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The Hedgehog gene (Hh) was first discovered due to its control of the growth of disorganized, hair-like bristles phenotype in Drosophila, much like hedgehog spines. Hh plays a crucial role in the development of organs and the maintenance of homeostasis in both invertebrates and vertebrates. However, while Drosophila has only one Hh protein, mammals have multiple functional Hedgehog proteins - Sonic (Shh), Desert (Dhh), and Indian Hedgehog (Ihh). All of these homologous proteins have adapted to...
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Inferring signaling pathways with probabilistic programming.

David Merrell1,2, Anthony Gitter1,2,3

  • 1Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.

Bioinformatics (Oxford, England)
|December 31, 2020
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Summary
This summary is machine-generated.

We developed a new Bayesian method using Markov Chain Monte Carlo to infer cellular signaling pathways from phosphoproteomic data. This approach efficiently samples sparse graphs, improving disease and therapy understanding.

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Area of Science:

  • Cellular biology
  • Systems biology
  • Computational biology

Background:

  • Cellular regulation relies on complex biochemical signaling pathways, often represented as networks.
  • Understanding these pathways is crucial for deciphering diseases and developing targeted therapies.
  • Inferring patient-specific signaling pathways from data is highly valuable due to disease-induced modifications.

Purpose of the Study:

  • To develop a novel computational method for inferring cellular signaling pathways.
  • To improve the accuracy and efficiency of pathway inference from phosphoproteomic time-course data.
  • To relax restrictive modeling assumptions common in existing pathway inference techniques.

Main Methods:

  • Formulated signaling pathway inference as a Dynamic Bayesian Network (DBN) structure estimation problem.
  • Employed a Bayesian approach utilizing Markov Chain Monte Carlo (MCMC) for posterior distribution estimation.
  • Introduced a novel proposal distribution for efficient sampling of sparse graphs.

Main Results:

  • Implemented the Sparse Signaling Pathway Sampling (SSPS) method in Julia using the Gen probabilistic programming language.
  • Evaluated SSPS on simulated data and the HPN-DREAM pathway reconstruction challenge, outperforming baseline methods.
  • Demonstrated the potential of probabilistic programming, specifically Gen, for biological network inference.

Conclusions:

  • The developed Bayesian method provides an efficient and flexible approach for inferring cellular signaling pathways.
  • Probabilistic programming languages like Gen offer powerful tools for complex biological network inference.
  • Accurate signaling pathway inference has significant implications for understanding cellular mechanisms in disease and therapy.