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Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

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Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
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Once a ligand binds to a receptor, the signal is transmitted through the membrane and into the cytoplasm. The continuation of a signal in this manner is called signal transduction. Signal transduction only occurs with cell-surface receptors, which cannot interact with most components of the cell, such as DNA. Only internal receptors can interact directly with DNA in the nucleus to initiate protein synthesis. When a ligand binds to its receptor, conformational changes occur that affect the...
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Multiple model-informed open-loop control of uncertain intracellular signaling dynamics.

Jeffrey P Perley1, Judith Mikolajczak2, Marietta L Harrison2

  • 1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States of America.

Plos Computational Biology
|April 12, 2014
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Summary

Researchers developed a new computational method to precisely control cell signaling pathways. This adaptive control strategy uses multiple mathematical models to improve accuracy and effectively direct cell behavior without real-time measurements.

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Area of Science:

  • Systems Biology
  • Control Theory
  • Computational Biology

Background:

  • Controlling intracellular signal transduction pathways is crucial for understanding cell biology.
  • Traditional control methods are limited by slow measurements and model inaccuracies.

Purpose of the Study:

  • To develop a computational control strategy for precise and selective tuning of intracellular signaling pathways.
  • To overcome limitations of real-time feedback and model uncertainty in biological systems.

Main Methods:

  • Combined nonlinear model predictive control with an adaptive weighting algorithm.
  • Utilized multiple mathematical models and pre-existing experimental data to create adaptive weight maps.
  • Designed an open-loop control framework to mitigate model uncertainty.

Main Results:

  • The proposed method reduced target tracking error by over 52% in silico compared to single-model and non-adaptive controllers.
  • In vitro experiments showed a 63% reduction in tracking error compared to the best single-model controllers.
  • Successfully directed Erk/MAPK signaling pathway dynamics in T cells.

Conclusions:

  • This adaptive, multi-model control approach offers a robust methodology for precise open-loop control of cellular signaling.
  • The strategy effectively utilizes existing model knowledge to guide cell behavior with reduced error.
  • Provides an experimentally validated framework for interrogating cell biology with enhanced precision.