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Related Concept Videos

Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Causal protein-signaling networks derived from multiparameter single-cell data.

Karen Sachs1, Omar Perez, Dana Pe'er

  • 1Biological Engineering Division, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.

Science (New York, N.Y.)
|April 23, 2005
PubMed
Summary

Machine learning identified causal links in cellular signaling networks using single-cell data. This approach revealed known and new pathway interactions, advancing our understanding of cell communication.

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Area of Science:

  • Systems Biology
  • Immunology
  • Computational Biology

Background:

  • Cellular signaling networks are complex and crucial for biological functions.
  • Understanding causal relationships within these networks is vital for disease research.
  • Current methods often struggle to capture dynamic, single-cell level interactions.

Purpose of the Study:

  • To develop an automated machine learning approach for deriving causal influences in cellular signaling networks.
  • To reconstruct signaling networks from primary human immune cells at the single-cell level.
  • To identify and experimentally validate novel causal relationships within these networks.

Main Methods:

  • Simultaneous measurement of phosphorylated proteins and phospholipids in thousands of primary human immune cells.
  • Application of machine learning, specifically Bayesian network computational methods, for automated causal inference.
  • Molecular perturbations to induce changes in signaling pathways and network ordering.
  • Experimental validation of predicted novel interpathway network causalities.

Main Results:

  • Automated derivation of causal influences in cellular signaling networks.
  • Elucidation of most traditionally reported signaling relationships.
  • Prediction and experimental verification of novel interpathway network causalities.
  • Successful reconstruction of network models from physiologically relevant primary single cells.

Conclusions:

  • Machine learning provides a powerful tool for automated causal inference in cellular signaling.
  • Single-cell network reconstruction offers insights into native-state tissue signaling.
  • This methodology can be applied to study complex drug actions and signaling in diseased cells.