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Seminars in CancerBiology 30 (2015) 7987

Contents lists available at ScienceDirect

Seminars in Cancer Biology

journal homepage: www.elsevier .com/ locate /semcancer

Review

Modeling metabolism: A window toward a comprehensiveinterpretation ofnetworks in cancer

Osbaldo Resendis-Antonioa,, Carolina Gonzlez-Torresa, GustavoJaime-Munoz a,b,Claudia Erika Hernandez-Patino a,c, Carlos Felipe Salgado-Munoz a,b

a Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genmica (INMEGEN), Mxico, DF,Mexicob Facultad de Medicina-UNAM,Mexicoc Undergraduate Program in Genomic Sciences-UNAM, Cuernavaca,Mexico

a r t i c l e i n f oKeywords:

Cancer metabolismMathematical modelsSystems biology: Constraint-basedmodelingP4 medicine

a b s t r a c t

Given the multi-factorial nature ofcancer, uncovering its metabolic alterations and evaluating their impli-cations is a major challenge in biomedical sciences that will help in the optimal design ofpersonalizedtreatments. The advance ofhigh-throughput technologies opens an invaluable opportunity to monitorthe activity at diverse biological levels and elucidate how cancer originates, evolves and responds underdrug treatments. To this end, researchers are confronted with two fundamental questions: how to inter-pret high-throughput data and how this information can contribute to the development ofpersonalizedtreatment in patients. A variety ofschemes in systems biology have been suggested to characterize thephenotypic states associated with cancer by utilizing computational modeling and high-throughput data.Thesetheoretical schemes are distinguished bythe level ofcomplexity ofthe biological mechanisms thatthey represent and by the computational approaches used to simulate them. Notably, these theoreti-cal approaches have provided a proper framework to explore some distinctive metabolic mechanismsobserved in cancer cells such as the Warburg effect. In this review, we focus on presenting a generalview ofsome ofthese approaches whose application and integration will be crucial in the transition fromlocal to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are

required to construct the bases ofan integrative and personalized medicine, which has been and remainsa fundamental task in the medicine ofthis century.

2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/3.0/).

1. Introduction

Multiple roads lead to cancer, it can emerge as the result ofmutations in regulatory or signaling proteins [1], as well as theimbalance of oxidative stress and anti-oxidative mechanisms [2],or by the continuous exposition of cells to a given stimulus [3].There are a myriad of causes for cancer, and the compounds orconditions that stop or delay its progress in some circumstances donot work in others. A variety of factors contribute to this complexbehavior, including the heterogeneity in cancer cell populations,even in tumors from the same patient, a fact that directly reducestheeffectiveness andreliabilityof thedrugs fortreating thedisease[4,5]. Furthermore, with the advent of high-throughput (HT) andNext Generation Sequencing (NGS) technologies, new biologicalmechanisms that are typical of cancer cells have been discovered

Corresponding author. Tel.: +52 01 55 53501900.E-mail address: oresendis@inmegen.gob.mx (O. Resendis-Antonio).

[6]. Thissceneryhascreatedtheneedtodevelopnewwaystointer-pret these data and elucidate their role in human diseases in acoherent and systematic fashion. At this stage, a biological systemcan be considered an interactive set of networks with componentsand relations at different biological levels that are the causes bywhich certain phenotype, functional or dysfunctional,emerges, seeFig. 1.

Currently, understanding how these biological networksorchestrate their activities to support cancer phenotypes is agreat challenge in medical science to prevent, suggest and predictstrategies with direct implications in clinical stages. Despite thisastonishing complexity which involves biological processes suchas signaling, regulation and metabolic networks, hallmark traitshave been suggested to characterize key processes in all humancancers, such as proliferation, invasion and metastasis [1,7,8].

