Intrinsic limits of information transmission in biochemical signalling motifs

Abstract

All living things have evolved to sense changes in their environment in order to respond in adaptive ways. At the cellular level, these sensing systems generally involve receptor molecules at the cell surface, which detect changes outside the cell and relay those changes to the appropriate response elements downstream. With the advent of experimental technologies that can track signalling at the single-cell level, it has become clear that many signalling systems exhibit significant levels of ‘noise,’ manifesting as differential responses of otherwise identical cells to the same environment. This noise has a large impact on the capacity of cell signalling networks to transmit information from the environment. Application of information theory to experimental data has found that all systems studied to date encode less than 2.5 bits of information, with the majority transmitting significantly less than 1 bit. Given the growing interest in applying information theory to biological data, it is crucial to understand whether the low values observed to date represent some sort of intrinsic limit on information flow given the inherently stochastic nature of biochemical signalling events. In this work, we used a series of computational models to explore how much information a variety of common ‘signalling motifs’ can encode. We found that the majority of these motifs, which serve as the basic building blocks of cell signalling networks, can encode far more information (4–6 bits) than has ever been observed experimentally. In addition to providing a consistent framework for estimating information-theoretic quantities from experimental data, our findings suggest that the low levels of information flow observed so far in living system are not necessarily due to intrinsic limitations. Further experimental work will be needed to understand whether certain cell signalling systems actually can approach the intrinsic limits described here, and to understand the sources and purpose of the variation that reduces information flow in living cells.

Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation

“…This may be due to inclusion of noise in a theoretical-like analysis. Common signaling motifs were found to contain 4-6 bits of information analytically, whereas the majority of biological systems transmit less than 1 bit experimentally [24, 25]. This discrepancy was speculated to be attributed to the functional necessity of real-world signaling networks and the realization of extrinsic noise in signal transduction in vivo.…”

ACID: A Low Dimensional Characterization of Markov-Modulated and Self-Exciting Counting Processes

“…From an engineering perspective maximizing the path MI between sensor and actuator could thus define a design principle to construct cellular circuits. Information theory has been used to reveal fundamental physical limits of information transmission guiding our understanding of gene regulatory motifs as information processing units [12], [28].…”

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

“…Each pathway within a biomolecular network requires input cues from the extracellular environment for onward downstream signal transduction (133)(134)(135). In the case of cancer, the biomolecular milieu constituting the tumor microenvironment (TME) acts as a niche for tumor development, metastasis, as well as therapy response (136) (Figure 1E and Table S4).…”