However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host’s immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as “metabolomics”. Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and  Infectious diseases continue to be the major cause of morbidity and mortality. Disability-related to infectious or communicable diseases have increased over the past several years (WHO) . The maiThe human immune system consists of two arms as part of the defense mechanism against infection. These are innate immunity and adaptive immunity . Innate Metabolomic analyses focus on the quantitative analyses of large numbers of metabolites in biological fluids, mentioned above . MetabolIn general, a biomarker is an analyte that can be quantified as a standard indicator of biological processes, pathogenic processes, or response to therapeutic intervention. According to the Food and Drug Administration (FDA), a biomarker must be replicable to the disease state for clinical interpretation. Biomarkers are of several types. These include prognostic biomarker, diagnostic biomarker, monitoring biomarker, predictive biomarker, safety biomarker, pharmacodynamic response biomarker, susceptibility/risk biomarker, and provisional biomarker . DiffereChemical properties of different metabolites of the human metabolic pathway are diverse. Two strategies are used to measure metabolites: targeted approaches and un-targeted approaches. Targeted approaches are used to measure the metabolites that are endogenously synthesized. Metabolites like amino acids, fatty acids, lipids, carbohydrates and bile acids belong to this group. Untargeted approaches, on the other hand, is used to measure metabolites, composition of which is diverse. Untargeted (or unbiased) approaches can provide global metabolite profiles. Untargeted analysis provides information on metabolites that are influenced by the host genome, associated microbiome, and environmental factors. As a result, this approach provides information on metabolites associated with drug dose effects and patient characterization integrating knowledge of genetic variation, metabolism and environmental interventions . Since cin vitro techniques to detect the biochemical reactions that occur in a subset of cellular processes. Cell-based assays, on the other hand, use live cells as model to assess the biochemical changes that occur in healthy or diseased cells . Thus, technological platforms that can be used to detect all metabolites present in the sample require a well-organized workflow to reduce technical artefacts and implement data for the identification of patient and disease specific biomarkers in clinics. Incorporation of metabolomics for personalized medicine workflow involves three basic steps. These are 1) pre-analytical steps, 2) analyte detection and 3) validation and clinical translation  Nordstr.This step involves well-organized experimental design on selection of sample type, quenching solution, technological platforms for metabolite extraction and separation, and selection on the type of analytical software to be used for metabolite detection. Factors that needs to be considered with importance at this steps are type of containers for sample collection and storage, sample handling, transport and extraction . RecentlThis encompasses biophysical methods that enable atomic- or molecular-scale investigation into the structure, characteristics, dynamics, or function of biomolecules. X-ray crystallography-based assays, chromatography, nuclear magnetic resonance spectroscopy (NMR), mass spectrometry (MS), and surface plasmon resonance SPR) are examples of common biophysical based methods. Of these, MS and NMR are utilized in both research labs and medical facilities .in vivo NMR, lipid and lipoprotein identification, and small molecule identification. NMR spectrometry is one-dimensional (1D) and two-dimensional (2D). 2D NMR are effective for reducing spectral complexity and identifying metabolites in complex samples. Although major advantage of NMR technology is that data are reproducible, drawbacks of the technology is low sensitivity compared to MS  just below visible red light. This RF is transformed into spectra in NMR . NMR is ed to MS   is an important tool for personalized medicine .13C-glucose into a biological system of organisms, cells or animals. Due to the metabolic reactions, the labelled carbon sources are converted into intermediate metabolites and secreted products. The metabolite’s overall isotopic composition patterns are influenced by the reaction fluxes and carbon atom rearrangements. In addition to 13C tracers, 15N and 2H labelled isotopes are also used in fluxomic studies. Flux is calculated based on the experimental external fluxes, mass distribution vectors (MDV) and a metabolic map that highlights all relevant reactions. Software such as, eiFlux, INCA, METRAN, OpenMedius and 13C2FLUX are used to support the metabolic model, flux estimation, and confidence interval calculation (Mycobacterium tuberculosis (Mtb). MFA has also been used to study the metabolic changes in host cell during viral infection  provide more precise data on the metabolic activities of specific pathways. The fluxome is constrained by the cell’s metabolic profile and corresponding stoichiometry. Each metabolic reaction’s rate is also influenced by a number of upstream factors, including gene expression and regulation, enzyme concentration, enzyme phosphorylation status, and metabolite concentrations related to the reaction. Therefore, metabolic flux measurements provide a complete and dynamic depiction of the metabolic state of the cell by capturing the net interplay of the transcriptome, proteome, regulome, and metabolome. Metabolic fluxes are of two different categories. Extracellular flux and intracellular flux. The extracellular flux crosses the cell membrane and shows the impacts of the medium, such as glucose uptake, or cell growth and biomass. By monitoring the changes in extracellular metabolite concentrations or biomass over time, extracellular fluxes can be measured directly. Intracellular flux, which does not cross the cell membrane, cannot be measured directly. Their impact can be observed by measuring the frequency of integration of an isotopically labelled substrate such as culation . Metabolnfection .Validation of metabolite based biomarkers aims for reproducibility across several cohorts and requires standardization. The concentration of metabolites and type of metabolites varies depending on the sample type. Therefore, metabolites to be used for clinical interpretation or used as biomarker require approval from regulatory bodies to interact this with the disease stage or treatment response. Biobanking of patient samples and longitudinal observation provide reliable data on a robust biomarker. However, the number of controlled studies specially examining infectious diseases and biobanking are very few . Furtherhttps://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates). According to World health Organization (WHO), infectious diseases cause disability even after post-infection sequel (The human immunodeficiency virus (HIV) causes symptoms resembling the flu (influenza) and if left untreated, can lead to acquired immunodeficiency syndrome (AIDS). The virus damages the immune system in such a way that the patient becomes more vulnerable to opportunistic infections, mostly by tuberculosis . Table 2+ T-cells even during the asymptomatic phase. Compared to HIV-1, HIV-2 is less contagious and has a slower rate of CD4+ T-cell depletion (Human immunodeficiency virus type 1 (HIV-1) and type 2 (HIV-2) both contribute to the development of AIDS. Although HIV-2 exhibits lower virulence and transmission than HIV-1, and only 30% of HIV-2 infections progress to AIDS, HIV-2 is nevertheless responsible for the majority of cases of AIDS worldwide. Before symptoms appear and the disease worsens, there is an asymptomatic stage that might continue years after the acute and original infection. As the virus adheres to and infects the cells to multiply within them, infection causes a progressive decline in CD4epletion .+ and HIV−. The distinction between HIV+ patients who had received antiretroviral medication (ART+) and HIV-positive patients, based on considerable alterations in glucose and lipid levels, was a more intriguing conclusion. Untargeted ultrahigh-performance liquid chromatography UHLC/MS/MS and GC/MS of plasma and cerebrospinal fluid (CSF) were used by Cassol et al. to validate these findings. The changes found in HIV+ ART+ samples involved neurotransmitters , myo-inositol, and ketone bodies, and their prevalence suggested an effect similar to increased aging. The results provided understanding of the inflammatory and neurotoxic processes at play because the discovered metabolites were also among the top classifiers for the emergence of HIV-associated neurocognitive disorders (HAND)  .+. NAD+ was not noticeably reduced despite the alterations in quinolinate levels that were seen. The kynurenine pathway’s impaired function has been linked to a number of conditions, including neurodegenerative illnesses and chronic inflammation. The immunological response has also been linked to tryptophan levels, and ongoing tryptophan depletion has been associated to T cell exhaustion and tryptophan catabolism towards immune activation. These findings raised the possibility that tryptophan levels may be responsible for the distinction between HIV-1 and HIV-2 pathogenicity , which are thought to be related to HIV-2 viral protein x (Vpx). SAMHD1, a host antiviral protein with dNTPase activity that works to reduce the availability of dNTP for viral reverse transcription, has been linked to Vpx’s role in degrading SAMHD1. LC-MS/MS metabolites were extracted from both HIV-uninfected and HIV-infected primary monocyte-derived macrophages. The HIV-1 strains were associated with increases in glyceraldehyde 3-phosphate (G3P) and fructose 1,6-bisphosphate (FBP), but the rise in quinolinate was the most notable modification. Quinolinate is an intermediate in the kynurenine pathway, which begins with the breakdown of tryptophan, which produces NADgenicity .