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Related Concept Videos

Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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Dynamic Sampling and Information Encoding in Biochemical Networks.

Garrett D Potter1, Tommy A Byrd2, Andrew Mugler2

  • 1Department of Physics, Oregon State University, Corvallis, Oregon.

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Summary
This summary is machine-generated.

Cells dynamically encode information using biochemical networks. Information extraction depends on cellular constraints and noise, with decoding acting as a low-pass filter, revealing limits of dynamic information storage.

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Area of Science:

  • Cellular biology
  • Biophysics
  • Systems biology

Background:

  • Cells process environmental cues via complex biochemical networks.
  • Cellular responses are dynamic, but information encoding mechanisms are unclear.
  • Understanding dynamic information transfer is crucial for cell signaling research.

Purpose of the Study:

  • Investigate dynamic information encoding in ATP-induced calcium signals in fibroblast cells.
  • Quantify information transfer using information theory.
  • Identify factors limiting cellular information processing.

Main Methods:

  • Utilized a vectorial (multi-time-point) information measure.
  • Analyzed ATP-induced calcium responses in fibroblast cells.
  • Compared experimental data to a minimal physical model.

Main Results:

  • Information extraction is constrained by sampling rate and memory capacity.
  • Intrinsic and extrinsic noise differentially impact information transfer.
  • Decoding mechanisms often function as simple low-pass filters, insensitive to detailed dynamics.

Conclusions:

  • Cellular information storage has inherent mechanisms and limitations.
  • Dynamic encoding is influenced by network properties and noise.
  • Simple decoding principles govern complex cellular signaling pathways.