Biomedical subjects

Vassily Hatzimanikatis

Publications and source records attributed to Vassily Hatzimanikatis.

At least 19 recordsLinked to original sources

PubMed2026

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

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. 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 hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed2025

Metabolic network reconstruction as a resource for analyzing Salmonella Typhimurium SL1344 growth in the mouse intestine.

Nontyphoidal Salmonella strains (NTS) are among the most common foodborne enteropathogens and constitute a major cause of global morbidity and mortality, imposing a substantial burden on global health. The increasing antibiotic resistance of NTS bacteria has attracted a lot of research on understanding their modus operandi during infection. Growth in the gut lumen is a critical phase of the NTS infection. This might offer opportunities for intervention. However, the metabolic richness of the gut lumen environment and the inherent complexity and robustness of the metabolism of NTS bacteria call for modeling approaches to guide research efforts. In this study, we reconstructed a thermodynamically constrained and context-specific genome-scale metabolic model (GEM) for S. Typhimurium SL1344, a model strain well-studied in infection research. We combined sequence annotation, optimization methods and in vitro and in vivo experimental data. We used GEM to explore the nutritional requirements, the growth limiting metabolic genes, and the metabolic pathway usage of NTS bacteria in a rich environment simulating the murine gut. This work provides insight and hypotheses on the biochemical capabilities and requirements of SL1344 beyond the knowledge acquired through conventional sequence annotation and can inform future research aimed at better understanding NTS metabolism and identifying potential targets for infection prevention.

Salmonella typhimurium↗

PubMed2006

Analysis of the maximum theoretical yield for the synthesis of erythromycin precursors in Escherichia coli.

The heterologous biosynthesis of complex polyketides in Escherichia coli was recently achieved through metabolic engineering. However, it was observed that less than 10% of the propionate carbon source is transformed into the erythromycin precursor, 6-deoxyerythronolide B (6dEB), resulting in a 1.4% molar yield. Therefore, metabolic flux analysis was performed using a model of the Escherichia coli metabolism with the addition of the enzymes required for 6dEB synthesis. The analysis shows that the maximum theoretical yield for 6dEB synthesis in E. coli is 11%. The maintenance energy requirement of E. coli and limitations in the specific oxygen uptake rate can further decrease the yield, suggesting that the observed 6dEB yield of 1.4% can be the result of these two factors. In addition, the results suggest that an increase in the specific carbon and oxygen uptake rates will increase the yield of 6dEB. The use of glucose as an alternative carbon source was also evaluated using metabolic flux analysis and the results suggest that the choice of glucose as the carbon source will allow a small improvement in performance relative to a propionate-based process.

Anti-Bacterial Agents↗

PubMed2006

Metabolic engineering under uncertainty--II: analysis of yeast metabolism.

Yeast metabolism has been used extensively in scientific investigations and industrial applications. Understanding the properties of the yeast metabolic network is crucial, yet unaccomplished due to its high complexity, the different culture conditions, and the uncertainty associated with kinetic parameters. We recently developed a computational and mathematical framework using Monte Carlo method in which parameter uncertainty can be addressed through large-scale sampling procedure. This framework was applied on the compartmentalized central carbon pathways of Saccharomyces cerevisiae metabolism considering the growth environment of batch and chemostat reactor and integrating information from metabolic flux analysis. Statistical analysis of the results indicates that yeast cells growing in batch culture condition exhibit dramatically different control schemes from those growing in a chemostat. The difference is mainly due to the feedback introduced by the constraints of the chemostat. The control of the enzymes on the rates of the substrate uptake, product excretion, and cell growth and its practical implication are discussed. Clustering of the reaction steps according to the similarity of their responses to enzyme activity perturbations reveals functional coupling of metabolic reactions.

Bioreactors↗

PubMed2006

Metabolic engineering under uncertainty. I: framework development.

