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Available online at www.sciencedirect.com Current Opinion in ScienceDirect

59 60 61 62 63 Q1 in systems medicine: an overview of 1 64 2 Q11 methods and applications 65 3 1,2,† 1,3,† 66 4 Q10 Effrosyni Karakitsou , Carles Foguet , 67 5 Pedro de Atauri1,3, Kim Kultima4, Payam Emami Khoonsari4, 68 6 5,6 5 69 7 Vitor A. P. Martins dos Santos , Edoardo Saccenti , 70 8 7 1,3 71 9 Antonio Rosato and Marta Cascante 72 10 73 11 The brave new world of systems medicine 74 12 Abstract 75 13 Patient-derived metabolomics offers valuable insights into the Systems biology treats biological systems as ensembles 76 14 metabolic phenotype underlying diseases with a strong meta- of networks at multiple levels, starting from the mo- 77 15 bolic component. Thus, these data sets will be pivotal to the lecular level and from there gradually addressing more 78 16 implementation of personalized medicine strategies in health complex systems such as cells, tissues, organs, whole 79 17 and disease. However, to take full advantage of such data sets, organisms, and finally analyzing population dynamics. 80 18 they must be integrated with other within a coherent Systems biology aims to describe and predict the 81 19 pathophysiological framework to enable improved diagnostics, behavior of groups of interacting components. To do so, 82 20 to identify therapeutic interventions, and to accurately stratify it uses mathematical and computational tools to analyze 83 21 Q2 patients. Herein, we provide an overview of the state-of-the-art measurements collected by systematic high-throughput 84 22 for different data analysis and modeling approaches applicable technologies such as (post), metabolomics, or 85 23 to metabolomics data and of their potential for systems among others. The goal of systems ap- 86 24 medicine. proaches is to boost our understanding of biology by 87 25 overcoming the limitations of reductive science, which 88 26 Addresses addresses individual genes, proteins, metabolites, 89 1 27 Department of Biochemistry and Molecular Biomedicine and Institute pathways, or cells, and thus does not account for the 90 28 of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona properties emerging from their interactions [1,2]. 91 29 (UB), Barcelona, Spain 2 92 30 Institute of Cancer and Genomic Sciences and Centre for Compu- tational Biology, University of Birmingham, B15 2TT, Birmingham, UK Current medical science is mostly conducted using the 93 31 3 Centro de Investigación Biomédica en Red de Enfermedades He- 94 32 reductionist approach [3,4]. This limits our ability to páticas y Digestivas (CIBEREHD), Metabolomics Node at INB- grasp how multiple variables interact with one another 95 33 Bioinformatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, 96 to create emergent effects [3] and hampers our under- 34 Spain 97 4 standing of diseases, as well as our capability of deliv- 35 Department of Medical Sciences, Clinical Chemistry, Uppsala Uni- 98 36 versity, Uppsala, Sweden ering better treatments. Systems medicine can be 5 Laboratory of Systems and Synthetic Biology, Wageningen University 99 37 regarded as the application of systems biology to human 100 & Research, Stippeneng 4, 6708WE, Wageningen, the Netherlands in a clinical context [5,6]. It addresses the 38 6 LifeGlimmer GmbH, Markelstraße 38, 12163 Berlin, Germany 101 39 7 Department of Chemistry and Magnetic Resonance Center, Univer- aforementioned issues by applying iterative and recip- 102 40 sity of Florence, Italy rocal between clinical research and practice 103 41 through computational, statistical, and mathematical 104 Corresponding authors: Saccenti, Edoardo (edoardo.saccenti@wur. 42 multiscale analysis. This includes modeling of disease 105 43 nl); Rosato, Antonio ([email protected]); Cascante, Marta (marta- [email protected]) progression and remission, treatment responses, and 106 44 † These authors contributed equally to this work. adverse events both at the epidemiological and patient 107 45 level. This new paradigm of systems science and med- 108 46 icine strongly complements the traditional reductionist 109 47 Current Opinion in Systems Biology xxxx, xxx:xxx approach (Figure 1). 110 48 Edited by Shinya Kuroda and Mariko Okada 111 49 This reviews comes from a themed issue on Gene regulation The functioning of the human body is regulated by the 112 50 interaction and interdependencies of biological mole- 113 51 For a complete overview see the Issue and the Editorial cules at multiple levels (proteineprotein, proteine 114 52 Available online xxx RNA, and proteineDNA networks and metabolic net- 115 53 https://doi.org/10.1016/j.coisb.2019.03.009 116 54 works) [8]. Therefore, it can only be efficiently analyzed 2452-3100/© 2019 Published by Elsevier Ltd. 117 55 by examining various omics concurrently. Systems medicine provides the appropriate framework to achieve 118 56 119 57 120 58 www.sciencedirect.com Current Opinion in Systems Biology xxxx, xxx:xxx 121 COISB239_proof ■ 15 April 2019 ■ 2/9

