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Cancer has been proposed as an example of systems biology disease or network disease. Accordingly, tumour cells differ from their normal counterparts in terms of intracellular network dynamics more than in terms of a number of specific molecules. Here we shall focus on a recently recognized hallmark of cancer, the enhanced reliance on glycolysis even under aerobic conditions, also known as Warburg effect. Glycolysis is known to be triggered by oncogene activation as well as by hypoxia in the tumour microenvironment. The constitutive activation of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway has been confirmed as an essential step towards cell transformation. Here we will consider how the effects of Akt activation are connected with cell metabolism. We will review existing models of the biochemical processes composing the metabolic network and we will discuss the current knowledge available to construct a kinetic model of the most relevant metabolic processes regulated by PI3K/Akt pathway. The model will enable a systems biology approach to predict the metabolic targets that may inhibit cell growth under constitutively active Akt conditions.
Keywords: PI3K/Akt pathway, metabolic network, systems biology, glycolysis, Warburg, cancer
ACC, Acetyl-CoA carboxylase; ACD, acyl-CoA dehydrogenase; ACL, ATP citrate lyase; ACSL, Long-chain-fatty-acid--CoA ligase; ADP, Adenosine Diphosphate; ALD, fructose 1,6 bisphosphate aldolase; AMP, Adenosine Monophosphate; ATP, Adenosine Triphosphate; BPG, 1,3-bisphosphoglycerate; CAC, carnitine acyl-carnitine carrier; CoA, Coenzyme A; CPT1A, Carnitine palmitoyltransferase 1A; CPT II, Carnitine palmitoyltransferase II ; DHAP, dihydroxyacetone phosphate; ECH, enoyl-CoA hydratase; EG, Extended Glycolysis, ENO, enolase; EP, Phosphoribulose epimerase; E4P, Erythrose-4-phosphate; FA, fatty acid; FASN, Fatty acid synthase; FASO, fatty acid synthesis and Î²-oxidation; F16BP, fructose-1,6-bisphosphate; F26BP, fructose 2,6-bisphosphate; F6P, fructose-6-phosphate; GAP, glyceraldehyde-3-phosphate; GMM, glutamine mithocondrial metabolism; G6PD, glucose-6-phosphate dehydrogenase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; Glc_in, intracellular glucose; Glc_out, extracellular glucose; GLUT, glucose transporter; G6P, glucose-6-phosphate; HCD, 3-Hydroxyacyl CoA Dehydrogenases; HK, hexokinase; HPI, hexose-6-phosphate isomerase; KI, Ribose phosphate isomerase; LDH, lactate dehydrogenase; MT, metabolite transporter; NAD, Nicotinamide Adenine Dinucleotides; NADH, Nicotinamide Adenine Dinucleotides; OCT, 3-oxoacyl-CoA thiolase; PDC, Pyruvate Dehydrogenase Complex; PEP, phosphoenolpyruvate; PFK-1, phosphofructokinase type 1; PFK-2, phosphofructokinase type 2; 6PG, 6-phosphogluconate; 2PGA, 2-phosphoglycerate; 3PGA, 3-phosphoglycerate; PGAM, 3-phosphoglycerate mutase; 6PGD, phosphogluconate dehydrogenase; PGK, phosphoglycerate kinase; PI3K, phosphatidylinositol 3-kinase; PYC, Pyruvate carrier; PYK, pyruvate kinase; PYR, pyruvate; PRPPS, Phosphoribosyl-pyrophosphate synthetase; RC, Respiratory Chain, R5P, Ribose-5-phosphate; Ru5P , ribulose-5-phosphate; S7P, Sedoheptulose-7-phosphate; TA, Transaldolase; TCA, tricarboxylic acid; TK1, Transketolase 1; TK2, Transketolase 2; TPI, triosephosphate isomerase; X5P, Xylulose-5-phosphate.
