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Metabolic Modeling and Omics-Integrative Analysis of Single and Multi-Organism Systems: Discovery and Redesign
Computations and modeling have emerged as indispensable tools that drive the process of understanding, discovery, and redesign of biological systems. With the accelerating pace of genome sequencing and annotation information generation, the development of computational pipelines for the rapid reconstruction of high-quality genome-scale metabolic networks has received significant attention. These models provide a rich tapestry for computational tools to quantitatively assess the metabolic phenotypes for various systems-level studies and to develop engineering interventions at the DNA, RNA, or enzymatic level by careful tuning in the biophysical modeling frameworks. in silico genome-scale metabolic modeling algorithms based on the concept of optimization, along with the incorporation of multi-level omics information, provides a diverse array of toolboxes for new discovery in the metabolism of living organisms(which includes single-cell microbes, plants, animals, and microbial ecosystems) and allows for the reprogramming of metabolism for desired output(s). Throughout my doctoral research, I used genome-scale metabolic models and omics-integrative analysis tools to study how microbes, plants, animal, and microbial ecosystems respond or adapt to diverse environmental cues, and how to leverage the knowledge gleaned from that to answer important biological questions. Each chapter in this dissertation will provide a detailed description of the methodology, results, and conclusions from one specific research project. The research works presented in this dissertation represent important foundational advance in Systems Biology and are crucial for sustainable development in food, pharmaceuticals and bioproduction of the future.
Engineering|Systematic biology|Chemical engineering|Bioengineering|Molecular biology|Physiology
Islam, Mohammad Mazharul, "Metabolic Modeling and Omics-Integrative Analysis of Single and Multi-Organism Systems: Discovery and Redesign" (2021). ETD collection for University of Nebraska - Lincoln. AAI28650957.