Information Theory as an Experimental Tool for Integrating Disparate Biophysical Signaling Modules
Year: 2022
Type: article
Abstract: There is a growing appreciation in the fields of Cell and Developmental Biology that cells collectively process information in time and space. While many powerful molecular tools exist to observe biophysical dynamics biologists must find ways to quantitatively understand these phenomena at the systems level. Here, we present a guide for application of well-established information theory metrics to biological datasets and explain these metrics using examples from cell, developmental and regenerative biology. We introduce a novel computational tool (CAIM) for simple, rigorous application of these metrics to timeseries datasets. Finally, we use CAIM to study calcium and cytoskeletal Actin information flow patterns between Xenopus laevis embryonic animal cap stem cells. The tools that we present here will enable biologists to apply information theory to develop systems level understanding of a diverse array of experimental systems.
DOI: 10.3390/ijms23179580
Institutions: Tufts University, Santa Fe Institute, Arizona State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,408
Awards: G1068584038, G3260603973, Synthetic Living Machines and the Emergence of Purpose, Harnessing bioelectric mechanisms of KRAS-dependent craniofacial abnormalities, G4457879867, G5502975794, G5504178353, G5743850235, G6438653580, G6713538507, G7655317158, G7930147608, Re-training Molecular Networks: A New Path Toward the Biomedicine of Cancer and Regeneration Revealed by a Basal Cognition Approach, G8004368094, G8456046689, G8771097231
OpenAlex ID: W4292998926