SwePub
Sök i LIBRIS databas

  Extended search

onr:"swepub:oai:research.chalmers.se:f4cc1c3c-c9c8-4b17-84a3-5ab62f21bb72"
 

Search: onr:"swepub:oai:research.chalmers.se:f4cc1c3c-c9c8-4b17-84a3-5ab62f21bb72" > Artificial neural n...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Artificial neural networks enable genome-scale simulations of intracellular signaling

Nilsson, Avlant, 1985 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Peters, Joshua M. (author)
Massachusetts Institute of Technology (MIT),Ragon Institute
Meimetis, Nikolaos (author)
Massachusetts Institute of Technology (MIT)
show more...
Bryson, Bryan (author)
Massachusetts Institute of Technology (MIT),Ragon Institute
Lauffenburger, Douglas A. (author)
Ragon Institute,Massachusetts Institute of Technology (MIT)
show less...
 (creator_code:org_t)
2022-06-02
2022
English.
In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
  • Journal article (peer-reviewed)
Abstract Subject headings
Close  
  • Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling. Many diseases are caused by disruptions to the network of biochemical reactions that allow cells to respond to external signals. Here Nilsson et al develop a method to simulate cellular signaling using artificial neural networks to predict cellular responses and activities of signaling molecules.

Subject headings

NATURVETENSKAP  -- Biologi -- Biofysik (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Biophysics (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

Publication and Content Type

art (subject category)
ref (subject category)

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Search outside SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view