Arguably the most fundamental trait of cancer cells is their abil-ity to sustain chronic uncontrolled proliferation [1]. Although it iswell accepted that this dysfunctional state is accompanied by arewiring of metabolic activity in the cell [9], it is not completely

http://dx.doi.org/10.1016/j.semcancer.2014.04.0031044-579X/2014 TheAuthors. Publishedby Elsevier Ltd. This is anopenaccess article underthe CCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

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Fig. 1. Mechanisms of metabolic regulation in cancer cells. Cancer cells display a variety of alterations in metabolic pathways for building the precursors that cells needto proliferate and sustain the anomalous state. Glycolysis, the TCA cycle and glutaminolysis, are a few pathways that guide the malignant transformation by covering themetabolicdemand of nucleotides, aminoacids, lipidsand other building materials thatcancer cellsneed to proliferate. Enzymes with an importantrole in cancer are depictedin the upper level of the figure (gray ovals), and metabolites participating in anabolic pathways are depicted in pink and orange arrows. These metabolic alterations aremodulatedby a variety ofmechanisms that involve othermolecules, such as oncogenes, tumor suppressors andmicroRNAs. Examples falling in thefirstcategory aregivenby

c-MYC, HIF-1 andP53, with activities that directly affectmetabolic activity (greencircles). c-Myc andHIF actin complexeswith other proteins to modulate theexpression ofglycolyticgenes or enzymes. c-Mycspecifically stimulatesglutaminolysis.P53 regulatesthe suppressionof glycolysisthroughTIGAR andincreasesmitochondrialmetabolismvia SCO2 (yellow circle), as depicted by the dashed blue arrows. In turn, these regulators modulate the expression of other target genes to enhance the cancer phenotype.c-MYC and HIF-1 promote the expression of cell cycle proteins, and mutations in P53, denoted as P53*, inhibit apoptotic genes, as depicted by the dashed green arrows.Moreover,the transcription of oncogenes andtumor suppressors can be controlled by theup-regulation of oncogenicmiRNAs (red) or inhibitory miRNAs,see green arrows.Simultaneously,metabolitescan playan importantrole in epigeneticregulation, seeyellow arrow. Furthermore,the stromal cellsinduceimportantmetabolic changes to favorcancer proliferation thought metabolic cross-talk (cyan arrow). GLUT1 Glucose transporter 1, HK-hexokinase, GLC Glucose, G6P Glucose-6 phosphate, F6P Fructose1,6phosphate, PFK Phosphofructokinase, G3P Glyceraldehyde-3phosphate, 3PG 3-Phosphoglycerate, PEP Phosphoenol pyruvate, LDH Lactate dehydrogenase, PYR Pyruvate, LAC Lactate, MCT1 Monocarboxylatetransporter 1, MCT4 Monocarboxylatetransporter 4, SLC1A5 Glutaminetransporter, GLNGlutamine, GLU Glutamate,CIT Citrate, ICIT Isocitrate,-KG--Ketoglutarate,SuCoA Succinylcoenzyme A, SUC Succinate, FUM Fumarate,MAL Malate.

clear how and which metabolic pathways differentially alters theiractivities in cancerversus normal tissues [10,11].

The most studied metabolic alteration in cancer is the degrada-tion of glucose via aerobic glycolysis, a less efficient pathway for

generating ATP compared with oxidative phosphorylation, despite

oxygen availability. This finding was reported almost a century agoby Otto Warburg in his seminal studies of metabolism in cancercells [12]. Notably, studies in cancer biology have demonstratedthat cancer genes are intimately linked to the Warburg effect and

other metabolic alterations [13,14], see Fig. 1. There is evidence

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that reprogramming signaling or regulatory networks in normalcells induces the expression and activation of several enzymes thatform part of the metabolic pathways that promote proliferationand survival in cancer cells [1517]. For instance, c-Myc regu-lates the expression of glycolytic genes, particularly enzymes suchas hexokinase (HK), phosphofructokinase (PFK), lactate dehydro-genase A (LDHA) and extracellular glucose transporter [1820].Furthermore, c-Myc controls glutaminolytic pathways that pro-mote the expression of glutamine transporters such as SLC1A5and the expression of glutaminase (GLS)[21]. c-Myc has also beenimplicated in the regulationof genesparticipatingin mitochondrialbiogenesis [22]. Similarly, HIF-1 modulates the activity of someenzymes involved in glycolytic metabolism at hypoxic conditions[23,24]. Moreover, mutations in many types of cancer disrupt thefunctionalrole of some tumor suppressors. For example, mutationsin P53 simultaneously promote glycolysis and inhibit mitochon-drial respiration in cancer cells through genes such as TIGAR[25]andSCO2 [26], see Fig.1.c-Myc, HIF-1 andP53are together involvedin other biological processes that sustain cancer. c-Myc and HIF-1 form a complex with the protein MAX, and consequently, thiscomplex up-regulates the expression of cyclins (proteins involvedin of cell cycle). Conversely, mutated P53 promotes cancer bydown-regulating apoptotic genes[24,27]. In turn, the transcriptionfactors mentioned above are regulated by microRNAs (miRNAs),small-noncoding RNAsof 1825 nucleotides in length[28]. miRNAstypically reduce the translation and stability of target mRNAs byspecific base-pairing interactions, but in some cases, they can pro-mote translation by indirect mechanisms [2830]. Some elementsof metabolic networks can affect other levels of regulation, whichadds another layer of complexity; for example, there is evidencethat metabolites can play an important role in epigenetic regula-tion [31], see Fig. 1. The above examples represent only a smallfraction of many cross-linking interactions at different biologicallevels that are together responsible for cancer phenotype.

