optimal transport

DriveWays: A Method for Identifying Possibly Overlapping Driver Pathways in Cancer

DriveWays identifies overlapping cancer driver pathways by solving an NP-hard optimization problem, outperforming existing methods on TCGA data.

Regulating enzymatic reactions in Escherichia coli utilizing light-responsive cellular compartments based on liquid-liquid phase separation

This paper introduces PhASE, a system for dynamically controlling enzymatic reactions in E. coli using light-responsive compartments formed by liquid-liquid phase separation, showing enhanced reaction efficiency.

MorphOT: Transport-based interpolation between EM maps with UCSF ChimeraX

MorphOT interpolates between EM maps using optimal transport to generate morphing trajectories, avoiding blending by displacing densities.

Optimizing interneuron circuits for compartment-specific feedback inhibition

The paper optimizes interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons using gradient-based optimization and biologically plausible models, revealing two interneuron classes resembling PV and SST.

A general technique for the detection of switch-like bistability in chemical reaction networks governed by mass action kinetics with conservation laws

The paper develops a generalized method to detect switch-like bistability in chemical reaction networks with conservation laws, validated on known bistable motifs.

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

This study shows that low-coverage single-cell RNA sequencing of more samples increases statistical power for cell-type-specific eQTL analysis compared to high-coverage sequencing of fewer samples.

Maximum Likelihood Reconstruction of Ancestral Networks by Integer Linear Programming

The paper presents an Integer Linear Programming approach for optimal ancestral PPI network reconstruction under the DMC model, showing improved likelihood and agreement with biological evidence.

Using optimal control to understand complex metabolic pathways

This paper develops a multicriteria optimal control framework to predict enzyme activation and metabolite concentrations in complex metabolic pathways, demonstrating its application to yeast and Bacillus subtilis.

A novel algorithm to optimize generalized gamma distributed multiplicative noise with implications on speckle removal from OCT images

This paper introduces an algorithm to optimize generalized gamma distributed multiplicative noise in OCT images, improving speckle removal and contrast-to-noise ratio.

Gait-symmetry-based human-in-the-loop optimization for unilateral transtibial amputees with robotic prostheses

This paper proposes a gait-symmetry-based human-in-the-loop optimization method for unilateral transtibial amputees, improving gait symmetry and reducing metabolic cost.

Amalgams: data-driven amalgamation for the reference-free dimensionality reduction of zero-laden compositional data

This paper introduces a data-driven method for dimensionality reduction of compositional data using amalgamation, achieving performance comparable to state-of-the-art methods while producing interpretable features.

Optimization of selective phenotyping and population design for genomic prediction

The paper optimizes population design for genomic prediction by selecting preexisting individuals to phenotype and extending this to connected population designs for maize hybrids.

Optoribogenetic control of regulatory RNA molecules

The paper introduces a method to optogenetically control regulatory RNA molecules by engineering chimeric RNAs that interact with a light-sensitive protein, enabling precise spatial and temporal regulation of gene expression.

Multi-Objective Approach for Identifying Cancer Subnetwork Markers

This paper identifies cancer subnetwork markers using a multi-objective approach combining differential gene expression and network topology, achieving better classification performance than existing methods.

Prioritizing transcriptomic and epigenomic experiments by using an optimization strategy that leverages imputed data

The paper develops a method to prioritize genomic and epigenomic experiments using imputed data to maximize information gain, and introduces a software framework called Kiwano.

Identifiability of Tissue Material Parameters from Uniaxial Tests using Multi-start Optimization

This paper assesses the identifiability of tissue material parameters from uniaxial tests using synthetic data and multi-start optimization, finding that not all parameters are uniquely identifiable.

Joint epitope selection and spacer design for string-of-beads vaccines

This paper presents a computational method that jointly selects epitopes and designs spacers for string-of-beads vaccines to improve immunogenicity and processing efficiency.

K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data

K-FIT accelerates kinetic parameterization using steady-state fluxomic data, achieving a thousand-fold speed-up over meta-heuristic methods for E. coli metabolic models.

Statistical inference of mechanistic models from qualitative data using an efficient optimal scaling approach

The paper develops an efficient optimal scaling approach for parameter inference in dynamical models using qualitative data, improving robustness and reducing computation time.

Maximizing cooperation in the prisoner's dilemma evolutionary game via optimal control

This paper develops an optimal control theory to maximize cooperation in the prisoner's dilemma game by dynamically adjusting payoffs over time, with applications in evolutionary biology and related fields.

An improved 3DMax algorithm to reconstruct the three-dimensional structure of the chromosome

The paper improves the 3DMax algorithm by integrating the XNadam optimization method to better predict chromosome 3D structures from Hi-C data.

PhISCS-BnB: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.

Designing combination therapies using multiple optimal controls

This paper applies optimal control theory to design combination therapies for acute myeloid leukaemia by analyzing control dynamics and pay-off functions.

An Optimization framework for Nonspiking Neuronal Networks and Aided Discovery of a Model for the Elementary Motion Detector

The paper develops an optimization framework to model nonspiking neuronal networks and applies it to uncover the mechanism of the elementary motion detector in Drosophila.

Selective Modulation of Heart and Respiration by Optical Control of Vagus Nerve Axons Innervating the Heart

This study uses optogenetics to selectively modulate heart and respiration by targeting vagus nerve axons innervating the heart, showing distinct physiological responses compared to whole nerve stimulation.

The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI

The paper introduces SILVER, a radial sampling method for MRI that improves SNR and uniformity compared to golden ratio sampling in dynamic imaging.

Identification of Conserved Evolutionary Trajectories in Tumors

This paper introduces CONETT, a method to identify conserved tumor evolution trajectories across multiple cancers, finding both previously reported and novel trajectories in renal and lung cancer datasets.

Gene Regulatory Network Inference as Relaxed Graph Matching

This paper presents a theoretical framework for PANDA, a gene regulatory network inference method, and shows it outperforms state-of-the-art methods in predicting transcription factor binding.

ComPotts: Optimal alignment of coevolutionary models for protein sequences

ComPotts aligns Potts models of proteins to improve pairwise sequence alignment by considering coevolutionary relationships.

A Computational Framework to Study the Primary Lifecycle Metabolism of Arabidopsis thaliana

This paper introduces a computational framework to model core carbon metabolism across the lifecycle of Arabidopsis thaliana, showing agreement with experimental data and enabling lifecycle metabolic studies.