The intrinsic differences between cancer and normal cells are key when trying to identify new targets for anticancer drugs and to overcome chemo-resistance to anticancer therapy. Like drivers on busy roads of big cities begin to turn around to reach their destination, intracellular networks allow cancer cells to bypass the effect of a drug using alternative pathways to exploit a critical function for their survival: it is thus increasingly believed that a systems biology approach, focused on the analysis of the structure and dynamics of these networks, can lead to a better comprehension of cancer disease and could aid the design of safe drugs and therapies (Wang, 2010). To summarize these concepts, cancer has been designated as a systems biology disease (Hornberg et al., 2006, Laubenbacher et al., 2009)
However, nowadays the development of systems biology in cancer research is still limited, especially when more specific applications are concerned. Here we will focus on a particular phenomenon found in several kinds of cancers, the enhanced activity of the glycolytic pathway (Warburg, 1956). Vis-à-vis possible limitations in oxygen supply, quite a few tumour cells produce the most of their ATP through the glycolytic pathway, thereby producing more lactate than their untransformed counterparts (DeBerardinis et al., 2008; Pedersen, 2007).
We will review current models of glycolysis and its related pathways and we will discuss the current knowledge available to construct a detailed kinetic model of the most relevant metabolic effects of the PI3K/Akt pathway. Such a kinetic model enables a systems biology approach to identify potential metabolic targets that exploit the addiction of tumour cells to increased glucose uptake and glycolysis.
Glycolysis and Warburg effect
Studies conducted in the early twentieth century demonstrated that, unlike normal tissues, tumour cells are highly dependent on fermentation reactions to survive. Starting from these studies Otto Warburg made one of the first hypotheses on the origins of cancer (Warburg, 1956). In addition to the six recognized hallmarks of cancer (Hanahan and Weinberg, 2000), aerobic glycolysis has been recently accepted as a metabolic property of most tumours (Hsu and Sabatini, 2008; Yeung et al., 2008). Overall energy metabolism is greatly affected during cellular transformation (reviewed in Vander Heiden et al., 2009). Primarily, cancer cells gain the ability to proliferate even in the absence of growth signals. Furthermore, oncogenic mutations often result in increased uptake of nutrients, particularly glucose. Glucose is then metabolized into lactate regardless of oxygen supply by the chain of reactions known as "aerobic glycolysis". This glycolytic switch was first described by Warburg, who proposed that this phenomenon was related to defects in mitochondria, but it has later been shown that oxidative phosphorylation is not always impaired in cancer cells (Moreno-Sánchez et al., 2007).
Besides an enhanced glycolytic activity, cancer cells also display an increased proliferation rate. In order to replicate, they uptake extra-cellular nutrients and convert them into biosynthetic precursors, such as nucleic acids, proteins and lipids (Tong et al., 2009). Tumour cells can achieve this goal through changes in the activation status of oncogenes, but, as a consequence, they become oncogene-addicted (Weinstein, 2000).
AKT relevance in cancer
The constitutive activation of the PI3K/Akt pathway has been confirmed by epidemiological and experimental studies as an essential step toward the initiation and maintenance of human tumours (Tokunaga et al., 2008). The PI3K/Akt pathway regulates several cellular functions, including proliferation, growth, survival and mobility (Carnero et al., 2008).
Notably, the PI3K/Akt pathway is related to both life and death signalling, and it plays a major role not only in tumour growth but also in the potential response of tumours to anticancer treatments (Huang et al., 2009). Alterations of this signalling pathway are frequent in human cancer and promote cancer cell resistance to anti-tumour drugs, by overcoming the apoptotic pathway (Asnaghi et al., 2004; LoPiccolo et al., 2007; Tokunaga et al., 2008).
In particular, the abnormal activation of the PI3K/Akt pathway in tumour cells prevents the down-regulation of cell metabolism, protein synthesis and cell growth when nutrients become limiting (Aki et al., 2003; Bruno et al., 2007; Elstrom et al., 2004). Accordingly, the effects of Akt activation on cell survival may be connected with its effects on cell metabolism. Here we will focus on the molecular targets of the PI3K/Akt pathway involved in energy metabolism and we will review how they are affected when Akt activity is up-regulated in cancer cells.
Role of Akt in cancer cell metabolism
The serine-threonine kinase Akt is a key molecule involved in the signal transduction pathways of many extra-cellular inputs (Kandel and Hay, 1999). One of the most important physiological functions of Akt is to stimulate glucose uptake in response to insulin (Welsh et al., 2005) (Figure 1). The Akt transduction pathway is responsible for transmitting insulin signal to the metabolic, transcription, and translation machinery of the cell (Burgering and Coffer, 1995; Manning and Cantley, 2007). In untransformed cells, the withdrawal of growth factors results in a depletion of ATP and glucose-derived metabolites within the cell (Rathmell et al., 2003). On the contrary, Akt constitutive activation allows cells to continue to import glucose and amino acids (Edinger and Thompson, 2002). Activated Akt has also been shown to increase the glycolitic flux (Robey and Hay, 2009; Young and Anderson, 2008). Then, where, precisely, does it act?
