arXiv subjects

Andrew Mugler

Publications and source records attributed to Andrew Mugler.

At least 19 recordsLinked to original sources

arXiv2026

Cell sensing: from physical limits to active behaviors

Physics sets the information contained in the signals that cells sense. But cells are active, not passive, sensors. They shape and reshape both the environment and themselves. These active behaviors allow cells to amplify, redistribute, share, and prioritize sensory information, often surpassing or obviating passive physical limits. Here, we review recent results on active sensing. After describing classic and more recent limits to sensory precision, we focus on four ways that cells implement active sensing: coordinating with other cells, reshaping their environment, dynamically updating themselves, and discriminating signals. We conclude with potential future directions.

physics.bio-ph↗

arXiv2026

Optimal information transmission in a sequential model for cell division

In proliferating cell populations, adaptive changes to biochemical reactions can change a cell's division time, which in turn can change the population size. However, biochemical reactions are subject to noise, and therefore the conditions for optimal information transmission from the molecular to the population scale are poorly understood. Here, we model cell proliferation as a Bellman-Harris branching process with age-dependent division times. We identify a class of division time distributions, built from a series of Markovian steps, for which the population size distribution at all times is hierarchically calculable. We use this feature to characterize the amount of influence that a given reaction step has on the population size via the mutual information. We find that information transmission is optimal for a characteristic number of steps until division: too few and the population size is unpredictable; too many and any given step has vanishing influence on the population size. Our work reveals the potential tradeoffs involved in adaptive decision making at the sub-cellular, cellular and population scales.

physics.bio-ph↗

arXiv2026

Distinguishable spreading dynamics in microbial communities

A packed community of exponentially proliferating microbes will spread in size exponentially. However, due to nutrient depletion, mechanical constraints, or other limitations, exponential proliferation is not indefinite, and the spreading slows. Here, we theoretically explore a fundamental question: is it possible to infer the dominant limitation type from the spreading dynamics? Using a continuum active fluid model, we consider three limitations to cell proliferation: intrinsic growth arrest (e.g., due to sporulation), pressure from other cells, and nutrient access. We find that memoryless growth arrest still results in superlinear (accelerating) spreading, but at a reduced rate. In contrast, pressure-limited growth results in linear (constant-speed) spreading in the long-time limit. We characterize how the expansion speed depends on the maximum growth rate, the limiting pressure value, and the effective fluid friction. Interestingly, nutrient-limited growth results in a phase transition: depending on the nutrient supply and how efficiently nutrient is converted to biomass, the spreading can be either superlinear or sublinear (decelerating). We predict the phase boundary in terms of these parameters and confirm with simulations. Thus, our results suggest that when an expansion slowdown is observed, its dominant cause is likely nutrient depletion. More generally, our work suggests that cell-level growth limitations can be inferred from population-level dynamics, and it offers a methodology for connecting these two scales.

physics.bio-ph↗

arXiv2026

Restoring information in aged gene regulatory networks by single knock-ins

A hallmark of aging is loss of information in gene regulatory networks. These networks are tightly connected, raising the question of whether information could be restored by perturbing single genes. We develop a simple theoretical framework for information transmission in gene regulatory networks that describes the information gained or lost when a gene is "knocked in" (exogenously expressed). Applying the framework to gene expression data from muscle cells in young and old mice, we find that single knock-ins can restore network information by up to 10%. Our work advances the study of information flow in networks and identifies potential gene targets for rejuvenation.

q-bio.MN↗

arXiv2025

Effects of cell-cell communication on bacterial chemotaxis

Bacteria track chemical gradients using a biased random walk, a process called chemotaxis. Experiments suggest that bacteria also communicate during this process. Using a mathematical model, we find that sufficiently strong communication succeeds in keeping a population of bacteria together but slows down chemotaxis. However, if the secretion of the communication molecule is coupled to the detection of the external chemoattractant, chemotaxis can be faster than without communication. Intriguingly, in this regime we predict that, even though blocking the communication receptors should slow down chemotaxis, partially blocking or underexpressing them should speed it up. Our work provides physical insights on how communication and chemotaxis are connected and may help explain why chemotaxing bacteria communicate.