2 Gene regulation

1 64 2 Figure 1 65 3 66 4 67 5 68 6 69 7 70 8 71 9 72 10 73 11 74 12 75 13 76 14 77 15 78 16 79 17 80 18 81 19 82 20 83 21 84 22 85 23 86 24 87 25 88 26 89 27 90 28 91 29 Overview of the core differences between reductionism and systems science, when analyzing the properties of a system; figure initially published in 92 30 Tillmann et al., 2015 [7] under the terms of Creative Commons Attribution 2.0 license. 93 31 94 32 95 33 96 34 97 35 this goal. The complementary perspectives offered by applied to analyze the complexity of the interactions 98 36 different data sets allow the genotype of an individual to within a biological network and link a priori knowledge 99 37 be linked to its observed phenotype as a function of from the literature and databases [11]. The application 100 38 lifestyle and environmental conditions. Eventually, this of network analysis allows the identification of nodes 101 39 could lead to defining how any healthy state can tran- with a high degree of connectivity (‘hubs’) and groups of 102 40 sition into a pathological one and vice versa and pave the highly interconnected nodes (‘modules’), identifying 103 41 way for personalized medicine. molecules functionally related to a disease state [12e 104 42 14]. 105 43 Multi-omics data integration 106 44 107 The integration of multiple omics data (sometimes also It is possible to outline a general strategy to integrate 45 various omics data sets based on network representa- 108 46 called trans-omics) will further enhance the contribu- 109 tion of omics science to our understanding of biomedi- tions. First, the network scaffold is defined by defining 47 how the individual components are interconnected. The 110 48 cine [9]. The example in Figure 2 shows the 111 connections among genomics, transcriptomics, prote- structure of the network can be identified based on the 49 data or prior knowledge (i.e., database information). 112 50 omics, and metabolomics, thus providing an overview of 113 Subsequently, the network itself can be separated into 51 the system from its potential (encoded in DNA) to the 114 modules. Finally, all the information can be combined 52 actual outcome (monitored by metabolomics). 115 53 with computational models of the whole system to 116 54 It is commonly accepted that the relationships between simulate and predict how the network determines the 117 55 genes, gene products, and metabolites participate in observed phenotype. In practice, if two omics elements 118 56 complex, interconnected networks (Figure 2). Various share a common driver, or if one perturbs the other, they 119 57 biological molecules can be represented as nodes in a will exhibit correlation or association. Various specialized 120 58 network and the interactions connecting them as edges. statistical approaches can be applied to measure these 121 59 For example, in metabolomics, metabolites would be the correlations. For example, a linear model taking into 122 60 nodes, and the edges would represent the enzymatic account age, gender, body mass index, and white blood 123 61 reactions interconnecting them. Graph theory can be cell count was used to find correlations between DNA 124 62 125 63 Current Opinion in Systems Biology xxxx, xxx:xxx www.sciencedirect.com 126 COISB239_proof ■ 15 April 2019 ■ 3/9

