Topics in cell signaling and bioinformatics: operating principles of Notch signaling pathway and supervised variational relevance learning

In the first part of this thesis, we studied the operating principles of cell fate decisions mediated by Notch signaling pathway. This pathway have important role in cell fate determination during embryonic development, wound healing and tumorigenesis. Notch signaling is activated by binding of Notch receptor of one cell to either of its ligand- Delta or Jagged- of another cell. Notch-Delta circuit forms an intercellular toggle switch, and two neighboring cells tend to adopt different fates - Sender (high ligand, low receptor) and Receiver (low ligand, high receptor). Here, we present a new tractable theoretical framework that incorporates both Delta and Jagged in Notch signaling, and show that Notch-Delta-Jagged circuit enables an additional fate - hybrid Sender/Receiver (S/R) (medium ligand, medium receptor) and behaves as a three-way switch. Further, we found that production rates of both the ligands and the asymmetric modulation of binding affinity of Notch to its ligands by glycosyltransferase Fringe severely affects the parameter range of the existence of these states and their relative stability - high levels of Jagged, but not that of Fringe or Delta, promote hybrid S/R state and lateral induction. We elucidate the role of Jagged in cell fate determination and discuss its possible implications in understanding tumor-stroma crosstalk, which frequently entails Notch-Jagged communication. We further evaluate the interplay among Notch signaling, inflamation and Cancer Stem Cell population. We show that inflammation can expand the population of Cancer Stem Cells (CSCs) by increasing the levels of Jagged in cells that can further activate Notch signaling pathway in neighboring non-CSCs. Our results suggest that, inhibiting the production of Jagged dampens the effect of inflammation in expanding the CSC population, indicating that inflammatory signal function through Notch-Jagged signaling to increase CSCs. Our results are consistent with observations in basal-like breast cancer, where loss of Fringe and constitutive activation of NF-kB-Jag1 axis promotes CSC population. Our computational framework can be tailored to include additional signals such as p53 and hypoxia that affect this plasticity to gain stemness, thus providing a platform that can be useful in designing novel therapies. In the second part of the thesis, we introduce a new method for feature selection: Supervised Variational Relevance Learning (Suvrel). This is a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. We propose a new methodology where the metric tensor can be calculated analytically. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. We also applied this methodology to study the relationship between global structural parameters and the Enzyme Commission hierarchy. Lastly, we propose a new methodology for identifying the differentially expressed genes using DNA microarray technology. Unlike traditional approaches, our methodology skips intermediate unnecessary preprocessing steps and therefore does not accumulate errors due to these analysis, resulting in a more sensitive and robust method. (AU)