Biology subjects

Mattingly, H. H.

Publications and source records attributed to Mattingly, H. H..

5 recordsLinked to original sources

bioRxiv2024

E. coli do not count single molecules

Organisms use specialized sensors to measure their environments, but the fundamental principles that determine their accuracy remain largely unknown. In Escherichia coli chemotaxis, we previously found that gradient-climbing speed is bounded by the amount of information that cells acquire from their environment, and that E. coli operate near this bound. However, it remains unclear what prevents them from acquiring more information. Past work argued that E. colis chemosensing is limited by the physics of molecules stochastically arriving at cells receptors, without direct evidence. Here, we show instead that E. coli are far from this physical limit. To show this, we develop a theoretical approach that uses information rates to quantify how accurately behaviorally-relevant signals can be estimated from available observations: molecule arrivals for the physical limit; chemotaxis signaling activity for E. coli. Measuring these information rates in single-cell experiments across multiple background concentrations, we find that E. coli encode two orders of magnitude less information than the physical limit. Thus, E. coli chemosensing is limited by internal noise in signal processing rather than the physics of molecule diffusion, motivating investigation of what specific physical and biological constraints shaped the evolution of this prototypical sensory system.

biophysics↗

bioRxiv2023

Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model

Chemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering new insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities. SIGNIFICANCE STATEMENTDuring collective cellular processes, cells often dynamically shape and respond to their chemical environments. Our understanding of these processes is limited by the ability to measure these chemical profiles in real time. For example, the Patlak-Keller-Segel model has widely been used to describe collective chemotaxis towards self-generated gradients in various systems, albeit without direct verification. Here we used a biocompatible fluorescent protein sensor to directly observe attractant gradients created and chased by collectively-migrating bacteria. Doing so uncovered limitations of the standard chemotaxis model at high cell densities and allowed us to establish an improved model. Our work demonstrates the potential for fluorescent protein sensors to measure the spatiotemporal dynamics of chemical environments in cellular communities.

biophysics↗

bioRxiv2021

A mechanism for migrating bacterial populations to non-genetically adapt to new environments

Populations of chemotactic bacteria can rapidly expand into new territory by consuming and chasing an attractant cue in the environment, increasing the populations overall growth in nutrient-rich environments. Although the migrating fronts driving this expansion contain cells of multiple swimming phenotypes, the consequences of non-genetic diversity for population expansion are unknown. Here, through theory and simulations, we predict that expanding populations non-genetically adapt their phenotype composition to migrate effectively through multiple physical environments. Swimming phenotypes in the migrating front are spatially sorted by chemotactic performance, but the mapping from phenotype to performance depends on the environment. Therefore, phenotypes that perform poorly localize to the back of the group, causing them to selectively fall behind. Over cell divisions, the group composition dynamically enriches for high-performers, enhancing migration speed and overall growth. Furthermore, non-genetic inheritance controls a trade-off between large composition shifts and slow responsiveness to new environments, enabling a diverse population to out-perform a non-diverse one in varying environments. These results demonstrate that phenotypic diversity and collective behavior can synergize to produce emergent functionalities. Non-genetic inheritance may generically enable bacterial populations to transiently adapt to new situations without mutations, emphasizing that genotype-to-phenotype mappings are dynamic and context-dependent.

cell biology↗

bioRxiv2021

E. coli chemotaxis is information-limited

Organisms must acquire and use environmental information to guide their behaviors. However, it is unclear whether and how information quantitatively limits behavioral performance. Here, we relate information to behavioral performance in Escherichia coli chemotaxis. First, we derive a theoretical limit for the maximum achievable gradient-climbing speed given a cells information acquisition rate. Next, we measure cells gradient-climbing speeds and the rate of information acquisition by the chemotaxis pathway. We find that E. coli make behavioral decisions with much less than the 1 bit required to determine whether they are swimming up-gradient. However, they use this information efficiently, performing near the theoretical limit. Thus, information can limit organisms performance, and sensory-motor pathways may have evolved to efficiently use information from the environment.

biophysics↗

bioRxiv2018

Spatial Self-Organization Resolves Conflicts Between Individuality and Collective Migration

Collective behavior can spontaneously emerge when individuals follow common rules of interaction. However, the behavior of each individual will differ due to existing genetic and non-genetic variation within the population. It remains unclear how this individuality is managed to achieve collective behavior. We quantified individuality in bands of clonal Escherichia coli cells that migrate collectively along a channel by following a self-generated gradient of attractant. We discovered that despite substantial differences in individual chemotactic abilities, the cells are able to migrate as a coherent group by spontaneously sorting themselves within the moving band. This sorting mechanism ensures that differences between individual chemotactic abilities are compensated by differences in the local steepness of the traveling gradient each individual must navigate, and determines the minimum performance required to travel with the band. By resolving conflicts between individuality and collective migration, this mechanism enables populations to maintain advantageous diversity while on the move.

biophysics↗