Upon insulin stimulation, Akt associates with glucose transporter 4 (Glut4)-containing vesicles (Calera et al., 1998) leading to Glut4 translocation to the plasma membrane. However, constitutively active Akt, is able to induce glucose uptake by stimulating translocation of Glut4 to the plasma membrane even in the absence of insulin (Kohn et al., 1996). The constitutively
active Akt also increases the synthesis of Glut1, the main glucose transporter in most cell types (Kohn et al., 1996). In particular, the activation of Akt enhances transcription and translocation of Glut1 from the cytosol to the plasma membrane, increasing glucose uptake (Barthel et al., 1999; Rathmell et al., 2003).
Akt activation can also alter glucose metabolism within cells. Glucose conversion into glucose 6-phosphate represents the first step of the glycolytic pathway and it is accomplished by cellular hexokinases (HKs). The activity of HK isoforms is finely regulated (reviewed by Pastorino and Hoek, 2008). HK isoforms I and II bind to the mitochondrial outer membrane, where high ATP concentrations favour enzymatic phosphorylation of glucose (Majewski et al., 2004). Upon Akt activation, translocation of HKs to the mitochondria is enhanced (Elstrom et al., 2004), although the mechanism by which mitochondrial binding of HK is stimulated remains elusive.
Akt has also effects on other regulatory elements of glycolysis; in fact it has been shown that increasing Glut1 and HK expression does not enhance the glycolytic flux to the levels observed with constitutive activation of Akt (Rathmell et al., 2003). Glycolysis downstream targets of Akt include Phosphofructo-Kinase-2 (PFK-2). Phosphorylation and activation of PFK-2 lead to allosteric activation of Phosphofructo-Kinase-1 (PFK-1) (Deprez et al., 1997). These enzymes convert Fructose-6-Phosphate (F6P) into Fructose-1,6-Bisphosphate (F16BP), a key step in glucose metabolism.
Furthermore Akt can contribute to fatty acid (FA) oxidation and FA synthesis by regulating multiple steps of these pathways (Figure 2). The tricarboxylic acid (TCA) cycle generates citrate that is next exported to the cytoplasm by the action of citrate transport proteins. Cytosolic citrate is used for lipid and cholesterol biosynthesis, it generates acetyl-CoA by ATP-citrate lyase (ACL), which is directly phosphorylated and activated by Akt (Berwick et al., 2002). Activation of ACL supports increased acetyl-CoA and malonyl-CoA production, inhibiting FA oxidation and inducing lipid synthesis in Akt-expressing cells (Buzzai et al., 2005). Activated PI3K/Akt pathway stimulates FA synthesis by a direct activation of ACL and inhibition of ï¢-oxidation by down-regulating the expression of the Î²-oxidation enzyme carnitine palmitoyltransferase 1A (CPT1A) as described by DeBerardinis et al. (2006). It has also been hypothesized that Akt might regulate other steps of lipid metabolism by suppressing expression of proteins required for FA oxidation, but further studies are needed to elucidate the mechanisms underlying additional roles of Akt in cancer cell metabolism.
1.4 Integrative systems biology approaches to identify molecular targets in anticancer therapy
As introduced earlier, an integrative systems biology approach is essential to assess the complex network of pathways connecting gene regulation, signalling and cell metabolism, and the relevant alterations occurring in malignant cells (Hornberg et al., 2006). A systems biology approach combines empirical, mathematical and computational tools to understand complex patho-physiological phenomena. As a consequence, a systems perspective on cancer prompts the application of mathematical and computational models in order to deal with the large amounts of data and the relationships within the datasets (Anderson and Quaranta, 2008).
Computational models of cancer should capture the fundamental biological processes that are the hallmarks of cancer. Progress has been made in understanding the properties of cancer cells from a systems biology point of view (Laubenbacher et al., 2009). It is now accepted that oncogenic mutations affect cell behaviour by changing the cellular network to trigger malignancy (Pawson and Warner, 2007; Bizzarri et al., 2008). Thus, network biology is useful to represent, compute and model biological relationships and to get further insight into cellular mechanisms (Kreeger and Lauffenburger, 2010).
