optimal transport

Cytoskeletal disassembly by optogenetic control of RhoA signaling termination

This study introduces an optogenetic tool to control RhoA signaling termination and shows it is faster than passive disactivation, revealing an asymmetry between activation and termination kinetics.

QuickSeg: A fast, versatile and accurate algorithm for genomic copy number segmentation using dynamic programming

QuickSeg improves copy number segmentation accuracy and efficiency using dynamic programming and median-based error function in cancer whole-genome sequencing data.

Predicting cellular responses to perturbation across diverse contexts with STATE

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

Gradient-based Optimization for mRNA Sequence Design

The paper introduces ID3, a framework for optimizing mRNA sequences using gradient-based methods, balancing multiple properties while preserving amino acid sequences.

MOSurvivor-Guided Joint CpG Selection and XGBoost Hyperparameter Optimization for Compact Epigenetic Age Prediction

This paper introduces MOSurvivor, a multi-objective framework for selecting CpGs and optimizing XGBoost hyperparameters to predict epigenetic age with fewer features while maintaining accuracy.

Probabilistic mouse-human brain correspondence by multimodal optimal transport

OTTER maps mouse and human brain regions using optimal transport, revealing evolutionary differences in association cortex and enabling cross-species predictions.

Optimising fertilisation kinetics models for broadcast spawning corals in the genus Acropora

This study optimises fertilisation kinetics models for two Acropora coral species using experimental data to improve predictions of reproductive success and inform reef management.

Beyond the Default: Optimizing Molecular Networking with arteMIS

The paper introduces arteMIS, a framework for optimizing molecular network parameters in metabolomics, showing improved network quality over default settings.

Cross-Cohort Optimal Transport Maps Macrophage Plasticity and Competing Routes to Inflammation and Fibrosis in Human Atherosclerotic Plaques

The paper develops a computational framework using optimal transport to analyze macrophage state transitions in atherosclerosis, identifying origin-dependent plasticity mechanisms and shared regulatory circuits in inflammation and fibrosis.

Conditional source attribution at plankton bloom onset: Identifiability and sharp bounds under environmental forcing

The paper develops a framework to determine if plankton blooms originate internally or externally under environmental forcing, using theoretical and computational methods to assess identifiability and bounds.

Optogenetic Control of cAMP Levels and HCN Channels: Implications in Cardiac Physiology and Parkinsons Disease

This study uses optogenetics to control cAMP and HCN channels in cardiac and Parkinson's disease models, showing effects on heart rate and motor function.

Discrete Inverse Rendering: Biological Image Analysis with Integer Programming

The paper introduces a discrete inverse rendering framework using integer programming to solve multiple biological image analysis tasks by jointly optimizing discrete decisions across time and space.

Methodologies for Manipulating Cardiomyocyte Physiology: In Vitro and In Vivo Perspectives

This study develops and applies methodologies to manipulate cardiomyocyte physiology in vitro and in vivo, revealing effects of optogenetic and chemogenetic tools on heart rate and calcium dynamics.

A distribution-aware and functionally relevant novel framework for generation and discovery of bioactive peptides

The paper proposes a two-phase framework combining distribution-learning and optimization to generate bioactive peptides, achieving high confidence scores and reduced KL divergence in IL-2 and IL-13 induction tasks.

A Novel ILP Framework to Identify Compensatory Pathways in Genetic Interaction Networks with GIDEON

The paper introduces GIDEON, an ILP framework to identify compensatory pathways in yeast genetic interaction networks, finding new BPM gene sets with potential antifungal drug target insights.

MOZAIC: Compound Growth via In Silico Reactions and Global Optimization using Conformational Space Annealing

MOZAIC generates chemically diverse drug-like molecules by combining fragment-based growth with global optimization, improving lead-like properties such as QED and synthetic accessibility.

Programmable Biochemical Logic Computation Through Repurposing Natural bZIP Protein Interaction Networks

This paper shows that natural bZIP protein interaction networks can perform Boolean logic operations through concentration tuning, without altering binding affinities.

Rank-Resolved Multi-Engine Docking and Optuna-Optimized Re-Ranking with ProDock for Virtual Screening

The paper improves virtual screening by combining multiple docking engines with optimized re-ranking, enhancing true-positive candidate identification.

Muon Reduces the Training Cost of Regulatory DNA Transformers

This paper compares Adam and Muon optimizers for training regulatory DNA Transformer models, finding Muon reduces compute costs while achieving similar performance.

A computational framework for designing micron-scale crisscross DNA megastructures

This paper develops a computational framework to design crisscross DNA megastructures, optimizing handle assignment and expanding handle libraries to enable large-scale fabrication.

UQ-PhysiCell: An extensible Python framework for uncertainty quantification and model analysis in PhysiCell

UQ-PhysiCell provides a Python framework for uncertainty quantification, calibration, and model analysis in PhysiCell simulations, enabling scalable and reproducible uncertainty-aware analysis of agent-based models.

Uncovering the spatiotemporal structure of neural avalanches through optimal transport and dynamic time warping

This paper introduces a method to analyze neural avalanches using optimal transport and dynamic time warping, revealing 12 propagation patterns and a shift toward stable patterns under psilocybin.

Breaking the Synthesis Barrier for AI-Designed DNA Libraries

This paper introduces PGLD, an AI method that designs cost-effective DNA libraries for high-throughput experiments by optimizing synthesis-aware stochastic libraries.

Scalable biophysical constraints for physiologically consistent metabolic states

The paper introduces METEOR-K, a framework that incorporates metabolite turnover rates as constraints to improve the physiological consistency of steady-state metabolic models.

Closed-loop optimization of a high-dimensional generative latent space for rhythmic visual response

This paper demonstrates closed-loop optimization of visual stimuli to modulate steady-state visual evoked response (SSVEP) power in non-invasive EEG recordings.

Riemannian Metric Learning for Alignment of Spatial Multiomics

This paper introduces MGW, a metric-learning framework for aligning spatial multi-omics data across different modalities, showing improved alignment over existing methods.

Personalized Immunotherapy via Multiscale Tumor-Immune Modeling and Optimal Control

This paper develops a multiscale tumor-immune model with optimal control for personalized immunotherapy, achieving 78% tumor reduction in simulations compared to standard-of-care.

Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport

This paper introduces SOCS, a trajectory inference method for time-series spatial transcriptomics that preserves structural coherence of biological units over time, improving accuracy compared to existing approaches.

LUstiGE, Light responsive Ustilago maydis Gene Expression: Optogenetic control of morphogenesis and pathogenesis in the corn fungal pathogen Ustilago maydis

This study develops blue-light inducible gene expression switches to control morphogenesis and pathogenesis in Ustilago maydis, demonstrating their utility in regulating fungal invasion and tumor formation.

ParTIpy: A Scalable Framework for Archetypal Analysis and Pareto Task Inference

ParTIpy scales archetypal analysis for biological trade-offs and integrates with single-cell workflows, enabling task inference in large datasets.