Biology subjects

Nikoloski, Z.

Publications and source records attributed to Nikoloski, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

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

The ModelSEED Biochemistry Database (https://modelseed.org) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to [~] 46,000 compounds, and [~] 56,000 reactions, featuring [~] 37,000 metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for [~] 33,000 reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at https://github.com/ModelSEED/ModelSEEDDatabase.

biochemistry↗

bioRxiv2026

Nitrogen metabolic map of Escherichia coli mediated by multi-functional aminotransferases

Nitrogen is a key element of organic molecules essential to all life. Unlike extensively characterized carbon metabolic maps, it remains elusive how assimilated nitrogen flows through metabolic networks. Here we determined the nitrogen metabolic map of Escherichia coli and used it to construct an enzyme-constrained metabolic model that includes multi-functionality of aminotransferase enzymes responsible for nitrogen transfer reactions. We characterized substrate specificities of sixteen E. coli aminotransferase enzymes by evaluating 2,528 reactions, uncovering 56 previously unrecognized activities. The cellular concentrations of these aminotransferase enzymes were quantified and used to estimate their catalytic rates across all enzyme-substrate pairs, leading to improved predictions of nitrogen flows in E. coli. This work advances our fundamental understanding of the nitrogen metabolic map critical for metabolism and growth.

biochemistry↗

bioRxiv2026

The controlled spectrum of plant form and function

Dimension reduction techniques with plant traits have suggested the existence of independent acquisitive-conservative spectra in leaves and roots across diverse plants. However, this finding may be due to third-variable effects that can alter the fundamental dimensionality of plant trait variation. Using a dimension reduction approach that controls for third-variable effects, we show that the acquisitive-conservative axis is not confined within an organ, but acts at the whole plant level.

ecology↗

bioRxiv2026

A hybrid machine learning and enzyme-constrained metabolic model for ab initio prediction of proteome reallocation

High expression of heterologous proteins in microbial cell factories frequently triggers a severe burden due to reallocation of finite cellular proteome. Conventional constraint-based models struggle to predict these resource shifts ab initio without relying on condition-specific omics data. To bridge this gap, we developed the Hybrid Transcription-Translation (HyTT) framework, combining multivariate adaptive regression splines (MARS) with enzyme-constrained metabolic models by enforcing an 80S ribosome integrity constraint. Cast as a mixed-integer linear programming problem, HyTT mathematically couples macroscopic spatial boundaries with microscopic, sequence-derived translational costs based on a bisection search. Validation against steady-state chemostat quantitative proteomics data demonstrated the superior capability of HyTT over contenders in predicting system-wide resource (re)allocation in Saccharomyces cerevisiae. Operating ab initio, the framework doubled the predictive accuracy of protein abundances (Pearson r=0.501) compared to conventional models, successfully segregating the minimal essential proteome from the cellular reserve pool. Crucially, HyTT autonomously captures complex stress responses vital for metabolic engineering. Upon simulating a 15% recombinant protein burden, the framework accurately predicted systemic growth retardation, decrease of ribosomal portion of the proteome, and surge of ethanol production, in line with the Crabtree effect. System-level analysis uncovered that cells adapt to restricted proteomic capacity through non-uniform metabolic rerouting, downregulating respiratory complexes in favor of high-turnover glycolytic enzymes, and relying on ribosomal paralog switching to minimize sequence-specific assembly costs. Ultimately, HyTT provides a computationally agile, sequence-driven platform for decoding dynamic resource reallocation, offering a powerful predictive tool to navigate metabolic trade-offs and guide rational strain design without requiring condition-specific multi-omics inputs.

systems biology↗

bioRxiv2026

COCOA.jl: A Julia package for high-performance analysis of concordance and kinetic modules in biochemical networks