Metabolomics in systems medicine Karakitsou et al. 3

1 64 2 Figure 2 the other hand, untargeted metabolomics typically fo- 65 3 cuses on capturing all the chemical compounds present 66 4 in a sample, including metabolites of unknown chemical 67 5 structure, thus generating notably large data sets. By 68 6 comparing the metabolome of the control and test 69 7 groups and focusing on the differences between their 70 8 metabolic profiles, the number of significant detected 71 9 signals becomes more manageable. Finally, the com- 72 10 pounds or metabolites identified are annotated using in 73 11 silico libraries when possible or by applying analytical 74 12 chemistry methods to explore the newly observed 75 13 structure [24]. 76 14 77 15 One of the technical challenges in connecting the 78 16 79 metabolome with other omics layers is matching the 17 80 identities of the same objects in different layers (ID 18 Multi-omics integration across different omics layers. Red arrows highlight 81 19 the top-down flow of interactions across layers: genes are transcribed, conversion). Various databases support this task: the 82 20 transcripts determine enzyme concentrations, and finally enzymes act on Kyoto Encyclopedia of Genes and Genomes (KEGG) 83 21 metabolites. Purple arrows highlight the bottom-up interactions, whereby integrates one computationally generated and fifteen 84 metabolite levels modulate enzyme activities, the DNA/RNA-binding af- 22 manually curated databases, allowing the users to link 85 finities of regulators or DNA methylation. Note that metabolites can also metabolites to reactions, enzymes, pathways, and genes 23 interact directly with transcripts. Black arrows are intra-omics networks, 86 24 which can be derived based on individual omics data sets (for a review of [25]; BRENDA provides information on enzymes, such 87 25 methods in metabolomics, see Rosato et al., 2018 [10]). Intra-omics as kinetic parameters for enzymatic reactions, allosteric 88 26 networks may describe direct physical interactions (e.g., protein–protein effectors, and association with diseases [26]; Reactome is 89 27 interactions) and correlations between their abundances (e.g., transcript a database that organizes metabolites into biological 90 levels or metabolite concentrations). Environmental stimuli (blue arrows) 28 can affect all omics layers. For example, they can trigger DNA mutations pathways and processes, using reactions to define re- 91 29 and transcriptional events and modify protein activity. In addition, the lationships [27]; and MetaCyc is a database of metabolic 92 30 environment is also a source of metabolites and xenobiotic molecules. pathways and enzymes, whereas BioCyc (BioCyc.org) 93 31 Overall, the different omics levels, which are a function of the environment collects organism-specific genomes and computationally 94 32 and the omics interactions, determine the phenotype. predicted metabolic networks [28]. For example, in a 95 33 multi-omics study on the flow of the insulin signal based 96 34 on time course data from the metabolome, phosphopro- 97 35 teome, and transcriptome, a global metabolism map was 98 36 methylation and metabolite concentrations in human generated by mapping quantitatively changed metabo- 99 37 blood serum [15]. An even broader study analyzed the lites and their corresponding metabolic enzymes to the 100 38 genome, transcriptome, proteome, metabolome, and KEGG database [29]. 101 39 metabolic fluxes in Escherichia coli to understand how its 102 40 metabolic state reacted to perturbations [16]. More 103 Finally, it is worth mentioning the Investigation/Study/ Q3 41 recently, weighted gene correlation network analysis was 104 42 Assay (ISA-Tab) format, which is a convenient standard 105 used to identify connectivity-based gene modules highly to store the metadata and the results of experiments 43 correlated to pathways identified by metabolomics [17]. 106 44 across the various omics, is already implemented in 107 45 metabolomic platforms such as MetaboLights or 108 Connecting the metabolome layer and other omics e 46 PhenoMeNal [30 32]. 109 layers 47 110 Metabolomics measures the metabolites present within 48 111 a cell, tissue, or organism. It is a core experimental omics Metabolic models 49 The metabolic phenotype is defined by two comple- 112 50 within systems biology as it delivers an integrated view 113 of biochemistry [18,19]. Current experimental ap- mentary omics, the metabolome and the fluxome. The 51 first offers a static view of metabolism (snapshot-like), 114 52 proaches in metabolomics are mostly based on nuclear 115 magnetic resonance and mass spectroscopy [20,21]. whereas the latter represents the rate at which metab- 53 olites are interconverted through metabolic pathways 116 54 Metabolomic studies can be divided into two major and therefore provides a dynamic view of the metabolic 117 55 groups: targeted and untargeted. 118 phenotype [33]. The fluxome emerges from complex 56 119 interactions among metabolites, enzymes, and trans- 57 Targeted metabolomics quantitatively measures the 120 58 abundance of a predefined group of known, well- membrane carriers. Thus, the fluxome cannot be 121 59 characterized metabolites in a sample. Usually, the aim directly measured and instead needs to be inferred 122 60 is to identify novel associations between metabolites in through the analysis of other omics measurements. One 123 61 the context of specific physiological states [22,23].On of the most informative techniques to determine the 124 62 125 63 www.sciencedirect.com Current Opinion in Systems Biology xxxx, xxx:xxx 126 COISB239_proof ■ 15 April 2019 ■ 4/9