Previously, we have outlined recent studies of one of the most commonly mutated signalling pathways in cancer: the PI3K/Akt pathway. Our focus is on how Akt oncogenic activation changes the metabolic network and how this information can be used to identify new targets and treatment strategies in anticancer therapy. In section 3, the available knowledge for the construction of a detailed kinetic model to simulate the PI3K/Akt pathway metabolic network will be described.
Such a model can be implemented linking different kinetic models to each other. To reach this goal, a number of guidelines should be addressed in order to ensure a uniform quality of the models. The members of the silicon cell project (Snoep, 2005), an international consortium which aims at making computer replica of sub-cellular systems, place the emphasis on using experimentally determined values for model parameters , wherein the measurements should be made on an isolated reaction step in order to ensure context independence. Moreover, they underline the importance of linking models that have been constructed under the same experimental conditions and of using an experimentally measured law to represent the (dynamic) behaviour of boundary metabolites (Snoep et al., 2006).
We will follow these guidelines to discuss the construction of an integrated model with detailed kinetic laws for the metabolic processes regulated by the PI3K/Akt pathway, whenever it is possible. In particular, the requirement of equal experimental conditions can be hardly met, since the currently available systems biology models have been constructed relying on a broad variety of experimental conditions.
The integrative model will capture the structure and dynamics of the metabolic network regulated by the PI3K/Akt pathway. In this model the effects of Akt activation will be reproduced acting on the rate of the biochemical processes regulated by PI3K/Akt pathway; more precisely, this can be done introducing quantitative changes in the enzyme kinetics values. This model might then be used to simulate normal and aberrant behaviour of the considered metabolic network, and to test hypotheses about the mechanisms underlying the PI3K/Akt pathway effects on tumour metabolism. The model will thus enable in silico experiments. Eventually, the results derived from this system biology approach might be experimentally validated in cancer cells. Through this approach, cancer systems biology will allow the integration of computational and experimental data at various levels and has the potential to hint possible anticancer therapeutic strategies.
Kinetic models of glucose/energy metabolism
Compared to signal transduction and gene regulatory networks, metabolic pathways are easier to study. The enzymes can be isolated and characterized in vitro while reaction fluxes can be quantified in vivo: therefore, it is possible to collect data concerning the kinetics of each biochemical reaction and the overall behaviour of a metabolic pathway. As a consequence, metabolic systems have been well characterized and were amongst the first to be reproduced by detailed kinetic models, especially glycolysis. With modern genome-sequencing capabilities, the size of metabolic models increased until the first genome-scale metabolic network was published (Edwards and Palsson, 1999). During the last decade the field of genome-scale metabolic network analysis has grown rapidly and nowadays more than 50 genome-scale metabolic reconstructions are available and span several species of bacteria and eukaryotes, including human. These models have already led to many advances both at the theoretical and practical level (see Oberhardt et al., 2009 for a review of the applications of genome scale metabolic reconstructions), although these so-called structural models focus on the reaction network structure and not on the kinetics. In fact, the integration of detailed kinetics into these models and the determination of an adequate amount of kinetics parameters (both steps enabling an accurate study of the system dynamics) are standing challenges. Even if some efforts are ongoing in this field (Herrgard et al., 2006, Lee et al., 2008), current detailed kinetics models include, compared to cellular level models, a relatively limited number of biochemical processes.
The earliest models concerning glycolysis appeared in 1960s (Chance et al., 1960, Garfinkel et al., 1964), and nowadays tens of models including the glycolytic reactions are publicly available in web resources. Currently, the BioModels Database (Li et al., 2010) at the European Bioinformatics Institute, one of the most important resources that allows users to store, search and retrieve published mathematical models of biological interest, provides about 40 models concerning glycolysis, half of which are classified as "curated". The availability of annotated models, for example according to the MIRIAM specifications (Le Novère et al., 2005) encoded in a standard language (such as the SBML (Hucka et al., 2003) or CellML (Cuellar et al., 2003)), is mostly important since it enables the direct usability of models by several computational tools.