SummaryRecent advances in analysis of biochemical networks have contributed the identification of their modular structure based on the concept of multi reaction dependencies and kinetic coupling of reaction rates (Kuken et al., 2022; Langary et al., 2025). Existing implementations of the algorithms to study modular structure do not scale well with the size of the networks, prohibiting their application with genome-scale networks. Here, we introduce COCOA.jl, a multithreaded Julia package for identification of concordant and kinetic modules, with applications in the study of concentration robustness. Availability and implementationCOCOA.jl is implemented in Julia 1.12.2 and is freely available under the MIT license at https://github.com/antoniofranky/COCOA.jl. It runs on Linux, macOS, and Windows; installation is supported via the Julia package manager. COCOA.jl can be called from Python via JuliaCall. Contactantonschaf@posteo.de; ankueken@uni-potsdam.de

systems biology↗

bioRxiv2026

TorchLIMIX: GPU-accelerated multivariate genome-wideassociation studies

SummaryWe introduce TO_SCPLOWORCHC_SCPLOWLIMIX, a GPU-accelerated PyTorch implementation of the LIMIX multivariate genome-wide association study pipeline. By leveraging batched GPU linear algebra, TO_SCPLOWORCHC_SCPLOWLIMIX achieves speedups of up to two orders of magnitude over the original CPU-based implementation while maintaining numerically equivalent results and full concordance of significantly associated loci. In simulation studies, replacing the default initialization of the genetic covariance factor with a QR-based strategy reduces genomic inflation factors to near-unity values under the common and interaction effect null hypotheses, ensuring well-calibrated type I error control. Applying TO_SCPLOWORCHC_SCPLOWLIMIX to metabolic traits of Arabidopsis thaliana measured in two experiments uncovered 37 additional associated SNPs at the same significance threshold used in the original univariate GWAS. AvailabilityThe TO_SCPLOWORCHC_SCPLOWLIMIX pipeline is openly available on GitHub at https://github.com/bi-horn/torchLIMIX. An adapted version of the multivariate association testing part of the original LIMIX pipeline is available at https://github.com/bi-horn/LIMIX_modified. Contactbibiana.horn@hpi.de Supplementary informationSupplementary materials are included with this submission.

bioinformatics↗

bioRxiv2026

Transcriptional regulation of the response to water availability in the resurrection plant Xerophyta elegans

Vegetative desiccation tolerance (VDT) has evolved independently across vascular plants, but its genetic basis remains poorly understood. Although VDT is associated with expansion of the ELIP gene family, the contribution of other lineage-specific expansions is unclear. We assembled genomes for Xerophyta elegans and Xerophyta humilis, identifying expanded gene families largely involved in chlorophyll metabolism and abscisic acid-mediated stress responses. Using a dense dehydration-rehydration transcriptome series in X. elegans seedlings, we reconstructed the regulatory network underlying VDT. Transcription factors from the ABF, ZAT and HSFC families were associated with early responses to desiccation. Key regulators of the seed maturation programme, including NAC transcription factors (ATAF1 and ANAC032), DOG genes and the trihelix factor ASIL1, were also implicated. These findings indicate that VDT arises through integration of abiotic stress signalling with rewiring of the seed maturation network, enabling desiccation tolerance in vegetative tissues.

plant biology↗

bioRxiv2025

KineticGP: a computational framework for genomic prediction of leaf photosynthesis traits

Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.

systems biology↗

bioRxiv2025

Kinetic parameter prediction using neural networks identifies limitations to C4 photosynthesis

Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically-relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.

bioinformatics↗

bioRxiv2025

Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize

Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design. Significance statementWe present a comprehensive evaluation of hyperspectral reflectance for predicting 25 physiological traits, including fluorescence kinetics, in maize across three seasons using rigorous nested cross-validation. By comparing ML models, prediction scenarios, and data aggregation strategies, our study reveals trait-specific limits of generalizability and offers a robust framework for deploying hyperspectral data in breeding applications.

plant biology↗

bioRxiv2025

CSR Calculator: An R package and Shiny application for assigning plant ecological strategies using trait data