4 Gene regulation

1 64 2 fluxome is stable isotope-resolved metabolomics unknown parameters of the kinetic equations (Figure 3) 65 3 (SIRM). In SIRM, a biological system is incubated with [41]. 66 4 a substrate labeled with a stable heavy isotope (e.g., 67 5 13C) that propagates to metabolites in the network There are two approaches to building large-scale kinetic 68 6 generating characteristic label patterns which are models: the bottom-up or forward reconstruction and 69 7 indicative of the underlying flux distribution [34]. the top-down or inverse reconstruction. In the former 70 8 method, the various subparts of the model are built 71 9 Metabolic models, mathematical representations of individually and then put together to form the final 72 10 metabolism, are the tools used by systems biology and model, whereas in the latter, the entire model is 73 11 systems medicine to integrate multiple layers of data reconstructed, and all the parameters are fitted at the 74 12 and predict metabolic fluxes. Nowadays, the vast avail- same time. The major issues in large-scale kinetic model 75 13 ability of genomic data and the functional annotations reconstruction are the many unknown parameters in the 76 14 allows the reconstruction of genome-scale metabolic model and the lack of knowledge of regulatory infor- 77 15 models (GSMMs). GSMMs are built starting from mation. Indeed, the greatest challenge to build large 78 16 79 genome annotations, which are used to identify enzyme- kinetic models is the parameter inference or fitting step. 17 80 coding genes. These can then be mapped to reactions Over the last few years, approaches such as structural 18 81 19 using biochemical databases, such as KEGG, BRENDA, kinetic modeling and mass action stoichiometric simu- 82 20 or MetaCyc. The resulting network is then curated to lation (MASS) modeling have been developed to tackle 83 21 account for misannotations and missing reactions. this step. 84 22 Finally, the built reconstruction is validated by simu- 85 23 lating the known metabolic functions of the target or- Structural kinetic modeling aims to quantitatively 86 24 ganism [35]. In 2007, the first human GSMMs were describe the dynamic performance of a system, rather 87 25 reconstructed [36,37]. They formed the basis for much than specifically define kinetic parameters, and con- 88 26 more in-depth human genome-scale reconstruction structs local linear approximations for each parameter 89 27 models including Human Metabolic Reaction, Recon 2, according to experimental data and feasible biochemical 90 28 and Recon3D [38e40]. states. Then, the reconstructed local linear models are 91 29 used for the interrogation of a solution parameter space 92 30 Metabolic simulations based on these genome-scale [43,44]. On the other hand, MASS models try to 93 31 networks, or a subset of them, are usually performed combine constraint-based stoichiometric re- 94 32 with either kinetic- or constraint-based modeling constructions with matrix-based kinetic modeling. More 95 33 (CBM) techniques. Kinetic models integrate kinetic specifically, MASS uses large-scale stoichiometric 96 34 properties of enzymes (e.g., their affinity for substrates, network reconstructions as scaffolds, onto which fluxo- 97 35 the number of catalytic cycles that they can undergo per mic and metabolomic data measured in vivo are inte- 98 36 unit of time, and their regulation by activators or in- grated, and then, kinetic parameters, explicit for the 99 37 hibitors) and allow to simulate the dynamic behavior of modeled steady state of the system, are estimated. If 100 38 fluxes and metabolites. However, they are limited by the simulations of growth conditions are performed, kinetic 101 39 102 complexity to build and parametrize kinetic models for constants for the evolution of the system can be calcu- 40 103 large networks. In contrast, CBM uses network stoi- lated, thus describing its dynamic behavior [45]. 41 104 42 chiometry and the assumption of the metabolic pseudo- 105 43 steady state (i.e., intracellular metabolite concentra- Constraint-based modeling 106 44 tions are constant in time) to simulate steady-state flux CBM assumes a metabolic pseudo-steady state to build 107 45 distributions. Although CBM is easily applied to large mass balance constraints around metabolites and iden- 108 46 networks such as GSMMs, it has a more limited capacity tify valid steady-state flux distributions. In this manner, 109 47 when it comes to studying the dynamic behavior of the stoichiometry of the network can be represented as 110 48 metabolic networks than kinetic models. a system of linear equations, and steady-state flux dis- 111 49 tributions can be simulated without the need for 112 50 Building large-scale kinetic models defining the kinetic equations for each enzyme 113 51 Kinetic models are systems of ordinary differential [36,37,46]. As the resulting system is usually under- 114 52 equations (ODEs) where metabolic fluxes are determined, additional constraints and optimizations 115 53 computed as a function of metabolite concentrations need to be applied to reduce the solution space toward a 116 54 through a set of defined kinetic equations. Each unique solution (Figure 4) [38,39]. 117 55 metabolite has an ODE equation representing its vari- 118 56 ation in time, and each reaction has a kinetic equation For instance, GSMMs generally need to be constrained 119 57 describing the dependency of reaction fluxes to by integrating transcriptomics or proteomics data. This 120 58 metabolite and enzyme concentrations. Metabolomic need arises because GSMMs define the entire meta- 121 59 data, taken at multiple time points, are the primary bolic potential for a given organism, whereas at any given 122 60 input to validate kinetic models and iteratively fit cell and time point, only a subset of enzymes are 123 61 124 62 125 63 Current Opinion in Systems Biology xxxx, xxx:xxx www.sciencedirect.com 126 COISB239_proof ■ 15 April 2019 ■ 5/9