Standard bioprocess conditions have been widely applied for the microbial conversion of raw material to essential industrial products. Successful metabolic engineering (ME) strategies require a comprehensive framework to manage the complexity embedded in cellular metabolism, to explore the impacts of bioprocess conditions on the cellular responses, and to deal with the uncertainty of the physiochemical parameters. We have recently developed a computational and statistical framework that is based on Metabolic Control Analysis and uses a Monte Carlo method to simulate the uncertainty in the values of the system parameters [Wang, L., Birol, I., Hatzimanikatis, V., 2004. Metabolic control analysis under uncertainty: framework development and case studies. Biophys. J. 87(6), 3750-3763]. In this work, we generalize this framework to incorporate the central cellular processes, such as cell growth, and different bioprocess conditions, such as different types of bioreactors. The framework provides the mathematical basis for the quantification of the interactions between intracellular metabolism and extracellular conditions, and it is readily applicable to the identification of optimal ME targets for the improvement of industrial processes [Wang, L., Hatzimanikatis, V., 2005. Metabolic engineering under uncertainty. II: analysis of yeast metabolism. Submitted].

Bioreactors↗

PubMed2005

An algorithmic framework for genome-wide modeling and analysis of translation networks.

The sequencing of genomes of several organisms and advances in high throughput technologies for transcriptome and proteome analysis has allowed detailed mechanistic studies of transcription and translation using mathematical frameworks that allow integration of both sequence-specific and kinetic properties of these fundamental cellular processes. To understand how perturbations in mRNA levels affect the synthesis of individual proteins within a large protein synthesis network, we consider here a genome-scale codon-wide model of the translation machinery with explicit description of the processes of initiation, elongation, and termination. The mechanistic codon-wide description of the translation process and the large number of mRNAs competing for resources, such as ribosomes, requires the use of novel efficient algorithmic approaches. We have developed such an efficient algorithmic framework for genome-scale models of protein synthesis. The mathematical and computational framework was applied to the analysis of the sensitivity of a translation network to perturbation in the rate constants and in the mRNA levels in the system. Our studies suggest that the highest specific protein synthesis rate (protein synthesis rate per mRNA molecule) is achieved when translation is elongation-limited. We find that the mRNA species with the highest number of actively translating ribosomes exerts maximum control on the synthesis of every protein, and the response of protein synthesis rates to mRNA expression variation is a function of the strength of initiation of translation at different mRNA species. Such quantitative understanding of the sensitivity of protein synthesis to the variation of mRNA expression can provide insights into cellular robustness mechanisms and guide the design of protein production systems.

Algorithms↗

PubMed2005

Bistability explains threshold phenomena in protein aggregation both in vitro and in vivo.

Neurodegenerative disease can originate from the misfolding and aggregation of proteins, such as Amyloid-beta, SOD1, or Huntingtin. Fortunately, all cells possess protein quality control machinery that sequesters misfolded proteins, either refolding or degrading them, before they can self-associate into proteotoxic oligomers and aggregates. This activity is largely performed by the stress response chaperones (i.e., Hsp70). However, the expression level of molecular chaperones varies widely among cell types. To understand the potential consequence of this variation, we studied the process of protein aggregation in the presence of molecular chaperones using mathematical modeling. We demonstrate that protein aggregation, in the presence of molecular chaperones, is a bistable process. Bistability in protein aggregation offers an explanation for threshold transitions to high aggregate concentration, which are observed both in vitro and in vivo. Additionally, we show that slight variations in chaperone concentration, due to natural fluctuations, have important consequences in a bistable system for the onset of protein aggregation. Therefore, our results offer a possible theoretical explanation for neuronal vulnerability observed in vivo and the onset of neurodegenerative phenotypes in neurons lacking an effective heat-shock response.

Animals↗

PubMed2005

Genome-scale thermodynamic analysis of Escherichia coli metabolism.

