Biology subjects

Nielsen, L. K.

Publications and source records attributed to Nielsen, L. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Improved Metabolic Flux Estimations through Compositional Data Analysis

Isotopic Metabolic Flux Analysis (I-MFA) is a standard approach for estimating intracellular metabolic fluxes. I-MFA infers fluxes by comparing simulated and measured metabolite isotopologue distributions (MIDs) of metabolites from isotope labeling experiments. MIDs represent fractional abundances that strictly sum to one for any given metabolite, thus they are inherently compositional data. However, state-of-the-art estimation approaches rely on calculating standard Euclidean distances between MIDs in a non-compositional paradigm, introducing a systemic bias. To resolve this, our study proposes compositional I-MFA. We demonstrate how to construct a meaningful orthonormal basis for MIDs via ordered sequential binary partitioning, which can be used to perform isometric log-ratio (ILR) transformation. As a minimal change to existing I-MFA workflows, we suggest estimating fluxes by minimizing Euclidean distances between ILR-transformed MIDs. We validated this framework against traditional methods using both a toy model and a biologically realistic model, evaluating point estimates, sensitivity across varied true fluxes, and confidence intervals. In the two examples, compositional I-MFA consistently outperformed traditional approaches, reducing mean squared error of flux point estimates by an average of 42.6% and substantially narrowing confidence intervals. We conclude that compositional data analysis significantly improves I-MFA and can be implemented as a simple drop-in replacement for current pipelines. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/742769v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1792c69org.highwire.dtl.DTLVardef@52b5beorg.highwire.dtl.DTLVardef@19cf29org.highwire.dtl.DTLVardef@6fb6db_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LINew compositional data approach improves metabolic flux estimation. C_LIO_LIThis data transformation requires minimal changes to existing workflows. C_LIO_LIThe new method reduced MSE of flux estimates by 42.6% in two examples tested. C_LIO_LIThe confidence intervals of the estimated fluxes were substantially narrowed. C_LIO_LIEstimation accuracy remained robust across a wide range of metabolic fluxes. C_LI

systems biology↗

bioRxiv2026

Light-dependent metabolism and proteome allocation in Synechocystis sp. PCC 6803: an enzyme-constrained metabolic model across light intensities

Genome-scale metabolic models (GEMs) are widely used to relate genotype to phenotype and to study how organisms respond to their environment, but conventional flux-balance analysis relies on simplifying assumptions that limit its predictive power. Enzyme-constrained models (ecModels) address this by bounding each reaction by the abundance and turnover number of its catalyzing enzyme. Here we reconstruct ecModels of Synechocystis sp. PCC 6803 at light-limited, light-saturated and photoinhibited intensities (27.5, 440 and 1100 {micro}mol photons m-{superscript 2} s-{superscript 1}) to study how light shapes its metabolism and enzyme usage. Using the GECKO framework, we constrained the model first by a total protein pool and then by condition-specific quantitative proteomics. The pool-constrained ecModel reproduced the decline in growth at high light as a consequence of a finite proteome, whereas the unconstrained GEM predicted growth to continue rising. Integrating proteomics reduced the median flux variability across reactions by up to two orders of magnitude relative to the conventional GEM. The enzyme budget was dominated by four subsystems (oxidative phosphorylation, transport, photosynthesis and carbon fixation), which together accounted for close to 70% of the minimum enzyme mass in every condition, and the total mass required tracked growth rate rather than light intensity. Enzyme-usage variability analysis found that only about 40% of usages were uniquely determined, a fraction stable across the light gradient. Enzyme constraints thus improve the models description of light-dependent cyanobacterial metabolism and identify the functional sectors that carry the metabolic protein budget.

systems biology↗

bioRxiv2026

Automated mini-bioreactors reveal the temporal dynamics and multi-omics responses of CRISPRi knockdowns in Pseudomonas putida

Characterizing CRISPR interference (CRISPRi) phenotypes presents a fundamental temporal challenge: pre-existing overabundance of target proteins can mask early silencing, requiring extended growth for dilution, yet prolonged repression rapidly selects for escaper mutants. To resolve this, we integrated a tightly regulated CRISPRi system in Pseudomonas putida with an automated mini bioreactor platform operating in turbidostat mode. By maintaining continuous exponential growth, we mapped the exact temporal dynamics of essential gene silencing. We identified a critical observation window between 17 and 27 hours (7-9.5 cell doublings) where repression exerts its maximum physiological impact, directly preceding population takeover by target-site mutated escapers. Applying this workflow to the arginine biosynthesis pathway, multi-omics profiling disentangled transient physiological buffering from long-term mutational events, revealing that argH and argG knockdowns trigger highly diverse metabolomic perturbations. This scalable framework overcomes batch culture limitations, ensuring precise temporal control for accurate phenotypic characterization and reliable functional genomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/709552v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c6795dorg.highwire.dtl.DTLVardef@49f52dorg.highwire.dtl.DTLVardef@17f5d80org.highwire.dtl.DTLVardef@d3bbca_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2026