+ sub-Saharan people, Bipath et al. carried out GC/MS analysis. They discovered elevated levels of indoleamine 2,3-dioxygenase (IDO) in the HIV+ samples. Using a DB-5 MS capillary column and 105 plasma samples from HIV+ sub-Saharan people, Bipath et al. carried out GC/MS analysis. They discovered elevated levels of indoleamine 2,3-dioxygenase (IDO) in the HIV+ samples. In contrast to the findings from HIV− and HIV+ samples from higher-income countries, this rise led to an accelerated breakdown of tryptophan and a buildup of kynurenine pathway intermediates such as quinolinate and compounds with neurotoxic characteristics . Studies have investigated changes suggestive of OS, such as altered amino acid metabolism, including those of alanine and glutamine, using a variety of techniques . Using a DB-5 MS capillary column and 105 plasma samples from HIVeristics .About 350 million infections and 600,000 deaths occur worldwide each year, primarily in Asia and Africa, due to the blood-borne hepatitis B virus (HBV). It can cause acute infection to chronic hepatitis B leading to LF, LC, and HCC. Metabolomics can have a significant influence on hepatitis B virus (HBV) research by offering a sensitive approach to determine the disease stage without the requirement for potentially harmful tests like biopsies and histopathology. The next stages are liver fibrosis (LF) and cirrhosis, which can advance to hepatocellular carcinoma (HCC), once an acute infection transforms into a chronic condition. Researchers used metabolomics platforms such as GC/TOF to identify metabolic biomarkers in serum samples to distinguish between HBV stages and enable the early detection of HCC. Asparagine and glutamate were found to be associated with HBV infection, cirrhosis progression, and changes to distinguish cirrhosis from HCC. For the development of HCC, major alterations of metabolites of the key pathways like glycolysis and the TCA cycle were reported. A blockage of the TCA cycle was reported with increases in malic acid, citric acid, and succinic acid, and a dependency on glycolysis. Additionally, palmitic acid for the detection of cirrhosis against HBV and phenylalanine, malic acid, and 5-methoxytryptamine was proposed as possible biomarkers for discriminating between HBV and controls. In order to examine the metabolome during chronic HBV and for disease staging, UPLC/MS was used to detect metabolites from serum samples. An elevated ornithine levels together with elevated citrulline and glutamate levels, which indicated dysregulation of the urea cycle could be linked to liver injury. Additionally, the virus also used the glycerol-3-phosphate NADH shuttle as a vehicle for its reproduction .According to the WHO (2018), 70% of individuals who contract the hepatitis C virus (HCV) proceed to develop a chronic illness. Hepatitis C (HCV) is caused by a flavivirus . While tEnzyme immunoassays (EIA) for the detection of anti-HCV and Polymerase chain reactions (PCR) for the identification of HCV-RNA are the standard techniques used in the screening and diagnosis of HCV-infections. These methods involve a lot of time and come with a risk of contamination, and the increased expense of the procedure prevents them from being used on a regular basis. Although liver biopsy is presently regarded as the gold standard method for staging the HCV-associated LF, it has several disadvantages, such as the possibility of bleeding, acute discomfort, and organ perforation. As a result, scientists are concentrating on developing alternative approaches that are easy to use, cheap to produce, safe to use, and quick to screen for HCV infection. Due to the significant incidence and fatal outcomes of the HCV-induced HCC worldwide, metabolomics studies have been used for early diagnosis and understanding of the correlation of metabolites with the progression of the disease .Utilizing NMR, biomarkers for disease detection have been discovered, and by combining MS with other chromatographic techniques, changes in sugar metabolism and elevated levels of metabolites, such as glucose, in the blood, were observed. Similar to HBV, metabolomics can offer a precise and non-invasive tool for disease staging, and numerous studies in this area have been carried out. Data mining and multivariate statistical analysis were used to create one of these algorithms, which attempted to use the methods of statistical analysis to create an algorithm to distinguish between stages by using amino acid ratios in plasma based on the formula [/ + (threonine + methionine + ornithine)/(proline + glycine)]. Biopsies were used to assess the extent of fibrosis in 53 individuals’ plasma samples. The formula successfully distinguished between F3-F4 and earlier phases, according to the results, and successfully distinguished F4 from all other stages. The area under the receiver operator curve was used to assess performance, and the findings showed good confidence . Although these findings are preliminary, they could be used to assess liver fibrosis without invasive procedures. Another study used LC/MS and GC/MS to find changes that may be used as fibrosis markers, such as cysteine and bile acids .1H-NMR technique was used to detect NMR spectra of serum samples from 67 patients with HCV, 50 with HBV, and 43 healthy controls in order to characterize their metabolic fingerprints , designated as DENV-1 through DENV-4. A minimum of 5% of DENV-mediated DF develops into severe forms, such as dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS), which can be fatal. Despite the fact that the majority of DENV-mediated DF is asymptomatic or only manifests mild symptoms . One serotype’s infection does not offer defense against another. Therefore, accurate diagnosis and prognosis depend on early and precise disease identification. Metabolomics is crucial for achieving this goal. Several studies that compared the metabolomes of DENV-infected and non-DENV-infected patients found metabolites that might be utilized to diagnose DENV and predict the prognosis of severe to mild DENV disease. These research articles describe how sphingolipids and glycerophospholipids are affected by DENV infection. Sera from DENV-infected patients exhibit lower amounts of phosphatidylcholine (PC), lysophosphatidylcholines (LPC), and lysophosphatidylethanolamines (LPE), as well as decreased levels of sphingomyelin (SM) in contrast to healthy control participants. The LPE correlates with the host’s later response while the increased SM correlates with the host’s early response. These investigations thereby indicate the potential of these lipids as diagnostic and prognostic biomarkers .Seasonal influenza is an acute respiratory infection which is caused by four types of seasonal influenza viruses. These are influenza virus A, B, C, and D. WHO estimates that seasonal Influenza may result in 290,000 to 650, 000 death each year due to respiratory diseases alone . Clinica1H-NMR and GC-MS methods, researchers were able to distinguish H1N1 pneumonia from bacterial CAP (community acquired pneumonia) and ventilated ICU control subjects through metabolomic profiling of plasma samples collected within 24 h of hospital admission. Additionally, by separating H1N1 non-survivors from survivors using samples taken from inside the study population, researchers further hypothesized that plasma metabolomics may be employed for the prognosis of mortality  is a strain of coronavirus family and causes the disease COVID-19. The outbreak caused by SARS-CoV-2 caused over 100 million confirmed cases in December 2019, and almost 2.5 million fatalities by 2021. Since there is no effective treatment, an immediate and accurate diagnosis is required. Metabolomics is an effective technique for combating COVID-19 because of its speed and ease of use. This method produces large amounts of information and enables the quick screening of molecules for the identification of biomarkers for the diagnosis and prediction of disease severity. With the use of LC/MS and NMR, several researchers found inflammation associated metabolites such as increased level of alpha-1-cis glycoprotein. There was an increased ratio of kynurenine/tryptophan in COVID-19 patients with diabetes and metabolic disorders. Changes in arginine and kynurenine ratio in plasma samples of COVID-19 patients were also detected by LC/MS/MS and patients  . M. tuberculosis predominantly adapts to intracellular habitats in macrophages. In a specialized phagosomal compartment within these host cells, M. tuberculosis survives and reproduces. The bacteria appear to be capable of escaping into the cytoplasm of the host cells under specific circumstances. Immediately upon infection, granulomas develop, which contain an infected macrophage core encircled by foamy macrophages, monocytes, and multinucleated giant cells. M. tuberculosis may also infect other organs in immune-compromised individuals, leading to extrapulmonary TB. It has been suggested that an essential reservoir for the persistence of M. tuberculosis is the visceral adipose tissue  and animals involved transcript profiling. Additionally, M. tuberculosis-infected cells and animal models were subjected to proteomics and metabolomics analyses. These investigations gave researchers important insights into the nature of the immunological responses that M. tuberculosis causes, as well as some hints about how it affects the host cells' metabolic processes  expression in mouse macrophages and other host cells, most likely through the TNF-α signaling pathway. The two-component system DosS/T appears to be the mechanism by which CO, one of the reaction products of HO-1, and NO produced by iNOS promote transcription of the Mtb dormancy regulon. In addition, elevated amounts of host cell proteins necessary for the production of ROS, such as the neutrophil cytosolic factor 1 (NCF1 or p47) and the p67phox component of NADPH oxidase, are detected. The production of the antioxidant Mn-dependent superoxide dismutase, which quenches ROS and hydrogen peroxide, is also increased  appeared to be involved in metabolism  and the pentose-phosphate pathway. This further supports the decreased levels of the TCA cycle intermediates such as oxaloacetate and fumarate, thereby decreased TCA cycle activity. However, the concentration of succinate, another intermediate of the TCA cycle showed increased levels , another metabolic response to protect against induced oxidative stress. Similar patterns of metabolites are found in the lung tissues and serum of infected guinea pigs when metabolite profiling is performed .M. tuberculosis infection leads to substantial metabolic host responses that affect the host’s ability to fight off the infection as well as increased lipid metabolism, glucose uptake, and proteolysis. These metabolic host responses, which were seen in vivo mouse and guinea pigs, appear to support intracellular replication of M. tuberculosis. Most of the identified metabolic host responses appear to be significantly influenced by the distinct mycobacterial cell wall components , which is the main causative agent of the disease (Approximately 150 million cases of urinary tract infections (UTIs) occur each year, making it one of the most prevalent bacterial infections worldwide . UTIs ma disease .E. coli (ExPEC). UPEC rarely creates any health issues in the colon. However, UPEC can spread and colonize in the urinary tract and the bloodstream. The organism secretes toxins, which can trigger an infection in the host. It is evident that UPEC’s ability to utilize nutritionally diverse habitats, including the intestines, urine, bladder, kidneys, and bloodstream, significantly contributes to the disease’s etiology. Because UPEC metabolism is tightly controlled and extremely responsive to the availability of nutrients, it can survive in a wide variety of environments that are both competitive and in fluctuating conditions. UTIs are typically initiated by UPEC that contaminate, colonize, and migrate into the urethra and bladder lumen. There is evidence that the majority of human UTIs are caused by UPEC strains that infiltrate the bladder epithelium and go through an intracellular infection cycle. The infection cycle is a complicated process that includes epithelial attachment, invasion of host cells, and intracellular proliferation. Ultimately, the bladder epithelial cell ruptures, causing infection to spread and infect neighboring epithelial cells. Lower urinary tract infections have the potential to spread to the kidneys and enter the bloodstream, leading to urosepsis, which can be fatal. As UPEC travels between the intestinal lumen and the urinary tract, they need to quickly adapt to new environments. One of the main sources of nutrients that UPEC receives in the urinary system is urine. It is yet unclear how the metabolic variety of urine lead to the development of UPEC in the urinary tract  cycle is required for the colonization and survival of the pathogen during bladder infection. These observations showed that the organism can adapt to different nutrient availability within the host environments .E. coli siderophore enterobactin. Siderocalin has low binding affinity to the siderophores that are associated with UPEC strains. As a result, these provide evolutionary advantage for the UPEC to scavenge host iron  was used to detect potential urine biomarkers in both healthy individuals and infected individuals. Following antimalarial therapy, the observed elevations in metabolite levels decreased. The differences in 1,3-diacetylpropane, N-acetylputrescine, and N-acetylspermidine levels between patients and controls make these molecules promising candidates to be used as biomarkers of infection. An abnormal level of amino acids and their metabolites such as threonine and trimethyl-L-lysine was detected too. These metabolic reprogramming are also related to kidney injury .Mycobacterium tuberculosis. GWAS are a powerful tool for establishing a causal relationship between genetic polymorphisms and specific diseases (The infection triggers a complex host response. The establishment and severity of the disease are significantly influenced by the virulence of the infecting pathogen. Host genetic variations play a key role in the vulnerability to disease and the duration of an infection. Genome-wide association studies (GWAS) have made it possible to identify biomarkers linked to pathogens such as the human immunodeficiency virus (HIV), hepatitis C virus (HCV), dengue virus, malaria, and diseases . Pharmacdiseases .Pharmacogenomics is emphasized in therapies that are approved for the treatment of infectious diseases on an individual basis. Through treatment tailoring and individualization based on numerous intrinsic  and extrinsic  aspects, clinical pharmacology plays a key role in precision medicine and drug discovery. Pharmacogenomics can be useful in detecting drug responders and non-responders, preventing side effects, and adjusting drug dosage. The primary objective of infectious disease management aims at the identification of genetic elements of the pathogen(s). The patient’s pharmacogenetic and/or immunogenetic profiles, however, may give the physician additional insights to help the patient effectively combat infection . Drugs cin vitro diagnostic tests that use biological samples such as blood, sweat, urine or tissue in order to diagnose a patient  is a medical device that is used in conjunction with therapeutic drugs to assess the effect and suitability of drugs on the human body . The CDxic tests . Companiic tests . The firic tests . For theic tests .While metabolomics can be used as biomarkers of disease diagnosis, drugs targeting the host metabolic pathway also possess potential benefit to manage infectious disease. Antimicrobial resistance has grown widely, necessitating the development of additional therapies in addition to new antibiotics. The development of a wide spectrum of host-directed medicines that target and modify biological pathways to achieve a successful therapeutic treatment outcome has been fueled by a renaissance in scientific research methodologies targeting host variables over the past 2 decades rather than directly targeting pathogen components. Host-directed therapies that have wide-ranging effectiveness may also be helpful in treating infectious diseases that have the potential to become epidemic and are related to high mortality cases. The unique advantage of host-directed treatments is that they can stop or slow the emergence of antibiotic resistance . Table 8Identification of robust markers is key to the success of personalized medicine. Metabolomics is used to identify biomarkers associated with disease diagnosis, prognosis and treatment. However, the complex chemical properties of different metabolites and overlapping roles in different metabolomic pathways requires the use of cross-validation platforms . Moreovein vitro diagnostic kits that use data from metabolomics, genetics, and even proteomics. Companion diagnostics, which are in vitro diagnostics kits, are helpful since they can contribute to the detection of pathogenic organisms for which there is no vaccine available. Drawbacks include these tests are targeted assays that may miss other related organisms and that detection equipment may not be available or affordable in all parts of the world. Several companion diagnostics tests have been developed in laboratories (LDTs). Because of this, these are only economical in a specific region. On the other hand, POCTs, which are also in vitro diagnostics kits, are affordable in countries and regions with limited resources. However, for these, accurate biomarkers must be identified; otherwise, the patient will be at risk of receiving an incorrect diagnosis, which would necessitate expenditure at the personal level  for sample collection, storage, preparation, and analysis in clinics to maintain the consistency of the data. In addition to this, the platforms encounter significant difficulties as a result of ongoing improvements to the data pre-processing software. Software development for metabolome study requires the involvement of personnel from bioinformatics, statistics, and computational biology. The majority of software programs are used to preprocess, analyze, visualize, and manage data as well as databases. Despite these, numerous metabolites in the bio-sample remain unidentified.Despite its inherent complexity and challenges, metabolomics is evolving into a crucial tool for defining patient phenotypes concurrently with other -omics platforms . In this


Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease. The integration of omics data such as genomics, transcriptomics, proteomics, and metabolomics has revolutionized our understanding of biological systems by providing a holistic view of the complex molecular processes associated with human health . Genome-GEMs serve as a valuable tool for predicting metabolic capabilities and identifying key regulatory nodes in biological systems, representing a paradigm shift in omics data analysis. GSMM has been used to study gut ecosystems, exploring the metabolic interactions between the host and the microbial communities in the gut ,12,13,14In this review, we explore how GSMM has contributed to our understanding of human metabolism. Specifically, we focus on two important approaches: (a) tissue-specific modeling and (b) host-microbiome modeling. We address key challenges associated with model reconstruction and validation to improve the reproducibility and reliability of GEM-based predictions. In addition, we emphasize the wide-ranging scope and diverse applications of GEMs in the field of precision medicine.Throughout the past three decades, GEMs have undergone continuous evolution, deepening our understanding of metabolic processes and their implications in various biological and clinical studies ,17. GEMshttps://metabolicatlas.org/ (accessed on 23 July 2015)), by integrating and curating the Recon and HMR model’s lineages. The entire development process followed a systematic approach. Moreover, Human1 and Metabolic Atlas were demonstrated to be valuable tools in identifying metabolic vulnerabilities, for instance, in acute myeloid leukemia; predicting essential genes for specific metabolic functions; and estimating metabolic fluxes and growth rates. These advancements enhance the capacity of GEMs to model metabolic pathways associated with health and disease.Over the past 15 years, there has been a focused and continuous effort by researchers to develop and enhance GEMs for human metabolism. Recon 1 was one of the first generic reconstructions of human metabolism, which aimed to integrate and analyze diverse biological datasets, providing a foundation for studying human metabolic pathways . EHMN  is a valuable tool dedicated to eliminating the unwanted sources of variations in RNA-seq data, such as batch effects or confounding factors [RUVSeq estimates and removes these sources of variation, enhancing the accuracy of downstream analyses. Limma and Limma-Voom are versatile tools that are extensively used for normalizing gene expression microarray data and RNA-seq data, respectively. These tools utilize linear modeling and empirical Bayes methods to effectively handle technical variations and batch effects, ensuring robust and accurate analysis. DESeq2 [DESeq2 effectively accounts for sequencing depth and sample-specific biases, enabling reliable differential expression analysis. In addition, edgeR [DESeq2, edgeR, and limma. Furthermore, NOMIS [A vast amount of data makes their integration and harmonization a daunting task. Diverse data types, formats, and measurement scales require meticulous effort with respect to data integration and standardization. The heterogeneity of data sources, i.e., datasets generated from different studies, experiments, or platforms, introduces variations in the data. Managing these technical variations is crucial to ensure the consistency of the results. Moreover, quality control measures such as outlier removal, artifact correction, and noise filtering are undertaken to improve data quality ,37,38,39ComBat  is a spemBat-seq  is an ad factors . Leverag. DESeq2  is a widn, edgeR  is anothe, NOMIS , a normae, NOMIS ,50.Furthermore, omics data may not capture the entire metabolic network or pathway, leading to incomplete coverage of the GEMs. Missing or unmeasured components can limit the model’s accuracy and predictive capabilities, particularly in scenarios where these components play critical roles. Integrating omics data into GSMMs involves complex mathematical and computational algorithms. The process requires sophisticated tools and expertise to handle large datasets, perform data preprocessing, and apply appropriate integration methods, which can be computationally demanding.COBRA  [Microbiome Modeling Toolbox [FastMM  [rBioNet [RAVEN  [Several standalone software suites, such as nalysis) ,52,53, M Toolbox , FastMM odeling) , rBioNet[rBioNet , and RAVetworks) ,58, offeGIMME (gene inactivity moderated by metabolism and expression) utilizes gene expression or transcriptomics data to predict active reactions in a GEM [iMAT , integrates gene expression data to identify active metabolic reactions [MADE  algorithm leverages gene expression data to predict metabolic adaptations in response to different conditions [MADE identifies reactions that are differentially regulated between conditions, shedding light on how the metabolic network adjusts its activity in response to cellular or environmental changes. The E-flux algorithm incorporates gene expression data to estimate the metabolic flux distribution within a tissue-specific GEM [E-flux estimates the distribution of metabolic fluxes and predicts the activity of specific reactions, offering a valuable tool for understanding the flow of metabolites through metabolic pathways in specific tissues. The tINIT  algorithm integrates gene expression data with a GEM to predict active metabolic reactions [tINIT estimates the transcriptional activity of metabolic genes and identifies active reactions within the metabolic network, providing a deeper understanding of tissue-specific metabolic activity by incorporating transcriptional regulation. The METRADE (metabolic and transcriptomics adaptation estimator) framework offers a comprehensive approach for integrating gene and protein expression data, allowing the combination of both types of omics data and enabling a more holistic understanding of biological processes [IOMA  platform provides an opportunity to integrate proteomic and metabolomic data [FASTCORE [FASTCORMICS [mCADRE [PRIME [RegrEX [CORDA [Tissue-specific GEMs have emerged as powerful tools for investigating the complex