Biology subjects

Hatzimanikatis, V.

Publications and source records attributed to Hatzimanikatis, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Genome-scale metabolic models for a synthetic soil microbial community as a path for understanding community functioning

Microbial communities drive essential ecosystem processes, yet community-level behavior often deviates from predictions based on single-species studies. This mismatch reflects the context-dependent nature of nutrient dependencies, metabolic interactions, and resource availability in complex environments. Here, we present a mechanistic, species-resolved framework to dissect metabolism and nutrient exchange in a defined 21-member soil synthetic community spanning five bacterial phyla. For each isolate, we reconstructed and manually curated thermodynamically constrained genome-scale metabolic models, reconciling predictions with growth phenotypes across rich and minimal media. We mapped minimal nutrient requirements, cataloged environment-specific uptake and secretion profiles, and compared metabolic pathways to identify potential competitive and cooperative interactions. Using community-scale metabolomics, we assigned putative producers and consumers for detected metabolites and revealed medium-dependent shifts in niche overlap. This approach exposes metabolic strategies that govern community function and resilience, offering a predictive basis for understanding and manipulating microbiomes. Our findings highlight how species-level metabolic maps can clarify why microbial interventions succeed or fail and guide targeted manipulations in soil ecosystems.

systems biology↗

bioRxiv2026

Scalable biophysical constraints for physiologically consistent metabolic states

Systems biology aims to develop predictive models that connect molecular mechanisms to cellular behavior. Genome-scale metabolic models are among the most widely used frameworks for integrating stoichiometric, thermodynamic, and omics-derived information to predict feasible metabolic phenotypes. However, cellular metabolism operates on timescales governed by enzyme kinetics and by the relationship between metabolic fluxes and metabolite pool sizes. In steady-state metabolic models, this relationship can be expressed in terms of metabolite turnover rates, defined as flux-to-pool-size ratios that quantify how rapidly metabolite pools are renewed. As a result, physiologically consistent steady-state solutions should not only satisfy mass-balance and thermodynamic constraints but also exhibit turnover rates consistent with enzyme-mediated cellular dynamics. Current constraint-based approaches can admit many steady-state flux-concentration states that do not account for turnover rates, resulting in phenotypes incompatible with realistic metabolic dynamics, even when multiple types of data are imposed. Here, we present METEOR-K, an optimization framework that links steady-state metabolic fluxes to metabolite concentrations via turnover rate constraints to identify dynamically plausible flux-concentration reference states. Because these constraints reshape the feasible solution space, we also introduce turnover-rate-aware sampling strategies to efficiently explore the resulting feasible region. We applied METEOR-K to models of increasing scope and scale, including a reduced glycolysis pathway, anaerobic E. coli, and near-genome-scale ovarian cancer models. METEOR-K narrowed the admissible steady-state solution space, reduced uncertainty in feasible flux-concentration states, and improved local dynamic behavior. In nonlinear ODE simulations of bioreactor cultivation and drug-response scenarios, METEOR-K-derived states produced intracellular response times compatible with growth-supporting metabolic operation and perturbation recovery. Overall, these results establish metabolite turnover rates as scalable biophysical constraints that improve the physiological consistency of steady-state metabolic modeling. Because turnover rates encode flux-to-pool-size timescale constraints, METEOR-K moves part of physiological-consistency assessment upstream of kinetic parameterization, yielding better-suited flux-concentration reference states for kinetic modeling and dynamic prediction.

systems biology↗

bioRxiv2026

Mechanistic reconstruction of receptor-to-transcription factor signaling integrating prior knowledge and omics

Omics profiling is ubiquitous, yet resolving how signal transduction shapes cellular physiology remains challenging. Most analyses interpret omics data by summarizing catalogs of pathways or by fitting networks to observations. Here we present SIGMA, a framework that converts curated prior knowledge into causally interpretable, elementally balanced signal-transduction cascades that connect sources to targets. By enforcing elemental balance, SIGMA links processes across pathway catalogs and reveals crosstalk beyond canonical definitions. It enumerates alternative cascades to expose parallel routing and identify context-dependent essential steps. We introduce balanced cascade enrichment analysis to map omics data onto alternative cascades and rank them by context-specific molecular support. Using SIGMA, we reconstructed signal flow from receptors to transcription factors that regulate metabolism, uncovering mechanisms that control metabolic reprogramming. In CD8+ T cells, SIGMA identified cascades connecting TGF-{beta} receptors to SP1 and indicated weakening of this axis in exhaustion. Overall, this framework enables mechanistic interpretation of signaling across datasets and guides the design of causal perturbations.