These different levels of biological information constitute oneaspect that determines metabolic capabilities in cancer cells, how-ever the microenvironment also plays a relevant role in supporting

cancer phenotypes. Epithelial cancer cells have been shown toinduce a type of aerobic glycolysis in neighboring fibroblasts [32].The proposed mechanism considers that cancer associated fibro-blasts (CAF) produce high-energy metabolites such as lactate andexportthem to the microenvironment. Then, epithelial cancer cellscan use these metabolites to fuel mitochondrial activity. This bio-chemical crosstalkbetweencell types is calledthe reverse Warburgeffect and induces a higher proliferative capacity in epithelial can-cercells [32]. Thesefindings reveal thatthe classicalWarburg Effectand the reverse Warburg effect are only the top of the iceberg onmetabolic alterations in cancer; there is thus an increased needto understand how metabolic pathways in cancer can be alteredby environmental cells in a tissue specific context. To constructa holistic view of this phenomenon, it is necessary to develop

systemic schemes capable of simultaneously: (1) representing bio-logical knowledge in a mathematical language; (2) integrating HTdata; and (3) formulating and assessing metabolic predictions insilico [33].

Significant advances have been reported in understanding can-cer metabolism through two parallel branches: experimentallyand computationally. A variety of biological assays in cancerhave discovered new metabolic changes that support growth andproliferation [10,3437]. Additionally, new theoretical schemeshave been suggested to analyze, describe and predict cellularmetabolism at a genome scale level [38]. Although both brancheshave been evolving, there remains a significant challenge inintegrating them and improving our understanding of cancermetabolism. In this review, we present some of the standard

formalisms to approach this problem. These methods represent

a cornerstone for quantitatively interpreting and understandinghow cells alter their metabolism to acquire their malignant pheno-type, an aim that could have strong implications for the design ofcombined medical treatments in cancer that achieve more efficientresults [3941].

1.1. Conceptual schemes in cancer systems biology

Complexity in cancer is astonishing, and understanding themetabolic mechanismsinvolved in this disease requires qualitativeand quantitative methods for systematicallyelucidating theirprin-ciples [4245]. To this end, a variety of theoretical frameworks havebeen suggested in the literature. These frameworks can be classi-fied intoa few categories: Topological, classical,stochastic, Booleanand Constraint-based modeling, see Table 1. Although there is norule for the selection of the approach, it can be guided by at leasttwo criteria: (1) the type of biological question to be addressed;and (2) the information that is available for the biological system.In this section, we present some examples of different formalismsthat one can find in the literature to analyze pathways in cancer.This formalism represents schemes that are capable of uncoveringand highlighting some mechanisms in cancer at diverse levels of

detail and complexity.

2. Balancing complexity, biological detail and physiological

knowledge in cancer modeling

Given the complexityof biological systems, mathematical mod-els are invaluable tool for integrating knowledge, understandingmechanisms and predicting phenotypic behavior. In the construc-tion of a model the balance between the degree of complexity andthe biological detail required should be addressed, Fig. 2. The strat-egy is guided by criteria such as the nature of the question and theavailable data to validate the outputs and predictions of the model.A variety of schemes have been applied to explore the behaviorof cancer cells in a broad spectrum of biological processes, such

as tumor growth, metabolism and treatment response [4549]. Toclassify these schemes is nota trivial task, however they canbe dis-tinguished in terms of the mathematical formalisms used, the levelof its biological detail andthe scopes of their predictions. Consider-ing these items, we suggest the classification in Table 1 in which atleast six approaches are identified: Topological, Classical, Boolean,Stochastic, Constraint-Based and Hybrid modeling.