The Role of Glycan Structures in Modulating GM-CSF Bioactivity: Insights from Glycoengineering

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a glycoprotein cytokine with therapeutic potential in cancer and neutropenia treatment. While glycosylation of GM-CSF reduces immunogenicity and enhances serum bioavailability, it can also diminish receptor binding and bioactivity. Based on transcriptomic analysis of human T lymphocytes reported previously, GM-CSF-producing cells exhibit elevated expression of Alpha-1,6-Mannosylglycoprotein 6-Beta-N-Acetylglucosaminyltransferase (MGAT5), which encodes N-acetylglucosaminyltransferase V, an enzyme involved in N-glycan branching. Given this role of MGAT5 in glycosylation, we produced GM-CSF variants using glycoengineered Chinese hamster ovary cells to generate diverse glycoforms and assessed their bioactivity. Testing their activity on TF-1 cell proliferation, we found that decreases in GM-CSF N-glycan branching significantly suppressed its activity. These findings underscore the importance of glycosylation in modulating the efficacy and safety of GM-CSF-based therapeutics, suggesting that precise glycoengineering may be key to optimizing GM-CSF performance in clinical applications.

bioengineering↗

bioRxiv2025

Semi-automated image analysis of root architecture and early root development in faba bean and white clover and genomic estimation of breeding values and correlations

Protein-rich leguminous plants, such as faba bean and white clover are prospectively interesting crops in the North-European countries for reducing dependence on soybean import. Significant expansion of the production area of leguminous crops is challenged by the sub-optimal climatic conditions in this region, especially by the increasing probability of year-to-year fluctuation of extreme weather conditions due to global climate change. To overcome these challenges, development of new climate-resilient varieties suitable for growing under Northern-European conditions are needed. Root architecture and early root development, as well as the availability of efficient root phenotyping technologies are crucial factors of advancing in breeding of adequate varieties. We report a study of a simple and affordable screening technology of early root development using rhizoboxes in connection with semi-automated image analysis and provide a conceptual pipeline for estimation of Genomic Estimated Breeding Values (GEBVs) and correlating greenhouse and field phenotype data. Based on bivariate models, high genetic correlation (r=0.83) could be detected between total root length values recorded in greenhouse rhizobox experiments and field grain yield in faba bean. In white clover, moderately positive genetic correlation (r=0.17) between estimated breeding values of rhizobox-detected total root length and field yield could be identified. Our results suggest that phenotyping and selection of early root development components could potentially be useful in breeding programs to increase the genetic gain for field yield.

plant biology↗

bioRxiv2025

A Robust Isotope Ratio LC-MS/MS Workflow for High-Throughput Metabolic Profiling of Bacteria

Stable isotope dilution mass spectrometry (IDMS) has become a cornerstone of quantitative metabolomics, enabling accurate intracellular metabolite quantification across a range of biological systems. However, the broader adoption of IDMS in high-throughput studies remains limited by the high costs of commercially available 13C-labeled internal standards (IS), labor-intensive in-house IS production, and the narrow applicability of existing methods to different organisms. Here, we present a robust and scalable IDMS-based LC-MS/MS workflow for high-throughput metabolic profiling of diverse bacteria. The analytical method couples ion-pairing liquid chromatography with multiple reaction monitoring (MRM) to quantify 96 intracellular metabolites in under 16 minutes, with an average RSD of 21%. We developed a protocol for large-scale production of high-quality 13C-labeled IS, considerably lowering the cost for high-throughput IDMS studies. We then applied the workflow to 5 bacterial species in different cultivation conditions. This work provides a versatile platform for microbial metabolomics, supporting systems biology and data-driven metabolic engineering at scale. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/669109v2_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1f3e9e9org.highwire.dtl.DTLVardef@81eb0eorg.highwire.dtl.DTLVardef@16efe09org.highwire.dtl.DTLVardef@1e83a06_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Bayesian Independent Component Analysis reconstructs independent modules of gene expression

