Doctoral Theses

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Browsing Doctoral Theses by Supervisor "Bagler, Ganesh"

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    Computational Gastronomy: Analysis of the basis of flavor in Indian cuisine and health impact of spices
    (Indian Institute of Technology Jodhpur, 2020-01) Bagler, Ganesh
    Cultures across the world have evolved diverse culinary repertoires that form an integral part of their identity. Traditional recipes have been shaped to incorporate ingredients driven by their taste and health considerations. The study of culinary practices has hitherto been mainly under the purview of humanities and social sciences. In this thesis, we take a computational gastronomy approach to conduct a data-driven investigation of traditional Indian recipes to study the basis for their flavor composition and health impact of culinary herbs and spices. The first part of the thesis explores the basis of flavor in Indian Cuisine through the principle of food pairing applied to its traditional recipes to show what ingredient combinations are generally followed in a typical Indian recipe and its regional cuisines. The study provides a basis for designing novel signature recipes, healthy recipe alterations and recipe recommender systems. Further, the thesis presents a repository of flavor compounds, FlavorDB, a comprehensive database for the exploration of flavor compounds in food ingredients. The latter and final sections of the thesis unearth the health significance of key dietary ingredients, spices and herbs from data available via published biomedical literature. By carrying out a data analytical approach, the thesis provides valuable insights into their therapeutic utility. Further, by integrating spice-phytochemical-disease associations, we identify bioactive spice phytochemicals potentially involved in their therapeutic effects. The results and data from this investigation are compiled and presented in the database SpiceRx.In summary, we take a data-driven approach to investigate the data of traditional Indian recipes to identify culinary fingerprints of its regional cuisines. Our computational gastronomical analysis led to the identification of spices as the molecular fulcrum of Indian recipes. We further investigated the therapeutic effects of culinary herbs and spices to highlight their broad-spectrum benevolence. We also created a data repository of flavor compounds (FlavorDB) and an integrated resource for empirical evidence of the health impacts of culinary herbs and spices (SpiceRx). We believe that these studies will provide an impetus for data-centric investigations of food, flavor, health and their related applications.
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    System Biological Investigation of Brain Networks
    (Indian Institute of Technology Jodhpur, 2017-06) Bagler, Ganesh
    Neuroscience has been driven by inquiry for principles of brain structure organization and its control mechanisms. Brain is a complex system comprising of large number of neurons that interact with each other giving rise to its functions. Hence, going beyond reductionist approaches, systems biological investigations using graph theoretical models of brain mechanisms is expected to provide better understanding of emergent properties of brain. With this view, in this thesis, we asked questions addressing brain structure organization, its control and network correlates of neuropathology. We modeled the neuronal connectivity of C. elegans as a network to characterise its graph theoretical properties. Using structural controllability analysis, we identified its ‘driver neurons’ and characterised them for their phenotypic and genotypic properties. The driver neurons were found to be primarily motor neurons located in the ventral nerve cord and contributing to biological reproduction of the animal. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a ‘distance constrained synaptic plasticity model’ that simultaneously explained small-world nature, saturation of feedforward neuronal motifs as well as number of driver neurons. Importantly, our model was able to accurately encode the identity of specific driver neurons matching with those observed empirically. By implementing a motif tuning algorithm, we observed that ‘number of driver neurons’ shows an asymmetric sigmoidal response, indicating robust control for saturation of feedforward motifs and a fragile behavior for their depletion. We further modeled the interplay of excitatory and inhibitory synapses for the study of structural balance in this neuronal system, to highlight the contribution of inhibitory synapses. Beyond investigating structural brain network in C. elegans, we constructed human functional brain network models to probe network correlates of schizophrenia. Thus, through systems biological investigations of brain networks, we have addressed questions related to brain structure organization, mechanisms of its control and network correlates of schizophrenia. Our studies highlight the importance of systems-level models of brain networks and provide insights into their structure, function and control.
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    Systems Modeling of Target and Chemical Profiles of Drugs to Predict Their Phenotypic Side Effects With Canonical Correlation Analysis
    (Indian Institute of Technology Jodhpur, 2017-06) Bagler, Ganesh
    Despite technological advances and improved understanding of biological systems, drug discovery remains an inefficient and arduous task, with the high attrition of candidate molecules. Side effects (adverse reactions) is one of the key factors contributing to the rejection of candidate molecules with therapeutic potential. Hence, accurate prediction of phenotypic side effects is an important problem in drug discovery. The action of drugs needs to be seen from the systems perspective knowing that cellular mechanisms form a web of interactions with intricate cross-talks among biomolecules. Availability of data capturing molecular interaction of drugs, and their phenotypic side effects have facilitated systems-level models aimed at prediction of potential side effects. Towards the goal of predicting side effects, objectives set in this thesis were driven by the idea of creating holistic models using empirical data, and devising mathematical as well as computational strategies. We integrated data from existing resources such as DrugBank and SIDER for systems-level investigations of side effects, and developed an integrative Generalized Canonical Correlation Analysis model which facilitates consolidation of various drugs features. We concluded that models implementing chemical profiles show more consistent accuracy than those based on target profiles. Further we constructed a graph theoretical model of biological space to account for associations among drug targets, and by comparing the performance of various network metrics inferred that simple network parameters are comparable to intricate parameters. Our studies performed for identification of minimal ‘known side effects’ set as a predictor for a class of adverse reactions suggest that, partial information of side effects profile could be used as a factor for arriving at the remaining side effects. Finally, towards the goal of obtaining drug features that contribute the most to side effects prediction, we developed a partial canonical correlation analysis model that facilitates enumeration of contribution from individual drug features. Our systems-level investigations offer insights into mechanisms of adverse drug reactions and provide data-driven methods for their prediction.