Summary: Bacteria display various shapes and rely on complex spatial organization of their intracellular components for many cellular processes. This organization changes in response to internal and external cues. Quantitative, unbiased study of these spatio‐temporal dynamics requires automated image analysis of large microscopy datasets. We have therefore developed MicrobeTracker, a versatile and high‐throughput image analysis program that outlines and segments cells with subpixel precision, even in crowded images and mini‐colonies, enabling cell lineage tracking. MicrobeTracker comes with an integrated accessory tool, SpotFinder, which precisely tracks foci of fluorescently labelled molecules inside cells. Using MicrobeTracker, we discover that the dynamics of the extensively studied Escherichia coli Min oscillator depends on Min protein concentration, unveiling critical limitations in robustness within the oscillator. We also find that the fraction of MinD proteins oscillating increases with cell length, indicating that the oscillator has evolved to be most effective when cells attain an appropriate length. MicrobeTracker was also used to uncover novel aspects of morphogenesis and cell cycle regulation in Caulobacter crescentus. By tracking filamentous cells, we show that the chromosomal origin at the old‐pole is responsible for most replication/separation events while the others remain largely silent despite contiguous cytoplasm. This surprising position‐dependent silencing is regulated by division.

Odors elicit spatiotemporal patterns of activity in the brain. Spatial patterns arise from the specificity of the interaction between odorants and odorant receptors expressed in different olfactory receptor neurons (ORNs), but the origin of temporal patterns of activity and their role in odor coding remain unclear. We investigate how physiological aspects of ORN response and physical aspects of odor stimuli give rise to diverse responses inDrosophilaORNs. We show that odor stimuli have intrinsic dynamics that depend on odor type and strongly affect ORN response. Using linear–nonlinear modeling to remove the contribution of the stimulus dynamics from the ORN dynamics, we study the physiological properties of the response to different odorants and concentrations. For several odorants and receptor types, the ORN response dynamics normalized by the peak response are independent of stimulus intensity for a large portion of the dynamic range of the neuron. Adaptation to a background odor changes the gain and dynamic range of the response but does not affect normalized response dynamics. Stimulating ORNs with various odorants reveals significant odor-dependent delays in the ORN response functions. However, these differences can be dominated by differences in stimulus dynamics. In one case the response of one ORN to two odorants is predicted solely from measurements of the odor signals. Within a large portion of their dynamic range, ORNs can capture information about stimulus dynamics independently from intensity while introducing odor-dependent delays. How insects might use odor-specific stimulus dynamics and ORN dynamics in discrimination and navigation tasks remains an open question.

Over the last decades, bacterial chemotaxis in Escherichia coli has emerged as a canonical system for the study of signal transduction. A remarkable feature of this system is the coexistence of a robust adaptive behavior observed at the population level with a large fluctuating behavior in single cells [Korobkova E, Emonet T, Vilar JMG, Shimizu TS, Cluzel P (2004) Nature 428:574–578]. Using a unified stochastic model, we demonstrate that this coexistence is not fortuitous but a direct consequence of the architecture of this adaptive system. The methylation and demethylation cycles that regulate the activity of receptor-kinase complexes are ultrasensitive because they operate outside the region of first-order kinetics. As a result, the receptor-kinase that governs cellular behavior exhibits a sigmoidal activation curve. We propose that the steepness of this kinase activation curve simultaneously controls the behavioral variability in nonstimulated individual bacteria and the duration of the adaptive response to small stimuli. We predict that the fluctuating behavior and the chemotactic response of individual cells both peak within the transition region of this sigmoidal curve. Large-scale simulations of digital bacteria suggest that the chemotaxis network is tuned to simultaneously maximize both the random spread of cells in the absence of nutrients and the cellular response to gradients of attractant. This study highlights a fundamental relation from which the behavioral variability of nonstimulated cells is used to infer the timing of the cellular response to small stimuli.

Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could serve as an alternative, or even evolutionary stepping-stone, to switching between multiple systems. We hypothesized that mutations in gene regulation could lead to heritable control of chemotactic diversity. By simulating foraging and colonization of E. coli using a single-cell chemotaxis model, we found that different environments selected for different behaviors. The resulting trade-offs show that populations facing diverse environments would ideally diversify behaviors when time for navigation is limited. We show that advantageous diversity can arise from changes in the distribution of protein levels among individuals, which could occur through mutations in gene regulation. We propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability.

Collective behavior can spontaneously emerge when individuals follow common rules of interaction. However, the behavior of each individual differs due to existing genetic and non-genetic variation within the population. It remains unclear how this individuality is managed to achieve collective behavior. We quantify individuality in bands of clonal Escherichia coli cells that migrate collectively along a channel by following a self-generated gradient of attractant. We discover 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.