2.1. Networks topology: unveiling structural organization in

metabolic networks

Cancer is a network disease resulting from alterations at diversebiological levels, and topological analysis is a primary strategy tosurvey the organization of the biological components and how

dysfunctional mechanisms can emerge. Thus, topological proper-ties such as robustness, centrality, modularity, motif discovery orminimal path-length provide clues concerning how organizationin biological networks can be associated with functional states[5054].

Genome-scalemetabolic reconstruction of human cells is a fast-movingareain whicha frameworkcan beestablishedto distinguishfunctional and dysfunctional states associated with diseases [38].For instance, the most current version of the human metabolicreconstruction (Recon) contains approximately 7400 reactions ofhuman metabolism, representing 123 metabolic pathways withexperimental and bioinformatics evidence in human cells [55,56].Notably, this curated metabolic reconstruction constitutes a com-putational platform to explore in silico the metabolic phenotypes

that characterize human diseases, such as cancer, obesity, diabetes

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Table 1

Modeling approaches in systems with applications in cancer systems biology. This table summarizes some properties associated with each approach. The square colors inthe first column label approach classification in concordance with Fig. 2.

Approach Description Characteristics Limitations Examples

Networktopology Mathematicalschemetouncover the organizingprinciples in biologicalnetwork

This approach has been able toelucidate organizing principlesin biology by taking intoaccount a large number ofbiological entities

Topological analysis isindependent of time andphysiological context

ProteinProtein interactionnetworksGene co-expressionnetworks

Classical models Basic models based on ODEor PDE for analyzingbiological systems with fewparameters

Consider few parameters of thesystem generally macroscopicproperties. Those parametersarerelated by simpleequations

The resolution of theinformation is restricted tothe scale used for thecalculations

LoktaVolterracompetition modelGompertz growth rate

Boolean models Biological networks whosecomponents can be in one oftwo states (0,1) and whosedynamic are given by logicalrules

It is a properframework toanalyze biological circuitswhere parameters are notwellcharacterized

The computational costmay rise depending uponthecomplexity of thenetwork

Transcriptional regulatorynetworksSignaling networks

Stochastic models Models describingphenomena in a probabilisticapproach due to significativefluctuations in thesystem

Eliminate the deterministic view ofthesystemand consider that noiseis fundamental to drive thephenotypeof a biological circuit

Computational simulation is atime consuming process, itincreases according thenumber of componentrises

Master equation systems

Cellular automaton modelsAgent basemodels

Constraint-basedmodels Modelsbasedon genomic

base information (omicstechnologies) andphysiological information.

With thecombination of the

omics information model canbe built forexploring therelationship between genotypewith phenotype.

The quality of theresults

depends of themetabolicreconstruction, which inturn dependon availableomics data andphysiological information.

Flux balance analysis

Flux variability analysis

Hybrid models Combine two or more type of models and mixes differentlevels of information.

These models becomemoredetailed and will be moreaccurate with the

Some limitations can besolved mixing models, butinstead others can appear.

Novel agent basemodelsMulti-scale models

and mental disorders. Given the relevance of this reconstruc-tion, community strategies have recently been accomplished forimproving data quality and, consequently, the predictive scope ofin silico models [55]. Although topology is independent of timeand physiological context, this approach can elucidate the orga-nizing principles that govern biological functions in cells [57]. Forexample, the presence of functional modules has been reportedat different levels of biological information, ranging from regula-tory to metabolic networks [58,59]. The concept of modules hasa variety of connotations: in gene expression analysis a mod-ule is understood as a co-expressed set of genes, whereas in anetworks context, a module is a set of nodes that have strong

interconnections among them, characterized by a high clusteringcoefficient [60]. Notably, although these concepts arise from dif-ferent criteria, there have been some efforts to combine them andexplore their relationships [42,57,59]. One such approach is basedon the reconstruction of co-expression networks that allows tostudy how genes coordinate their expression to sustain a specificbiological state [42,6164]. These methods have been applied ina variety of tissues to elaborate hypotheses and identify drug tar-gets [6570]. Notable observations have been made with this typeof analysis, such as the finding that up-regulated genes in lungcancer present a high degree of connection and centrality [71].Although the representation of biological data through networks

Fig. 2. Theoretical approaches used in cancer modeling. This figure depicts some theoretical schemes that have been applied to analyze different aspects of cancer bycombining different levels of complexity and detail. Models that representhigh biological complexity and a low level of detail (reduced number of variables) are located tothe left of the figure. Conversely, models that representlow biological level of complexity and a high level of detail are located on theright side. The colors of theexamples

are in concordance with those in theclassification proposed in Table 1.