physics.bio-ph↗

arXiv2025

Exosome-mediated chemotaxis optimizes leader-follower cell migration

Cells frequently employ extracellular vesicles, or exosomes, to signal across long distances and coordinate collective actions. Exosomes diffuse slowly, can be actively degraded, and contain stochastic amounts of molecular cargo. These features raise the question of the efficacy of exosomes as a directional signal, but this question has not be systematically investigated. We develop a theoretical and computational approach to quantify the limits of exosome-mediated chemotaxis at the individual cell level. In our model, a leader cell secretes exosomes, which diffuse in the extracellular space, and a follower cell guides its migration by integrating discrete exosome detections over a finite memory window. We combine analytical calculations and stochastic simulations and show that the chemotactic velocity exhibits a non-monotonic dependence on the exosome cargo size. Small exosomes produce frequent but weak signals, whereas large exosomes produce strong but infrequent encounters. In the presence of nonlinear signal transduction, this tradeoff leads to an optimal cargo size that maximizes information throughput, as quantified by the average speed of the follower cell. Using a reduced one-dimensional model, we derive closed-form expressions coupling the optimal cargo size to follower speed as a function of secretion rate, memory time, and detection sensitivity. These results identify molecular packaging and memory integration as key determinants of exosome-mediated information transmission and highlight general design principles for optimization of migration under guidance by discrete and diffusible signaling particles.

physics.bio-ph↗

arXiv2025

General mechanism for concentration-based cell size control

Cells control their size to cope with noise during growth and division. Eukaryotic cells exhibiting "sizer" control (targeting a specific size before dividing) may rely on molecular concentration thresholds, but simple implementations of this strategy are not stable. We derive a general criterion for concentration-based sizer control and demonstrate it with a mechanistic model that resolves the instability by using multistage progression towards division. We show that if molecular dynamics in one stage satisfy the sizer criterion, then sizer control follows for the whole progression. We predict that perturbations to the molecular dynamics in non-sizer stages shift the size statistics without disrupting sizer control, consistent with recent experiments in fission yeast.

physics.bio-ph↗

arXiv2025

Near-critical gene expression in embryonic boundary precision

Embryonic development relies on the formation of sharp, precise gene expression boundaries. In the fruit fly Drosophila melanogaster, boundary formation has been proposed to occur at a dynamical critical point. Yet, in the paradigmatic case of the hunchback (hb) gene, evidence suggests that boundary formation occurs in a bistable regime, not at the dynamical critical point. We develop a minimal model for hb expression and identify a single parameter that tunes the system from its monostable regime to its bistable regime, crossing the critical point in between. We find that boundary precision is maximized when the system is weakly bistable--near, but not at, the critical point--optimally negotiating the tradeoff between two key effects of bistability: sharpening the boundary and amplifying its noise. Incorporating the diffusion of Hb proteins into our model, we show that boundary precision is maximized simultaneously at an optimal degree of bistability and an optimal diffusion strength. Our work elucidates design principles of precise boundary formation and has general implications for pattern formation in multicellular systems.

physics.bio-ph↗

arXiv2024

Quantifying cellular autonomy in multi-cue environments

A cell routinely responds to one of many competing environmental cues. A fundamental question is whether the cell follows the cue prioritized by its internal signaling network or the cue that carries the most external information. We introduce a theoretical framework to answer this question. We derive information limits for four types of directional cues: external and self-generated chemical gradients, fluid flow, and contact inhibition of locomotion. When the cues compete as pairs, these limits predict which cue a cell should follow if its decision is based on environmental information alone. We compare these predicted decision boundaries with data from our and others' cell migration experiments, finding cases where the boundary is obeyed and cases where it is violated by orders of magnitude. Both outcomes are informative, and we find that they rationalize known properties, or predict putative properties, of cells' internal signaling networks. Our work introduces a physical framework to quantify the degree to which a cell acts like an autonomous agent, rather than a passive detector, favoring a cue even when it is less informative.