O_LIThe competitor, stress-tolerator, ruderal (CSR) theory, first proposed by John Philip Grime, is a useful framework for understanding plant ecological strategies and predicting responses to environmental changes and pressures. However, current tools for assigning CSR strategies are limited to an Excel sheet format and have yet to be integrated into modern computational platforms that enable reproducible research. C_LIO_LIWe present CSRcalculator (https://github.com/TeddyGaskin/CSRcalculator), an open-source R package and shiny application that calculates CSR scores and assigns strategies based on user-uploaded trait data. CSRcalculator supports CSR assignments according to the three most prominent models: The original soft approach, the global StrateFy, and a morpho-physiological model. C_LIO_LIThe R package outputs a table including CSR scores, assigned strategies and intermediate traits for the selected model. The shiny application produces this same table alongside an interactive ternary plot to visualise the strategy distribution and an optional summary table describing the overall CSR strategy distribution, using metrics such as, the modal strategy, axis means, standard deviations, and ranges. Group-level analyses calculate the same statistics across user-defined categories. C_LIO_LIWe provide a worked example using our tool to assign CSR strategies to plants for an example dataset. C_LI

plant biology↗

bioRxiv2025

Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning

Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines (Hordeum vulgare) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R2 [≥] 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R2 [≥] 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.

plant biology↗

bioRxiv2025

Comparative analysis of genomic prediction approaches for multiple time-resolved traits in maize

Ability to accurately predict multiple growth-related traits over plant developmental trajectories has the potential to revolutionize crop breeding and precision agriculture. Despite increased availability of time-resolved data for multiple traits from high-throughput phenotyping platforms of model plants and crops, genomic prediction is largely applied to a small number of traits, often neglecting their dynamics. Here, we compared and contrasted the performance of MegaLMM and dynamicGP as well as their hybrid variants that can handle high-dimensional temporal data for multi-trait genomic prediction. The comparative analysis made use of time series for 50 geometric, colour, and texture traits in a maize multiparent advanced generation inter-cross (MAGIC) population. The performance of the approaches was assessed using snapshot accuracy and longitudinal accuracy, providing insight into the ability to predict multiple traits at a single time point or the dynamics of individual traits over the considered time domain, respectively. We found that MegaLMM outperforms dynamicGP in terms of snapshot accuracy, while dynamicGP proved superior in terms of longitudinal accuracy. This study paves the way for careful investigation of factors that affect the capacity to predict dynamics of multiple traits from genetic markers alone.

bioinformatics↗

bioRxiv2025

Advances in multi-trait genomic prediction approaches: Classification, comparative analysis, and perspectives

Traits in any organism are not independent, but show considerable integration, observed in a form of couplings and trade-offs. Therefore, improvement in one trait may affect other traits, often in undesired direction. To account for this problem, crop breeding increasingly relies on multi-trait genomic prediction (MT-GP) approaches that leverage the availability of genetic markers from different populations along with advances in high-throughput precision phenotyping. While significant progress has been made to jointly model multiple traits using a variety of statistical and machine learning approaches, there is no systematic comparison of advantages and shortcomings of the existing classes of MT-GP models. Here, we fill this knowledge gap by first classifying the existing MT-GP models and briefly summarizing their general principles, modeling assumptions, and potential limitations. We then perform an extensive comparative analysis with ten traits measured in an Oryza sativa diversity panel using cross-validation scenarios relevant in breeding practice. Finally, we discuss directions that can enable the building of next generation MT-GP models in addressing pressing challenges in crop breeding.

bioinformatics↗

bioRxiv2024

Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana

Temperature is a critical environmental factor affecting nearly all plant processes, including growth, development, and yield. Yet, despite decades of research, we lack the ability to predict plant performance at different temperatures, limiting the development of climate-resilient crops. Further, there is a pressing need to bridge the gap between the prediction of physiological and molecular traits to improve our understanding and manipulation of plant temperature responses. Here, we developed the first enzyme-constrained model of Arabidopsis thalianas metabolism, facilitating predictions of growth-related phenotypes at different temperatures. We showed that the model can be employed for in silico identification of genes that affect plant growth at suboptimal growth temperature. Using mutant lines, we validated the genes predicted to affect plant growth, demonstrating the potential of metabolic modeling in accurately predicting plant thermal responses. The temperature-dependent enzyme-constrained metabolic model provides a template that can be used for developing sophisticated strategies to engineer climate-resilient crops.

