Modeling metabolism: A window toward a comprehensive interpretation of networks in cancer (original) (raw)
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Modeling Core Metabolism in Cancer Cells: Surveying the Topology Underlying the Warburg Effect
Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. This work presents a constraint-base modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart. Alterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most experimentally studied pathways sustaining cancer cell growth. Remarkably, in silico model was able to resemble the physiological conditions in cancer cells and successfully identified some enzymes currently studied by its therapeutic effect. Overall, we supplied evidence that constraint-based modeling constitutes a promising computational platform to: 1) integrate high throughput technology and establish a crosstalk between experimental validation and in silico prediction in cancer cell phenotype; 2) explore the fundamental metabolic mechanism that confers robustness in cancer; and 3) suggest new metabolic targets for anticancer treatments. All these issues being central to explore cancer cell metabolism from a systems biology perspective.
Frontiers in Physiology, 2013
One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.
Reconstruction of a generic metabolic network model of cancer cells
A promising strategy for finding new cancer drugs is to use metabolic network models to investigate the essential reactions or genes in cancer cells. In this study, we present a generic constraint-based model of cancer metabolism, which is able to successfully predict the metabolic phenotypes of cancer cells. This model is reconstructed by collecting the available data on tumor suppressor genes. Notably, we show that the activation of oncogene related reactions can be explained by the inactivation of tumor suppressor genes. We show that in a simulated growth medium similar to the body fluids, our model outperforms the previously proposed model of cancer metabolism in predicting expressed genes.
Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism
Scientific reports, 2017
Malignant transformation is often accompanied by significant metabolic changes. To identify drivers underlying these changes, we calculated metabolic flux states for the NCI60 cell line collection and correlated the variance between metabolic states of these lines with their other properties. The analysis revealed a remarkably consistent structure underlying high flux metabolism. The three primary uptake pathways, glucose, glutamine and serine, are each characterized by three features: (1) metabolite uptake sufficient for the stoichiometric requirement to sustain observed growth, (2) overflow metabolism, which scales with excess nutrient uptake over the basal growth requirement, and (3) redox production, which also scales with nutrient uptake but greatly exceeds the requirement for growth. We discovered that resistance to chemotherapeutic drugs in these lines broadly correlates with the amount of glucose uptake. These results support an interpretation of the Warburg effect and gluta...
Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models
Computational Biology and Chemistry, 2016
Reconstruction of tissue-specific, constraint-based core models for breast, Highlights • In the context of metabolic networks, genome-scale modeling represents an increasingly used approach, as it has been shown to be successful in modeling the entire human, as well as cancer cells metabolism for the prediction of selective drug. In spite of the potentialities linked to their comprehensiveness, these reconstructions are difficult to control and often include errors, such as improper filling of network gaps. • Starting from already existing genome-scale metabolic models, we aim at reconstructing manually curated core models that zoom-in on cancer metabolic rewiring. In order to study the role of the Warburg effect and more in general the role of all cancer metabolic alterations in supporting the neoplastic proliferation, we focused on the three most harmful neoplasias: liver, breast and lung tumors.
The evolution of genome-scale models of cancer metabolism
Frontiers in Physiology, 2013
The importance of metabolism in cancer is becoming increasingly apparent with the identification of metabolic enzyme mutations and the growing awareness of the influence of metabolism on signaling, epigenetic markers, and transcription. However, the complexity of these processes has challenged our ability to make sense of the metabolic changes in cancer. Fortunately, constraint-based modeling, a systems biology approach, now enables one to study the entirety of cancer metabolism and simulate basic phenotypes. With the newness of this field, there has been a rapid evolution of both the scope of these models and their applications. Here we review the various constraint-based models built for cancer metabolism and how their predictions are shedding new light on basic cancer phenotypes, elucidating pathway differences between tumors, and dicovering putative anti-cancer targets. As the field continues to evolve, the scope of these genome-scale cancer models must expand beyond central metabolism to address questions related to the diverse processes contributing to tumor development and metastasis.
