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Related Concept Videos

Intracellular Signaling Cascades01:24

Intracellular Signaling Cascades

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...
Intracellular Signaling Cascades01:24

Intracellular Signaling Cascades

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...
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

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...
Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

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 the...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

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
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Overview of Cell Signaling01:23

Overview of Cell Signaling

Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
Cells respond to many types of information, often through receptor proteins positioned on the membrane. For example, skin cells respond to and transmit touch...

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Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device
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Published on: April 17, 2021

Learning cyclic signaling pathway structures while minimizing data requirements.

K Sachs1, S Itani, J Fitzgerald

  • 1karensachs@stanford.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 13, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces Generalized Bayesian Networks (GBNs) to model cyclic biological pathways, overcoming limitations of traditional acyclic methods. The novel algorithm enables cyclic structure learning using biologically relevant data, including singly perturbed samples.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Bayesian network structure learning is crucial for understanding biomolecular pathway regulation.
  • Existing methods are constrained by acyclicity, which is biologically unrealistic due to prevalent cycles.
  • Modeling cyclic pathways is essential for a comprehensive understanding of biological systems.

Purpose of the Study:

  • To introduce a novel method for modeling cyclic pathways in biology.
  • To extend Bayesian network structure learning to accommodate cycles.
  • To enable learning with biologically relevant data, including singly perturbed samples.

Main Methods:

  • Development of Generalized Bayesian Networks (GBNs).
  • An algorithm for cyclic structure learning using GBNs.
  • Application of the algorithm to simulated and real-world biological data.

Main Results:

  • Demonstrated theoretical arguments for the GBN approach.
  • Achieved successful cyclic structure learning on simulated biological data.
  • Validated the method on a real-world dataset of T-cell signaling pathways.

Conclusions:

  • Generalized Bayesian Networks provide a powerful framework for modeling cyclic biological pathways.
  • The novel algorithm effectively learns cyclic structures from biologically relevant data.
  • This approach advances the study of complex regulatory networks in biology.