physics.bio-ph↗

arXiv2023

Role of signal degradation in directional chemosensing

Directional chemosensing is ubiquitous in cell biology, but some cells such as mating yeast paradoxically degrade the signal they aim to detect. While the data processing inequality suggests that such signal modification cannot increase the sensory information, we show using a reaction-diffusion model and an exactly solvable discrete-state reduction that it can. We identify a non-Markovian step in the information chain allowing the system to evade the data processing inequality, reflecting the nonlocal nature of diffusion. Our results apply to any sensory system in which degradation couples to diffusion. Experimental data suggest that mating yeast operate in the beneficial regime where degradation improves sensing.

physics.bio-ph↗

arXiv2023

Collective effects in flow-driven cell migration

Autologous chemotaxis is the process in which cells secrete and detect molecules to determine the direction of fluid flow. Experiments and theory suggest that autologous chemotaxis fails at high cell densities because molecules from other cells interfere with a given cell's signal. Based on observations of collective cell migration in diverse biological contexts, we propose a mechanism for cells to avoid this failure by forming a collective sensory unit. Formulating a simple physical model of collective autologous chemotaxis, we find that a cluster of cells can outperform single cells in terms of the detected anisotropy of the signal. We validate our results with a Monte-Carlo-based motility simulation, demonstrating that clusters chemotax faster than individual cells. Our simulation couples spatial and temporal gradient sensing with cell-cell repulsion, suggesting that our proposed mechanism requires only known, ubiquitous cell capabilities.

physics.bio-ph↗

arXiv2023

Effects of molecular noise on cell size control

Cells employ control strategies to maintain a stable size. Dividing at a target size (the `sizer' strategy) is thought to produce the tightest size distribution. However, this result follows from phenomenological models that ignore the molecular mechanisms required to implement the strategy. Here we investigate a simple mechanistic model for exponentially growing cells whose division is triggered at a molecular abundance threshold. We find that size noise inherits the molecular noise and is consequently minimized not by the sizer but by the `adder' strategy, where a cell divides after adding a target amount to its birth size. We derive a lower bound on size noise that agrees with publicly available data from six microfluidic studies on Escherichia coli bacteria.

physics.bio-ph↗

arXiv2022

Detection of signaling mechanisms from cellular responses to multiple cues

Cell signaling networks are complex and often incompletely characterized, making it difficult to obtain a comprehensive picture of the mechanisms they encode. Mathematical modeling of these networks provides important clues, but the models themselves are often complex, and it is not always clear how to extract falsifiable predictions. Here we take an inverse approach, using experimental data at the cell level to {deduce} the minimal signaling network. We focus on cells' response to multiple cues, specifically on the surprising case in which the response is antagonistic: the response to multiple cues is weaker than the response to the individual cues. We systematically build candidate signaling networks one node at a time, using the ubiquitous ingredients of (i) up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding. In each case, our method reveals a minimal, interpretable signaling mechanism that explains the antagonistic response. Our work provides a systematic way to {deduce} molecular mechanisms from cell-level data.

q-bio.MN↗

arXiv2022

Multigenerational memory in bacterial size control

Cells maintain a stable size as they grow and divide. Inspired by the available experimental data, most proposed models for size homeostasis assume size control mechanisms that act on a timescale of one generation. Such mechanisms lead to short-lived autocorrelations in size fluctuations that decay within less than two generations. However, recent evidence from comparing sister lineages suggests that correlations in size fluctuations can persist for many generations. Here we develop a minimal model that explains these seemingly contradictory results. Our model proposes that different environments result in different control parameters, leading to distinct inheritance patterns. Multigenerational memory is revealed in constant environments but obscured when averaging over many different environments. Inferring the parameters of our model from {\it Escherichia coli} size data in microfluidic experiments, we recapitulate the observed statistics. Our work elucidates the impact of the environment on cell homeostasis and growth and division dynamics.