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has contributed to understanding the organization of dysfunc-tional pathways in cancer, some elements require more attentionto ensureproper interpretations. For instance, the topological anal-ysis of metabolic networks is usually performed with only onetype of element as node, i.e. either reactions or metabolites, as aconsequence, there is a loss of information in these approaches.To overcome this fact, the representation of metabolism throughbipartite networks and the development of methods to exploretheir architecture are needed to uncover more general organizingprinciples [72]. In a more general context, theproper networks rep-resentation and coherent interpretation of heterogeneous HT datais a major challenge in the field, achieving that will advance ourunderstanding of cancer in at least two ways: (1) uncovering orga-nization principles by which cancer cells response and evolve intissues, and(2) applying this knowledgeto contribute to thedesignof optimal treatments for cancer. Both goals are closely linked tothe purposes of P4 medicine: predictive, preventive, personalized,and participatory [46].

2.2. Classicalmathematical models

In classical models of cancer, ordinary differential equations(ODE) or partial differential equations (PDE) play important roles

in describing processes in cancer. A myriad of ODE models haveanalyzed cancer from different points of view that range frommodeling the growth rate to calculating radiation doses in ther-apy [45]. In this classification, we include all those models whosedescription is characterized by simplifying the biological systemthrough a reduced number of mathematical variables associatedwith biological macroscopic properties (such as cell growth). Aseminal example falling into this classification is the Gompertzmodel that reproduces growth rate measurements of cancer cellsin vitro [73]. Some other models based on ODE focus on analyz-ing drug responses in cancer cultures, which is an essential pointfor the optimal design of therapeutic strategies [74]. A detaileddescription of these methods and a broader number of examplescan be found in the literature [45]. Although these schemes have

contributed significantly to understanding global mechanisms incancer such as growth rate, angiogenesis or metastasis they donot include detailed information regarding specific alterations insignaling, regulatory or metabolic networks underlying the pheno-type. To integrate this latter information one necessary step is tomove toward genome-scale modeling, which allows us to explorethe relationship amongtopology, dynamic and biological function-ality [42,75,76].

2.3. Booleanmodels

Another option for mathematical modeling in cancer is theBoolean formalism, a proper scheme for analyzing the dynamicbehavior of transcriptional regulatory (TRN) or signaling networks

without the need to include kinetic parameters of the processesinvolved. Given a network, this approach assumes that the expres-sion of each biological element can be in one of two states (zeroor one) whose selection depends on a set of Boolean rules definedby the state of the elements that regulate it. Hence, with networktopology and Boolean rules, the dynamic behavior of genes can beobtained by applying the rules at discrete intervals of time in a syn-chronic or asynchronic fashion. As a result, the state of the entirenetwork changes at a discrete interval of time until it reaches afixed or cyclic state associated with a phenotypestate, known as anattractor [77,78]. To ensure a proper simulation, one should defineBoolean rules in terms of physiological knowledge [79]; however,this information is often absent, and the combinatory effects ofthe possible mechanisms significantly increase as a function of the

number of regulators involved. Nevertheless, this approach is an

appealing formalism when one desires to explore global questionsaboutthedynamicorganizationofthenetworkwithoutanaccurateknowledge of mathematical parameters. In cancerstudies, Booleannetworks have an important role to elucidate the mechanisms thatcan sustain the cancer phenotype [8183]. Hence, to characterizethe biological profile in cancer, some methods applying a Booleanscheme have explored the physiological states of transcriptionalregulatory networks (TRN) and its transition between normal anddysfunctional states [80,81]. For instance, Fumi et al. constructeda regulatory protein network integrating signaling pathways rele-vant in cancer and characterized its phenotypes through Booleanstates. The dynamic behavior of this network was such that theattractors identified were associated with one of these physio-logical states: proliferative, quiescent or apoptotic. Furthermore,gene mutation analysis carried out in silicoevaluate the stability ofcancers attractors, and their simulations concluded that perturba-tions in some nodes elicit transitions among physiological states.Notably, this finding suggest the possibility of controlling the dis-ease evolution through the proper selection of targets along thenetwork [82].