metabolic processes that occur within specific cell or tissue types ,8,80. Thin a GEM ,82, aimieactions . By combnditions . By consific GEM . By combeactions . By consrocesses . Similarmic data . Other aFASTCORE , FASTCORTCORMICS , mCADRE  [mCADRE , PRIME [E [PRIME , RegrEX  [RegrEX , and CORX [CORDA , and a dX [CORDA ,95. COBRA [RAVEN [KBase [ModelSEED [Automated methodologies have been utilized to develop tissue- and cell-specific models by combining human metabolic reconstructions, primarily from HMR and Recon lineages, along with omics data ,91. SemiCOBRA ,52,53 anA [RAVEN ,58, whicA [RAVEN . AdditioN [KBase  and ModeodelSEED  have beePNPLA3, TM6SF2, and HSD17B13) linked to NAFLD, providing insights into metabolic dysregulation in the liver and its contribution towards NAFLD progression. Notable examples of tissue-specific models include liver GEMs that are specifically tailored to mimic the metabolism of the human liver ,8,98,99.To unravel the contributions of various brain cells in neurodegenerative diseases, researchers have employed the brain-specific GEMs of neurons, astrocytes, and microglia, along with multi-omics data ,101. TheAdipocyte-GEM has been instrumental in studying the metabolic processes in adipocytes, which are the cells primarily responsible for storing and releasing energy as fat . By inteserine palmitoyltransferase (SPT) in promoting the production of proinflammatory cytokines by Th17 cells [Furthermore, by incorporating gene expression data and lipidomic experiments into HMR2, cell-specific GEMs were developed to study the activation and differentiation of CD4+ T cell subsets. The study revealed specific metabolic changes during CD4+ T cell activation and differentiation. Importantly, the significance of ceramide and glycosphingolipid biosynthesis pathways in Th17 cell differentiation was highlighted. Moreover, model predictions were validated using gene knockdown experiments, demonstrating the role of In addition, a comprehensive metabolic reconstruction of human small intestinal epithelial cells (sIECs) was assembled and curated . The sIEIn a study conducted by Gustafsson et al., they introduced a novel approach for generating cell-type models by integrating single-cell RNA sequencing (scRNA-seq) with GEMs. By clustering single-cell RNA profiles, the authors demonstrated the effectiveness of combining scRNA-seq and GEMs in advancing the understanding of human metabolism. This method shows promising potential, particularly as single-cell datasets and GEMs become increasingly accessible . A schemRecently, multi-tissue modeling frameworks were developed to study the interactions between cell and tissue-specific models ,110. BorThe emergence of high-throughput “meta”-omics technologies has significantly improved our understanding of the gut microbiome and its significance in human health and disease. Nevertheless, the vast amount of data generated by gut microbiome research demands the development of innovative computational tools and mathematical models, while approaches such as 16S rRNA amplicon sequencing and whole-genome shotgun metagenomics sequencing (WGS) have been utilized for microbiome profiling ; these gAGORA  project, which successfully conducted semi-automatic genome-scale metabolic reconstructions of 773 human gut bacteria, encompassing 205 genera and 605 species [AGORA2, an expanded resource of human gut microbial metabolic reconstructions incorporating 7302 strains, was developed [AGORA2 facilitated personalized modeling by predicting diverse drug conversion potential in the gut microbiomes of patients with colorectal cancer vs. controls. It outperformed other resources by predicting microbial drug transformations and enabling the personalized modeling of gut microbiomes in colorectal cancer patients. By using the AGORA framework [AGORA reconstructions are publicly accessible via the VMH  database [GSMM has emerged as a valuable approach for investigating the metabolic interactions between microbial communities and their host in gut ecosystems ,127,128. species . Recentleveloped . AGORA2 ramework ,16, metaBiGG models [KBase [ModelSEED [COMET [BacArena [dOptCom [MatNet [DyMMM [MCM [CASINO [CASINO, for instance, successfully predicted interactions along the diet-microbiota-host axis in obesity and overweight individuals [In addition, G models  and the G models ,62 serves [KBase  and ModeodelSEED  integratD [COMET , BacArenBacArena , dOptCom[dOptCom , MatNet  [MatNet , DyMMM [t [DyMMM , MCM [13MMM [MCM , and CAS [CASINO , have beividuals . A comprividuals .Of note, the development of personalized microbiota models tailored to individual subjects is an area of recent interest. Leveraging metagenomics, meta-transcriptomics, meta-proteomics, and metabolomics data, estimating the pathway activities within the gut becomes possible, offering an approximation of the metabolic functionality specific to the gut microbes under defined conditions ,138. PerHMR 2.0 database and a task-driven model reconstruction algorithm (tINIT), to develop personalized GEMs for six HCC patients based on proteomics data. These personalized GEMs served as a foundation for identifying potential anticancer drugs using the concept of antimetabolites. To assess the toxicity of each antimetabolite, the authors examined the in silico functionality of 83 healthy cell-type-specific GEMs. The study predicted several antimetabolites that could effectively inhibit tumor growth in all HCC patients.The integration of high-throughput omics data with GEMs enables a deeper understanding of the molecular mechanisms underlying disease and facilitates the development of personalized treatment strategies. By constructing patient-specific metabolic models based on individual genomic, proteomic, and transcriptomic data, GEMs can predict metabolic alterations that are associated with specific diseases and identify potential therapeutic targets. In a study by Lewis et al. , personaA personalized whole-body metabolic (WBM) reconstruction serves as a powerful tool that provides a comprehensive view of an individual’s metabolic profile by considering the intricate relationship between different biological systems. A WBM reconstruction enables a deeper understanding of how metabolism is influenced by factors like diet, lifestyle, and gut microbial composition; it facilitates the investigation of individual-specific responses to dietary interventions, drug treatments, and disease conditions . In a grIn summary, GEMs serve as valuable tools in precision medicine, providing a multifaceted approach to understanding disease mechanisms, facilitating biomarker discovery, and guiding personalized treatment strategies that are tailored to specific patient groups.GEMs have certain limitations that need to be considered. In the context of human metabolism, GEMs face the challenge of incomplete knowledge about metabolic pathways and regulatory mechanisms. GEMs rely on the available genomic and biochemical information, which may result in gaps in the model, leading to an incomplete representation of metabolic pathways ,26. WhenAnother limitation is the static nature of GEMs since they primarily represent steady-state conditions and may not fully capture the dynamic and temporal changes in cellular metabolism. This means that they might not accurately model changes in metabolite concentrations and reaction rates over time . To overGECKO method has been developed to construct GEMs with enzymatic constraints by incorporating kinetic and omics data [cat values. By constraining protein abundance in this manner, GECKO effectively reduces flux variability and enhances the accuracy of predictions.Estimating parameter values in GEMs is subject to uncertainty ; the absics data . This isA common limitation of GEMs is their lack of tissue and cell-type specificity. When generic metabolic networks are reconstructed to represent specific tissues, they may not fully capture the unique metabolic characteristics of each tissue or cell-type. To address this issue and improve the contextualization of GEMs for specific conditions, the integration of multiple omics datasets becomes crucial. While algorithms have been developed to generate tissue-specific models, most rely on a single omics dataset that may not provide a comprehensive representation of cell/tissue metabolism. Therefore, there is a need for new tools and methods that effectively integrate multi-omics data to create more accurate models. Recently, a promising approach using scRNA-seq data was introduced, which provided insights into the heterogeneity and interactions within a specific cell type . To ensure the biological relevance of GEMs, it is important to validate their predictions by comparing them against experimental data and existing knowledge. By conducting targeted experiments, researchers can assess the accuracy and reliability of the model’s predictions and gain further insights into the underlying biological processes. The experimental validation of GSMMs involves various techniques, such as in vitro assays, cell culture experiments, and animal studies. These experiments aim to measure and compare metabolic fluxes, metabolite concentrations, enzyme activities, and other relevant parameters predicted by the model against experimental observations. Using these validations, researchers can evaluate the model’s ability to capture the complex dynamics of cellular metabolism and assess its predictive power. Furthermore, the experimental validation of GSMMs also involves testing specific hypotheses generated by the model. Researchers can design experiments to manipulate specific genes, enzymes, or metabolites within the metabolic network to confirm the model’s predictions and understand the functional consequences of these perturbations.mummichog framework, which enables the direct prediction of pathway activity using spectral features from metabolomics data [Lilikoi, developed by AlAkwaa et al., specializes in personalized pathway-based classification modeling using metabolomics data [Lilikoi incorporates metabolomic profiles to generate personalized pathway profiles and identify pathways significantly associated with specific disease phenotypes. Machine learning (ML) techniques have demonstrated their ability to identify metabolic pathways from chemical compounds; various ML methods have also been employed for metabolic pathway prediction, analysis, and modeling ,158,159.ics data . Anotherics data . Unlike There is a growing interest in combining ML techniques with GSMM ,161,162.DeepMetabolism, a knowledge-based deep learning (DL) system that predicts cellular phenotypes from transcriptomics data using a combination of unsupervised and supervised approaches [Furthermore, GSMM and ML methods have been applied to determine metabolite secretion, flux quantification, protein turnover rate estimation, gene essentiality, metabolic gene prediction, and the assessment of a medication effect ,162,163.proaches . The fraproaches ,161,170.To summarize, ML techniques together with GSMM can unravel the complexity of biological data. ML enables the consolidation of large omics datasets, extracting key features that are linked to the desired outcomes. In contrast, GSMM offers a mechanistic comprehension, generating testable hypotheses regarding the specific metabolic processes taking place in cells, tissues, organs, or microbial communities via the incorporation of omics data. The integration of ML and GSMM holds immense promise in achieving a comprehensive understanding of human metabolism in healthy and diseased states.Today, the field of GSMM has witnessed widespread application