The differences among the available models regarding glycolysis concern a number of factors leading to the inclusion of a different set of biochemical processes or to a different mathematical formulation. There are "core" metabolic models, where only the most important reactions (e.g. important regulatory steps and branches) are included (e.g. Galazzo et al., 1990) and detailed models, where more or less every biochemical reaction of the studied pathway is considered. An important aspect to be considered is the approach used for the definition of the kinetic parameters: while some models have been defined relying on extensive fitting of kinetics values on systemic datasets (e.g. Rizzi et al., 1997), other exploit values experimentally determined by studying isolated enzymes (e.g. Teusink et al., 2000). Even though each model can be quantitatively different from another, it is possible to distinguish the largest fraction of models in which glycolytic intermediates reach a (stable) steady state, from models exhibiting oscillations of some metabolites (e.g. Nielsen et al., 1998). Glycolysis models have been constructed for several organisms and cell types; a large number of models exist for yeast, while for the human species, detailed kinetic models have been constructed mainly for erythrocytes (e.g. Mulquiney and Kuchel, 1999), skeletal muscle (e.g. Lambeth and Kushmerick, 2002) and pancreatic beta-cells (e.g. Jiang et al., 2007).
Even if not ubiquitous in every glycolysis model, a number of (species-specific) glycolytic branches, such as those concerning disaccharides and polysaccarides metabolism, and biochemical reactions concerning the pyruvate destiny, such as those for lactic acid or ethanol fermentation and acetyl-CoA production, are usually considered (Conant and Wolfe, 2007), while the pentose phosphate shunt is detailed only in a few models (Holzhütter, 2004). Due to the importance of the TCA cycle, some models exist for it (Singh and Ghosh, 2006), even coupled with the respiratory chain (Nazaret at al., 2009), with the fatty acid Î²-oxidation and the mitochondrial inner-membrane metabolite transport system (Yugi and Tomita, 2004).
A detailed kinetic model to study the PI3K/Akt pathway-mediated metabolic effects
During the construction of a kinetic model of the PI3K/Akt pathway metabolic effects, it has to be found the optimal compromise between two requirements with an opposite outcome. On the one hand, it is important to consider as many metabolic reactions directly and indirectly related to the PI3K/Akt pathway as possible, in order to ensure an accurate reproduction of intracellular kinetics. On the other hand, as the set of reactions to be included gets larger, model construction and analysis becomes harder, due to a series of issues such as the lack of enzyme kinetics data, the number of parameters with uncertain values and computational requirements.
In this section we will describe a model that is an optimal solution according to (i) the metabolic pathways influenced by Akt activation and (ii) the knowledge available in the literature required for modelling such metabolic processes.
It is possible to describe the model as composed of six modules: the "extended" glycolysis (EG), the TCA cycle, the respiratory chain (RC), the FA synthesis and Î²-oxidation (FASO), the glutamine mitochondrial metabolism (GMM) and the metabolite transporter (MT), as shown in Figure 3 and Table 1.
The EG module (Figure 1) includes the glucose transporter, the glycolytic pathway, the glycogen branch, the pentose phosphate shunt and two reactions concerning the pyruvate fate. We considered the reactions and kinetic laws provided by Marín-Hernández et al. (in press) for the glucose transporter, the ten glycolytic reactions, the glycogen branch and the lactic acid fermentation. The authors used enzyme-specific kinetic laws and kinetic parameter values experimentally determined in cytosolic extracts of HeLa cells under the same experimental conditions. The authors simplified the glycogen branch into two irreversible reactions representing the glycogen synthesis and degradation rates which are, however, supported by experimental data concerning the glycogen metabolism (e.g. glycogen content, synthesis and degradation fluxes). Their model also considers the pentose phosphate shunt and the mitochondrial pyruvate destiny with simplified reactions. As both these pathways play a relevant role in relation to the PI3K/Akt pathway-mediated metabolic effects, we replaced these parts with detailed reactions from other models available in the literature. Regarding the pentose phosphate shunt, it has been proposed that one of the potential benefits to a cancer cell of a high glycolytic rate is the availability of glucose for the production of NADPH by means of the oxidative arm of the pentose phosphate cycle, which may be important in maintaining the redox state of a cell under oxidative stress (Elstrom et al., 2004). Therefore, we considered the pentose phosphate shunt reactions and kinetic laws provided in the model by Holzhütter et al. (2004), which is in turn the integration of previous works where the kinetic parameters were experimentally determined (McIntyre et al., 1989; Boyer, 1962; Barman, 1969; Lueck and Fromm, 1974). Lastly, we considered the pyruvate transport into mitochondria and its transformation into acetyl-CoA with the kinetic laws included in the model by Yugi and Tomita (2004). This model is particularly interesting since the authors reconstructed the mitochondrial metabolism, integrating enzyme-specific kinetic laws from several studies. The kinetic parameter values used in this model were found in the literature or computationally estimated in order to fulfil the Lineweaver-Burk plots of each enzyme.