Systems Biology↗

bioRxiv2026

Pyocyanin disrupts airway regeneration to favor chronic Pseudomonas aeruginosa infection

During chronic infections, host tissues undergo repeated cycles of injury and repair while being exposed to bacterial products. Although many virulence factors trigger immune responses, whether bacterial metabolites alter tissue architecture by disrupting regenerative programs remains largely unexplored. Here, using a primary-cell-derived human lung microtissue model, we show that the redox-active metabolite pyocyanin (PYO), produced by the opportunistic pathogen Pseudomonas aeruginosa, disrupts airway epithelial regeneration and reshapes tissue architecture, amplifying infection. PYO exposure impairs epithelial repair, leading to defects in mucociliary clearance and promoting bacterial growth. Single-cell transcriptomics and imaging reveal that PYO exposure during basal cell differentiation alters epithelial cell-type composition. Keratin-13-rich cells promote the emergence of squamous regions with defective ciliogenesis, resulting in functional defects. After characterizing how PYO remodels the epithelium, we tested how these changes impact bacterial fitness. Using transposon-insertion sequencing and live imaging of infections, we show that these regeneration defects improve the fitness of mutants that tend to form biofilms, a common feature of chronic infections. Together, our results reveal that PYO reshapes regenerative trajectories and tissue architecture in ways that subsequently alter infection outcomes, highlighting the role of secreted metabolites as potential modulators of tissue regeneration during chronic lung infections.

microbiology↗

bioRxiv2025

Computational Inference of Metabolic Programs: A Case Study Analyzing the Effect of BRCA1 Loss

Metabolic reprogramming is a hallmark of cancer, yet how oncogenic drivers shape tumor metabolism across disease progression remains incompletely understood. In this study, we present iMSEA (in silico Metabolic State and Enrichment Analysis), a computational framework that infers flux-based metabolic states from omics profiles. Applying iMSEA to isogenic BRCA1-mutant and BRCA1-wild-type ovarian cancer cells, we identified a shift toward glycolysis, nucleotide biosynthesis, and redox imbalance, coupled with impaired oxidative phosphorylation. These predictions were validated with metabolomics, Seahorse, and SCENITH assays, demonstrating the accuracy of our approach. Extending the analysis to homologous recombination deficient patient tumors at single-cell resolution, we found that BRCA1-deficient cancers display heightened metabolic activity and site-specific adaptations, including altered central carbon fluxes, mitochondrial function, nucleotide biosynthesis, and lipid metabolism. By linking transcriptional programs to functional metabolic states, iMSEA reveals hidden metabolic liabilities in BRCA1-deficient ovarian cancer and provides a broadly applicable strategy for dissecting metabolic heterogeneity and therapeutic vulnerabilities in cancer.

systems biology↗

bioRxiv2025

Multi-omics-driven kinetic modeling reveals metabolic vulnerabilities and differential drug-response dynamics in ovarian cancer

BRCA1-deficient ovarian cancer cells undergo extensive metabolic reprogramming, yet the network-level dynamics underlying their proliferation and treatment response remain poorly resolved. Here, we construct large-scale multi-omics-driven kinetic model populations of ovarian cancer metabolism to track how tumor cells adapt to changes in nutrient use, energy production, and metabolite dynamics over time. Across BRCA1 wild-type and mutant cells, these models expose distinct metabolic strategies shaped by transcriptional regulation and prioritize 28 enzyme-mediated vulnerabilities, including 24 linked to existing experimental or approved drugs and 4 previously uncharacterized targets in nucleotide and lipid synthesis. They further recapitulate a ceramide-linked metabolic stress signature shared across diverse chemotherapies. Mechanistic analysis traces the effects of BRCA1 loss to transcription-factor-mediated shifts in enzyme activity, outlining regulatory routes for network-level rewiring. Beyond ovarian cancer, this framework offers a generalizable blueprint for predicting metabolic vulnerabilities, drug responses, and adaptive mechanisms across diverse cancer and metabolic disease contexts. By coupling dynamic metabolism to therapeutic prediction, it delivers actionable hypotheses for biomarker discovery, patient stratification, target prioritization, and precision metabolic medicine.

cancer biology↗

bioRxiv2025

in silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity

Understanding how metabolic capabilities diverge across microbial species is fundamental for deciphering community function, ecological interactions, and for guiding synthetic microbiome design. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity poses significant challenges to direct comparison. Here, we introduce NIS, a computational approach that uses the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce GEMs to interpretable modules. NIS enables direct comparison of fueling pathways, biosynthetic routes, and environmental exchange processes across organisms, while preserving key metabolic information. We demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, revealing both conserved and divergent patterns in central metabolism, biomass biosynthesis, and substrate utilization. We further apply NIS to members of the core honeybee gut microbiome, uncovering distinct metabolic traits and complementarity that explain coexistence and interaction potential. Our framework offers a robust and scalable method to dissect microbial metabolic networks and supports the rational design and ecological understanding of microbial communities.

