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Type of Document Dissertation Author Huang, Chengbang Author's Email Address chuang1@nd.edu URN etd-07212005-085435 Title Multiscale Computational Methods for Morphogenesis and Algorithms for Protein-Protein Interaction Inference Degree Doctor of Philosophy Department Computer Science and Engineering Advisory Committee
Advisor Name Title Jesus A. Izaguirre Committee Chair Amitabh Chaudhary Committee Member Edward J. Maginn Committee Member Gregory R. Madey Committee Member Mark S. Alber Committee Member Keywords
- Extended Potts Model
- Limb Growth Simulation
- Morphogenesis
- Set Cover
- Reaction Diffusion
- Protein Interaction Prediction
- Maximum Specificity Set Cover
Date of Defense 2005-07-07 Availability unrestricted Abstract Biocomplexity is the study of the complex relationships among biological entities that are responsible for life. This dissertation addresses two important computational in biocomplexity: morphogenesis and protein-protein interaction networks. Morphogenesis is the development of multicellular organisms. I develop models, algorithms, and software that integrate the genetic regulatory network and physical or generic cellular mechanisms that explain how cells interact to form tissues. The genetic regulation is modeled by a combination of a rule-based state automaton and a set of partial differential equations (PDEs); the generic cellular mechanisms include cell adhesion, cell differentiation, cell growth, mitosis, secretion of morphogens, haptotaxis and chemotaxis.
Protein networks are part of signaling, metabolic, and other biological pathways important to cells and organisms. We develop a new algorithm called Maximum Specificity Set Cover (MSSC) to predict protein-protein interactions. The predictions by MSSC preserve not only the topological characteristics of protein interaction networks, but also the protein co-expression. Our method outscores other prediction methods in quality.
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