2.4. Stochasticmodels

Biological processes in cells are constantly subject to internalfluctuations that are induced by the fact that concentrations ofsome reacting species, such as transcriptional factors or metabo-lites, are extremely low inside the cell. Notably, biological circuitshave the ability to control this noise to favor certain phenotypicstates in cells according to the environmental conditions [83].Noise functions at many levels of biological complexity, includinggenetic regulatory mechanisms, metabolism and cell populations[5,84,85]. Cancer cells are not excluded from this fundamentaleffect, and a clear consequence is observed at a population levelwhere subspecies of cancer cells in a tumor coexist to potentiallyfavorand sustain the malignantphenotype[5]. Todescribethecon-sequences that noise has on cell activity, a stochastic approach ismore convenient than a deterministic one is more convenient to

elucidate the cellular mechanism by which noise is controlled[86].For simplicity, we subdivided stochastic models into two types:those that involve master equations; and those based on com-putational techniques such as cellular automaton or Monte Carlosimulations. The first scheme is frequently used to investigate ana-lytically the statistical properties of biological circuits with a lownumber of components in a homogenized mixture [86], whereas the second scheme overcomes these limits by computation-ally simulating interacting cells and biological networks subjectedto diffusion and population effects. Thus, cellular automaton hasbeen a central piece of the in silico models developed to simulateheterogeneities in tumors by considering vascularization, geneticmutations, metabolism and other biological processes [87]. Forinstance, computational models have simulated tumor growth in

a set of interacting cancer cells in three dimensional space, wheregradient effects of key metabolites, such as glucose or oxygen, playimportant factors in determining the disease. Overall, computa-tional and mathematical models are complementary strategies forunderstanding the mechanisms by which cells regulate intrinsicandextrinsicnoiseandexploringhowthesebiologicalmechanismscontribute to the cancer phenotype.

2.5. Constraint-basedmodeling

New HT technologies have contributed significantly to movetoward the integration and coherent interpretation of biologi-cal data. To this end, constraint-based modeling is a paradigmin systems biology that uses a biochemical, genetic and genomic

(BiGG) knowledge base to explore the metabolic capabilities in

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microorganismandhumantissues[8890]. Constraint-basedmod-eling integrates computational methods that contribute to linkthe fundamental relationship between the genotype and thephenotype such as the identification of gene essentiality inmicroorganisms [90]. One of the most applied methods is flux bal-ance analysis (FBA) that calculate the flux through a metabolicnetworksthatensure an optimalproduction of a specific physiolog-ical objective function, such as biomass production in a cell [91,92].FBA has been successful in exploring the metabolic capacities ofcancer cell lines and identifying potential enzymes with thera-peutic applications [33,93]. Given that most human diseases haveconsequences in metabolic activity, constraint-based modeling isconsidered a useful paradigm for understanding the mechanismsinvolved in human diseases [56].

2.6. Hybridmodels

Computational or mathematical models based on previousschemes have guided our understanding of cancer at different lev-els of descriptions [44]. However, each approach has limits and tomove toward more realistic models, there is an interest in com-bining more than one scheme simultaneously. In most cases, this

integrative effort is propelled by the type of biological question tobe explored and the experimental technologies that can be used toassess the conclusions. Currently, some strategies have been pro-posed to model metabolism in solid tumorsby integrating differentlevels of complexity, such as metabolism and tumor development[94,95]. Advances in these types of modeling will create the oppor-tunity to analyze the behavior of cancer cells with a greater level ofdetail, including additional processes such as cell cycle, angiogen-esis, metastasis and the effect of metabolite gradients during thegrowth of solid tumors [95,96].

Overall, these paradigms constitute a valuable conceptual plat-form to characterize, understand and identify the mechanismssustaining cancer [43,97]. As soon as these approaches increasetheir capacity to integrate biological information and heteroge-

neous high-throughput data, their role for moving toward anintegrative analysis of diseases and the development of person-alized, preventive and predictive medicine will become evident inclinical areas.

