Biology subjects

Kirkegaard, J. B.

Publications and source records attributed to Kirkegaard, J. B..

4 recordsLinked to original sources

bioRxiv2026

Discrete Inverse Rendering: Biological Data Analysis with Integer Programming

Biological image and video analysis is full of discrete decisions: whether an object is present, which multi-hypothesis detections are real, whether two detections are tracking the same object, or whether a cell divides or not. Standard pipelines resolve these locally and in sequence, e.g through non-max suppression, per-frame segmentation, distance-based linking, or dedicated lineage rules. Image evidence and temporal evidence are thus rarely weighed against each other in a single objective. We recast these discrete steps as discrete inverse rendering : candidate renderings are generated, and then a integer programming solver selects the subset that best reconstructs the observed video subject to problem-specific constraints. We demonstrate that the same solver and the same reconstruction principle can handle three otherwise separate motifs: suppression of overlapping detections in dense C. elegans tracking, extraction of a single connected curve in sperm tracking, and event-structured tracking in single cell tracking.

bioinformatics↗

bioRxiv2026

Differentiable Vertex Model: Exploring Gradient-Based Optimization for Tissue Morphogenesis

Vertex models are widely used within the field of developmental biology to study tissue morphogenesis. These models are well-suited for modeling deformation at the cellular level where movement is driven by local forces. However, understanding how these microscopic movements coordinate to yield macroscopic phenomena such as the shapes of entire tissues remains a challenge. Here we study a top-down approach using differentiable programming on a simplified vertex model of a laminar tissue, and investigate whether the attributes of individual cells can be tuned to make the mesh as a whole acquire a predefined shape. We let the mesh evolve according to simple rules defined by the input to each polygon, and evaluate the resulting shape against a target boundary. Additionally, we show how the high degeneracy of the output can be reduced by constraining the polygon distributions: first, by adding simple penalties on tissue-wide attributes; and second, by dividing the tissue into regions, within which we bias the attributes toward characteristic values. Our study shows how a simple vertex model can be combined with differentiable programming to model developing tissues, and provides insight into the way individual cells must coordinate to yield macroscopic phenomena such as pre-programmed shapes.

biophysics↗

bioRxiv2026

A Modular Framework for Automated Segmentation and Analysis of AFM Imaging of Chromatin Organization

Chromatin organization underlies essential genome functions, but its nanoscale organization remains challenging to capture and quantify with precision. Atomic force microscopy (AFM) offers direct structural readouts of DNA and chromatin, yet translating these rich images into reproducible biological metrics has been limited by the lack of standardized, scalable analysis tools. Here we present DNAsight, an automated analysis framework that integrates machine learning (ML)-based segmentation with modular, base-pair-calibrated quantification of DNA spatial organization, looping, nucleosome spacing, and protein clustering. Applied across diverse chromatin-associated proteins, DNAsight reveals protein-specific organizational signatures, including topology-dependent compaction by integration host factor (IHF), condition-dependent changes in loop-like DNA structures in cohesin-CTCF-precocious dissociation of sisters 5A (PDS5A) reactions, and promoter-driven multimerization of GAGA factor (GAF) clusters. The framework further enables direct extraction of nucleosome spacing distributions from raw AFM images, providing a label-free route to investigate chromatin fiber architecture. Together, these advances establish DNAsight as a generalizable and scalable approach for converting AFM measurements into quantitative insights into the physical principles of chromatin organization. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/708946v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1225883org.highwire.dtl.DTLVardef@1e1041dorg.highwire.dtl.DTLVardef@1d53d18org.highwire.dtl.DTLVardef@9e0a2a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Spontanously breaking of symmetry in overlapping cell instance segmentation using diffusion models

Instance segmentation is the task of assigning unique identifiers to individual objects in images. Solving this task requires breaking the inherent symmetry that semantically similar objects must result in distinct outputs. Deep learning algorithms bypass this break-of-symmetry by training specialized predictors or by utilizing intermediate label representations. However, many of these approaches break down when faced with overlapping labels that can appear, e.g., in biological cell layers. Here, we discuss the reason for this failure and offer a novel approach for instance segmentation based on diffusion models that breaks this symmetry spontaneously. Our method outputs pixel-level instance segmentations matching the performance of models such as cellpose on the cellpose fluorescent cell dataset while also permitting overlapping labels.

bioinformatics↗