Inhibition of Non-flux-Controlling Enzymes Deters Cancer Glycolysis by Accumulation of Regulatory Metabolites of Controlling Steps

Glycolysis provides precursors for the synthesis of macromolecules and may contribute to the ATP supply required for the constant and accelerated cellular duplication in cancer cells. In consequence, inhibition of glycolysis has been reiteratively considered as an anti-cancer therapeutic option. In previous studies, kinetic modeling of glycolysis in cancer cells allowed the identification of the main steps that control the glycolytic flux: glucose transporter, hexokinase (HK), hexose phosphate isomerase (HPI) and glycogen degradation in human cervix HeLa cancer cells and rat AS-30D ascites hepatocarcinoma. It was also previously experimentally determined that simultaneous inhibition of the non-controlling enzymes lactate dehydrogenase (LDH), pyruvate kinase (PYK) and enolase (ENO) brings about significant decrease in the glycolytic flux of cancer cells and accumulation of intermediate metabolites, mainly fructose-1,6-bisphosphate (Fru1,6BP) and dihydroxyacetone phosphate (DHAP), which are inhibitors of HK and HPI, respectively. Here it was found by kinetic modeling that inhibition of cancer glycolysis can be attained by blocking downstream non flux-controlling steps as long as Fru1,6BP and DHAP, regulatory metabolites of flux-controlling enzymes, are accumulated. Furthermore, experimental results and further modeling showed that oxamate and iodoacetate inhibitions of PYK, ENO and glyceraldehyde3-phosphate dehydrogenase (GAPDH), but not of LDH and phosphoglycerate kinase, induced accumulation of Fru1,6BP and DHAP in AS-30D hepatoma cells. Indeed, PYK, ENO and GAPDH exerted the highest control on the Fru1,6BP and DHAP concentrations. The high levels of these metabolites inhibited HK and HPI and led to glycolytic flux inhibition, ATP diminution and accumulation of toxic methylglyoxal. Hence, the anticancer effects of downstream glycolytic inhibitors are very likely mediated by this mechanism. In parallel, it was also found that uncompetitive inhibition of the flux-controlling steps is a more potent mechanism than competitive and mixed-type inhibition to efficiently perturb cancer glycolysis

Bayesian inference for protein signalling networks

Cellular response to a changing chemical environment is mediated by a complex system of interactions involving molecules such as genes, proteins and metabolites. In particular, genetic and epigenetic variation ensure that cellular response is often highly specific to individual cell types, or to different patients in the clinical setting. Conceptually, cellular systems may be characterised as networks of interacting components together with biochemical parameters specifying rates of reaction. Taken together, the network and parameters form a predictive model of cellular dynamics which may be used to simulate the effect of hypothetical drug regimens. In practice, however, both network topology and reaction rates remain partially or entirely unknown, depending on individual genetic variation and environmental conditions. Prediction under parameter uncertainty is a classical statistical problem. Yet, doubly uncertain prediction, where both parameters and the underlying network topology are unknown, leads to highly non-trivial probability distributions which currently require gross simplifying assumptions to analyse. Recent advances in molecular assay technology now permit high-throughput data-driven studies of cellular dynamics. This thesis sought to develop novel statistical methods in this context, focussing primarily on the problems of (i) elucidating biochemical network topology from assay data and (ii) prediction of dynamical response to therapy when both network and parameters are uncertain

Analysis of Biochemical Reaction Networks using Tropical and Polyhedral Geometry Methods

The field of systems biology makes an attempt to realise various biological functions and processes as the emergent properties of the underlying biochemical network model. The area of computational systems biology deals with the computational methods to compute such properties. In this context, the thesis primarily discusses novel computational methods to compute the emergent properties as well as to recognize the essence in complex network models. The computational methods described in the thesis are based on the computer algebra techniques, namely tropical geometry and extreme currents. Tropical geometry is based on ideas of dominance of monomials appearing in a system of differential equations, which are often used to describe the dynamics of the network model. In such differential equation based models, tropical geometry deals with identification of the metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. The application of such properties in model reduction and symbolic dynamics are demonstrated in the network models obtained from a public database namely Biomodels. Extreme currents are limiting edges of the convex polyhedrons describing the admissible fluxes in biochemical networks, which are helpful to decompose a biochemical network into a set of irreducible pathways. The pathways are shown to be associated with given clinical outcomes thereby providing some mechanistic insights associated with the clinical phenotypes. Similar to the tropical geometry, the method based on extreme currents is evaluated on the network models derived from a public database namely KEGG. Therefore, this thesis makes an attempt to explain the emergent properties of the network model by determining extreme currents or metastable regimes. Additionally, their applicability in the real world network models are discussed

