No abstract

Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from single-cell sequencing or flow-cytometry experiments into regulatory dynamics. We show how static population snapshots of covariability can be exploited to rigorously infer properties of gene expression dynamics when gene expression reporters probe their upstream dynamics on separate timescales. This can be experimentally exploited in dual-reporter experiments with fluorescent proteins of unequal maturation times, thus turning an experimental bug into an analysis feature. We derive correlation conditions that detect the presence of closed-loop feedback regulation in gene regulatory networks. Furthermore, we show how genes with cell-cycle-dependent transcription rates can be identified from the variability of coregulated fluorescent proteins. Similar correlation constraints might prove useful in other areas of science in which static correlation snapshots are used to infer causal connections between dynamically interacting components.

No abstract

Cells in natural environments like tissue or soil sense and respond to extracellular ligands with intricately structured and non-monotonic spatial distributions that are sculpted by processes such as fluid flow and substrate adhesion. Nevertheless, traditional approaches to studying cell sensing assume signals are either uniform or monotonic, neglecting spatial structures of natural environments. In this work, we show that spatial sensing and navigation can be optimized by adapting the spatial organization of signaling pathways to the spatial structure of the environment. By viewing cell surface receptors as a sensor network, we develop an information theoretic framework for computing the optimal spatial organization of a sensing system for a given spatial signaling environment. Applying the framework to simulated environments, we find that spatial receptor localization maximizes information acquisition in many natural contexts, including tissue and soil. Receptor localization extends naturally to produce a dynamic protocol for redistributing signaling receptors during cell navigation and can be implemented in a cell using a feedback scheme. In a simulated tissue environment, dynamic receptor localization boosts navigation efficiency by 30-fold. Broadly, our framework readily adapts to studying how the spatial organization of signaling components other than receptors can be modulated to improve cellular information processing.