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Visualization and analysis strategies for dynamic gene-phenotype relationships and their biological interpretation

Secrier, Maria

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The complexity of biological systems is one of their most fascinating and, at the same time, most cryptic aspects. Despite the progress of technology that has enabled measuring biological parameters at deeper levels of detail in time and space, the ability to decipher meaning from these large amounts of heterogeneous data is limited. In order to address this challenge, both analysis and visualization strategies need to be adapted to handle this complexity. At system-wide level, we are still limited in our ability to infer genetic and environmental causes of disease, or consistently compare and link phenotypes. Moreover, despite the increasing availability of time-resolved experiments, the temporal context is often lost. In my thesis, I explored a series of analysis and visualization strategies to compare and connect dynamic phenotypic outcomes of cellular perturbations in a genetic and network context. More specifically, in the first part of my thesis, I focused on the cell cycle as one of the best examples of a complex, highly dynamic process. I applied analysis and data integration methods to investigate phenotypes derived from cell division failure. I examined how such phenotypes may arise as a result of perturbations in the underlying network. To this purpose, I investigated the role of short structural elements at binding interfaces of proteins, called linear motifs, in shaping the cell division network. I assessed their association to different phenotypes, in the context of local perturbations and of disease. This analysis enabled a more detailed understanding of the regulatory mechanisms beyond the malfunctioning of cell division processes, but the ability to compare phenotypes and track their evolution was limited. Exploring large-scale, time-resolved phenotypic screens is still a bottleneck, especially in the visualization area. To help address this question, in the subsequent parts of the thesis I proposed novel visualization approaches that would leverage pattern discovery in such heterogeneous, dynamic datasets and enable the generation of new hypotheses. First, I extended an existing visualization tool, Arena3D, to enable the comparison of phenotypes in a genetic and network context. I used this tool to continue the exploration of phenotype-wide differences between outcomes of gene function suppression within mitosis. I also applied it to an investigation of systemic changes in the network of embryonic stem cell fate determinants upon downregulation of the pluripotency factor Nanog. Second, time-resolved tracking of phenotypes opens up new possibilities in exploring how genetic and phenotypic connections evolve through time, an aspect that is largely missing in the visualization area. I developed a novel visualization approach that uses 2D/3D projections to enable the discovery of genetic determinants linking phenotypes through time. I used the resulting tool, PhenoTimer, to investigate the patterns of transitions between phenotypes in cell populations upon perturbation of cell division and the timing of cancer-relevant transcriptional events. I showed the potential of discovering drug synergistic effects by visual mapping of similarities in their mechanisms of action. Overall, these approaches help clarify aspects of the consequences of cell division failure and provide general visualization frameworks that should be of interest to the wider scientific community, for use in the analysis of multidimensional phenotypic screens.

Item Type: Dissertation
Supervisor: Steinmetz, Prof. Dr. Lars
Place of Publication: Heidelberg, Germany
Date of thesis defense: 5 June 2013
Date Deposited: 09 Jul 2013 07:26
Date: 2013
Faculties / Institutes: The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
Subjects: 500 Natural sciences and mathematics
Uncontrolled Keywords: visualization, gene-phenotype relationships, time-course data, linear motifs, cell cycle, networks
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