systems biology↗

bioRxiv2025

Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

Generative machine learning methods that utilize neural networks to parameterize large-scale and near-genome-scale kinetic models have yielded significant efficiency gains in model construction, paving the way for high-throughput dynamic metabolism studies in biomedical and biotechnological applications. Nevertheless, challenges remain in interpreting the outputs of generative neural networks and developing strategies to quickly adapt these networks to different organisms and physiological contexts without having to restart the modeling process from scratch. Here, we present a systematic framework for repurposing generative neural networks trained under one physiological context to build large-scale kinetic models tailored to another. We showcase the effectiveness of this framework through three case studies in Escherichia coli: (i) adjusting the speed of the dynamic response of aerobic metabolism, (ii) improving interpretability by identifying key enzymatic steps that limit the dynamic response speed of the metabolic models, and (iii) adapting a trained generator to capture the distinct dynamic behavior of anaerobic metabolism. To assess robustness and generalizability beyond E. coli, we extend our approach to large-scale kinetic models of Saccharomyces cerevisiae, systematically exploring latent-space-driven control of network dynamics across generators at different training stages and across multiple representative regions of the latent input space. Together, these results demonstrate that latent space exploration provides a transferable and computationally efficient strategy for controlling the dynamic behavior in large-scale kinetic models across species and physiological regimes. Given the growing adoption of generative neural networks in biological systems modeling, our approach has the potential to facilitate applications in personalized medicine and accelerate the high-throughput design of cell factories by streamlining model construction across diverse living organisms.

systems biology↗

bioRxiv2024

Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production

The use of kinetic models of metabolism in design-build-learn-test cycles is limited despite their potential to guide and accelerate the optimization of cell factories. This is primarily due to difficulties in constructing kinetic models capable of capturing the complexities of the fermentation conditions. Building on recent advances in kinetic-model-based strain design, we present the rational metabolic engineering of an S. cerevisiae strain designed to overproduce p-coumaric acid (p-CA), an aromatic amino acid with valuable nutritional and therapeutic applications. To this end, we built nine kinetic models of an already engineered p-CA-producing strain by integrating different types of omics data and imposing physiological constraints pertinent to the strain. These nine models contained 268 mass balances involved in 303 reactions across four compartments and could reproduce the dynamic characteristics of the strain in batch fermentation simulations. We used constraint-based metabolic control analysis to generate combinatorial designs of 3 enzyme manipulations that could increase p-CA yield on glucose while ensuring that the resulting engineering strains did not deviate far from the reference phenotype. Among 39 unique designs, 10 proved robust across the phenotypic uncertainty of the models and could reliably increase p-CA yield in nonlinear simulations. We implemented these top 10 designs in a batch fermentation setting using a promoter-swapping strategy for down-regulations and plasmids for up-regulations. Eight out of the ten designs produced higher p-CA titers than the reference strain, with 19 - 32% increases at the end of fermentation. All eight designs also maintained at least 90% of the reference strains growth rate, indicating the critical role of the phenotypic constraint. The high experimental success of our in-silico predictions lays the foundation for accelerated design-build-test-learn cycles enabled by large-scale kinetic modeling.

systems biology↗

bioRxiv2024

Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infections

Pseudomonas aeruginosa causes antibiotic-resilient acute and chronic pneumonia, but the mechanisms by which it adapts to the airway environment are poorly understood. Here, we investigated P. aeruginosa pathoadaptive mechanisms in tissue-engineered human airway organoids. Using transposon sequencing in situ, we decoded how P. aeruginosa survives on the mucosal surface during antibiotic treatment. Biofilm formation emerged as a major driver of P. aeruginosa colonization. Mutants that extensively produce biofilms on mucus show limited exploratory behavior, which limits nutrient access, slowing down their growth. Conversely, biofilm-dwelling P. aeruginosa better tolerate antibiotics via biophysical mechanisms. Finally, biofilms can shelter less-tolerant but more cytotoxic strains, thereby contributing to genotypic heterogeneity. P. aeruginosa must therefore adapt to conflicting physical and biological selective pressures to initiate chronic infections.

microbiology↗

bioRxiv2024

Designing pathways for bioproducing complex chemicals by combining tools for pathway extraction and ranking

