Biology subjects

Wittmann, B.

Publications and source records attributed to Wittmann, B..

4 recordsLinked to original sources

bioRxiv2026

FLIP2: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications

Machine learning methods that predict protein fitness from sequence remain sensitive to changes in data distributions, limiting generalization across common conditions encountered in protein engineering. Practically, protein engineers are thus left wondering about the effective utility of ML tools. The FLIP benchmark established protocols for testing generalization under some domain shifts, but it was limited to measurements of thermostability, binding, and viral capsid viability. We introduce FLIP2, a protein fitness benchmark spanning seven new datasets, including enzymes, protein-protein interactions, and light-sensitive proteins, as well as splits that measure generalization relevant to real-world protein engineering campaigns. Evaluating a suite of benchmark models across these datasets and splits reveals that simpler models often matched or outperformed fine-tuned protein language models on FLIP2, challenging the utility of existing transfer learning techniques. Provenance for all datasets has been recorded and we redistribute all data CC-BY 4.0 to facilitate continued progress.

bioengineering↗

bioRxiv2025

In Vivo Network-Level Cerebrovascular Mapping Reveals the Impact of Flow Topology on Capillary Stalls After Stroke

Cerebral microvasculature is essential for brain function, but how flow and large-scale connectivity contribute to its resilience or failure remains poorly understood. To address this, we developed OMNIMap, a framework for mesoscale in vivo mapping of functional microvascular networks, capturing flow dynamics and connectivity across thousands of capillaries. OMNIMap integrates extended-focus optical coherence microscopy and learning-based segmentation with global vessel-graph optimization to resolve artery-vein classification and branching order, linking capillary flow and stalls to broader network context. Applied to over 40,000 capillaries in the mouse cortex before and after ischemic stroke, we observe heterogeneous vulnerability patterns: while most capillaries stall or reduce flow after arterial occlusion, some experience accelerated flow. Further analysis revealed that stall-prone flow topology subtypes were less prevalent than their robust counterparts. Notably, the overall distribution of these subtypes remains largely preserved after stroke, revealing a previously unrecognized, system-level organizing principle that alleviates the impact of individual capillary stalls to maintain network-level perfusion.

neuroscience↗

bioRxiv2024

Bessel Beam Optical Coherence Microscopy Enables Multiscale Assessment of Cerebrovascular Network Morphology and Function

Understanding the morphology and function of large-scale cerebrovascular networks is crucial for studying brain health and disease. However, reconciling the demands for imaging on a broad scale with the precision of high-resolution volumetric microscopy has been a persistent challenge. In this study, we introduce Bessel beam optical coherence microscopy with an extended focus to capture the full cortical vascular hierarchy in mice over 1000 x 1000 x 360 m3 field-of-view at capillary level resolution. The post-processing pipeline leverages a supervised deep learning approach for precise 3D segmentation of high-resolution angiograms, hence permitting reliable examination of microvascular structures at multiple spatial scales. Coupled with high-sensitivity Doppler optical coherence tomography, our method enables the computation of both axial and transverse blood velocity components as well as vessel-specific blood flow direction, facilitating a detailed assessment of morpho-functional characteristics across all vessel dimensions. Through graph-based analysis, we deliver insights into vascular connectivity, all the way from individual capillaries to broader network interactions, a task traditionally challenging for in vivo studies. The new imaging and analysis framework extends the frontiers of research into cerebrovascular function and neurovascular pathologies.

bioengineering↗

bioRxiv2021

FLIP: Benchmark tasks in fitness landscape inference for proteins

Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Critical to its use in designing proteins with desired properties, machine learning models must capture the protein sequence-function relationship, often termed fitness landscape. Existing bench-marks like CASP or CAFA assess structure and function predictions of proteins, respectively, yet they do not target metrics relevant for protein engineering. In this work, we introduce Fitness Landscape Inference for Proteins (FLIP), a benchmark for function prediction to encourage rapid scoring of representation learning for protein engineering. Our curated tasks, baselines, and metrics probe model generalization in settings relevant for protein engineering, e.g. low-resource and extrapolative. Currently, FLIP encompasses experimental data across adeno-associated virus stability for gene therapy, protein domain B1 stability and immunoglobulin binding, and thermostability from multiple protein families. In order to enable ease of use and future expansion to new tasks, all data are presented in a standard format. FLIP scripts and data are freely accessible at https://benchmark.protein.properties.

bioengineering↗