Metabolomics in systems medicine Karakitsou et al. 5

1 64 2 65 3 66 4 Figure 3 67 5 68 6 69 7 70 8 71 9 72 10 73 11 74 12 75 13 76 14 77 15 78 16 79 17 80 18 81 19 82 20 83 21 84 22 85 23 86 24 87 25 88 26 89 27 90 28 91 29 92 30 93 31 94 32 95 33 96 34 97 35 98 36 99 37 100 38 101 39 102 40 103 41 104 42 105 43 106 44 107 45 108 46 109 47 110 48 111 49 112 50 113 51 114 52 115 53 116 Kinetic model of upper glycolysis (Puigjaner et al., 1997 [42]). The network has four metabolites (Glc: glucose, G6P: glucose 6-phosphate, F6P: fructose Q4 54 6-phosphate) connected by three reactions (HK: hexokinase, GPI: glucose 6-phosphate isomerase, PFK: phosphofructokinase). HK has a Michaelis– 117 55 Menten kinetic law with an uncompetitive inhibition by G6P; GPI, a reversible Michaelis–Menten kinetic law; and PFK, a Hill cooperative kinetic law. Each 118 56 kinetic law is parametrized from measurements of mice muscle extracts (Vmax: maximal reaction rate, Km/Kms/Kmp/Shalve: concentration at which half of 119 57 the Vmax is achieved, Ki: Concentration at which half of the inhibition is achieved, h: Hill cooperativity coefficient). From network stoichiometry, the 120 58 parametrized kinetic laws are combined to build a system of ODEs, with each equation describing the dependent dynamic of a metabolite concentration. 121 59 Starting with initial metabolomic values, solving the system of ODEs simulates time courses for metabolite concentrations and reaction fluxes, which can 122 be compared with additional metabolomics data for validation. ODE, ordinary differential equation. 60 123 61 124 62 125 63 www.sciencedirect.com Current Opinion in Systems Biology xxxx, xxx:xxx 126 COISB239_proof ■ 15 April 2019 ■ 6/9