In our model we considered the TCA and RC modules due to their central role in metabolism and, as a consequence, in cancer cells metabolism (Kroemer, 2006). These modules were entirely taken from the model by Yugi and Tomita (2004). More precisely, the TCA module includes the nine metabolic processes of the TCA cycle and the anaplerotic reaction of the pyruvate carboxylase. The FASO module encompasses the anabolic pathway of FAs synthesis and the catabolic process that leads to the conversion of cytosolic FAs into mitochondrial Acetyl-CoA (Figure 2). We included these processes since the PI3K/Akt pathway regulates FA metabolism by acting, at least, on two proteins: it promotes the FAs synthesis increasing the activity of ACL and it inhibits the Î²-oxidation decreasing the concentration of CPT1A. We describe FAs synthesis using four enzymatic reactions, respectively catalyzed by ACL, acetyl carboxylase (ACC), FA synthase (FASN), and an irreversible reaction representing the flux towards lipid metabolism. The ACL, ACC and FASN kinetic laws provided with experimentally determined kinetic parameters can be found in different studies (respectively Plowman and Cleland, 1967, Kaushik et al., 2009 and Cox et al., 1983). We considered a mass action kinetics for the irreversible reaction representing the flux towards lipid metabolism. The reactions for the entry of FAs into mitochondria and Î²-oxidation were taken from the model by Yugi and Tomita (2004). Since this model lacks the pre-step of Î²-oxidation, catalysed by the acyl-CoA synthetase (ACSL), we considered the kinetic law and parameters experimentally determined for purified murine ACSL (Hall et al., 2003).
Glutamine, which is highly transported into proliferating cells (Wise et al., 2008), is a major source for energy and nitrogen for biosynthesis, and a carbon substrate for anabolic processes in cancer cells (DeBerardinis et al., 2010). In cancer, glutamine can serve as an alternative substrate for the TCA cycle in order to produce ATP and can become critical for biosynthesis and survival. Hence, it can be used as an energy substrate when glucose supply is limited (Yuneva, 2008), the so-called "glutamine addiction" phenomenon (Wise and Thompson, 2010). Thus, we included a series of reactions which account for the glutamine metabolization in the TCA cycle. More precisely, we considered three biochemical processes. The first one is the transport of glutamine from the cytosol into mitochondria, for which we considered the kinetic law provided by Steib et al. (1986), with kinetic parameters experimentally determined in rat brain. The second process is the conversion of glutamine into glutamate catalyzed by the mitochondrial glutaminase; here we took into account the kinetic law and parameters experimentally determined in rat kidney (Haser et al., 1995). Finally, we considered the conversion of glutamate into oxoglutarate by the glutamate dehydrogenase and the associated kinetic law included in the model of ammonium assimilation in E. coli (Bruggeman et al., 2005).
Lastly, we considered a MT module which includes the malate-asparate shuttle, the acetyl groups transporter shuttle, and other transporters not included in the other modules, but required to reproduce the metabolites traffic (or translocation) between cytosol and mitochondria. Essentially, we considered all the carriers described in the model by Yugi and Tomita (2004) plus the malic enzyme (considered to be part of the acetyl groups shuttle system), with its specific kinetic law and kinetic parameters experimentally determined in rat skeletal muscle (SwierczyÅ„ski, 1980).
Modelling of the Akt-mediated metabolic effects
To correctly reproduce the PI3K/Akt signalling pathway effects on the model, it is fundamental to take into account the specific ways in which the PI3K/Akt pathway affects the activity of the metabolic players it regulates: Glut, PFK-1, HK, ACL and CPT1A (see Figure 3, where the global regulation of the PI3K/AKT pathway on the different modules is shown). Once the final effect of the PI3K/Akt pathway over the activity of a particular protein is known, this can be simulated acting on the kinetic law used to describe the rate of the process regulated by the protein.
The PI3K/Akt pathway-mediated regulation of Glut leads to an increase of its concentration. Thus, we can reproduce this effect increasing the Vmax parameter (the maximum velocity) value appearing in the monosubstrate Michaelis-Menten equation used to model the rate of glucose transport inside the cell. Similarly, but with the opposite effect, the CPT1A concentration is decreased by the activation of the PI3K/Akt pathway and thus we can reduce the value of the appropriate Vmax in the kinetic law of this transporter. The increased PFK-1 activity due to Akt is ultimately determined by a higher concentration of one of the PFK-1 allosteric activators: F26BP. As this positive interaction is explicitly considered in the PFK-1 kinetic law that we use, we can increase the F26BP concentration value to reproduce the increased PFK-1 activity due to the PI3K/Akt pathway.