Transcriptional regulation--the modulation of gene expression in response to environmental stimuli--is fundamental to cellular function. Identifying groups of co-regulated genes helps elucidate gene functions and characterize how an organism has evolved to respond to various stimuli. In previous works, signal processing algorithms have been applied to characterize the transcriptional regulatory modes, known as iModulons, of bacteria. However, these methods do not quantify uncertainty of the results and are difficult to integrate with different sources of information. In this work, we propose a Bayesian model of Independent Component Analysis that addresses these issues by providing a formal structure to quantify the uncertainty of gene activations and membership of co-regulated genes, achieving state-of-the-art alignment with known regulators. Furthermore, we expand this Bayesian model to explain and integrate first multi-strain and then multi-omics data. Author summaryUnderstanding how genes are turned on and off is crucial for deciphering how living organisms respond to their environment. Genes often work together in groups, and identifying these co-regulated groups can reveal their functions and how organisms adapt to changes. Previous methods have used complex mathematical techniques to find these gene groups in bacteria, but they come with limitations: they do not measure how confident we can be in the results and are hard to combine with other types of biological information. In our study, we introduce a new approach using Bayesian statistics to overcome these challenges. This method not only helps us identify groups of co-regulated genes more accurately but also allows us to quantify our confidence in these findings. Additionally, our approach can easily integrate different kinds of data, such as information from various bacterial strains or other biological processes. This makes our method a powerful tool for exploring gene regulation, with potential applications in understanding diseases, developing new treatments and advancing biotechnology.

systems biology↗

bioRxiv2025

A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells

Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present iCHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, iCHO3K includes 3489 protein structures and structural descriptors for >70% of its 7377 metabolites, enabling deeper exploration of the link between molecular structure and cellular metabolism. We used iCHO3K to contextualize transcriptomics and metabolomics data from a CHO cell line in which lactate secretion is abolished. We found the reduced glycolytic flux and enhanced TCA cycle flux were accompanied by an elevated NADH and PEP levels in these cells, consistent with experimental measurements. Leveraging iCHO3Ks structural annotations, we identified candidate binding interactions of NADH and PEP with glycolytic enzymes showing model-predicted differential flux, suggesting novel allosteric regulation associated with the observed decrease in glucose uptake and glycolysis. Overall, iCHO3K offers a valuable framework for systematic integration of omics data, improved flux predictions, and structure-guided insights, thus advancing CHO cell engineering and enhancing biomanufacturing efficiency.

systems biology↗

bioRxiv2025

Comparative genomic assessment of the Cupriavidus necator species for one-carbon based biomanufacturing

The transition from a petroleum-based manufacturing to biomanufacturing is an important step towards a sustainable bio-economy. In particular biotechnological processes which use one carbon (C1) compounds as feedstock represent an interesting avenue. Many bacterial species evolved naturally to thrive on such compounds, among them Cupriavidus necator, which has been studied in the past due to its range of metabolic capabilities in utilization and production of compounds of interest. Cupriavidus necator strain H16 is the reference laboratory strain for this species and by far the most extensively studied. In contrast, research efforts and genomic characterization of other strains within this species have been limited and sporadic. Therefore, the genomic diversity and full metabolic potential across the broader species remain poorly understood. In this work, we collected publicly available genomes along with newly sequenced ones. From a collection of 44 genomes we curated a final collection of 22 genomes deemed to be C. necator. We examined hallmark metabolic functions, including carbon dioxide fixation, formate assimilation, and hydrogen utilization. We identified methylation motifs and restriction modification systems. Finally, strains ATCC 25207, TA06, and 1978 are proposed as candidate strains of interest based on their genomic make-up and observations from literature. This work provides a comprehensive genomic resource for the C. necator species, facilitating its development as a biomanufacturing platform and advancing our understanding of its metabolic diversity and potential applications. ImportanceThe green transition from petroleum-based manufacturing to a biomanufacturing economy requires new solutions. One of these is the development of bacterial platforms for production of valuable chemicals, in particular from CO2 and derived molecules. In this context, Cupriavidus necator is one of the most studied platform strains, although the genomic potential of this species has been seldom explored. This work provides a curated set of C. necator genomes, collected from public repositories and additional newly sequenced genomes. Through multiple analyses, C. necator ATCC 25207, TA06, and 1978 are proposed as strains of interest for further use. These findings lay the groundwork for advancing C. necator as a key organism in sustainable biomanufacturing, highlighting its potential for efficient carbon fixation, formate assimilation, and hydrogen utilization. The identification of methylation motifs and restriction modification systems further supports genetic engineering efforts, making these strains highly valuable for future biotechnological applications.

microbiology↗

bioRxiv2025

A Diverse Genetic Basis for Metabolic Reactions is Revealed Through Pangenome analysis

