Collective Search With Finite Perception: Transient Dynamics and Search Efficiency

Motile organisms often use finite spatial perception of their surroundings to navigate and search their habitats. Yet standard models of search are usually based on purely local sensory information. To model how a finite perceptual horizon affects ecological search, we propose a framework for optimal navigation that combines concepts from random walks and optimal control theory. We show that, while local strategies are optimal on asymptotically long and short search times, finite perception yields faster convergence and increased search efficiency over transient time scales relevant in biological systems. The benefit of the finite horizon can be maintained by the searchers tuning their response sensitivity to the length scale of the stimulant in the environment, and is enhanced when the agents interact as a result of increased consensus within subpopulations. Our framework sheds light on the role of spatial perception and transients in search movement and collective sensing of the environment

GlnK Facilitates the Dynamic Regulation of Bacterial Nitrogen Assimilation

Ammonium assimilation in Escherichia coli is regulated by two paralogous proteins (GlnB and GlnK), which orchestrate interactions with regulators of gene expression, transport proteins, and metabolic pathways. Yet how they conjointly modulate the activity of glutamine synthetase, the key enzyme for nitrogen assimilation, is poorly understood. We combine experiments and theory to study the dynamic roles of GlnB and GlnK during nitrogen starvation and upshift. We measure time-resolved in vivo concentrations of metabolites, total and posttranslationally modified proteins, and develop a concise biochemical model of GlnB and GlnK that incorporates competition for active and allosteric sites, as well as functional sequestration of GlnK. The model predicts the responses of glutamine synthetase, GlnB, and GlnK under time-varying external ammonium level in the wild-type and two genetic knock-outs. Our results show that GlnK is tightly regulated under nitrogen-rich conditions, yet it is expressed during ammonium run-out and starvation. This suggests a role for GlnK as a buffer of nitrogen shock after starvation, and provides a further functional link between nitrogen and carbon metabolisms

Dynamical adaptation in biology on multiple scales: from cells to collectives

Bacteria and higher organisms alike have evolved sophisticated mechanisms to dynamically adapt to changing environments. We study temporal and spatial aspects of adaptation on multiple scales and at different levels of biological description. Firstly, in the context of Escherichia coli nitrogen assimilation, we study the cellular mechanisms facilitating dynamical adaptation to time-varying nitrogen abundance. We develop a concise mechanistic model, based on previously characterised biochemical interactions, that predicts the states of key enzymes in vivo during changes in ammonium abundance. Experimental data and model predictions reveal the dynamic role of enzymatic interactions in the signalling and regulation of the cellular nitrogen status. We suggest a novel, history-dependent mechanism regulating the ammonium uptake with the role to anticipate and buffer nitrogen shock. Secondly, we consider the chemotaxis of E. coli to study the functional role of cellular memory in chemotactic navigation. Agent-based simulations show that cells with memory achieve higher drift speeds in rugged attractant gradients than predicted by the Keller- Segel (KS) model describing gradient sensing. This behaviour is captured by a second- order correction to the KS drift velocity containing the effect of spatial correlations. Our results are consistent with the chemotactic pathway processing spatial correlations shedding light on the spatial interpretation of filtering. Thirdly, we generalise to study how the spatial information acquired at the microscopic level influences the spatial search by a population of random walkers. We extend the KS model of local search as an optimal control problem that describes the movement of searchers with spatial perception, quantified by a time horizon. Simulations and analytical arguments show that spatial perception induces faster convergence to steady state. Our results highlight the importance of transients in search; while asymptotically local strategies are optimal, biological timescales favour strategies based on finite spatial information.Open Acces

Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature

Describing networks geometrically through low-dimensional latent metric spaces has helped design efficient learning algorithms, unveil network symmetries and study dynamical network processes. However, latent space embeddings are limited to specific classes of networks because incompatible metric spaces generally result in information loss. Here, we study arbitrary networks geometrically by defining a dynamic edge curvature measuring the similarity between pairs of dynamical network processes seeded at nearby nodes. We show that the evolution of the curvature distribution exhibits gaps at characteristic timescales indicating bottleneck-edges that limit information spreading. Importantly, curvature gaps are robust to large fluctuations in node degrees, encoding communities until the phase transition of detectability, where spectral and node-clustering methods fail. Using this insight, we derive geometric modularity to find multiscale communities based on deviations from constant network curvature in generative and real-world networks, significantly outperforming most previous methods. Our work suggests using network geometry for studying and controlling the structure of and information spreading on networks. The analysis of networks and network processes can require low-dimensional representations, possible for specific structures only. The authors propose a geometric formalism which allows to unfold the mechanisms of dynamical processes propagation in various networks, relevant for control and community detection

Interpretable statistical representations of neural population dynamics and geometry

The dynamics of neuron populations during diverse tasks often evolve on low-dimensional manifolds. However, it remains challenging to discern the contributions of geometry and dynamics for encoding relevant behavioural variables. Here, we introduce an unsupervised geometric deep learning framework for representing non-linear dynamical systems based on statistical distributions of local phase portrait features. Our method provides robust geometry-aware or geometry-agnostic representations for the unbiased comparison of dynamics based on measured trajectories. We demonstrate that our statistical representation can generalise across neural network instances to discriminate computational mechanisms, obtain interpretable embeddings of neural dynamics in a primate reaching task with geometric correspondence to hand kinematics, and develop a decoding algorithm with state-of-the-art accuracy. Our results highlight the importance of using the intrinsic manifold structure over temporal information to develop better decoding algorithms and assimilate data across experiments.Comment: Version before peer revie

barahona-research-group/PyGenStability: 0.2.3

<h2>What's Changed</h2> <ul> <li>Add matrix plot by @arnaudon in https://github.com/barahona-research-group/PyGenStability/pull/83</li> <li>Update README.md by @michaelschaub in https://github.com/barahona-research-group/PyGenStability/pull/84</li> <li>Small improvements by @arnaudon in https://github.com/barahona-research-group/PyGenStability/pull/85</li> <li>FIx: make dtypes consistent by @arnaudon in https://github.com/barahona-research-group/PyGenStability/pull/89</li> <li>Feat: Make some deps optional by @arnaudon in https://github.com/barahona-research-group/PyGenStability/pull/90</li> </ul> <h2>New Contributors</h2> <ul> <li>@michaelschaub made their first contribution in https://github.com/barahona-research-group/PyGenStability/pull/84</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/barahona-research-group/PyGenStability/compare/v0.2.2...v0.2.3</p&gt

Proteomic/metabolic data and mathematical model of nitrogen assimilation

Experimental data and mathematical model accompanying the paper 'GlnK facilitates the dynamic regulation of bacterial nitrogen assimilation' by Gosztolai et al. (2017) Biophys J. <div><br></div><div>Data.xlsx contains concurrent <i>in vivo</i> time-courses of metabolite, total protein and PTM protein concentrations in response to time-varying external ammonium levels. </div><div><br></div><div>Model.txt contains the mathematical model in the paper in SBML language.</div