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Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
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Quantifying information of intracellular signaling: progress with machine learning.

Ying Tang1,2,3, Alexander Hoffmann1,2

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Cells use intracellular signaling pathways to transmit environmental information. This review explores information theory and machine learning to understand how cells adapt to changes using temporal coding.

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cellular signalingimmune responsesinformation processingmachine learningmutual informationregulatory dynamics

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Area of Science:

  • Cell biology
  • Systems biology
  • Information theory

Background:

  • Cells sense and transmit environmental cues internally.
  • Understanding information flow in biological systems is crucial.
  • Signaling pathways are complex, pleiotropic, and stochastic.

Purpose of the Study:

  • To review information-theoretic approaches for quantifying signal transmission.
  • To explore the role of molecular stochasticity and pleiotropy.
  • To understand cellular adaptation via temporal coding.

Main Methods:

  • Information-theoretic analysis of signaling pathways.
  • Application of machine learning to temporal trajectory data.
  • Review of existing literature on cellular information processing.

Main Results:

  • Information theory quantifies transmission in complex pathways.
  • Machine learning addresses challenges in analyzing temporal data.
  • Temporal coding is a key mechanism for cellular adaptation.

Conclusions:

  • Information-theoretic and machine learning methods advance understanding of cellular signaling.
  • Cells dynamically adapt to environmental perturbations through temporal coding.
  • This framework provides insights into fundamental living system principles.