iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.

Autor: Anand V Sastry, Yuan Yuan, Saugat Poudel, Kevin Rychel, Reo Yoo, Cameron R Lamoureux, Gaoyuan Li, Joshua T Burrows, Siddharth Chauhan, Zachary B Haiman, Tahani Al Bulushi, Yara Seif, Bernhard O Palsson, Daniel C Zielinski

Publikováno v: PLoS Computational Biology, Vol 20, Iss 10, p e1012546 (2024)

Public gene expression databases are a rapidly expanding resource of organism responses to diverse perturbations, presenting both an opportunity and a challenge for bioinformatics workflows to extract actionable knowledge of transcription regulatory

Externí odkaz: https://doaj.org/article/012b0c0d41224b3b907edb195e4abdcc

Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials.

Autor: James T Yurkovich, Miguel A Alcantar, Zachary B Haiman, Bernhard O Palsson

Publikováno v: PLoS Computational Biology, Vol 14, Iss 8, p e1006356 (2018)

Allosteric regulation has traditionally been described by mathematically-complex allosteric rate laws in the form of ratios of polynomials derived from the application of simplifying kinetic assumptions. Alternatively, an approach that explicitly des

Externí odkaz: https://doaj.org/article/236d2f241ebb48b8a06c628e29c56c0f

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

Autor: Colton J. Lloyd, Zachary B. Haiman, Daniel C. Zielinski, Martin J. Lercher, Nathan Mih, Abdelmoneim Amer Desouki, David Heckmann, Yuanchi Ha, Bernhard O. Palsson

Publikováno v: Nature Communications, Vol 9, Iss 1, Pp 1-10 (2018)
Heckmann, D, Lloyd, C J, Mih, N, Ha, Y, Zielinski, D C, Haiman, Z B, Desouki, A A, Lercher, M J & Palsson, B O 2018, ' Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models ', Nature Communications, vol. 9, 5252 . https://doi.org/10.1038/s41467-018-07652-6
Nature communications, vol 9, iss 1
Nature Communications

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bfa038068e6cf5b7d4f5904e6b70df5
http://link.springer.com/article/10.1038/s41467-018-07652-6

MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

Autor: Daniel C. Zielinski, James T. Yurkovich, Bernhard O. Palsson, Zachary B. Haiman, Yuko Koike

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulatin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5cdc410e63f4c34c1affc9e6c660b96b
https://doi.org/10.1101/2020.07.31.230334

Catalytic potential and disturbance rejection of glycolytic kinases in the human red blood cell

Autor: James T. Yurkovich, Miguel A. Alcantar, Bernhard O. Palsson, Zachary B. Haiman

The allosteric regulation of metabolic enzymes plays a key role in controlling the flux through metabolic pathways. The activity of such enzymes is traditionally described by allosteric rate laws in complex kinetic models of metabolic network functio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbd5596b6b1982c504da9382d07a4fe0
https://doi.org/10.1101/227058