Browsing by Author "Qutub, Amina A"

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    Improved Methods for Constraint Based Modeling of Mammalian Systems
    (2017-06-26) Schultz, Andre; Qutub, Amina A
    Genome-wide metabolic reconstructions have been widely applied to study metabolism at a genome scale. To date, most of the work in the field has been performed in the study of unicellular organisms, however, and many of the methods developed in this context do not transfer for the study of mammalian systems. For instance, (1) the larger size of mammalian reconstructions makes the application of computationally expensive algorithms such as pathway decomposition infeasible. Also, (2) the optimization of a cellular objective, commonly defined to be biomass production in unicellular organisms, does not transfer to mammalian cells, where a cellular objective is neither well defined nor optimized. Finally, (3) the generalized human reconstruction needs to be tailored to specific tissues or cell lines for a context specific analysis, since only a subset of the metabolism defined in the human genome takes place in each cell. In this project, we aim to develop better methods for the analysis of mammalian systems using genome-scale models. We demonstrate that (1) the removal of currency metabolites and energy related loops from the model leads to a more feasible and biologically relevant application of pathway decomposition analysis. We also show that merging sets of fully coupled reactions, and using a combination of two algorithms, leads to a significantly faster implementation of Monte-Carlo sampling. Furthermore, (2) by fixing the cellular objective and optimizing metabolic resources, we demonstrate that a sub-optimal objective oriented approach can significantly improve flux prediction results. Finally, (3) we present a context-specific algorithm that is faster, agrees better with experimental data, and yields better tissue-specific predictions when compared to previous methods. After validating these methods, we apply them to the study of E. coli metabolism, cancer cells in a subtype specific manner, and to the study of hypoxia adaptation in mouse cardiomyocytes. Results from our predictions provided biological insight in both applications: including the role of Hexosamine synthesis pathways as an energy regulator in cancer cells, and the role of evolution in the adaptation of mouse populations to altitude conditions.
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    Insulin Signaling & Hypoxic Response in Brain Cancer
    (2015-12-14) Lin, Ka Wai; Qutub, Amina A
    Glioblastoma patient survival rate has stagnated for the past 30 years, with median survival time less than 1 year. Only 20% of the young (0-19 years old) glioblastoma patients survive past 5 years, and this number drops to just less than 5% for patients above 40 years old. Due to poor survival rate of glioblastoma patients, there is a pressing need to develop more effective treatment methods. In this project, we investigated the effects of the insulin signaling pathway on the glioblastoma growth. Glioblastoma growth has been shown to be promoted by three key molecular signaling factors: IGFI, IGFBP2 and HIF1α. We used both the current literature data and our own experimental data to understand the interactions between these molecular factors on glioblastoma growth via a mathematical model. The model predicted that the activation of HIF1α by IGFBP2 is a crucial driver in the glioblastoma growth. We then used in vitro experiments to validate the findings of the model, and to further explore the response of glioblastoma progression by the stimulation of IGFBP2 and IGFI. This research demonstrates how IGFI and IGFBP2 influence glioblastoma growth, and suggest that future research should investigate the effects of both IGFI and IGFBP2 to control the glioblastoma progression system as a whole.
