Costas D. Maranas

The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics

The paper updates the ModelSEED Biochemistry Database with expanded compounds and reactions, integrating multi-source thermodynamic data and grading reaction reliability.

Engineered Pseudomonas putida reconfigures metabolic fluxes to support energy demands during muconate bioproduction from lignin-related aromatics

This paper investigates how engineered Pseudomonas putida reconfigures metabolic fluxes to enhance muconate production from lignin-related aromatics.

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

OpenKinetics Predictor unifies thirteen enzyme kinetic predictors behind one interface and API; on E. coli iML1515, eight kcat predictors correlate pairwise at r 0.08 to 0.67.

BindPred: A Framework for Predicting Protein-Protein Binding Affinity from Language Model Embeddings

BindPred predicts protein-protein binding affinity from amino acid sequences using language model embeddings and gradient boosting trees, achieving high correlation on a benchmark dataset.

MechFind: A computational framework for de novo prediction of enzyme mechanisms

MechFind predicts enzyme mechanisms from reaction stoichiometry, achieving 72% accuracy on a training set and generating 18,000 novel hypotheses across 28,412 reactions.

Parameterization of cell-free systems with time-series data using KETCHUP

The paper extends the KETCHUP tool to parameterize kinetic models using time-course data from cell-free systems, demonstrating accurate simulation of multi-enzyme systems.

novoStoic2.0: An integrated framework for pathway synthesis, thermodynamic evaluation, and enzyme selection

novoSstoic2.0 streamlines pathway design by integrating stoichiometry, thermodynamics, and enzyme selection to identify efficient bioproduct synthesis routes.

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki

CatPred predicts in vitro enzyme kinetic parameters using deep learning, achieving competitive performance with uncertainty quantification on diverse datasets.

Genome streamlining to improve performance of a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973

This paper streamlines the genome of Synechococcus elongatus UTEX 2973 to improve growth and productivity using CRISPR/Cas3 and computational tools.

Standard-GEM supports reproducible development of genome-scale metabolic models

Standard-GEM provides a template for reproducible genome-scale metabolic model development following FAIR principles and software best practices.

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

This study uses resource balance analysis to evaluate proteome allocation and metabolic fluxes in yeast under different environmental conditions, identifying key limitations in biosynthetic pathways.

Proteome capacity constraints favor respiratory ATP generation

The paper shows mitochondrial respiration is more proteome-efficient than aerobic glycolysis in yeast and mammals, with flexibility arising from maintaining a fermentation-capable proteome.

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on Ensemble Learning

EnZymClass predicts plant acyl-ACP thioesterase substrate specificity using ensemble learning, achieving high accuracy and outperforming existing methods.

Quantifying the propagation of parametric uncertainty on flux balance analysis

This paper quantifies how uncertainty in biomass coefficients and non-steady-state conditions affect flux balance analysis results.

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

dGPredictor improves thermodynamic analysis of metabolic reactions by incorporating stereochemistry and enables accurate prediction of free energy changes for novel reactions and pathway design.

Dissecting the Metabolic Reprogramming of Maize Root under Nitrogen Limiting Stress Condition

This study develops a genome-scale metabolic model for maize root to analyze metabolic reprogramming under nitrogen stress, identifying key metabolites involved in biomass growth and stress tolerance.

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.

Development of a genome-scale metabolic model of Clostridium thermocellum and its applications for integration of multi-omics datasets and strain design

This study develops a genome-scale metabolic model for Clostridium thermocellum and applies it to integrate multi-omics data and design strains for biofuel and biochemical production.

Engineering sensitivity and specificity of AraC-based biosensors responsive to triacetic acid lactone and orsellinic acid

This paper improves AraC-based biosensors for triacetic acid lactone and orsellinic acid by reducing background expression, enhancing sensitivity, and increasing specificity through dual-selection and computational methods.

A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data

The paper constructs a genome-scale metabolic model for Rhodosporidium toruloides IFO0880, integrating functional genomics and phenotypic data to improve predictions of gene essentiality and metabolic capabilities.