Genome-scale metabolic models are an invaluable tool for analyzing metabolic systems as they provide a more complete picture of the processes of metabolism. We have constructed a genome-scale metabolic model of Escherichia coli based on the iJR904 model developed by the Palsson Laboratory at the University of California at San Diego. Group contribution methods were utilized to estimate the standard Gibbs free energy change of every reaction in the constructed model. Reactions in the model were classified based on the activity of the reactions during optimal growth on glucose in aerobic media. The most thermodynamically unfavorable reactions involved in the production of biomass in E. coli were identified as ATP phosphoribosyltransferase, ATP synthase, methylene-tetra-hydrofolate dehydrogenase, and tryptophanase. The effect of a knockout of these reactions on the production of biomass and the production of individual biomass precursors was analyzed. Changes in the distribution of fluxes in the cell after knockout of these unfavorable reactions were also studied. The methodologies and results discussed can be used to facilitate the refinement of the feasible ranges for cellular parameters such as species concentrations and reaction rate constants.

ATP Phosphoribosyltransferase↗

PubMed2005

Theoretical considerations and computational analysis of the complexity in polyketide synthesis pathways.

The emergence of antimicrobial resistance has led to an increase in research directed toward the engineering of novel polyketides. To date, less than 10 000 polyketide structures have been discovered experimentally; however, the theoretical analysis of polyketide biosynthesis performed suggests that over a billion possible structures can be synthesized. Polyketide synthesis, which involves the formation of a linear chain and its subsequent cyclization, is catalyzed by an enzyme complex called polyketide synthase (PKS). There are a number of variables in the linear chain synthesis controlled by the PKS: the number, identity, stereochemistry and sequence of the monomer units used in the elongation steps, and the degree of reduction that occurs after each of the condensation reactions. The theoretical analysis performed demonstrates that changes in these variables lead to the formation of different polyketide linear chains and, consequently, a high diversity of polyketide structures. The complexity in the number of possible structures led to the implementation of this system in BNICE, a computational framework that generates all possible biochemical pathways using a given set of enzyme reaction rules. This formulation allowed the analysis of the evolution of diversity in the synthesis mechanism and the construction of the pathway architecture of polyketide biosynthesis. It is expected that, after future implementation of the cyclization reactions, this framework can be used to identify all possible polyketides and their corresponding synthesis pathways. Consequently, this formulation would prove useful in guiding experimental approaches to engineer novel polyketides, a number of which will likely have medicinal properties.

Computational Biology↗

PubMed2004

Mathematical modeling of the eukaryotic heat-shock response: dynamics of the hsp70 promoter.

The heat-shock response in humans and other eukaryotes is a highly conserved genetic network that coordinates the cellular response to protein damage and is essential for adaptation and survival of the stressed cell. It involves an immediate and transient activation of heat-shock transcription factor-1 (HSF1) which results in the elevated expression of genes encoding proteins important for protein homeostasis including molecular chaperones and components of the protein degradative machinery. We have developed a mathematical model of the critical steps in the regulation of HSF1 activity to understand how chronic exposure to a stress signal is converted into specific molecular events for activation and feedback regulated attenuation of HSF1. The model is utilized to identify the most sensitive steps in HSF1 activation and to evaluate how these steps affect the expression of molecular chaperones. This analysis allows the formulation of hypotheses about the differences between the heat-shock responses in yeast and humans and generates a model with predictive abilities relevant to diseases associated with the accumulation of damaged and aggregated proteins including cancer and neurodegenerative diseases.

Adaptation, Physiological↗

PubMed2004

Exploring the diversity of complex metabolic networks.