across various domains, owing to the abundance of biological data, advancements in automatic reconstruction tools, and the introduction of novel mathematical modeling techniques. As GEMs continue to evolve, they will encompass a broader range of biological pathways and gene-protein-reaction (GPR) associations. To further enhance their capabilities, incorporating additional biochemical information into GEMs becomes necessary, i.e., going beyond metabolism. This includes aspects such as protein allocation, cellular macromolecular composition, and detailed structural information about the proteins. Notably, certain molecular processes such as enzyme-substrate interactions, protein-protein complex structures, and post-translational modifications require careful consideration.The integration of omics data into GEMs has revolutionized our understanding of biological systems. However, there are still challenges that need to be addressed to improve the reliability and reproducibility of GEM-based predictions. Efforts should be directed towards standardizing and harmonizing omics data, developing automated pipelines and tools for data integration and normalization, and incorporating ML algorithms to enhance accuracy and robustness. ML algorithms can play a crucial role in automating the reconstruction of GEMs by leveraging omics data and existing metabolic network reconstructions. Furthermore, they can be utilized to validate and refine GEMs using experimental data, ensuring an accurate representation of metabolic characteristics. ML algorithms can enable the integration of diverse omics data, allowing for the accurate prediction of active metabolic reactions under specific conditions. This is particularly important for developing high-quality models that are specific to cell types or tissues. Additionally, the incorporation of single-cell omics data enhances the accuracy of GEMs, enabling the construction of cell-type-specific models and expanding our knowledge of cellular metabolism.In conclusion, the integration of GEMs with multi-omics data, powered by the incorporation of ML approaches, has the potential to significantly enhance our understanding of biological systems and their underlying mechanisms. These advancements pave the way for precision medicine and the development of targeted interventions for human health and disease.


Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies. Disentangling how evolutionary history and environmental adaptation shape metabolic phenotypes is an open problem, especially for microbes whose phenotypes cannot be determined directly and are inferred from genomic information. Here, Ramon & Stelling propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and link genotype and environment to phenotype for 245 bacterial species. Understanding the drivers of metabolic functional diversity requires disentangling links between metabolic gene repertoires, realized metabolic phenotypes, taxonomy to represent evolutionary history, and environmental characteristics. However, inferring these links, and ultimately determining how all factors combined shape metabolic phenotypes, is an open problem3. One challenge is that, especially in complex microbial communities, few phenotypes can be determined directly4. Instead, potential phenotypes are often inferred from genomic information5. Analyses of the global ocean microbiome illustrate common approaches based on metagenomics data: to infer metabolic functions from gene repertoires6, or to use species-level functional annotations7, which are then associated with taxonomy and environment. However, this does not consider interdependencies of genes, cellular networks their products establish, and phenotypes. Since network context shapes gene functions, and the whole network generates metabolic phenotypes. genetic epistasis8 and variable trait relations along the phylogeny3 indicate a need to incorporate interactions in cellular networks.Metabolic reactions as well as entire metabolic networks establish function by yielding phenotypes in terms of metabolic flux distributions inside the cell and in the cell’s interaction with the environment. Such metabolic phenotypes of potentially complex cell communities impact many areas, including biogeochemical cycles9, their topological analysis can predict environments11, and they were instrumental in analyzing enzyme evolution12, but all for single species. However, there are only a few studies that employed model-predicted phenotypes beyond the species level15. A recent, comparative GSM-based study of bacterial phenotype evolution13 did not make links to genotype or environment, while other comparative studies linked specific 15 or generic (sampled)14 environments to species and ecological relations, but not to detailed metabolic functions. To bridge the corresponding gaps, here we exploit the concept that genotype–phenotype relationships connect differences at the genomic level (and in environments) with differences in phenotypes16. Specifically, we quantify how perturbations in enzyme-catalyzed reactions affect metabolic fluxes to compare identical biochemical reactions and subsystems across species with varying metabolic network structures.Genome-scale metabolic network models (GSMs) make these dependencies explicit. They can reliably predict metabolic phenotypes17 to characterize perturbation effects in metabolic networks . Here, we compute absolute sensitivities, which are obtained analytically (see Methods) and do not assume a specific operating state of the network or a specific environment17. These sensitivities are considered to capture adjustments to infinitesimal perturbations; they are valid unless a network’s operating state is exactly equal to specific constraints such as the availability of nutrients in a specific environment, which is unlikely. To compare two common reactions  in two GSMs, we correlate the sensitivities of all common reactions to perturbations of these two reactions . In particular, many reactions have a low Jaccard index, but a high sensitivity correlation because sensitivities account for the whole network’s response to a perturbation; they can distribute over large graph distances  such as the Jaccard index. For this purpose, we quantified the similarities of the neighborhoods of each common reaction in the ces Fig. . Jaccardces Fig. .E. coli and B. subtilis  because B. subtilis can adapt by active riboflavin transport across its membrane, but E. coli lacks this transport21. Correspondingly, the correlation increased to 0.71 when augmenting the E. coli GSM with riboflavin exchange and transport  in lis Fig. , but it n = 154 orthologues, n = 1’140’254 non orthologues). In addition, the correlations span a large range of values, confirming that orthologs are not functionally equivalent22, despite often catalyzing the same biochemical reaction.To assess the plausibility of reaction-level predictions as well as the potential of comparing biological functions with sensitivity correlations in more complex networks, we next used human and yeast GSMs. We analyzed Enzyme Commission (EC) number similarities between pairs of enzymes, defined as the number of shared levels in the four-level EC number classification. As expected, enzyme pairs with the highest EC number similarities showed higher sensitivity correlations than more unrelated pairs Fig. . However23 such as here  and give identical results for the applications  could have too similar functions to be resolved unambiguously by sensitivity correlations. A previous method for functional network alignment25 reported 85% correct alignments for 100% common reactions when aligning the yeast GSM iMM90426 with itself. In contrast, more than 92% of the metabolic reactions were correctly aligned even when using only 1% of the reactions to compute sensitivity correlations . Functional alignment is challenging because even phylogenetically closely-related organisms can have very different metabolic repertoiresons Fig. . Importa27 that represent 15 species from all kingdoms of life. Consistent with a previous GSM-based analysis of phenotypic evolution13, average sensitivity correlations decrease with increasing species divergence time and they saturate at high divergence times .With alignments being at the basis of phylogenetic analyses, we hypothesized that the sensitivity concept could extend to such comparisons over multiple networks. We define the global similarity of two GSMs as the average sensitivity correlation of all common reactions (Methods). To validate this measure, we compared the yeast model with a randomly reduced version of itself. As expected, both the Pearson and copula correlations decrease as the number of deleted reactions increases Fig. . We thenmes Fig. . HoweverMethanosarcina barkeri), but not with others. For example, the metabolically extreme organisms M. barkeri (a methanogen) and Thermotoga maritima (which does not produce ATP when growing on sulfur) are outliers. Yeast clusters with Gram-positive bacteria (Mycobacterium tuberculosis and B. subtilis), and not with multicellular eukaryotes. Note that the two yeast models with different network coverage clustered together, indicating a certain robustness to GSM accuracy or completeness. We also confirmed that the inferred species tree is robust to the tree construction method  diversity  GSMs for bacteriaity Fig.  and reprity Fig. . Howeverity Fig. , confirm28 annotations of subsystems and Pearson correlations. Importantly, this classification is stable over evolutionary distances between species  to aid interpretation and because of heteroskedacity of sensitivity correlations per reaction , sensitivities indicate conservation Fig.  and the 35, but functional conservation vs variable repertoire for arginine and proline. There, alternative biosynthesis pathways exist across species, but the functional requirement of providing the amino acids is conserved. We predict especially proline biosynthesis to be functionally conserved  and variable  subsystems agree . Note that these feature annotations based on widely used databases (see Methods) are incomplete and subject to uncertainties that may affect the analysis. Because taxonomy and environment almost always jointly contribute to predicted functional conservation, we used joint estimation by linear regression to untangle their influences .Environmental contributions to metabolic function cluster according to previously defined categories of environmental variabilityher Fig. . However45, considering that reduced modularity implies reduced module function45. For oxygen requirements, we capture diversity of anaerobic metabolism, and facultative anaerobes as generalists separate. As a negative control, Gram status has no significant influence – it is part of taxon definitions.Predicted metabolic functions for bacteria living at low-to-average temperature are conserved, but variable for thermophiles. Functional variability of thermophiles is consistent with previous analyses that showed metabolic networks of thermophiles to be less modular than those of other bacteria5. Our approach predicts the experimentally determined impact of taxonomy  vs. environment (low variability in soil) on metabolic phenotypes in soil ecosystems4  phenotypes according to evolutionary history (represented by taxonomy) and environment are largely unknown and corresponding experimental studies are rarems4 Fig. . It alsoms4 Fig. , we expems4 Fig. . Novel h46.Estimates of normalized sensitivity biases adjusted for taxonomic and environmental influences Fig.  overall 47; we predict these subsystems as significant