Biology subjects

Wyart, M.

Publications and source records attributed to Wyart, M..

3 recordsLinked to original sources

bioRxiv2026

Whole organism 3D mapping reveals universal branching topology and biophysical optimization governs vascular and nervous system development

The vascular and nervous systems are transport networks essential for life, yet whether universal geometric and topological principles govern their formation remains unclear. The organism-wide molecular and biophysical coordination required to build and maintain these networks during embryonic development has inspired decades of theoretical work, offering predictions about their expected organization. However, the lack of complete three-dimensional (3D) data has limited validation to isolated structures, leaving whole-organism networks unexplored. Here, we developed a computational pipeline for whole-organism 3D imaging to reconstruct the complete vascular and nervous systems of rhesus macaque, mouse, and turtle embryos. Our analysis reveals that both networks share structural principles, including binary branching and scale-invariant bifurcation geometry, maintained across species and throughout development. Yet, from these shared rules emerge fundamentally different architectures. Vasculature exhibits fractal topology with a fractal dimension [~]3, forming space-filling trees that prioritize proximity to every cell in the body. Nervous system networks exhibit a fractal dimension [~]2, forming sheet-like arbors that prioritize electrical signal transmission. This architectural divergence originates from distinct biophysical constraints operating in bifurcations, where vascular junctions minimize energy expenditure while conserving fluid flow and nerve junctions maximize conduction velocity while conserving electrical current. These local optimization rules, iterated across generations, construct organism-wide networks governed by distinct physical constraints, revealing how evolution generated different solutions for fluid versus electrical transport.

developmental biology↗

bioRxiv2019

Mechanics of allostery: contrasting the induced fit and population shift scenarios

In allosteric proteins, binding a ligand can affect function at a distant location, for example by changing the binding affinity of a substrate at the active site. The induced fit and population shift models, which differ by the assumed number of stable configurations, explain such cooperative binding from a thermodynamic viewpoint. Yet, understanding what mechanical principles constrain these models remains a challenge. Here we provide an empirical study on 34 proteins supporting the idea that allosteric conformational change generally occurs along a soft elastic mode presenting extended regions of high shear. We argue, based on a detailed analysis of how the energy profile along such a mode depends on binding, that in the induced fit scenario there is an optimal stiffness [Formula] for cooperative binding, where N is the number of residues involved in the allosteric response. We find that the population shift scenario is more robust to mutation affecting stiffness, as binding becomes more and more cooperative with stiffness up to the same characteristic value [Formula], beyond which cooperativity saturates instead of decaying. We confirm numerically these findings in a non-linear mechanical model. Dynamical considerations suggest that a stiffness of order [Formula] is favorable in that scenario as well, supporting that for proper function proteins must evolve a functional elastic mode that is softer as their size increases. In consistency with this view, we find a significant anticorrelation between the stiffness of the allosteric response and protein size in our data set.

biophysics↗

bioRxiv2019

Characterizing and inferring epistasis via direct evolutionary couplings in models of allostery

In allosteric proteins, the binding of a ligand modifies function at a distant active site. Such al-losteric pathways can be used as target for drug design, generating considerable interest in inferring them from sequence alignment data. Currently, different methods lead to conflicting results, in particular on the existence of long-range evolutionary couplings between distant amino-acids mediating allostery. Here we propose a resolution of this conundrum, by studying epistasis and its inference in models where an allosteric material is evolved in silico to perform a mechanical task. We find four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range and have a simple mechanical interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA predicts well mutation costs but is a rather poor generative model. Strikingly, it can predict short-range epistasis but fails to capture long-range epistasis, in agreement with empirical findings. We propose that such failure is generic when function requires subparts to work in concert. We illustrate this idea with a simple model, which suggests that other methods may be better suited to capture long-range effects.\n\nAuthor summaryAllostery in proteins is the property of highly specific responses to ligand binding at a distant site. To inform protocols of de novo drug design, it is fundamental to understand the impact of mutations on allosteric regulation and whether it can be predicted from evolutionary correlations. In this work we consider allosteric architectures artificially evolved to optimize the cooperativity of binding at allosteric and active site. We first characterize the emergent pattern of epistasis as well as the underlying mechanical phenomena, finding four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range. The numerical evolution of these allosteric architectures allows us to benchmark Direct Coupling Analysis, a method which relies on co-evolution in sequence data to infer direct evolutionary couplings, in connection to allostery. We show that Direct Coupling Analysis predicts quantitatively mutation costs but underestimates strong long-range epistasis. We provide an argument, based on a simplified model, illustrating the reasons for this discrepancy and we propose neural networks as more promising tool to measure epistasis.

biophysics↗