Tag: network science

Criticality in Biochemical Networks

J Royal Society Interface

Researchers from our center, in collaboration with State University of New York, Binghamton University and the Instituto Gulbenkian de Ciência developed a mathematical and computational framework to understand how biochemical networks contribute to the evolvability, robustness, and resilience of biological organisms.

In a paper in the journal Journal of the Royal Society InterfaceLuis Rocha, George J. Klir Professor of Systems Science, and Drs. Manuel Marques-Pita and Santosh Manicka (who earned his Ph.D. in complex networks and systems from the Luddy School), show that a large amount of redundancy exists in how genes, proteins and other biochemical components process signals. This results in much robustness to perturbations, allowing biological systems to exist in a stable or near-critical dynamical regime, despite being composed of thousands of biochemical variables which would ordinarily result in chaotic dynamics.

The measure of effective connectivity developed by Rocha and Marques-Pita captures redundancy in automata networks and is shown in the paper to be highly predictive of dynamical regime of biochemical systems ranging from flower development to breast cancer in humans. The approach thus adds empirical validity to several  well-known hypotheses in theoretical biology: 1) that canalization adds robustness to biological development put forth by C.H. Waddington, 2) that redundancy is essential for evolvability put forth by Michael Conrad, and 3) that biological organisms exist in a near-critical dynamical regime put forth by Stuart Kauffman. The new work further connects the three hypotheses by equating canalization with redundancy, providing a  measure of effective connectivity based on dynamical redundancy, and further showing that this measure very accurately predicts the dynamical regime of biochemical networks.

You can read the article following the links in reference:

Manicka Santosh, Marques-Pita Manuel and Rocha Luis M. [2022]. “Effective connectivity determines the critical dynamics of biochemical continue reading.

Team led by Luis Rocha awarded NIH grant to improve chronic-disease management with Data and Network Science

Luis M. RochaThe National Institutes of Health, under the National Library of Medicine’s program on data science research, awarded a $1.55 million grant to an interdisciplinary team lead by Luis Rocha, a professor of informatics, member of CNETS and the director of the NSF-NRT complex networks & systems program at the School of Informatics, Computing, and Engineering. The four-year project, a collaboration between SICE and the Indiana University School of Nursing, will employ innovative data- and network-science methods to produce myAURA, an easy-to-use web service for epilepsy patients. myAURA will be based on a large-scale epilepsy knowledge graph built by integrating data from social media, electronic health records, patient discussion boards, scientific literature databases, advocacy websites, and mobile app data. The knowledge graph will, in turn, be used to fuel recommendation and visualization algorithms based on the automatic inference of relevant associations. The inference will follow algorithms developed by Rocha’s team to remove redundancy and extract factual information from large knowledge graphs as well as parsimonious network visualizations developed by Katy Börner, Distinguished Professor of Engineering & Information Science at SICE. … continue reading.

CNetS team awarded NIH grant to improve chronic-disease management with Data and Network Science

Luis M. RochaThe National Institutes of Health, under the National Library of Medicine’s program on data science research, awarded a $1.55 million grant to an interdisciplinary team lead by Luis Rocha, a professor of informatics, member of CNETS and the director of the NSF-NRT complex networks & systems program at the School of Informatics, Computing, and Engineering. The four-year project, a collaboration between SICE and the Indiana University School of Nursing, will employ innovative data- and network-science methods to produce myAURA, an easy-to-use web service for epilepsy patients. myAURA will be based on a large-scale epilepsy knowledge graph built by integrating data from social media, electronic health records, patient discussion boards, scientific literature databases, advocacy websites, and mobile app data. The knowledge graph will, in turn, be used to fuel recommendation and visualization algorithms based on the automatic inference of relevant associations. The inference will follow algorithms developed by Rocha’s team to remove redundancy and extract factual information from large knowledge graphs as well as parsimonious network visualizations developed by Katy Börner, Distinguished Professor of Engineering & Information Science at SICE. … continue reading.