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    Multiplexed Spatial Analyses in situ and in living cells
    (2015-12-04) Zimak, Jan; Diehl, Michael R; Qutub, Amina A; Wagner, Daniel S
    High-content spatial analyses are critical to understanding the structural organization and dynamics of many complex biological processes. Increasing the number of cellular components that can be visualized will help delineate the functions of many interacting and competing cellular pathways. However, the physical limitations of spectral bandwidth and the experimental difficulty of genomic manipulation have hampered traditional approaches to multiplex molecular analyses in both fixed samples and live cells. The programmable and predictable nature of the DNA molecule makes it a tantalizing candidate for an engineering tool to help alleviate some of these limitations. This thesis seeks to harness both the chemical and biological utility of DNA as a building block to multiplex the color and control the number and location of fluorescent reporters in biological samples. First in the context of in situ immunofluorescence imaging of fixed cells or tissues, And second in the context of live-cell imaging of genomically engineered cells. In the first case, by utilizing the stand displacement chemical reaction between dynamic DNA complexes and DNA-conjugated antibodies we selectively couple fluorophores to, and then remove them, from their protein targets. We leverage this mechanism to facilitate multiple sequential round of fluorescence microscopy where the same color dye molecules are used reiteratively to visualize different antibody-tagged markers. By optimizing the DNA-antibody conjugation chemistry and incubation protocol we now routinely perform 9 marker analyses of paraffin-fixed tissue sections with these DNA probes. Then automating the sequence design process enabled more complex probe designs to be use for balancing marker levels appropriately for hyperspectral imaging experiments. Here, discrete and reconfigurable control over amplification gains, greatly improved the spectral un-mixing of different antibody signals. Secondly, we focus on dissecting network-level functions of cytoskeletal regulatory proteins during epithelial cell polarization and morphogenesis. DNA-based STORM microscopy revealed that a scaffold protein, IQGAP1, associates with specialized actin filaments within cell-cell junctions and with basket-like structures in the basal actin cortex of normal epithelial cells. This work uses IQGAP1 as a platform, as it lies at the nexus of cell signaling and cytoskeletal regulatory networks. We construct multi-gene systems that simultaneously sense and control intracellular expression levels of IQGAP1 and track the actin cytoskeleton. By combining novel molecular biology techniques to manipulate the DNA in live cells. We use a barcoded self-assembly technique to construct large vectors that contain several transcriptional elements. These multi-gene systems are then stably incorporated into cells engineered with genomic ‘landing pads’ using locus-specific integration. Finally, we demonstrate functional circuits by linearly controlling intracellular IQGAP1 levels. These results will support future single-cell and multiplexed population-level analyses of IQGAP1 functions in epithelial cells, allow us to study IQGAP1 recruitment to epithelial cell-cell junctions and to examine how it influences cellular transitions.
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    Network Analysis of Developing Neural Progenitor Cells
    (2018-08-01) Mahadevan, Arun; Qutub, Amina A
    The architecture of the mammalian brain has been characterized through decades of innovation in the field of network neuroscience. However, the assembly of the brain from neural progenitor cells (NPCs) during embryonic development is an immensely complex process that has yet to be well characterized. The complex interplay between ‘programmed’ genetic behavior of individual cells and cell-cell interactions among NPCs shapes how neural circuits self-assemble. A lack of tractable experimental methods and accompanying analytical techniques have hindered understanding of this process. To address these challenges, I designed, developed and implemented two new technologies: (1) cytoNet – a computational platform to enable quantitative characterization of cell-cell interactions and (2) Living Neural Networks – an experimental-computational assay to capture the development of neural network formation from NPCs. The first technology, cytoNet, is a software tool designed to characterize multicellular topology from microscopy images. Accessible through a web-based interface, cytoNet quantifies the spatial relationships in cell communities using principles of graph theory, and evaluates the effect of cell-cell interactions on individual cell phenotypes. cytoNet’s capabilities are demonstrated in two applications relevant to regenerative medicine: quantifying the morphological response of endothelial cells to neurotrophic factors present in the brain after injury, and characterizing cell cycle dynamics of differentiating neural progenitor cells. For development of the Living Neural Networks technology, I leveraged the cytoNet technique to characterize the evolution of spatial and functional network features in human NPCs during the formation of neural networks in vitro. Results from the Living Neural Networks analysis show that the rise and fall in spatial network efficiency is a characteristic feature of the transition from immature NPC networks to mature neural networks. Furthermore, networks at intermediate stages of differentiation that display high spatial network efficiency also show high levels of network-wide spontaneous electrical activity. These results support the view that network-wide signaling in immature progenitor cells gives way to a hierarchical form of communication in mature neural networks. In addition to identifying global trends in neural network formation, I also leveraged graph theory to study the spatial features of individual cell types in developing cultures, uncovering spatial features of polarized neuroepithelium. Finally, I employed the method to uncover aberrant network features in a neurodevelopmental disorder using induced pluripotent stem cell (iPSC) models. The techniques introduced by my thesis work bridge the gap between developmental neurobiology and network neuroscience, and offers insight into the relationship between developing and mature neural networks in health and disease.