6 Gene regulation

1 64 2 Figure 4 65 3 66 4 67 5 68 6 69 7 70 8 71 9 72 10 73 11 74 12 75 13 76 14 77 15 78 16 79 17 80 18 81 19 82 20 83 21 84 22 85 23 86 24 87 25 88 26 89 27 90 28 91 29 92 30 93 31 94 32 95 33 96 34 97 35 98 36 99 37 100 38 101 39 102 40 103 41 104 42 105 43 106 44 107 45 Constraint-based modeling. First, the stoichiometry of the metabolic network is written as a stoichiometric matrix (s), where the sm,r element of the matrix 108 is the stoichiometric coefficient of the metabolite m in the reaction r. From an infinite space of possible flux (v) solutions, a feasible solution space which 46 contains possible steady-state solutions is obtained by applying the steady-state constraint (s.v = 0) and defining the directionality of reactions. A 109 47 condition-specific solution space can be obtained by integrating condition-specific omics such as transcriptomics, proteomics, or metabolomics. Finally, 110 48 an optimization can be performed in the solution space to select the best solution(s). For instance, biomass production can be maximized so that the 111 49 solution(s) that optimizes growth efficiency can be selected. 112 50 113 51 114 52 115 53 116 expressed and only a subset of reactions will be active. two or more different conditions or time points [47e 54 117 55 There are several approaches to integrate such data, but 52]. 118 56 they are generally based on maximizing the consistency 119 57 between the transcript and protein abundances of en- The range of feasible flux values can be further 120 58 zymes and the flux through reactions catalyzed by them. constrained by metabolomics data. Metabolomics from 121 59 Integrating transcriptomics and proteomics allows to the extracellular media can be used to constrain extra- 122 60 obtain maps of active/inactive reactions, as well as to cellular fluxes (i.e., rates of uptake of secretion for 123 61 characterize the changes in flux distributions between extracellular metabolites). Concerning intracellular 124 62 125 63 Current Opinion in Systems Biology xxxx, xxx:xxx www.sciencedirect.com 126 COISB239_proof ■ 15 April 2019 ■ 7/9