3. Tissue specific metabolism in cancer

Metabolic alterations are important avenues to propitiatetumorigenesis and sustain cancer, these changes are heavily influ-enced by the physiological and microenvironmental conditionsin human tissues. The human body integrates tissues with cellsthat are specialized in several processes, such as storing fat, trans-forming carbohydrates, creating hormones and covering energeticdemands, among others functions. Tissues have physiological pur-

poses, and when a disease emerges, its evolution is stronglyinfluencedby tissue-specificmetabolicambiance[32,46,96,98]. Forinstance, prostate cancer appears to prefer fatty acid metabolismas a source of acetyl-CoA synthesis, whereas breast cancer appearsto prefer the consumption of glucose as a main carbon source tosustain malignant phenotype [99,100]. Undoubtedly, the charac-terization of specific metabolic states in each tissue will contributeto improve the descriptive and predictive capacities of com-putational models in different types of cancer. To address thisvaluable and fundamental topic, there have been efforts to cre-ate manually curated networks (bottom-up scheme) for a coupleof human tissues [101104]. This type of reconstruction has theadvantage of including high-quality curated information regardingbiological networks. In recent years there has also been increased

interest in implementing computational algorithms that integrate

high-throughput data to reconstruct tissue-specific metabolicnetworks (top-down scheme). In the latter case, these compu-tational methods start from a generic non-tissue-specific humanmetabolic reconstruction such as Recon [55,56,100], EHMN [105]or HumanCyc [106] and then select the subnetwork that bet-ter fits available biological data, such as microarrays, proteomicsor metabolomics. Currently, a variety of methods are available inthe literature, some of which have been applied to reconstruct thetissue-specific metabolism in cancer [38,107]. Conceptual foun-dations and properties of some of these methods are brieflydescribed in Table 2. Some differences can be highlighted amongthem, for instance, INIT (Integrative Network Inference for Tis-sues) allows for a small net accumulation in the metabolic fluxto have a network able to synthesize molecules such as NADH,rather than only being able to use such molecules as cofactors[108]. Although the final tissue-specific metabolic reconstructionin mCADRE (context-specificity assessed by deterministic reac-tion evaluation) is constrained to produce universally importantmetabolites from simple precursors such as glucose, INIT allowsthe model to use precursors that the cell is known to take up[109].Remarkably, the achievements of these methods constitute a firststep toward a human metabolic atlas that will serve as a platformto understand the metabolic alterations in cancer and potentiallydesign drugs and more effective treatments for patients.

4. Outlook

Mathematical models represent a simplified version of real-ity whose essential purpose is to help us handle and understandthe complexity of biological phenomena. Systems biology enablesthe systematic analysis and coherent interpretation of high-throughput data, and this paradigm is fundamental to achieve abetter understanding of metabolic mechanisms involved in can-cer in a coherent and systematic fashion. The implications of thisperspective should be reflected in practical aims oriented towardbasic questions in biomedical sciences and clinical areas, such

as the uncovering of metabolic alterations in cancer, discoveringpossible therapeutic targets or properly designing personalizedtreatments. However, these aims are not trivial tasks, and oneimportant piece in the puzzle is the development of computationalmodeling methods that are capable of integrating informationwith HT data and physiological behavior in cancer. In this review,we present some theoretical schemes that have contributed tothe understanding of key mechanisms in cancer. Each approach,whether mathematically or computationally, has its own scopesthat are distinguished by their levels of complexity and the num-berofvariablesusedtomodelthephenotype.Thus,systemsbiologyschemes are a paradigm to advance our knowledge of how can-cer emerges in tissues and how to design novel drugs treatments.Although there have been some importantadvances in this area,the

practical implication of these findings for cancer therapy remainsa challenge in systems biology. This challenges addresses addi-tional problems, such as the building of a common language tocommunicate between the clinical and theoretical scientists andpreparinga new generationof scientistswithskills in mathematics,genomic and computational sciences. In our opinion, these strate-gies are required to move toward a quantitative description ofcancer phenotype that contributes to the advent of a systemic andpersonalized medicine.

Funding

The authors thank thefinancialsupport from theResearch Chairon Systems Biology-INMEGEN-FUNTEL Mexico.

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