for human-associated habitats  in marine environments. This could reflect both the importance of taurine as C- and N-source in general, and the depth-dependence of the availability of taurine and alternative nutrient sources50.To take marine habitats as another example, we predict conserved siderophore metabolism Fig. . To scavSalmonella enterica and E. coli involved methionine and galactose51; those subsystems of γ-proteobacteria are variable among mostly conserved amino acid and carbohydrate metabolism , ambiguities in widely used subsystem annotations, and potential biases in the collections of analyzed models limit the biological accuracy of these predictions. For example, detailed reaction-level comparisons of B. subtilis and E. coli required manual checking. Hypotheses generated by our integrated analysis of bacterial metabolic diversity therefore require empirical validation – and they can simultaneously guide corresponding studies.We propose sensitivity correlations as a measure to quantify effects of perturbations in metabolic networks to link metabolic repertoires, functions, and their relations to evolutionary history and environment. In contrast to prior work, we do not need to define13C metabolic flux analysis53 can enable systematic testing of sensitivity-based predictions for individual enzymes, provided targeted  perturbations can be introduced and a sufficient number of common reaction fluxes can be resolved. For example, one could investigate iso-enzymes predicted to have a most dissimilar effect on network operation in humans and (pathogenic) yeasts as candidates for novel antibiotics. Second, more indirect experiments could be designed that use targeted  or untargeted  mutations and indirect readouts such as growth on different nutrient sources for different bacteria to test predictions on subsystem functional conservation with largest effect size. Finally, studies of bacterial ecology in different natural habitats4 could be designed on our corresponding predictions on drivers of metabolic diversity, for example, focusing on the pronounced predicted differences between β-/γ- and α-/ε-proteobactria.We envisage different levels of empirical validation. First, recent advances that increase accuracy and throughput of 54. Incorporating quantitative characteristics of environments could lead to finer ecological resolution55. By allowing such refinements directly, our framework will be instrumental for detailed and systematic studies of relations between metabolic repertoires, phenotypes, evolution, and environment.We consider the concept of sensitivity correlations as the main contribution of this work, and the range of applications presented as proofs-of-principle. Increased species diversity and thereby statistical power could increase taxonomic depth and functional specificity, for example, regarding ‘accessory’ genomes that enable intra-species metabolic exchanges in microbiomesBacillus subtilis (iYO844), and Escherichia coli (iJO1366) were retrieved from the BIGG database56. We added missing reactions to iYO844, namely a nitric oxide synthase reaction and exchange reactions for nitric oxide. 16 GSMs  as well as pairwise genetic distances between organisms from ref. 13. We obtained the reference bacterial phylogenetic tree from ref. 40.GSMs of yeast (iMM904), human Recon1), , Bacillu58. As a sanity check, we verified that the biomass production before and after the removal of the blocked reactions was identical. When this could not be verified, the original model instead of the reduced model was used (24 models out of 321). The cases where the model reduction did not work are indicated in Supplementary Data For every SEED model, we checked that the biomass reaction carries a strictly positive flux when exchange reactions allow uptake of every possible exchanged metabolite. Under this condition, whenever possible, we removed the structurally blocked reactions (reactions that cannot carry any flux) using flux variability analysis 59 as well as the GTDB taxonomy42. Mapping between models and the corresponding databases was performed by species names, including by species synonyms retrieved from the MACADAM database60. For habitat and physiology annotations, we used fusionDB61. For species with multiple habitat assignments, we automatically identified the main categories , and subsumed entries that could not be categorized or of low frequency for SEED models under ‘other’. With available evidence , we classified ‘host’ as ‘human’ more specifically . To obtain corresponding KEGG28 subsystem annotations, we relied on reaction aliases provided by ModelSEED  method18. Here, we computed absolute structural sensitivities that neither require the definition of a reference flux distribution (as in MOMA18), nor the definition of an environment through constraints on exchange fluxes . Hence, absolute structural sensitivities characterize network responses independent of a cell’s metabolic state or environment.Structural sensitivity analysis17. For model Specifically, we first characterized each reaction in each GSM using structural sensitivity analysisThe sensitivity represents the effect of a perturbation on any reaction relative to the strength of the perturbation. Correspondingly, we define the vector of sensitivities of each reaction of model This vector has elements To make sensitivity computations efficient, GSMs were pre-processed by removing blocked reactions. For reaction pairs in pairs of GSMs, we characterized functional similarity (distance) by correlations (lack of correlations) of absolute structural sensitivities over all common reactions after perturbing a single reaction. For gene-based analysis, we computed sensitivities by simultaneously perturbing all reactions associated with a gene (via the GSM’s gene-reaction associations) with the same perturbation magnitude.To define the similarity of responses of two models We extend these concepts from a single reaction This is a quite natural extension because The average model similarity of two GSMs is then defined as the average sensitivity correlation over all common reactions:This directly yields an average dissimilarity for pairwise GSM combinations that is interpretable:The Jaccard index quantifies the similarity of two sets, and we use it to compare the reaction contents of two GSMs To expand this concept to quantify the context similarity of a reaction The Jaccard index for reactions then becomes:To account for potential biases due to different sets of common reactions in GSM pairs, we compute normalized similarities asHere, ata Fig. , these dWe expand this normalization to more general similarities by replacing To study variability of metabolic subsystems across all models, we partition the set of unique reactions into disjoint subsets For sensitivity-based analysis, the average normalized bias (i)Subsystem mean by reaction: Distribution of (ii)Subsystem mean, alignments: Distribution of (iii)Subsystem mean, bootstrap: Distribution of The classification of subsystems into categories relied on three alternative approaches:For analyses based on reaction sets, we define a corresponding measure to compare subsystems For a subsystem classification that is consistent with the sensitivity-based approach, we compute normalized Jaccard indices To validate sensitivity-based predictions with independent measures, we relied on Enzyme Commission (EC) numbers. They provide a hierarchical numerical classification for enzyme functions composed of four levels (numbers) that represent a progressively finer classification. To compare two enzymes, we define an EC similarity level according to the maximal level up to which their EC numbers coincide. This leads to five levels of similarity between zero (no similarity) and four . When several EC numbers were mapped to the same enzyme, we used the maximal similarity level of all possible pairwise comparisons. We proceed identically when several EC numbers were mapped to one reaction through its catalyzing enzymes.To globally characterize the aligned reactions in two GSMs 66, which selects the set of pairs of reactions with total minimum sensitivity distance. Compared to ref. 25, our method is faster  because it requires only one optimization per reaction. It returns a set of mapped reactions with their individual sensitivity distances, and a set of unmapped reactions in the larger GSM. For validations, we aligned the yeast GSM with a copy of itself, after randomizing the order of reactions in the copy and varying the number of common reactions. We measured performance by the number of correctly aligned reactions.Sensitivity distances between all possible pairs of reactions characterize all possible mappings between reactions in two GSMs. The assignment problem corresponds to identifying the best reaction mapping using solely sensitivity distances, with blinded reaction identities. We solved it using the Jonker-Volgenant algorithm67.We characterized the pairwise distance between GSMs by sensitivity-based average dissimilarity as well as Jaccard index distances. Trees for analyses of habitat and taxonomy used estimated regression coefficients (see below). We applied the Unweighted Pair-Group Method with Arithmetic mean (UPGMA) for all tree constructions. The phylogenetic tree based on divergence times was retrieved from TimeTreeWe analyze metabolic flux phenotypes with respect to five classes of features . The features in each class can be mutually exclusive , or not . Importantly, the feature classes are not independent . Therefore, all features have to be accounted for simultaneously in the analysis.F, where Fl is the feature vector for model Fl, We encode features in row feature vectors X with one row To quantify how similarities depend on features, we estimate linear models of the form Y are Y are To assess the impact of features on average normalized model similarity, the elements of the response matrix To assess significance of normalized biases for subsystem classification, two tests were used as indicated in text and figures: (i) two-sided Wilcoxon signed rank test with For taxonomy, habitat, and physiology analysis, we evaluated the impact of features on subsystem variability via significant coefficient estimates of significant linear regressions. Significance of linear regressions was determined by F-tests against the constant null model without correction for multiple testing  and Gurobi Optimizer (version 8.1.1).Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Reporting Summary


Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients’ health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable  that represents the system’s coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models  were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79–0.95  in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.The online version contains supplementary material available at 10.1007/s13755-023-00218-x. In the current understanding, a metabolic disorder can be traced to a particular set of biochemical reactions and metabolites, whose abnormal changes lead to syndrome manifestation and progression. These altered metabolic states typically have complex causes and develop due to individual genetic variations, changes in the environment, behavioral factors, lifestyle, or as a side effect of disease treatment , 2. As tDespite advances in large-scale and high-precision techniques for metabolite measurements and their successful applications in identification of certain inborn metabolic disorders, quantifying the progress of chronic diseases remains challenging –5. To enStandard enzymatic kinetics analysis is applied to quantify the effects of several genetic variants of the arachidonate 15-lipoxygenase type II, an enzyme with a potential role in the development of coronary artery disease . The autIn a clinical study of glucose disposal with 88 healthy volunteers receiving four types of insulin infusions, kinetic parameters of glucose concentration for an M–M model of glucose metabolism were determined . The autStudy  applied At the same time, kinetic models have been proven of limited value for clinical relevant applications, because the models require a large number of biochemical reaction parameters, the physiological values of which are difficult to determine in a clinical setting.Mathematical methods developed within the metabolic modeling research area, such as enzymatic kinetic models, metabolic flux analysis, or stochastic models, were proposed to analyze metabolic systems. Traditional enzymatic kinetics models using the Michaelis–Menten and the Briggs–Haldane equations , have meIn contrast, MFA allows a more coarse-grain representation of a complex biochemical system of reactions and binding processes as a composition of metabolic fluxes connected with stoichiometric and mass balance relations –23. ThisTypically, MFA computational models in clinical application studies include the basics of the carbon metabolism network  complemented with selected pathways from the amino acid, lipid or other types metabolic or signaling network elements if necessary. With detail up to individual reactions, the models are just focused of specific aspects of the networks behavior and do not look into potentially more global metabolism changes. The limited opportunities provided by such type of modelling are illustrated by the study from Gregory et al.  who explThe work of Karlstädt et al.  is an exThe human health state, being a specific case of a phenotype at a given point of a disease development trajectory, can thus be analyzed using MFA in principle. Yet, at present, the formalism is not ready for this purpose. There are several issues. First, the number of parameters in MFA models grows linearly with the number of reactions. Although this makes MFA a more scalable approach to build mechanistic models of complex systems, the number of parameters in biochemical reaction-oriented, detailed models is still enormous and finding proper values for them is problematic. Second, the metabolite concentrations typically used in MFA calculations are not commonly available from clinical laboratories. Third, at the same time, many biochemical and physiological parameters, such as urine albumin or blood pressure, currently measured in clinics, are not incorporated into MFA models.To reduce the demand on the input data scale, we apply GMFA to a reduced, coarse-grain metabolic network, wherein individual metabolic entities/fluxes are grouped in accordance with biological processes.We map non-metabolic clinical modalities and measurements onto the metabolic network.To make health states and their progression comparable across patients, we replace the time variable with the extent variable quantifying the degree of progression between two extreme health states—the healthy state and the fully manifested disease state, assuming quasi-linearity of metabolite concentration changes along the trajectory of states.We applied our methodology to the analysis of observational data of two cross-sectional cohorts of diabetes type 2 patients. We constructed a simplified metabolic map including key components of glucose and lipid metabolism, onto which physiological characteristics, for example, the pulse wave velocity and carotid intima-media thickness were mapped. With statistical rigor, we show that fluxes calculated with the data from patients are predictive for the development of individual ophthalmic complications in type 2 diabetes.The GMFA approach introduced in this work implements a number of methodical innovations that enhance the suitability of MFA for clinical applications. To evaluate the applicability of the proposed methods to clinical data, we studied the EVAS multi-ethnic cohort of 289 patients with type 2 diabetes visiting a tertiary medical center in Singapore  (data codiq080).In the EVAS patient cohort, we examined data with regards to diagnosis of ophthalmic complications of diabetes at baseline, as well as their development over a period of up to 3 years after enrolment into the cohort study Table . In bothThe summary biochemical characteristics of the patients are listed in Table M metabolite species (N reactions/connections (enumerated S (size M metabolites as well as of fluxes N reactions. The concentrations and fluxes changing in time (t) are related via the linear system of N flux balance equations:A and B is commonly quantified by the variable expressing the number of molecular transformations of one particular type that has to occur while the system transforms from state A to state B. By analogy with a single biochemical reaction progress, where this number is represented by a scalar variable Following the MFA theory, a metabolic network system described by a set of t, fluxes and metabolite concentration changes can then be expressed with respect to changes expressed in the units of the extent t and metabolite concentrations A and B, respectively) can be described as follows:A and the final state B. If we observe an ensemble of distinct metabolic systems  observed at states A and B, their evolution on the extent scale Similar to Eq. If in the system, whose metabolic state evolution is linear . The construction and primary analysis of digital twins was implemented in Python (v.3.8) programming language.The metabolic map and the generalized stoichiometry matrix were designed to include the metabolites measured in the study, to quantify the fluxes through the major biochemical and physiological pathways implicated in diabetes . We illustrate this approach by finding a stoichiometric coefficient connecting the metabolic variable oxidized low-density lipoprotein (ox-LDL) with the physiological variable pulse wave velocity (Online Appendix B).For each patient, a personalized digital model was constructed and initiated with all available metabolite and physiological readings measured from the patient. The missing data were imputed as population averages. Then, the best fit generalized flux vectors were obtained by the quadratic optimization procedure as the solution of the system of stoichiometric equations that minimizes the squared deviations between the vector of patient’s readings and the vector of the metabolite concentrations and physiological values in the model. Hereby, the patient’s initial data were used as soft constraints. Their respective values in the model were permitted to deviate within ± 20%. The constraints limiting the permitted flux values were introduced based on reference literature (see Online Appendix B for more details).The state of a given digital twin is defined by the vector of all generalized fluxes in the network at a given generalized extent. The proximity of two states can be assessed by applying a distance metric. We tested the Euclidean distance as well as the health state distance, a manifold-based metric (see Online Appendix A for the formulas).A) was selected to represent a healthy individual with demographic characteristics similar to the studied population. The end-point state (state B) represented advanced type 2 diabetes, where common complications, such as hypertension and nephropathy are fully manifested. Each state was characterized by measured values of key metabolite concentrations and quantitative physiological parameters of vascular health  table files containing cross-sectional data arranged by year and by the variable group. The files were converted to CSV tables, using the R programming environment. The data were aggregated across all the years and the available variables and saved in a single CSV file. The variables were interpreted according to the official description of the tables as categorical or numerical. All the statistical methods were implemented using the R programming environment (R v.3.4.4).Each component of the metabolite and flux vectors was then evaluated as an independent predictor of the patient classification by one of the following phenotypes representing diabetic complications detected by the sample collections time point: diabetic retinopathy or cataract. Statistical associations between each vector component and each phenotype were assessed using the Wilcoxon–Mann–Whitney non-parametric test with the null hypothesis of the vector components being equal between two groups patients corresponding to two phenotypic (disease) states. Multiple hypothesis testing bias was controlled via the false discovery rate assessment. The false discovery rate was calculated according to the Benjamini–Yekutieli procedure , and theglm.fit function) [To identify at the baseline and to predict the development of the ophthalmic complications at the follow-up time points, we employed the binomial logistic regression model, implemented in the R v.3.4.4 standard library as a Generalized Linear Model (the unction) .The magnitudes and signs of individual’s generalized fluxes, obtained at the baseline time point were used as the inputs of the model. The diagnosis at the follow-up time was taken as the expected output to obtain the best logistic regression coefficients parametrizing the model.pROC [The quality of the model was assessed using the Receiver Operating Characteristic (ROC) and the c-index , as implemented in the R package pROC , 35.To further increase the specificity of the models, samples with the intermediate values of the calculated logistic function (the “twilight zone” exclusion) were removed from the analysis. We tested the results obtained from removing the samples from the following percentile ranges: 45 to 55, 40 to 60, 35 to 65, 33 to 67, and 25 to 75. At each range, we obtained ROC estimates as described above.To balance the test design to ensure robustness, we sampled 50% of positive cases and an equal number of negative cases to train the logistic model We used the other 50% of the cases to test it. We repeated the sampling 50 times to obtain median estimates of the ROC curves.To quantify the correlations between the health state distance metric and diagnosis of the patients, we applied a binomial logistic regression model in R v.3.4.4 . We usedWe considered ophthalmic complications of type 2 diabetes of our patient cohort  to exploAnalysis of metabolite concentrations and physiological measurements . Studies report equivocal effects of protein consumption on diabetic retinopathy development . At the Development of cataract was significantly associated with the extended flux leading from the reactive hyperemia index (RHI) characterizing the vascular health state, to carotid intima-media thickness (P = 1.1e−4). Notably, we also found that the flux leading to induction of ferritin and the flux converting bilirubin to bile, were also associated with cataract development . Both associations were not detected on the level of individual metabolites. Recently, evidence was found that blood bilirubin might be a compound protecting retina from degradation in diabetes patients –52.Thus, we observed that with respect to specific clinical phenotypes, statistical results obtained with metabolic and physiological flux models are not contradicting the results obtained with metabolic and physiological variables alone. Moreover, flux models have the potential to provide more biomarkers characterizing the disease and to improve