arXiv subjects

Pawel Gniewek

Publications and source records attributed to Pawel Gniewek.

5 recordsLinked to original sources

arXiv2026

Theory of post-jamming rigidity in feedback-regulated cellular packings

Budding-cell packings jam before all buds are mechanically constrained, so the post-jamming state is set not by the pressure $P$ alone but also by the fraction $u$ of buds that remain unconstrained. We develop a mean-field theory in these two variables for this regime. Stress feedback suppresses growth on the loaded buds, so continued growth is redirected onto the remaining free ones. As those buds are completed they add contacts that raise the excess coordination without a comparable rise in prestress. We introduce a modified Maxwell count, a bud-depletion relation, and a flux-partition argument to predict the post-jamming coordination, the density at which the reservoir of initially free buds is exhausted, and how strong feedback can stiffen the packing while generating little internal pressure. Because the added contacts raise the rigidity while the prestress stays low, we conclude that feedback-regulated growth provides a distinct mechanism of self-rigidification.

cond-mat.soft↗

arXiv2022

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly approaching a size beyond that which permits explicit enumeration, presenting new challenges for virtual screening. To overcome these challenges, we propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE). The proposed generative model represents such libraries as a differentiable, hierarchically-organized database. Given a compound from the library, the molecular encoder constructs a query for retrieval, which is utilized by the molecular decoder to reconstruct the compound by first decoding its chemical reaction and subsequently decoding its reactants. Our design minimizes autoregression in the decoder, facilitating the generation of large, valid molecular graphs. Our method performs fast and parallel batch inference for ultra-large synthesis libraries, enabling a number of important applications in early-stage drug discovery. Compounds proposed by our method are guaranteed to be in the library, and thus synthetically and cost-effectively accessible. Importantly, CSLVAE can encode out-of-library compounds and search for in-library analogues. In experiments, we demonstrate the capabilities of the proposed method in the navigation of massive combinatorial synthesis libraries.

q-bio.QM↗

arXiv2021

Learning physics confers pose-sensitivity in structure-based virtual screening

In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or "hits" for therapeutic protein targets from docked poses at specific binding sites. However, while generally successful at this task, many deep learning methods are known to be insensitive to protein-ligand interactions, decreasing the reliability of hit detection and hindering discovery at novel binding sites. Here, we overcome this limitation by introducing a class of models with two key features: 1) we condition bioactivity on pose quality score, and 2) we present poor poses of true binders to the model as negative examples. The conditioning forces the model to learn details of physical interactions. We evaluate these models on a new benchmark designed to detect pose-sensitivity.

q-bio.QM↗

arXiv2019

Fluid flow through packings of elastic shells

Fluid transport in porous materials is commonly studied in geological samples (soil, sediments etc.) or idealized systems, but the fluid flow through compacted granular materials, consisting of substantially strained granules, remains relatively unexplored. As a step towards filling this gap, we study a model of liquid transport in packings of deformable elastic shells using Finite Element and Lattice-Boltzmann methods. We find that the fluid flow abruptly vanishes as the porosity of the material falls below a critical value, and the flow obstruction exhibits features of a percolation transition. We further show that the fluid flow can be captured by a simplified permeability model in which the complex porous material is replaced by a collection of disordered capillaries, which are distributed and shaped by the percolation transition. To that end, we numerically explore the divergence of hydraulic tortuosity $\rmτ_H$ and the decrease of a hydraulic radius $\rm R_h$ as the percolation threshold is approached. We interpret our results in terms of scaling predictions derived from the percolation theory applied to random packings of spheres.

cond-mat.soft↗

arXiv2018

Jamming by growth

Growth in confined spaces can drive cellular populations through a jamming transition from a fluid-like state to a solid-like state. Experiments have found that jammed budding yeast populations can build up extreme compressive pressures (over 1MPa), which in turn feed back onto cellular physiology by slowing or even stalling cell growth. Using extensive numerical simulations, we investigate how this feedback impacts the mechanical properties of model jammed cellular populations. We find that feedback directs growth toward poorly-coordinated regions, resulting in an excess number of cell-cell contacts that rigidify cell packings. Cell packings posses anomalously large shear and bulk moduli that depend sensitively on the strength of feedback. These results demonstrate that mechanical feedback on the single-cell level is a simple mechanism by which living systems can tune their population-level mechanical properties.

cond-mat.soft↗