Development of a Workflow for a Robust Cytometry of Reaction Rate Constant

Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell population heterogeneity based on the activity of cellular processes. The original CRRC workflow did not account for cell motility, which led to inaccurate measurements in motile cells. Here, we report on the development of a new CRRC workflow that makes it applicable to motile cells. We confirmed the robustness of the new workflow to cell movement by performing a comparative CRRC workflow study of cross-membrane transport in motile cells. Using the new workflow, preliminary progress was made on the investigation of cytochrome p450 (CYP) activity. We validated the CRRC experimental procedure to conduct such study but found that the CRRC CYP assay had considerable variability. Nonetheless, the development of the new CRRC workflow is a step in the right direction with more work needing to be done to understand the variability found in the CRRC CYP assay

Development of a Workflow for a Robust Cytometry of Reaction Rate Constant

Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell population heterogeneity based on the activity of cellular processes. The original CRRC workflow did not account for cell motility, which led to inaccurate measurements in motile cells. Here, we report on the development of a new CRRC workflow that makes it applicable to motile cells. We confirmed the robustness of the new workflow to cell movement by performing a comparative CRRC workflow study of cross-membrane transport in motile cells. Using the new workflow, preliminary progress was made on the investigation of cytochrome p450 (CYP) activity. We validated the CRRC experimental procedure to conduct such study but found that the CRRC CYP assay had considerable variability. Nonetheless, the development of the new CRRC workflow is a step in the right direction with more work needing to be done to understand the variability found in the CRRC CYP assay

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

Using Mathematical Modelling and Electrochemical Analysis to Investigate Age‐Associated Disease

People are living longer. With this rise in life expectancy, a concomitant rise in morbidity in later life is observed; with conditions including cardiovascular disease (CVD), and cancer. However, ageing and the pathogenesis of age related disease, can be difficult to study, as the ageing process is a complex process, which affects multiple systems and mechanisms. The aim of this research was two‐fold. The first aim was to use mathematical modelling to investigate the mechanisms underpinning cholesterol metabolism, as aberrations to this system are associated with an increased risk for CVD. To better understand cholesterol from a mechanistic perspective, a curated kinetic model of whole body cholesterol metabolism, from the BioModels database, was expanded in COPASI, to produce a model with a broader range of mechanisms which underpin cholesterol metabolism. A range of time course data, and local and global parameter scans were utilised to examine the effect of cholesterol feeding, saturated fat feeding, ageing, and cholesterol ester transfer protein (CETP) genotype. These investigations revealed: the model behaved as a hypo‐responder to cholesterol feeding, the robustness of the cholesterol biosynthesis pathway, and the impact CETP can have on healthy ageing. The second aim of this work was to use electrochemical techniques to detect DNA methylation within the engrailed homeobox 1 (EN1) gene promoter, which has been implicated in cancer. Hypermethylation of this gene promoter is often observed in a diseased state. Synthetic DNA, designed to represent methylated and unmethylated variants, were adsorbed onto a gold rotating disk electrode for electrochemical analysis by 1) electrochemical impedance spectroscopy (EIS), 2) cyclic voltammetry (CV) and 3) differential pulse voltammetry (DPV). The technique was then applied to bisulphite modified and asymmetrically amplified DNA from the breast cancer cell line MCF‐7. Results indicated that electrochemical techniques could detect DNA methylation in both synthetic and cancer derived DNA, with EIS producing superiorresults. These non‐traditional techniques ofstudying age related disease were effective for the investigation of cholesterol metabolism and DNA methylation, and this work highlights how these techniques could be used to elucidate mechanisms or diagnose/monitor disease pathogenesis, to reduce morbidity in older peopl