MOTIVATION: Metabolism, the network of chemical reactions that make life possible, is one of the most complex processes in nature. We describe here the development of a computational approach for the identification of every possible biochemical reaction from a given set of enzyme reaction rules that allows the de novo synthesis of metabolic pathways composed of these reactions, and the evaluation of these novel pathways with respect to their thermodynamic properties. RESULTS: We applied this framework to the analysis of the aromatic amino acid pathways and discovered almost 75,000 novel biochemical routes from chorismate to phenylalanine, more than 350,000 from chorismate to tyrosine, but only 13 from chorismate to tryptophan. Thermodynamic analysis of these pathways suggests that the native pathways are thermodynamically more favorable than the alternative possible pathways. The pathways generated involve compounds that exist in biological databases, as well as compounds that exist in chemical databases and novel compounds, suggesting novel biochemical routes for these compounds and the existence of biochemical compounds that remain to be discovered or synthesized through enzyme and pathway engineering. AVAILABILITY: Framework will be available via web interface at http://systemsbiology.northwestern.edu/BNICE (site under construction). CONTACT: vassily@northwestern.edu or broadbelt@northwestern.edu SUPPLEMENTARY INFORMATION: http://systemsbiology.northwestern.edu/BNICE/publications.

Amino Acids, Aromatic↗

PubMed2004

Metabolic control analysis under uncertainty: framework development and case studies.

Information about the enzyme kinetics in a metabolic network will enable understanding of the function of the network and quantitative prediction of the network responses to genetic and environmental perturbations. Despite recent advances in experimental techniques, such information is limited and existing experimental data show extensive variation and they are based on in vitro experiments. In this article, we present a computational framework based on the well-established (log)linear formalism of metabolic control analysis. The framework employs a Monte Carlo sampling procedure to simulate the uncertainty in the kinetic data and applies statistical tools for the identification of the rate-limiting steps in metabolic networks. We applied the proposed framework to a branched biosynthetic pathway and the yeast glycolysis pathway. Analysis of the results allowed us to interpret and predict the responses of metabolic networks to genetic and environmental changes, and to gain insights on how uncertainty in the kinetic mechanisms and kinetic parameters propagate into the uncertainty in predicting network responses. Some of the practical applications of the proposed approach include the identification of drug targets for metabolic diseases and the guidance for design strategies in metabolic engineering for the purposeful manipulation of the metabolism of industrial organisms.

Algorithms↗

PubMed2004

A model-based optimization framework for the inference on gene regulatory networks from DNA array data.

MOTIVATION: Identification of the regulatory structures in genetic networks and the formulation of mechanistic models in the form of wiring diagrams is one of the significant objectives of expression profiling using DNA microarray technologies and it requires the development and application of identification frameworks. RESULTS: We have developed a novel optimization framework for identifying regulation in a genetic network using the S-system modeling formalism. We show that balance equations on both mRNA and protein species led to a formulation suitable for analyzing DNA-microarray data whereby protein concentrations have been eliminated and only mRNA relative concentrations are retained. Using this formulation, we examined if it is possible to infer a set of possible genetic regulatory networks consistent with observed mRNA expression patterns. Two origins of changes in mRNA expression patterns were considered. One derives from changes in the biophysical properties of the system that alter the molecular-interaction kinetics and/or message stability. The second is due to gene knock-outs. We reduced the identification problem to an optimization problem (of the so-called mixed-integer non-linear programming class) and we developed an algorithmic procedure for solving this optimization problem. Using simulated data generated by our mathematical model, we show that our method can actually find the regulatory network from which the data were generated. We also show that the number of possible alternate genetic regulatory networks depends on the size of the dataset (i.e. number of experiments), but this dependence is different for each of the two types of problems considered, and that a unique solution requires fewer datasets than previously estimated in the literature. This is the first method that also allows the identification of every possible regulatory network that could explain the data, when the number of experiments does not allow identification of unique regulatory structure.

Algorithms↗

PubMed2004

Metabolic networks: enzyme function and metabolite structure.

Metabolism is one of the most complex cellular processes. Connections between biochemical reactions via substrate and product metabolites create complex metabolic networks that may be analyzed using network theory, stoichiometric analysis, and information on protein structure/function and metabolite properties. These frameworks take into consideration different aspects of enzyme chemistry, enzyme structure and metabolite structure, and demonstrate the impact of metabolic biochemistry on the systemic properties of metabolism. The integration of these approaches and the systematic classification of enzyme function and the chemical structure of metabolites will enhance our understanding of metabolism, and could improve our ability to predict enzyme function and novel metabolic pathways.