Analysis and Modeling of Metabolism of Cancer
Biomechanics of Cells and Tissues, 2013
Metabolism comprises a set of chemical reactions that are performed in biological systems in order to sustain life. Metabolism is responsible for deriving energy and biomolecules from the cells' surrounding. Tumour cells' very high metabolic needs have to be fulfilled under suboptimal conditions. Thus, tumour cells and tissues have a remarkably different metabolism than the tissues that they derive from. Many key oncogenic signaling pathways converge to create this change in order to support growth and survival of cancer cells. Some of these metabolic alterations are initiated by oncogenes and are required for malignant transformation. Altered metabolism allows cancer cells to sustain higher proliferative rates with faster energy and molecular building block production while resisting cell death signals particularly those that are mediated by increased oxidative damage. The very specific metabolic phenotype of cancer provides an interesting avenue for diagnosis and treatment and several examples of such applications are already in place. Novel methods for metabolic profiling, comprised under the term metabolomics, provide tools for collection of data on cancer cell and tissue's metabolic properties in steady state and as a function of time and/or treatment. The time, i.e. flux data can provide components for creation of more detailed kinetic models of metabolic processes in cancer leading to more information about possible markers as well as platforms for in silico treatment testing. Once a more detailed understanding of the characteristics of M. Cuperlovic-Culf (B)
PLOS Computational Biology, 2018
Cancer metabolism has received renewed interest as a potential target for cancer therapy. In this study, we use a multi-scale modeling approach to interrogate the implications of three metabolic scenarios of potential clinical relevance: the Warburg effect, the reverse Warburg effect and glutamine addiction. At the intracellular level, we construct a network of central metabolism and perform flux balance analysis (FBA) to estimate metabolic fluxes; at the cellular level, we exploit this metabolic network to calculate parameters for a coarse-grained description of cellular growth kinetics; and at the multicellular level, we incorporate these kinetic schemes into the cellular automata of an agent-based model (ABM), iDynoMiCS. This ABM evaluates the reaction-diffusion of the metabolites, cellular division and motion over a simulation domain. Our multi-scale simulations suggest that the Warburg effect provides a growth advantage to the tumor cells under resource limitation. However, we identify a non-monotonic dependence of growth rate on the strength of glycolytic pathway. On the other hand, the reverse Warburg scenario provides an initial growth advantage in tumors that originate deeper in the tissue. The metabolic profile of stromal cells considered in this scenario allows more oxygen to reach the tumor cells in the deeper tissue and thus promotes tumor growth at earlier stages. Lastly, we suggest that glutamine addiction does not confer a selective advantage to tumor growth with glutamine acting as a carbon source in the tricarboxylic acid (TCA) cycle, any advantage of glutamine uptake must come through other pathways not included in our model (e.g., as a nitrogen donor). Our analysis illustrates the importance of accounting explicitly for spatial and temporal evolution of tumor microenvironment in the interpretation of metabolic scenarios and hence provides a basis for further studies, including evaluation of specific therapeutic strategies that target metabolism.
Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer
eLife, 2014
Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.
Probabilistic model checking of cancer metabolism
Scientific Reports, 2022
Cancer cell metabolism is often deregulated as a result of adaption to meeting energy and biosynthesis demands of rapid growth or direct mutation of key metabolic enzymes. Better understanding of such deregulation can provide new insights on targetable vulnerabilities, but is complicated by the difficulty in probing cell metabolism at different levels of resolution and under different experimental conditions. We construct computational models of glucose and glutamine metabolism with focus on the effect of IDH1/2-mutations in cancer using a combination of experimental metabolic flux data and patient-derived gene expression data. Our models demonstrate the potential of computational exploration to reveal biologic behavior: they show that an exogenously-mutated IDH1 experimental model utilizes glutamine as an alternative carbon source for lactate production under hypoxia, but does not fully-recapitulate the patient phenotype under normoxia. We also demonstrate the utility of using gene expression data as a proxy for relative differences in metabolic activity. We use the approach of probabilistic model checking and the freelyavailable Probabilistic Symbolic Model Checker to construct and reason about model behavior. Deregulation of cell metabolism has recently been designated an emerging hallmark of cancer 1. This designation recognizes both the important role that reprogramming of energy metabolism plays in cancer, as well as the need to better understand the mechanisms by which this reprogramming is achieved, its relation to other signalingrelated hallmarks, and ultimately how it can be exploited to develop new therapies. The classical cancer metabolic phenotype, known as the Warburg phenotype 2 , is characterized by a shift toward relying on aerobic glycolysis-an inefficient way of metabolizing glucose to supply cellular energetics that is typically deployed in hypoxic conditions. The Warburg phenotype has been observed in many aggressive cancers, and is thought to be an adaptation that enables fast-growing cells to fuel biosynthesis through glycolytic intermediates, and thrive in the tumor micro-environment characterized by gradients of hypoxia. In some cancers metabolism is directly affected through mutation of key metabolic enzymes, most notably the IDH family of enzymes. IDH1, IDH2, and IDH3 catalyze the conversion of (iso)citrate to α-ketogluterate (aKG) in the cytoplasm (IDH1) and mitochondria (IDH2 and IDH3). Single amino acid missense mutations in IDH1 at arginine 132 (R132) or the analogous residue in IDH2 (R172 or R140) lead to gain of novel catalytic function-the conversion of aKG to (D)2-hydroxyglutarate (2HG) 3. These mutations are found in more than 80% of lower grade gliomas and secondary glioblastomas, as well as in acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms, and cholangiocarcinoma 4,5. The accumulation of 2HG , which has no physiologic function in mammals, has an oncogenic effect through a number of mechanisms, which include epigenetic silencing of transcription. IDH mutation status has also been shown to have prognostic value: patients with IDH-mutant gliomas live longer than those with IDH-wild-type gliomas, even when controlling for other prognosticators like age at diagnosis and grade. The discovery of the oncogenic role of 2HG fueled hopes that inhibiting mutant IDH and therefore its ability to produce 2HG would be an effective therapy against IDH-mutant cancers. However, inhibition of the mutant IDH function has not translated into better outcomes. These failed efforts might be due to the high levels of 2HG playing an initiator role in cancer genesis (for example, IDH mutations in gliomas have been shown to proceed TP53 mutations or 1p/19q chromosome deletions) that once started becomes independent of the levels of 2HG. However, other consequences of IDH mutations have been discovered: for example, recent studies have shown that the mutations compromise the ability of wild-type IDH to catalyze the reverse conversion of aKG or glutamate to citrate 6-8. Many unanswered questions remain on the role of IDH mutations in cancer. For example, while typically slow growing, IDH-mutant gliomas eventually progress to an aggressive phenotype with Warburg-like metabolism 9. How these metabolically-defective cells adapt to the high energy demands of fast-growing cells is not known.