bioinformatics↗

bioRxiv2024

Evaluating plant growth-defence trade-offs by modelling the interaction between primary and secondary metabolism

Understanding the molecular mechanisms behind plant response to stress can enhance breeding strategies and help us design crop varieties with improved stress tolerance, yield and quality. To investigate resource redistribution from growth-to defence-related processes in an essential tuber crop, potato, here we generate a large-scale compartmentalised genome-scale metabolic model, Potato-GEM. Apart from a large-scale reconstruction of primary metabolism, the model includes the full known potato secondary metabolism, spanning over 600 reactions that facilitate the biosynthesis of 182 distinct potato secondary metabolites. Constraint-based modelling identifies that the activation of the largest amount of secondary (defence) pathways occurs at a decrease of the relative growth rate of potato leaf, due to the costs incurred by defence. We then obtain transcriptomics data from experiments exposing potato leaves to two biotic stress scenarios, a herbivore and a viral pathogen, and apply it as constraints to produce condition-specific models. We show that these models recapitulate experimentally observed decreases in relative growth rates under treatment, enabling us to pinpoint the metabolic rewiring underlying growth-defence trade-offs. Potato-GEM thus presents a useful resource to study and broaden our understanding of potato and general plant defence responses under stress conditions.

systems biology↗

bioRxiv2024

Integrating promiscuous enzyme activities in protein-constrained models pinpoints the role of underground metabolism in robustness of metabolic phenotypes

The integration of enzyme parameters in constraint-based models have significantly improved the prediction of physiological and molecular traits, including enzyme resource usage and distribution. However, current approaches largely neglect the set of promiscuous enzyme activities that jointly comprise the so-called underground metabolism. To allow enzyme-constrained study of underground metabolism, we developed the CORAL Toolbox. This toolbox reworks enzyme usage into subpools for each reaction catalysed by a promiscuous enzyme, increasing the resolution of modelled enzyme resource allocation. Applying CORAL with an enzyme-constrained genome-scale metabolic model of Escherichia coli, we found that underground metabolism resulted in larger flexibility in metabolic fluxes and enzyme usage. Knocking out the main activity of a promiscuous enzyme led to small enzyme redistribution to the side activities. Further, knocking out pairs of main activities showed that non-promiscuous enzymes exhibited larger effect on growth. In addition, we demonstrated these findings are robust with respect to the parameterization of the models with catalytic rates from different prediction tools. Together, our results from modelling underground metabolism in enzyme-constrained models indicated that promiscuous enzyme activities are vital to maintain robust metabolic function and growth.

bioinformatics↗

bioRxiv2024

Phototropin connects blue light perception to starch metabolism in green algae

In photosynthetic organisms light acts as an environmental signal to control their development and physiology, and as energy source to drive the conversion of CO2 into carbohydrates used for growth or storage. The main storage carbohydrate in green algae is starch, which accumulates during the day and is broken down at night to meet cellular energy demands. The signalling role of light quality in the regulation of starch accumulation remains unexplored. Here, we identify PHOTOTROPIN-MEDIATED SIGNALLING KINASE 1 (PMSK1) as a key regulator of starch metabolism in Chlamydomonas reinhardtii. In its phosphorylated form (PMSK1-P), it activates GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE (GAP1), promoting starch biosynthesis. We show that blue light, perceived by PHOTOTROPIN, induces PMSK1 dephosphorylation that in turn represses GAP1 mRNA levels and reduces starch accumulation. These findings reveal a novel blue light-mediated signaling pathway that advances our understanding of photoreceptor-controlled carbon metabolism in microalgae. One-Sentence SummaryBlue light perception by PHOTOTROPIN triggers kinase-mediated signaling to inhibit starch accumulation in the green alga Chlamydomonas.

plant biology↗