The synthesis of many important biochemicals involves complex molecules and many reactions. Therefore, the design and optimization of whole-cell biocatalysts to produce these molecules requires the use of metabolic modeling. Such modeling involves the extraction of the production pathways from biochemical databases and their integration into genome-scale metabolic models of the host organism. However, the synthesis of such complex molecules requires reactions from multiple pathways operating in balanced subnetworks that are not assembled in existing databases. Here we present SubNetX, a novel computational algorithm that extracts reactions from a given reaction database and assembles balanced reaction subnetworks to produce a target biochemical from a selected set of precursor metabolites, energy currencies, and cofactors of the host organism. These subnetworks can be directly integrated into whole-cell metabolic models, and using available methods, we can then reconstruct all alternative biosynthetic pathways and rank them according to design criteria such as yield, pathway length, and other optimization goals. We applied SubNetX to eight selected secondary metabolites and one non-natural chemical used as pharmaceuticals to demonstrate the potential of this pipeline.

systems biology↗

bioRxiv2024

Thermodynamics determines the coupling between growth and byproduct production

Genetic manipulation of cells to couple byproduct production and growth rate is important in bioengineering and biotechnology. In this way, we can use growth rate as a selective pressure, where the mutants with higher growth have higher production capacity. Computational methods have been proposed to find knockouts that couple growth and byproduct production. However, none of these methods consider the energetic and thermodynamic feasibility of such knockout strategies. Furthermore, there is no computational study of how variations in metabolite concentrations affect the coupling between growth and byproduct formation. One of the computational methods to find knockouts that couple growth and byproduct formation is OptKnock. OptKnock is a bi-level optimization problem. Here, we integrated thermodynamic constraints into the bilevel formulation of OptKnock to create TOptKnock. We show that the computational efficiency of TOptKnock is comparable to that of OptKnock. TOptKnock can account for the thermodynamic viability of the knockouts and examine how variations in metabolite concentrations affect the coupling. We have shown that the coupling between growth and byproduct formation can change in response to variations in concentrations. Thus, a knockout strategy might be optimal for one intracellular condition but suboptimal for another. If metabolomics data are available, TOptKnock can search for optimal knockout interventions under the given condition. We also envision that the TOptKnock framework will help develop strategies for manipulating metabolite concentrations to couple growth and byproduct formation.

systems biology↗

bioRxiv2024

A constraint-based framework to reconstruct interaction networks in microbial communities

Microbial communities live in diverse habitats and significantly impact our health and the environment. However, the principles that govern their formation and evolution remain poorly understood. A crucial step in studying microbial communities is to identify the potential metabolic interactions between the community members, such as competition for nutrients or cross-feeding. Due to the size and complexity of the metabolic network of each organism, there may be a variety of connections between each pair of organisms, which poses a challenge to unraveling the metabolic interactions. Here, we present ReMIND, a computational framework to reconstruct the interaction networks in microbial communities based on the metabolic capabilities of individual organisms. We applied ReMIND to a well-studied uranium-reducing community and the honeybee gut microbiome. Our results provide new perspectives on the evolutionary forces that shape these ecosystems and the trade-off between metabolite exchange and biomass yield. By enumerating alternative interaction networks, we systematically identified the most likely metabolites to be exchanged and highlighted metabolites that could mediate competitive interactions. We envision that ReMIND will help characterize the metabolic capacity of individual members and elucidate metabolic interactions in diverse communities, thus holds the potential to guide many applications in precision medicine and synthetic ecology.

systems biology↗

bioRxiv2023

Host metabolic pathways essential for malaria and related hemoparasites in the infection of nucleated cells

Apicomplexan parasite diseases, including malaria (Plasmodium) and theileriosis (Theileria), pose a significant threat to global health and the socioeconomic well-being of low-income countries. Despite recent advances, the common host metabolic proteins essential for these highly auxotrophic pathogens remain elusive. Here, we present a comprehensive investigation integrating a metabolic model of P. falciparum parasites in hepatocytes and a genome-wide CRISPR screen targeting Theileria schizont-infected macrophages. We reveal unifying host metabolic enzymes critical for the intracellular survival of these related hematozoa. We show that pathways such as host purine and heme biosynthesis are essential for both Theileria survival and Plasmodium liver development, while genes involved in glutathione and polyamine biosynthesis are predicted to be essential for Plasmodium only under certain metabolic conditions. Our work highlights the importance of host porphyrins for the viability of liver-stage Plasmodium. Shared parasite vulnerabilities provide a resource for exploring alternative therapeutic approaches to combat these crippling diseases.