statistical power. The ability of flux models to describe additional details of long-term dynamics is complementary to the descriptive power of metabolites alone. These conclusions confirm the applicability of the computational framework provided by GMFA to address the practical needs of integrative biochemical, physiological and clinical data analysis for holistic assessment.We used the extent variable Diabetes durationHbA1c value evolutionThe Euclidean distance between the patient flux profilesThe health state distance, the manifold-based metricThe results of this analysis are presented in Tables We analyzed the influence of choosing any of four definitions of the distance between health states:When considering all diabetes patients Table , we findHowever, qualitatively different performances of the four distance metrics are observed if we explore the development of diabetic complications. In Table Results for the diabetic patients with cataracts  can correlate with the trend towards an ophthalmic complication. As the output of the model on each patient, the value of the logistic function, ranging between 0 and 1, quantified the probability of the present patient’s diagnosis. This value is plotted against the flux distance metric in Fig. entclass1pt{minimaentclass1pt{minimaUsing the distance metric of the flux profiles and the generalized flux-driven syndrome identification, patients with high and low risk of complications can be classified into the respective high- and low-risk groups. Here, we assigned the patients to the high-risk group by their proximity to the diagnosed syndrome state if they fall into the upper quartile simultaneously by (i) their logistic function value and (ii) the distance (see the distance metric above) between their current health states (flux profiles) and the complication-free state. This group corresponds to the top-right quadrant in Fig. To characterize the high- and low-risk groups, we calculated their median flux profiles and displayed them as graphs with edge weights (representing the fluxes magnitudes) proportional to the normalized deviation from their median values in the entire patient cohort. These values are displayed in a graphical form in Fig. Thus, we can conclude that the distance between a patient’s flux profile and a typical flux profile of a complication-free diabetes patient is indicative of the degree to which a particular diabetic complication is  manifested in a given patient.Together these results show that, for a given diabetic complication, it is possible to find such a disease progression metric that would better correlate with the evolution of key progression characteristics than direct clinical parameters, such as diabetes duration or HbA1c values. Further analysis of the flux profiles may uncover the mechanisms underlying syndrome development.To explore the potential of using the GMFA-based digital twins as diagnostic and predictive tools, we used them as inputs of logistic regression models for detection existing and predicting future occurrence of ophthalmic complications of type 2 diabetes in the EVAS dataset  and cataract . The results are reported in Fig. Our analysis of relationship between the patient’s health state distance and the ability of the logistic model to identify the patient’s risk group Fig.  suggestsTo validate our findings in an independent patient cohort, we analyzed the National Health and Nutrition Examination Survey (NHANES) dataset, which included patient survey and measurements data collected across the United States . From thHaving demonstrated the evidence of GMFA-based digital twin models indicating the presence of T2DM ophthalmic complications, we tested if the models can be used to predict development of these complications in the future, within 3 years from the baseline time point. The results are presented in Fig. We found that the GMFA models can predict all retinopathy  is a flexible method of systems biology that have been tested in applications that span from bacterial models to higher eukaryotes –23. HowePrecision medicine and digital health initiatives are driving the adoption of advanced computational tools for holistic analysis and interpretation of individual patients’ physiological and health states. At the same time, clinical science is trending towards a focus on an integrative picture of personalized health. Today, a holistic assessment of a person’s health state implies integration of detailed profiles of multiple physiologically inter-connected subsystems: metabolism, cellular signalling, immune responses, nervous system, body structure and microbiome. At present, there is no methodological framework to unify quantitative modelling of all these components.By pooling metabolites and fluxes along the biological processes and mechanisms, we both keep the biological logic of the systems and, at the same time, greatly simplify the network, making it much more coarse-grained.We further enhance the information content of the model network by mapping non-metabolic clinical modalities and measured clinical laboratory parameters onto the network.We analyze the system’s trajectories along the disease extent progression coordinate, rather than the time scale. In this way, the progression of various patients along the path from health to the manifested disease state becomes comparable.We calculate the system fluxes by quadratic optimization using the clinical readings as soft constraints. The optimality of the system’s profile in the space of generalized fluxes is formulated as the best fit ensuring the minimal squared difference between the observed measured variables and their values predicted from the given flux solution under constraints.By applying GMFA to accessible clinical data we create descriptive and predictive personalized mathematical models of an individual patient’s metabolic state. A digital twin can be defined as a mechanistic numerical model of a particular patient calibrated to the individual’s phenotypic and clinical data at a particular time point. Thus, GMFA is used as a novel approach for creating digital twins based on evaluating observed metabolic and physiological data.In the present study, we describe the Generalized Metabolic Flux Analysis (GMFA). The combined impact of several technical innovations and novel concepts of GMFA enables the computational simulation of complex, clinically relevant networks. The critical points are:Within the GMFA framework, we analyzed two cohorts of diabetes type 2 patients, EVAS  and NHANWe built a coarse-grained metabolic map Fig.  that incThe observed associations correlate with recent clinical and experimental studies reporting similar conclusions , 48, 49.Thus, the GMFA method provides mechanistic insights into disease progression along a path in the health states space and allows us to delineate subgroups of patients that can be predicted to develop diabetic eye complications Our GMFA based method for creating digital twins as representations of health states that vary on the scale of the progress extent, has the advantage of relating the information obtained in a cross-sectional study of the population with evolution of health state in time. In the future, this approach may be further developed to evaluate different health state transitions with respect to reversibility. This would bring a novel perspective on options for chronic disease management.In the last decade, there has been an upsurge in the use of data-driven machine learning models for the prediction of clinical outcomes. Machine learning provides prediction on outcomes of complex biological processes by ploughing through databases of inputs (exposures) and outputs (outcomes) for a given problem. These models bypass the need to understand complex mechanisms. In contrast, mechanistic modeling involves the generation of novel hypotheses for causal mechanisms that are generated through clinical observations in the datasets. A mechanistic model obtained by fitting its parameters to the available observations, would complement data-driven analyses by reducing the requirements for the data set volumes and compensating for occasional incompleteness of observations. The GMFA methodology described here, can be a candidate for this role. The advantages of similar systems biology methods can be found in integrating multi-level biological systems information, from genomics to proteomics , 29. ThiOur present study revealed several limitations. Despite the GMFA methodology is able to model medium- and large-scale metabolic networks, in the clinical setting only small-scale models can be practically applied, since a typical biochemical analysis includes only a small number of common biochemical tests. The available data sets included only up to two longitudinal data points per patient, often collected at an interval of a few years. Having a larger number of longitudinal data points would allow us test the linearity of the disease progression extent, one of the key assumptions of the analysis. Moreover, such design would allow us to test the ergodicity of the generalized fluxes. If the generalized fluxes are indeed ergodic, as predicted from the GMFA equations, this methodology could in the future be used to utilize large volumes of cross-sectional data as sources of information on longitudinal changes in the metabolism of patients in homogeneous populations. Despite many of diabetes patients receive medications, we were unable to effectively use this information, since the variation in the treatment regimes was great, while our patient cohorts were relatively small. This did not allow us to stratify our patients by treatment type into smaller subgroups, while having enough patients in each subgroup to achieve statistically significant conclusions.Overall, our work demonstrates an example of using metabolic and physiological data to construct predictive digital twin models of patients from routinely accessible clinical data. We provide a novel analytical framework, which opens up possibilities for the elucidation of disease mechanisms in personalized health assessments. The GMFA approach was applied to modeling health states in diabetes patients and showed potential to predict the development of ophthalmic complications in patients with diabetes.With further development and validation, the GMFA approach could be applied in the clinical setting for patient risk assessment. In the future, we plan to use our methods across a broad range of patient cohorts, phenotypes and diseases. A potential area of application of this methodology is in the stratification of patients in the process of population screening.The Generalized Metabolic Flux Analysis method described here aims to apply systems biology analysis principles to small- and medium-scale metabolic profiles, such as those obtained in clinical settings. The key novelty making the approach suitable for future clinical practice is building best-fit personalized constraint-based computational models , which quantify the expected rates of inter-connected metabolic processes, based on a single time point data input. We validated this approach by generating the GMF digital twins from the biochemical and physiological data of type 2 diabetes patients. This allowed us to characterize the present metabolic states of individuals and to predict the health state progression into development of ophthalmic complications. The predictive performance in multiple patient cohorts, measured as ROC-AUC was in the range 0.79–0.95.Supplementary file1 (PDF 192 kb)Supplementary file2 (PDF 94 kb)Below is the link to the electronic supplementary material.