physics.bio-ph↗

arXiv2022

Temporal signals drive the emergence of multicellular information networks

Coordinated responses to environmental stimuli are critical for multicellular organisms. To overcome the obstacles of cell-to-cell heterogeneity and noisy signaling dynamics within individual cells, cells must effectively exchange information with peers. However, the dynamics and mechanisms of collective information transfer driven by external signals is poorly understood. Here we investigate the calcium dynamics of neuronal cells that form confluent monolayers and respond to cyclic ATP stimuli in microfluidic devices. Using Granger inference to reconstruct the underlying causal relations between the cells, we find that the cells self-organize into spatially decentralized and temporally stationary networks to support information transfer via gap junction channels. The connectivity of the causal networks depend on the temporal profile of the external stimuli, where short periods, or long periods with small duty fractions, lead to reduced connectivity and fractured network topology. We build a theoretical model based on communicating excitable units that reproduces our observations. The model further predicts that connectivity of the causal network is maximal at an optimal communication strength, which is confirmed by the experiments. Together, our results show that information transfer between neuronal cells is externally regulated by the temporal profile of the stimuli, and internally regulated by cell-cell communication.

physics.bio-ph↗

arXiv2021

Autologous chemotaxis at high cell density

Autologous chemotaxis, in which cells secrete and detect molecules to determine the direction of fluid flow, is thwarted at high cell density because molecules from other cells interfere with a given cell's signal. Using a minimal model of autologous chemotaxis, we determine the cell density at which sensing fails and find that it agrees with experimental observations of metastatic cancer cells. To understand this agreement, we derive a physical limit to autologous chemotaxis in terms of the cell density, the Péclet number, and the length scales of the cell and its environment. Surprisingly, in an environment that is uniformly oversaturated in the signaling molecule, we find that sensing not only can fail, but can be reversed, causing backwards cell motion. Our results get to the heart of the competition between chemical and mechanical cellular sensing and shed light on a sensory strategy employed by cancer cells in dense tumor environments.

physics.bio-ph↗

arXiv2021

Precision of protein thermometry

Temperature sensing is a ubiquitous cell behavior, but the fundamental limits to the precision of temperature sensing are poorly understood. Unlike in chemical concentration sensing, the precision of temperature sensing is not limited by extrinsic fluctuations in the temperature field itself. Instead, we find that precision is limited by the intrinsic copy number, turnover, and binding kinetics of temperature-sensitive proteins. Developing a model based on the canonical TlpA protein, we find that a cell can estimate temperature to within 2%. We compare this prediction with in vivo data on temperature sensing in bacteria.

physics.bio-ph↗

arXiv2020

Multicellular sensing at a feedback-induced critical point

Feedback in sensory biochemical networks can give rise to bifurcations in cells' behavioral response. These bifurcations share many properties with thermodynamic critical points. Evidence suggests that biological systems may operate near these critical points, but the functional benefit of doing so remains poorly understood. Here we investigate a simple biochemical model with nonlinear feedback and multicellular communication to determine if criticality provides a functional benefit in terms of the ability to gain information about a stochastic chemical signal. We find that when signal fluctuations are slow, the mutual information between the signal and the intracellular readout is maximized at criticality, because the benefit of high signal susceptibility outweighs the detriment of high readout noise. When cells communicate, criticality gives rise to long-range correlations in readout molecule number among cells. Consequently, we find that communication increases the information between a given cell's readout and the spatial average of the signal across the population. Finally, we find that both with and without communication, the sensory benefits of criticality compete with critical slowing down, such that the information rate, as opposed to the information itself, is minimized at the critical point. Our results reveal the costs and benefits of feedback-induced criticality for multicellular sensing.

physics.bio-ph↗