microbiology↗

bioRxiv2023

Enzyme promiscuous profiles for protein sequence and reaction annotation

1.1Novel sequencing techniques and biochemical pathway prediction resources provide a wealth of data on novel proteins and computationally predicted enzymatic reactions. Accurate matching of protein sequences to enzymatic activities is crucial for advancing synthetic biology and metabolic engineering efforts. Here we present BridgIT+, a computational workflow that accounts for enzyme promiscuity and accurately predicts protein-reaction and reaction-protein associations. BridgIT+ builds upon the promiscuity-based method for annotating orphan and novel reactions with enzymatic activities, BridgIT, and utilizes position-specific scoring matrices (PSSM). The framework uses sequence alignment and enzyme promiscuity predictions to analyze protein sequences, identify sequence patterns, and create promiscuous protein sequence profiles for each reaction. These profiles allow us to predict the protein sequences most likely involved in the reaction. We showcase BridgIT+ by annotating (i) computationally predicted reactions with proteins and (ii) unannotated proteins of E. coli proteome with enzymatic functions. We demonstrated the performance of BridgIT+ on several biochemical assays and compared it to three current state-of-the-art methods for matching proteins and reactions. We anticipate that the proposed conceptual framework will enhance our understanding of gene-protein-reaction relations and advance biological sequence and reaction annotation in biology and synthetic biology studies.

bioinformatics↗

bioRxiv2023

Application of genome-scale models of metabolism and expression to the simulation and design of recombinant organisms

The production of recombinant proteins in a host using synthetic constructs such as plasmids comes at the cost of detrimental effects such as reduced growth, energetic inefficiencies, and other stress responses, collectively known as metabolic stress. Increasing the number of copies of the foreign gene increases the metabolic load but increases the expression of the foreign protein. Thus, there is a trade-off between biomass and product yield in response to changes in heterologous gene copy number. This work proposes a computational method, rETFL (recombinant Expression and Thermodynamic Flux), for analyzing and predicting the responses of recombinant organisms to the introduction of synthetic constructs. rETFL is an extension to the ETFL formulations designed to reconstruct models of metabolism and expression (ME-models). We have illustrated the capabilities of the method in four studies to (i) capture the growth reduction in plasmid-containing E. coli and recombinant protein production; (ii) explore the trade-off between biomass and product yield as plasmid copy number is varied; (iii) predict the emergence of overflow metabolism in recombinant E. coli in agreement with experimental data; and (iv) investigate the individual pathways and enzymes affected by the presence of the plasmid. We anticipate that rETFL will serve as a comprehensive platform for integrating available omics data for recombinant organisms and making context-specific predictions that can help optimize recombinant expression systems for biopharmaceutical production and gene therapy.

systems biology↗

bioRxiv2023

Transcriptional reprogramming by IL-2 variant generates metabolically active stem-like T cells

Interleukin-2 receptor (IL-2R)-mediated intracellular signaling is a key regulator of T-cell fate decisions. While the potent signals generated by IL-2 engagement execute effector differentiation, elevated metabolic activities and rapid cellular expansion, IL-15 binding induces a stemness/memory phenotype and a quiescent metabolic state. Here, we demonstrate that weak but sustained signaling generated by a non-IL-2R-binding variant of IL-2 (IL-2v) drive proliferation/metabolic and stemness transcriptional programs, thereby reprogramming CD8+ T cells into a hybrid metabolically active stem-like state. We further show that IL-2v-induced T cells are capable of superior engraftment, persistence, and tumor control when utilized in adoptive cell therapy. Taken together, our study highlights the ability to fine-tune cytokine engagement of cognate receptors in order to generate therapeutically relevant T-cell states and further reveals the metabolic plasticity of the T-cell memory program.

systems biology↗

bioRxiv2023

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Generating large omics datasets has become routine practice to gain insights into cellular processes, yet deciphering such massive datasets and determining intracellular metabolic states remains challenging. Kinetic models of metabolism play a critical role in integrating omics data, as they provide explicit connections between metabolite concentrations, metabolic fluxes, and enzyme levels. Nevertheless, the challenges associated with determining the kinetic parameters that underlie cellular physiology pose significant obstacles to the broader acceptance and adoption of these models within the research community. Here, we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration and consolidation of diverse omics data and other relevant information, like extracellular medium composition, physicochemical data, and expertise of domain specialists, we show that the proposed framework accurately characterizes unknown intracellular metabolic states, including metabolic fluxes and metabolite concentrations in E. colis metabolic network. Moreover, we show that RENAISSANCE successfully estimates missing kinetic parameters and reconciles them with sparse and noisy experimental data, resulting in a substantial reduction in parameter uncertainty and a notable improvement in the accuracy and reliability of the parameter estimates. The proposed framework will be invaluable for researchers who seek to analyze metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnological studies.

systems biology↗