Mathematical Modeling of Oxygen Transport, Cell Killing and Cell Decision Making in Photodynamic Therapy of Cancer

In this study we present a model of in vitro cell killing through type II Photodynamic Therapy (PDT) for simulation of the molecular interactions leading to cell death in time domain in the presence of oxygen transport within a spherical cell. By coupling the molecular kinetics to cell killing, we develop a modeling method of PDT cytotoxicity caused by singlet oxygen and obtain the cell survival ratio as a function of light fluence or initial photosensitizer concentration with different photon density or irradiance of incident light and other parameters of oxygen transport. A systems biology model is developed to account for the detailed molecular pathways induced by PDT treatment leading to cell killing. We derive a mathematical model of cell decision making through a binary cell fate decision scheme on cell death or survival, during and after PDT treatment, and we employ a rate distortion theory as the logical design for this decision making proccess to understand the biochemical processing of information by a cell. Rate distortion theory is also used to design a time dependent Blahut-Arimoto algorithm of three variables where the input is a stimulus vector composed of the time dependent concentrations of three PDT induced signaling molecules and the output reflects a cell fate decision. The concentrations of molecules involved in the biochemical processes are determined by a group of rate equations which produce the probability of cell survival or death as the output of cell decision. The modeling of the cell decision strategy allows quantitative assessment of the cell survival probability, as a function of multiple parameters and coefficients used in the model, which can be modified to account for heterogeneous cell response to PDT or other killing or therapeutic agents. The numerical results show that the present model of type II PDT yields a powerful tool to quantify various processes underlying PDT at the molecular and cellular levels and to interpret experimental results of in vitro cell studies. Finally, following an alternative approach, the cell survival probability is modeled as a predator - prey equation where predators are cell death signaling molecules and prey is the cell survival. The two models can be compared to each other as well as directly to the experimental results of measured molecular concentrations and cell survival ratios for optimization of models, to gain insights on in vitro cell studies of PDT.  Ph.D

MilkGuard: Predictive Modeling and Mobile App Development for Affordable, Usable Breast Milk Diagnostic

Breast milk is considered the gold standard of infant nutrition, but some infants around the world lack access to it due to maternal health complications or other considerations. Human breast milk banks do exist to try to alleviate this problem, but most are underfunded and have high operational costs, making it difficult for some infants to obtain safe, reliable donated breast milk. Existing methods of testing breast milk are expensive, so the MilkGuard project was conceptualized in 2017 as a fast, economical, and highly usable bacterial contamination detection system. Prior to this year, previous MilkGuard teams had developed a system that was faster and more affordable than prior methods, but its main drawbacks were that it was difficult to use and that it lacked the sensitivity to detect low Escherichia coli (E. coli) contamination levels. To ameliorate these drawbacks, our goals for this year were 1) to improve MilkGuard’s sensitivity to the Human Milk Banking Association of North America’s (HMBANA) lower limit of detection standard of 104 CFU/mL, 2) to increase the ease of the assay process, and 3) to achieve these objectives in an economical and environmentally-friendly way. Through COMSOL Multiphysics software simulations, we proved the possibility of realistically optimizing biosensor parameters on a computer. Since the simulations were virtual, we discovered an optimal biosensor configuration without the need to purchase, manufacture, and test hundreds of physical sensors. Future teams can quickly confirm these results by building a physical sensor in the lab. We also developed the MilkGuard app, which greatly simplifies the colorimetric analysis process for the user. This mobile app uses our improved color-analysis algorithm which improves detection sensitivity around the HMBANA’s lowered limit of detection standard, given the same image data to analyze. The efficacy of our new color analysis algorithm can be confirmed by future teams in the lab, and our current regression curve can be made more robust with a larger sample size. Taken together, our developments this year have increased the usability and sensitivity of the MilkGuard system, which can improve bacterial contamination testing by milk banks and move one step closer to equitable access to safe breast milk for infants around the world