Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided

A Review of Mathematical Models for the Formation of\ud Vascular Networks

Mainly two mechanisms are involved in the formation of blood vasculature: vasculogenesis and angiogenesis. The former consists of the formation of a capillary-like network from either a dispersed or a monolayered population of endothelial cells, reproducible also in vitro by specific experimental assays. The latter consists of the sprouting of new vessels from an existing capillary or post-capillary venule. Similar phenomena are also involved in the formation of the lymphatic system through a process generally called lymphangiogenesis.\ud \ud A number of mathematical approaches have analysed these phenomena. This paper reviews the different modelling procedures, with a special emphasis on their ability to reproduce the biological system and to predict measured quantities which describe the overall processes. A comparison between the different methods is also made, highlighting their specific features

GENOME WIDE DISCOVERY OF DISEASE MODIFIERS

Disease modifiers are genes that when activated can alter the expression of a phenotype associated with a disease. This can be done directly through affecting the expression of another gene that is causing the disease, or indirectly by affecting other factors that contribute to the phenotype’s variability. Identification of disease modifiers is of great interest from both treatment and genetic counseling perspectives. We set here to develop computational approaches to identify and study disease modifiers. We focus on two research avenues for studying disease modifiers: (1) One aimed at identifying and investigating modifiers of cancer, a complex disease influenced by multiple genetic and environmental factors, and (2) the other focuses on the identification of disease modifiers for monogenetic disorders which involve a single disease causing gene. Towards the first aim of studying cancer modifiers we take four complimentary approaches. (a) First, we developed a computational approach to identify metabolic drivers of cancer that when applied to colorectal cancer, successfully identified FUT9 as a gene that strongly modifies tumors aggressiveness. (b) Second, to study metabolic pathway-level modifications in cancer, we developed an algorithm that summarizes cancer modifications to generate pathway compositions that best capture cancer associated alterations, which, as we show, enhances cancer classification and survival prediction. (c) Third, to identify modifiers of cancer immunotherapy treatment, we developed a new computational approach that robustly predicts the response to immune checkpoint blockage therapy. (d) Fourth, to identify modifiers of cancer radiotherapy treatment we built a robust predictor of rectal cancer patients’ response to chemo-radiation-therapy (CRT), identifying a signature of genes that may serve a potential targets for modifying patients’ response to CRT. Towards the second aim of studying genetic modifiers of Mendelian diseases, we developed a computational approach for identifying a specific expression pattern associated with genes that are modifying disease severity. We show that we can successfully prioritize genes that are modifying disease severity in cystic fibrosis and spinal muscular atrophy, where we have identified a new modifier and validated it experimentally. As will become evident from reading my dissertation, my work has naturally focused on developing a variety of computational approaches to analyze research questions that were of interest to me. Obviously, my work has greatly benefited and has been significantly enriched by close collaboration with many experimental labs that have kindly embarked on testing the predictions made, and to whom I am indebted. In sum, we developed methods to identify and study disease modifiers for both cancer and Mendelian diseases. The applications of these methods generates a few promising leads for advancing the treatment for these diseases and improving clinical decision-making

Spotlight on iron and ferroptosis: research progress in diabetic retinopathy

Iron, as the most abundant metallic element within the human organism, is an indispensable ion for sustaining life and assumes a pivotal role in governing glucose and lipid metabolism, along with orchestrating inflammatory responses. The presence of diabetes mellitus (DM) can induce aberrant iron accumulation within the corporeal system. Consequentially, iron overload precipitates a sequence of important adversities, subsequently setting in motion a domino effect wherein ferroptosis emerges as the utmost pernicious outcome. Ferroptosis, an emerging variant of non-apoptotic regulated cell death, operates independently of caspases and GSDMD. It distinguishes itself from alternative forms of controlled cell death through distinctive morphological and biochemical attributes. Its principal hallmark resides in the pathological accrual of intracellular iron and the concomitant generation of iron-driven lipid peroxides. Diabetic retinopathy (DR), established as the predominant cause of adult blindness, wields profound influence over the well-being and psychosocial strain experienced by afflicted individuals. Presently, an abundance of research endeavors has ascertained the pervasive engagement of iron and ferroptosis in the microangiopathy inherent to DR. Evidently, judicious management of iron overload and ferroptosis in the early stages of DR bears the potential to considerably decelerate disease progression. Within this discourse, we undertake a comprehensive exploration of the regulatory mechanisms governing iron homeostasis and ferroptosis. Furthermore, we expound upon the subsequent detriments induced by their dysregulation. Concurrently, we elucidate the intricate interplay linking iron overload, ferroptosis, and DR. Delving deeper, we engage in a comprehensive deliberation regarding strategies to modulate their influence, thereby effecting prospective interventions in the trajectory of DR’s advancement or employing them as therapeutic modalities

THE APPLICATION OF METABOLIC NETWORK ANALYSIS IN UNDERSTANDING AND TARGETING METABOLISM FOR DRUG DISCOVERY

Metabolic networks provide a vital framework for understanding the cellular metabolism in both physiological and pathophysiological states, which will ultimately facilitate network analysis-based drug discovery. In this thesis, we aim to employ a metabolic network analysis approach to study cancer metabolism (a pathophysiological state) and the metabolism of the bacterial pathogen, S. aureus (a physiological state), in order to understand, predict, and ultimately target cell metabolism for drug discovery. Cancer cells have distinct metabolism that highly depend on glycolysis instead of mitochondrial oxidative phosphorylation alone, even in the presence of oxygen, also called aerobic glycolysis or the Warburg effect, which may offer novel therapeutic opportunities. However, the origin of the Warburg effect is only partially understood. To understand the origin of cancer metabolism, our theoretical collaborator, Prof. Alexei Vazquez, developed a reduced flux balance model of human cell metabolism incorporating the macromolecular crowding (MC) constraint and the maximum glucose uptake constraint. The simulations successfully captured the main characteristics of cancer metabolism (aerobic glycolysis), indicating that MC constraint may be a potential origin of the Warburg effect. Notably, when we experimentally tested the model with mammalian cells from low to high growth rates as a proxy of MC alteration, we find that, consistent with the model, faster growing cells indeed have increased aerobic glycolysis. Moreover, the metabolic network analysis approach has also been shown to be capable of predicting the drug targets against pathogen metabolism when completely reconstructed metabolic networks are available. We deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and demonstrated experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. Our results indicate that the metabolic network analysis approach is able to facilitate the understanding of cellular metabolism by identifying potential constraints and predicting as well as ultimately targeting the metabolism of the organisms whose complete metabolic networks are available through the seamless integration of virtual screening with experimental validation

A review of mathematical models for the formation of vascular networks

Two major mechanisms are involved in the formation of blood vasculature: vasculogenesis and angiogenesis. The former term describes the formation of a capillary-like network from either a dispersed or a monolayered population of endothelial cells, reproducible also in vitro by specific experimental assays. The latter term describes the sprouting of new vessels from an existing capillary or post-capillary venule. Similar mechanisms are also involved in the formation of the lymphatic system through a process generally called lymphangiogenesis. A number of mathematical approaches have been used to analyse these phenomena. In this article, we review the different types of models, with special emphasis on their ability to reproduce different biological systems and to predict measurable quantities which describe the overall processes. Finally, we highlight the advantages specific to each of the different modelling approaches. The research that led to the present paper was partially supported by a grant of the group GNFM of INdA