Metabolomics in systems medicine Karakitsou et al. 7

1 64 2 metabolomics, if a metabolite is detected, the model CBM has also been widely used in systems medicine to 65 3 can be constrained to have at least one reaction active, perform multi-omics data integration in the framework 66 4 where this metabolite is produced [50]. Furthermore, of GSMMs. For example, Mardinoglu et al. [65] inte- 67 5 quantitative metabolomics of intracellular metabolites grated proteomics and transcriptomics to build an 68 6 allows setting the rate at which intracellular metabolites adipocyte-specific GSMM and identified several puta- 69 7 must be synthesized to maintain a steady state in tive therapeutics against obesity. GSMMs have also 70 8 proliferating or growing systems [53]. Finally, SIRM- been widely applied to identify genes or sets of genes 71 9 based metabolic flux analysis can be applied to iden- that are essential for a disease-related process [59,66e 72 10 tify the range of flux values underlying a given set of 68]. For instance, Folger et al. [69] created a GSMM 73 11 SIRM measurements. The resulting flux ranges can be of cancer metabolism that predicted 52 cytostatic drug 74 12 added to the GSMM as flux bounds [34]. targets, 40% of which were targeted by known anti- 75 13 cancer drugs. Similarly, Agren et al. [70] built 27 76 14 Even after integrating transcriptomics or proteomics and patient-specific GSMMs of hepatocellular carcinoma 77 15 metabolomics, GSMMs are generally still undeter- and identified 101 potential drug targets, many of which 78 16 79 mined. Flux balance analysis aims to identify a unique had a strong correlation with disease progression. 17 80 optimal solution by maximizing or minimizing one or GSMMs have also shown great potential in biomarker 18 81 19 more fluxes in the metabolic network [54]. The choice discovery, for example, in liver diseases and type 2 dia- 82 20 of objective depends on the system under study, for betes [71,72]. 83 21 instance, to study rapidly proliferating systems, such as 84 22 cancer cells, the synthesis of biomass is used as the Conclusions and future perspectives 85 23 objective, but other objectives can be set depending on The primary goal of systems medicine is to explain, 86 e 24 the system of study [54 59]. predict, and prevent the progression of disease based on 87 25 clinical, environmental, and multi-omics data. Given the 88 26 Applications in systems medicine inherent network structure of metabolic processes, 89 27 The integration of multiple omics data in a systems network modeling and the analysis of multi-omics data 90 28 medicine manner is an emerging field. Nevertheless, it provide powerful and flexible inference tools to deci- 91 29 has already provided new insights into the interplay pher the complex interactions in biological systems. 92 30 among different regulatory layers. However, consensus models built from samples from 93 31 94 many individuals, albeit informative, might fail to cap- 32 Q5 For example, by studying the associations between 95 ture the heterogeneity that is present in a population 33 SNPs and metabolomics measurements, it has been 96 [73]. This limits the elucidation of the molecular drivers 34 demonstrated that the variability of metabolite con- 97 for an individual-specific phenotype (either healthy or 35 centrations in the blood between individuals is 98 pathological), which result from the differential regu- 36 explained to a large extent by common genetic variants 99 lation or dysfunction of individual-specific networks. 37 [60]. In another study, associations using epigenome- 100 38 wide association data in combination with cytosinee 101 Toward that end, methods are being proposed to build 39 guanine dinucleotide methylation data and other 102 patient-specific networks that capture the subject’s 40 multi-omics data suggested a causal effect of metabolite 103 specificity of clinical manifestation with the goal of 41 levels on methylation of obesity-associated cytosinee 104 understanding diseases at the individual level and 42 guanine dinucleotide sites [61]. 105 43 providing targeted and personalized treatments [74e 106 44 77]. In principle, a personalized database could be 107 Furthermore, even if the reconstruction of large-scale 45 generated for each individual, containing his/her omics 108 kinetic models still poses a big challenge, several ex- 46 information (e.g., genomics, urine and blood metab- 109 amples of kinetic models in systems medicine demon- 47 olomics, gut microbiome), together with lifestyle data 110 strate their great potential. For instance, a kinetic model 48 across time. This information, if properly analyzed, can 111 of human erythrocytes was used to identify metabolic 49 provide the means to build patient-specific networks to 112 targets that would selectively kill the parasite Trypano- 50 identify the best diagnostic, therapeutic, and prevention 113 soma brucei with minimal collateral damage to human 51 strategies for each individual and enable predictive, 114 cells [62]. Berndt et al. [63] reconstructed a kinetic 52 preventive, personalized and participatory medicine 115 model of the liver, and they used it to characterize the 53 [78,79]. 116 54 metabolic phenotype of hepatocytes and the metabolic 117 55 reprogramming that they have undergone during carci- 118 56 nogenesis. Bordbar et al. [64] have simulated individual Acknowledgements Q6 119 AR acknowledges financial support from CIRMMP. VAPMDS and ES 57 responses to drug exposure including side effect inci- acknowledge financial support from the EU FP7 funded project INFECT 120 58 dence and demonstrated that enzyme activities and (contract no. 305340; www.fp7infect.eu/). MC acknowledges support from 121 59 MINECO-European Commission FEDER funds e “Una manera de hacer 122 cellular dynamics, rather than metabolomics, are the Europa” (SAF2017-89673-R and SAF2015-70270-REDT), Instituto de 60 most accurate representation of the genotype. Salud Carlos III and Centro de Investigacio´n Biome´dica en Red de 123 61 Enfermedades Hepa´ticas y Digestivas (CIBEREHD CB17/04/00023), 124 62 125 63 www.sciencedirect.com Current Opinion in Systems Biology xxxx, xxx:xxx 126 COISB239_proof ■ 15 April 2019 ■ 8/9

8 Gene regulation

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