Sequenced genomes for thousands of strains of a bacterial species allow for a comprehensive analysis of its pangenome. We present a pangenome study of Escherichia colis metabolism by formulating gene-to-protein-to-reaction associations (GPRs) for about 2,700 metabolic reactions in 2,377 fully sequenced strains. On one hand, these GPRs reconstruct strain-specific networks that allow computational predictions (and experimental validation) of metabolic phenotypes, while on the other hand, they give the genetic basis for a given metabolic reaction in every strain. A pangenome-wide analysis of GPRs shows that: 1) We can reveal the genetic basis for a specific metabolic property at the species level; 2) The genetic basis for many metabolic reactions is diverse; 3) Many rare genes show variation in the genes genomic neighborhood which often contain genes from transposable elements, 4) Many rare genes show large-scale fragmentation and horizontal gene transfer (>11,000 rare genes in 2,377 strains); and 5) The aromatic amino acids and branched chain amino acids pathways are enriched with rare genes, with Acetolactate synthase having 29 distinct genes. Thus, analysis of GPRs across the pangenome reveals a complex dynamic evolutionary history of metabolism, revealing the role of conserved, fragmented, and horizontally transferred metabolic genes.

systems biology↗

bioRxiv2024

Multiplex genome editing eliminates the Warburg Effect without impacting growth rate in mammalian cells

The Warburg effect is ubiquitous in proliferative mammalian cells, including cancer cells, but poses challenges for biopharmaceutical production, as lactate accumulation inhibits cell growth and protein production. Previous efforts to eliminate lactate production via knockout have failed in mammalian bioprocessing since lactate dehydrogenase has proven essential. However, here we eliminated the Warburg effect in Chinese hamster ovary (CHO) and HEK293 cells by simultaneously knocking out lactate dehydrogenase and regulators involved in a negative feedback loop that typically inhibits pyruvate conversion to acetyl-CoA. In contrast to long-standing assumptions about the role of aerobic glycolysis, Warburg-null cells maintain wildtype growth rate while producing negligible lactate. Further characterization of Warburg-null CHO cells showed a compensatory increase in oxygen consumption, a near total reliance on oxidative metabolism, and higher cell densities in fed-batch cell culture. These cells remained amenable for production of diverse biotherapeutic proteins, reaching industrially relevant titers and maintaining product glycosylation. Thus, the ability to eliminate the Warburg effect is an important development for biotherapeutic production and provides a tool for investigating a near-universal metabolic phenomenon.

bioengineering↗

bioRxiv2024

Pseudo batch transformation: A novel method to correct for mass removal through sample withdrawal of fed-batch fermentations

SummaryWe present a novel "pseudo batch" transformation algorithm that maps analytical data obtained for fed-batch bioreactor cultivations onto a constant volume batch process, significantly decreasing the complexity of characterizing the fed-batch process. Availability and implementationOur method is implemented in both Excel and Python and is available with tutorials and example data from https://github.com/biosustain/pseudobatch. The Python package is also available on PYPI under the name "pseudobatch". ContactLars Keld Nielsen, e-mail: lars.nielsen@uq.edu.au Supplementary informationA comprehensive explanation of the simulated fed-batch, parameter estimation procedures, and the Bayesian model can be found in supplementary information (S1-S5).

bioinformatics↗

bioRxiv2024

Evaluating apoptotic gene efficiency for CHO culture performance using targeted integration

Chinese hamster ovary (CHO) cells have long been the favoured platform for producing complex biopharmaceuticals such as monoclonal antibodies (mAbs). Cell death is a critical factor in all CHO cultures, dictating duration until harvest in batch cultures and viable cell density in perfusion. The programmed cell death, or apoptosis, pathway has been widely studied due to its relevance in affecting cell culture performance and the extensive knowledge about its protein-to-protein interaction network. However, clonal variation seen with random integration has confounded results and it remains unclear which effector genes should be overexpressed. Here, we employed the recombinase-mediated cassette exchange (RMCE) strategy to develop isogenic cell lines expressing one copy of erythropoietin, as model protein product, and various anti-apoptotic genes: bcl-2 from CHO and human origin, bcl-xL from CHO and human origin, mcl-1 and bhrf-1. We tested the generated isogenic cell lines in the presence of sodium butyrate, a well-known apoptotic initiator, in batch culture. The most promising candidates were cultured in fed-batch in the microbioreactor ambr(R)15 system. The observed phenotype varied significantly depending on the overexpressed gene, therefore the metabolic differences were further characterized using multiplexed quantitative proteomics. We showed that overexpressing bcl-2 from CHO origin significantly improved productivity and established a methodology to successfully test candidate genes via targeted integration. This will enable future metabolic engineering strategies to be more comparable and overcome the challenges faced thus far.

