Literature DB >> 34139170

Advances in systems biology modeling: 10 years of crowdsourcing DREAM challenges.

Pablo Meyer1, Julio Saez-Rodriguez2.   

Abstract

Computational and mathematical models are key to obtain a system-level understanding of biological processes, but their limitations have to be clearly defined to allow their proper application and interpretation. Crowdsourced benchmarks in the form of challenges provide an unbiased assessment of methods, and for the past decade, the Dialogue for Reverse Engineering Assessment and Methods (DREAM) organized more than 15 systems biology challenges. From transcription factor binding to dynamical network models, from signaling networks to gene regulation, from whole-cell models to cell-lineage reconstruction, and from single-cell positioning in a tissue to drug combinations and cell survival, the breadth is broad. To celebrate the 5-year anniversary of Cell Systems, we review the genesis of these systems biology challenges and discuss how interlocking the forward- and reverse-modeling paradigms allows to push the rim of systems biology. This approach will persist for systems levels approaches in biology and medicine.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; biological networks; cell lineage; crowdsourcing; modeling; neural networks; parameter estimation; promoters; single cell; systems biology; whole cell models

Mesh:

Year:  2021        PMID: 34139170     DOI: 10.1016/j.cels.2021.05.015

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  3 in total

Review 1.  Human-centered explainability for life sciences, healthcare, and medical informatics.

Authors:  Sanjoy Dey; Prithwish Chakraborty; Bum Chul Kwon; Amit Dhurandhar; Mohamed Ghalwash; Fernando J Suarez Saiz; Kenney Ng; Daby Sow; Kush R Varshney; Pablo Meyer
Journal:  Patterns (N Y)       Date:  2022-05-13

2.  Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd.

Authors:  Attila Gabor; Marco Tognetti; Alice Driessen; Jovan Tanevski; Baosen Guo; Wencai Cao; He Shen; Thomas Yu; Verena Chung; Bernd Bodenmiller; Julio Saez-Rodriguez
Journal:  Mol Syst Biol       Date:  2021-10       Impact factor: 13.068

3.  Learning processes in hierarchical pairs regulate entire gene expression in cells.

Authors:  Tomoyuki Yamaguchi
Journal:  Sci Rep       Date:  2022-05-09       Impact factor: 4.996

  3 in total

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