Enzymes↗

PubMed2003

Insights into the relation between mrna and protein expression patterns: II. Experimental observations in Escherichia coli.

There is a need for improved appreciation of the importance of genome-wide mRNA and protein expression measurements and their role in understanding translation and in relation to genome-wide mathematical frameworks for gene expression regulation. We investigated the use of a high-density microarray technique for mRNA expression analysis and a two-dimensional protein electrophoresis-tandem mass spectrometry method for protein analysis to monitor changes in gene expression. We applied these analytical tools in the context of an environmental perturbation of Escherichia coli cells-the addition of varying amounts of IPTG. We also tested the application of these tools to the study of a genetic perturbation of Escherichia coli cells-the ability of certain strains to hypersecrete the hemolysin protein. We observed a lack of correspondence between mRNA and protein expression profiles. Although our data do not include measurements on all expressed genes (because the ability to measure protein expression profiles is limiting), we observed that the qualitative and quantitative behavior of the measurements of a subset of expressed genes is similar to the behavior of the entire system. The change in observed average mRNA and protein amplification factors for 77 and 52 genes coincided with the observed change in mRNA amplification factor for the entire system. Furthermore, we found that the use of relative changes in expression could be used to elucidate mechanisms of gene expression regulation for the system studied, even when measurements were made on a small subset of the system.

Adaptation, Physiological↗

PubMed2003

Insights into the relation between mRNA and protein expression patterns: I. Theoretical considerations.

Translation is a central cellular process in every organism and understanding translation from the systems (genome-wide) perspective is very important for medical and biochemical engineering applications. Moreover, recent advances in cell-wide monitoring tools for both mRNA and protein levels have necessitated the development of such a model to identify parameters and conditions that influence the mapping between mRNA and protein expression. Experimental studies show a lack of correspondence between mRNA and protein expression profiles. In this study, we describe a mechanistic genome-wide model for translation that provides mapping between changes in mRNA levels and changes in protein levels. We use our model to study the system in detail and identify the key parameters that affect this mapping. Our results show that the correlation between mRNA and protein levels is a function of both the kinetic parameters and concentration of ribosomes at the reference state. In particular, changes in concentration of free and total ribosomes in response to a perturbation; changes in initiation and elongation kinetics due to competition for aminoacyl tRNAs; changes in termination kinetics; average changes in mRNA levels in response to the perturbation; and changes in protein stability are all important determinants of the mapping between mRNA and protein expression.

Bacteria↗

PubMed2002

Jay Bailey as mentor--the students' perspective.

Professor James E. Bailey was not only a world-renowned leader and pioneer in biochemical engineering but also a mentor to the many graduate students and postdoctoral researchers in his group. To provide non-"Bailey-ites" with an impression of Jay as a mentor, we begin with a brief review of his career, focusing on the dynamics of the research group. Typical student experiences of being part of the Bailey group are then discussed, including the recognition of a particular research style and Jay's expectations for hard work. Finally, we provide some thoughts on Jay's mentoring style, which was marked by an ability to foster independence, a sense of quality, and passion for research. Jay's contributions as a mentor can perhaps be recognized as being as significant as his research achievements.

Biochemistry↗

PubMed2002

A memorial review of Jay Bailey's contribution in prokaryotic metabolic engineering.

When mentioning prokaryotic metabolic engineering, most people will immediately think of Jay Bailey. Jay's contribution to this fast-growing field is evident and familiar to many. Therefore, instead of a detailed technical review, we attempt in this article to summarize his contribution and dissect reasons for his success in this area from a standpoint of one of his former students (VH) and of a colleague in the field (JCL). This short review is by no means complete and provides only a partial view of Jay's contribution to the metabolic engineering of prokaryotes.

Biochemistry↗