Application of differential metabolic control analysis to identify new targets in cancer treatment

In the quest for anti-cancer drugs with high efficacy and low toxicity, cancer metabolism has increasingly been a focus of interest in clinical research. Enhanced glycolysis and robust production of lactate constitute characteristic traits that discriminate many cancerous cells from their normal counterparts. This, in principle, may provide researchers with a general handle on such a complex disease, regardless of the intrinsic genotypic heterogeneity of the single transformed cells. The work carried out during this project and presented in this thesis consists of developing and applying analytical approaches, mainly drawn from the field of metabolic control analysis (MCA), to the study of cancer metabolism. The ultimate goal is to assess whether, and to what extent, the metabolic features of cancer cells may be exploited in the attempt to attack the malignancy more specifically than through traditional clinical approaches. The underlying idea consists of identifying enzymes that represent points of fragility specifically characterising the cancerous metabolic phenotype. These enzymes are such that an alteration in their activity (due for example to the action of an anticancer drug) would elicit the desired response in cancer cells, without affecting their normal counterparts. The application of MCA relies on a mathematical representation of the system under study. Creating such a model is often hampered by the lack of data about the precise kinetic laws governing the different reaction steps and the value of their corresponding parameters. The most important result reached during this project shows that the metabolic quantities defining the normal and cancer phenotypes (such as fluxes and metabolite concentrations), together with heuristic assumptions about the properties of typical enzyme-catalyzed reactions, already allow for a fast and efficient way to explore the effectiveness of putative drug targets with respect to criteria of high efficacy and low toxicity. The relevance of this result lies in the fact that the quantities defining a metabolic phenotype are experimentally more accessible than the kinetic parameters of the different enzymatic steps in the system.EThOS - Electronic Theses Online ServiceBBSRCEPSRCGBUnited Kingdo

Capturing the essence of a metabolic network: A flux balance analysis approach

As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux Balance Analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented. Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of a FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole. Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined

Why does yeast ferment? A flux balance analysis study

Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation

Understanding principles of the dynamic biochemical networks of life through systems biology

Systems Biology brings the potential to discover fundamental principles of Life that cannot be discovered by considering individual molecules. This chapter discusses a number of early, more recent, and upcoming discoveries of such network principles. These range from the balancing of fluxes through metabolic networks, the potential of those networks for truly individualized medicine, the time dependent control of fluxes and concentrations in metabolism and signal transduction, the ways in which organisms appear to regulate metabolic processes vis-à-vis limitations therein, tradeoffs in robustness and fragility, and a relation between robustness and time dependences in the cell cycle. The robustness considerations will lead to the issue whether and how evolution has been able to put in place design principles of control engineering such as infinite robustness and perfect adaptation in the hierarchical biochemical networks of cell biology © 2014 Elsevier Inc. All rights reserved