synthetic biology↗

bioRxiv2024

Unlocking DOE potential selecting the most appropriate design for rAAV optimization

The production of recombinant adeno-associated virus (rAAV) for gene therapy via triple transfection is a highly intricate process involving many cellular interactions. Each of the different elements encoded in the three required plasmids--pHelper, pRepCap, and pGOI-- play a distinct role and affect different cellular pathways when producing rAAVs. The expression balance of these different elements emphasizes the critical need to fine-tune the concentration of all three plasmids and transfection reagents effectively. The use of design of experiments (DOE) to find optimal plasmid and transfection reagent ratios is a powerful method to streamline the process. However, the choice of the DOE method and the design construction is crucial to avoid misleading results. In this work, we examined and compared four distinct DOE approaches: a rotatable central composite design (RCCD), a Box-Behnken design (BBD), a face-centered central composite design (FCCD), and a mixture design (MD). We compared the ability of the different models to predict optimal ratios, interactions among the three plasmids and transfection reagent, and the essentiality of studying the variability caused by uncontrolled random effects using blocking. Our findings revealed that MD, when coupled with FCCD, outperformed all other tested models. This outcome underscores the importance of selecting a model that can effectively account for the biological context, ultimately yielding superior results in optimizing rAAV production.

bioengineering↗

bioRxiv2023

Pangenome reconstruction of Lactobacillaceae metabolism predicts species-specific metabolic traits

Strains across the Lactobacillaceae family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available Lactobacillaceae genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the Lactobacillaceae family and a collection of predictive computational metabolic models that enable a variety of practical uses.

systems biology↗

bioRxiv2023

ASTA-P: a pipeline for the detection, quantification and statistical analysis of complex alternative splicing events

Alternative splicing dramatically increases the repertoire of the human transcriptome and plays a critical role in cellular differentiation. Long read sequencing has dramatically improved our ability to explore isoform diversity directly. However, short read sequencing still provides advantages in terms of sequencing depth at low cost, which is important in comparative quantitative studies. Here, we present a pipeline called ASTA-P for profiling, quantification, and differential splicing analysis of tissue-specific, arbitrarily complex alternative splicing patterns. We discover novel events by supplementing existing annotation with reconstructed transcripts and use spliced RNA-seq reads to quantify splicing changes accurately based on their unique assignments. We used simulated RNA-seq data to demonstrate that ASTA-P provides a good trade-off between discovery and accuracy compared with several popular methods. Further, we applied ASTA-P to analyse AS patterns in real data from hiPSC derived cranial neural crest cells capturing the transition from primary neural cells into migratory cranial neural crest cells, differentiated by their expression of the transcription factor, SOX10. Our analysis revealed a significant splicing complexity, i.e., numerous AS events that cannot be described using the conventionally analysed 2D splicing event patterns. Such events are misclassified when analysed using current differential splicing analysis methods. Thus, ASTA-P provides a new approach for studying both conventional and complex splicing across different cellular conditions and the dynamic regulation of AS. The pipeline is available at https://github.com/uqktiwar/ASTAP/tree/main

bioinformatics↗

bioRxiv2022

MAMBA: a model-driven, constraint-based multiomic integration method

The inclusion of omic data into constraint-based modeling (CBM) has improved metabolic network characterization of biological models. However, the integration of semi-quantitative metabolomic data into CBM remains challenging. Here, we present MAMBA (Metabolic Adjustment via Multiomic Blocks Aggregation), a CBM approach that enables the use of semi-quantitative metabolomic data together with a gene-centric omic data type (e.g. transcriptomics, ChIP-seq or chromatin accessibility data, among others), and the combination of different time points and conditions. MAMBA outperformed other methods in terms of metabolic network characterization and metabolite prediction accuracy. As a case study, we applied MAMBA to a yeast multiomic dataset with time series design where two different yeast strains where exposed to heat stress. MAMBA captured known biology of heat stress in yeast and identified novel affected metabolic pathways. MAMBA was implemented as an integer linear programming (ILP) problem to guarantee efficient computation, and coded for MATLAB (freely available at github.com/ConesaLab/MAMBA).

bioinformatics↗

bioRxiv2022

The giant diploid faba genome unlocks variation in a global protein crop

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emission and loss of biodiversity. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value. Faba bean (Vicia faba L.) has a high yield potential and is well-suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has grown to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, though with significant copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association (GWA) analysis to dissect the genetic basis of hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate improvement of sustainable protein production across Mediterranean, subtropical, and northern temperate agro-ecological zones.

plant biology↗