Even if the mechanism by which the PI3K/Akt pathway regulates the HK is not completely understood, it is known that Akt activation increases the concentration of mitochondrial bound HK, leading to a more efficient conversion of Glucose to Glucose-6-Phosphate. Hence, it is possible to reproduce this effect increasing the Vmax value of the bi-substrate Michaelis-Menten kinetic law used for this reaction.
The activation of the PI3K/Akt pathway determines the phosphorylation of ACL, which in turn increase the activity of the enzyme. This event can be reproduced in silico introducing in the ACL kinetic law the experimentally determined quantitative values of Vmax and Km for phosphorylated ACL (Potapova et al., 200).
The constitutive activation of the PI3K/Akt pathway has been confirmed as an essential step towards cell transformation. Cancer cells use the PI3K/Akt pathway signalling to alter their metabolism in several points in order to gain a number of selective advantages compared to the other cells. Currently, several therapeutic strategies that target the PI3K/Akt pathway for the treatment of cancer have been proposed and are under clinical development (Engelman, 2009). At the same time it is becoming more and more evident that targeting single gene products or pathways yields low rates of response and should not be expected to cure cancer (Hayden, 2008). PI3K/Akt pathway-mediated effects modify the activity of several proteins which, in turn, control mass and energy flux distribution through a tightly interconnected metabolic network. As a consequence, the altered metabolic network shows different dynamics compared to the normal one. In this context, an effective therapeutic approach should attack the altered system in order to switch it off without affecting the normal system. Indeed, Metabolic Control Analysis (MCA) and the oncologic clinical practice, have both demonstrated that control of function is shared by multiple steps (Moreno-Sánchez et al., 2010). The availability of a mathematical model makes easier the calculation of the control coefficients, a crucial step in the MCA operational framework (Moreno-Sánchez et al., 2010). Moreover, a kinetic model can be perturbed in order to study in silico a large number of simulated conditions, limiting the wet experiments toward the most promising (predicted) scenarios. By comparing MCA control coefficients between normal and altered model parameterizations it is possible to predict the enzymes or transporters which show the highest control difference between the two conditions. Considering the model of PI3K/Akt pathway-mediated metabolic effects that we described above, the proteins with the highest control coefficient differences between normal and altered condition will be candidates to be selective drug targets. Targeting these proteins might be a good strategy to obtain one or a combination of desirable outcomes, such as the inhibition of the aerobic glycolysis and glutamine metabolization towards mitochondria, the re-activation of FAs ï¢-oxidation or the inhibition of FAs synthesis. Importantly, due to the complex behaviour of such metabolic network, it is not granted that the optimal intervention points are one or more of the metabolic targets of the PI3K/Akt pathway that we discussed above (i.e. GLUT, HK, PFK-1, CPT and ACL). With the aim of enabling such a systems biology approach, we here reviewed and discussed the framework of knowledge available to model the metabolic network regulated by the PI3K/Akt pathway, emphasizing the use of enzyme-specific kinetic laws with experimentally derived kinetic parameters.
Although the analysis of the current knowledge suggests that a series of detailed kinetic laws are available for the most important biochemical processes that link together in an integrated metabolic network all the processes regulated by the PI3K/Akt pathway, the differences existing among the experimental conditions used for the characterisation of enzyme and transporters kinetics demand a computational phase of parameter estimation to obtain a coherent model which fit the experimental observations, before it can be exploited to gain in silico predictions.
Nevertheless, new data sets are continuously made available and measuring everything is not mandatory (Alberghina and Westerhoff, 2005). The systems biology approach presented here indeed allows to devise testable hypothesis based on a quantitative model that might ultimately aid in understanding the complex metabolic network mediated by the PI3K/Akt pathway.
This work has been supported by the EGEE-III, BBMRI, EDGE European projects and by CNR Italian Bioinformatics Network, the MIUR FIRB ITALBIONET (RBPR05ZK2Z) and (RBIN064YAT003), LITBIO (RBLA0332RH), BIOPOPGEN (RBIN064YAT) initiatives.