Information Theory as an Experimental Tool for Integrating Disparate Biophysical Signaling Modules

There is a growing appreciation in the fields of cell biology 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 the 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 named after its intended purpose, calcium imaging, (CAIM) for simple, rigorous application of these metrics to time series 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 should enable biologists to apply information theory to develop a systems-level understanding of information processing across a diverse array of experimental systems.

Automated summary

There is a growing recognition of the importance of collective information processing by cells in the fields of cell biology and developmental biology. To quantitatively understand these processes at the systems level, a guide for applying established information theory metrics to biological datasets is presented. A computational tool called CAIM is introduced for the simple and rigorous application of these metrics to time series datasets. CAIM is used to study calcium and cytoskeletal actin information flow patterns between Xenopus laevis embryonic animal cap stem cells.