Zoran Nikoloski

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

The paper maps nitrogen metabolism in E. coli by characterizing aminotransferase enzyme activities and using them to improve nitrogen flow predictions.

The paper shows that the acquisitive-conservative axis of plant traits operates at the whole plant level, not just within individual organs, after controlling for third-variable effects.

The paper develops a hybrid model to predict proteome reallocation in yeast under heterologous protein expression, showing improved accuracy over existing methods.

C4TUNE predicts photosynthesis kinetic parameters from response curves, enabling efficient simulation and identification of limiting factors in maize genotypes.

COCOA.jl identifies concordant and kinetic modules in biochemical networks, enabling analysis of concentration robustness with improved scalability.

TorchLIMIX accelerates multivariate GWAS using GPU computing, finding 37 additional SNPs in Arabidopsis metabolic traits compared to univariate GWAS.

This study identifies gene families and transcription factors involved in the desiccation tolerance of the resurrection plant Xerophyta elegans through genome and transcriptome analysis.

This study compares genomic prediction methods for multiple time-resolved traits in maize, finding MegaLMM excels in snapshot accuracy while dynamicGP is better for longitudinal accuracy.

KineticGP improves prediction of leaf photosynthesis traits in maize by integrating genomic data with kinetic models of C4 photosynthesis.

This study uses high-throughput phenotyping and machine learning to predict harvest-related traits in barley under drought conditions, achieving high accuracy with early and late-stage predictors.

The paper develops a genome-scale metabolic model for potato to study growth-defence trade-offs by integrating primary and secondary metabolism and applying transcriptomics data from stress experiments.

This study evaluates ML models for predicting maize traits using hyperspectral data across three seasons, revealing trait-specific generalizability limits and offering a robust framework for breeding applications.

The paper introduces an R package and Shiny app for calculating CSR ecological strategies using plant trait data, supporting multiple models and providing visualizations.

This paper classifies and compares multi-trait genomic prediction models using an Oryza sativa diversity panel to identify strengths and limitations of existing approaches.

The paper identifies a blue light signaling pathway in green algae that inhibits starch accumulation via PMSK1 dephosphorylation and GAP1 repression.

The paper develops a temperature-dependent enzyme-constrained metabolic model for Arabidopsis thaliana to predict growth responses and identify genes affecting thermal growth.

The paper develops a toolbox to model promiscuous enzyme activities in metabolic models, showing that underground metabolism enhances metabolic flexibility and robustness.

This study compares metabolic models of microbial communities generated by different automated tools and a consensus approach, finding that reconstruction methods significantly affect model structure and functionality.

CAMEL predicts protein abundances by combining constraint-based modeling and machine learning, achieving high accuracy in E. coli and moderate performance in S. cerevisiae.

The paper analyzes gene expression in tetraploid wheat under different nitrogen conditions to identify selection signatures during domestication.

The paper integrates proteomics and constraint-based modeling to estimate in vivo enzyme turnover numbers for Chlamydomonas reinhardtii, improving metabolic model predictions.

This paper introduces two novel methods for gene regulatory network inference using mixed-norm regularization and shows they outperform existing approaches on simulated and experimental data.

The paper analyzes cell shape and packing in Arabidopsis thaliana cotyledons, finding that microtubule-mediated shape irregularity enhances tissue packing density.

The paper presents factorizations to categorize balanced complexes in metabolic networks and shows their presence across all kingdoms of life.

The paper shows that integrating data improves turnover number estimates and metabolic predictions for E. coli and S. cerevisiae.

The paper constructs an enzyme-constrained genome-scale metabolic model of Rhizophagus irregularis to predict its metabolic capabilities and physiological responses to environmental cues.

The paper predicts cell division events and their tissue-level consequences in Arabidopsis thaliana's shoot apical meristem using network properties and biological features.

This paper introduces a model reduction approach for biochemical networks that preserves steady-state fluxes and reduces metabolite counts by up to 99% in Escherichia coli models.

The paper introduces effective deficiency to better assess the capacity of constrained biochemical networks to permit steady state variations, showing it is smaller than classical deficiency.