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Related Concept Videos

Signal Transduction: Overview01:26

Signal Transduction: Overview

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Cells respond to many types of information, often through receptor proteins positioned on the membrane. They respond to chemical signals, such as hormones, neurotransmitters, and other signaling molecules, initiating a series of molecular reactions to produce an appropriate response. This is called signal transduction. Cells also coordinate different responses elicited by the same signaling molecule via mediators, allowing molecular cross-talk.
Typically, signal transduction involves three...
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Interactions Between Signaling Pathways01:19

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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
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Amplifying Signals via Enzymatic Cascade01:22

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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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Diversity in Cell Signaling Responses01:22

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The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
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Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
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Intracellular Signaling Cascades01:24

Intracellular Signaling Cascades

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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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Related Experiment Video

Updated: Apr 11, 2026

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lpNet: a linear programming approach to reconstruct signal transduction networks.

Marta R A Matos1, Bettina Knapp2, Lars Kaderali1

  • 1Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany and.

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Summary

This study introduces a novel network inference method using linear programming to reconstruct biological interactions from high-throughput data. The approach efficiently reveals cellular regulatory networks and disease mechanisms, implemented as an R package.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput technologies enable simultaneous measurement of numerous cellular factors (mRNA, proteins).
  • Extracting regulatory and signaling interactions from this data remains a significant challenge.

Purpose of the Study:

  • To present a flexible network inference approach based on linear programming.
  • To reconstruct biological interaction networks using combined perturbation and steady-state/time-series data.

Main Methods:

  • Network inference using linear programming.
  • Reconstruction of interactions from perturbation/non-perturbation and steady-state/time-series data.
  • Implementation as an R package available via bioconductor.

Main Results:

  • Demonstrated fast and efficient reconstruction of underlying networks on simulated and real data.
  • The method provides insights into biological processes.
  • The approach aids in understanding disease mechanisms.

Conclusions:

  • The developed linear programming approach offers an efficient method for network inference.
  • This tool can illuminate complex biological interactions and disease pathways.
  • The R package facilitates accessibility and application of the method.