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Reprogramming alters the gene expression in somatic cells, transforming them into induced pluripotent stem (iPS) cells over several generations. Scientists can reprogram cells by introducing genes for four transcription factors—Oct4, Sox2, Klf4, and c-Myc (OSKM) by viral or non-viral methods. These factors are also known as Yamanaka factors after Shinya Yamanaka, who first generated iPS cells using mouse skin cells. Yamanaka was awarded the Nobel Prize in Physiology or Medicine in 2012...
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Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems.

Lucy Ham1,2,3, Taylor E Woodward2, Megan A Coomer3,4

  • 1ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems, University of Melbourne, Parkville, Victoria, Australia.

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Summary
This summary is machine-generated.

Mathematical models help understand cellular decision-making. By analyzing complex biological data, researchers can uncover design principles to guide cellular behavior in various organisms.

Keywords:
epigenetic landscapemolecular networksstochastic processessynthetic biologywhole-cell models

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Area of Science:

  • Systems Biology
  • Cellular Biology
  • Computational Biology

Background:

  • Cellular processes rely on information processing and decision-making.
  • Analyzing complex, heterogeneous biological data presents a significant challenge.
  • Understanding cellular behavior requires quantitative, predictive, and mechanistic approaches.

Purpose of the Study:

  • To discuss the role of mathematical modeling in cell-fate decision-making systems.
  • To explore how design principles can be learned from cellular behavior.
  • To investigate the application of these principles in guiding or redesigning cellular functions.

Main Methods:

  • Review and discussion of mathematical models applied to cell-fate decisions.
  • Analysis of information processing in both single-celled and multicellular organisms.
  • Focus on extracting design principles from observational and modeling data.

Main Results:

  • Mathematical models are crucial for understanding complex cellular decision-making.
  • Design principles can be identified and utilized to influence cellular behavior.
  • These principles are applicable across diverse life forms, from single cells to complex organisms.

Conclusions:

  • Integrating mathematical modeling with experimental data offers a powerful framework for systems biology.
  • Understanding cellular design principles enables the potential for bioengineering and therapeutic applications.
  • A quantitative and mechanistic approach is essential for advancing our knowledge of cellular information processing.