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Whole-cell computational models integrate genotype, molecular species, interactions, and environment to predict cellular phenotypes.
  • These models hold significant potential for transforming bioscience, bioengineering, and medicine.
  • However, developing accurate and comprehensive whole-cell models faces numerous challenges.

Purpose of the Study:

  • To outline the potential and challenges of whole-cell computational models.
  • To highlight recent advancements enabling the development of these models.
  • To anticipate future developments in scalable whole-cell modeling.

Main Methods:

  • Leveraging progress in measurement technology and bioinformatics.
  • Utilizing data sharing initiatives for model development.
  • Employing rule-based modeling and multi-algorithmic simulation techniques.

Main Results:

  • Researchers are beginning to construct the first generation of whole-cell models.
  • Progress in key areas is overcoming previous limitations.
  • The development of scalable modeling tools is underway.

Conclusions:

  • Ongoing efforts are paving the way for more comprehensive and accurate whole-cell models.
  • Future models are expected to include complex systems like human cells.
  • Whole-cell modeling promises to revolutionize biological and medical research.