Modeling cellular metabolism in multiple scales
type: CreativeWork
description: Computational studies of biological systems have evolved significantly, to the point where we can combine quantitative information on the expression of thousands of proteins and RNAs in large models of metabolism or gene expression. We can recover and explain fluctuations observed in single-cell experiments, and connect them to physiological behavior. In order to advance the field, it is necessary to employ state-of-the-art computational tools, and integrate even more complex biological contexts into the models and modeling tools themselves. In order to enhance our description of physiological variability in metabolic phenotypes of yeast, we improved the Population FBA method to dynamically constrain a genome-wide metabolic reconstruction of S. cerevisiae using comprehensive proteomics and microarray data sets. Moreover, a genetic algorithm approach was used to explore the variability of observed metabolic phenotypes, identifying a surprising robustness in overall growth rate distribution. Within cell-wide metabolic or gene expression networks, key regulatory checkpoints are modulated by specific molecular interactions. They are essential for the formation of multi-protein complexes, for the binding of ligands in active sites, and for allosteric activation of enzymes. In order to identify essential residues for communication pathways and signaling elements in biomolecular complexes, we developed an evolution of the dynamical network analysis framework. The new implementation was applied to the leucine tRNA synthetase complexed with its cognate tRNA and adenylate, revealing experimentally verified identity elements throughout the tRNA:protein interface, as well as essential catalytic residues in the active site. This enhanced and updated methodology will provide the community with an intuitive and interactive interface, that can be easily applied to large macroolecular complexes. Beyond the study of protein:ligand interactions, in order to analyze specific enzymatic …
abstract: Computational studies of biological systems have evolved significantly, to the point where we can combine quantitative information on the expression of thousands of proteins and RNAs in large models of metabolism or gene expression. We can recover and explain fluctuations observed in single-cell experiments, and connect them to physiological behavior. In order to advance the field, it is necessary to employ state-of-the-art computational tools, and integrate even more complex biological contexts into the models and modeling tools themselves. In order to enhance our description of physiological variability in metabolic phenotypes of yeast, we improved the Population FBA method to dynamically constrain a genome-wide metabolic reconstruction of S. cerevisiae using comprehensive proteomics and microarray data sets. Moreover, a genetic algorithm approach was used to explore the variability of observed metabolic phenotypes, identifying a surprising robustness in overall growth rate distribution. Within cell-wide metabolic or gene expression networks, key regulatory checkpoints are modulated by specific molecular interactions. They are essential for the formation of multi-protein complexes, for the binding of ligands in active sites, and for allosteric activation of enzymes. In order to identify essential residues for communication pathways and signaling elements in biomolecular complexes, we developed an evolution of the dynamical network analysis framework. The new implementation was applied to the leucine tRNA synthetase complexed with its cognate tRNA and adenylate, revealing experimentally verified identity elements throughout the tRNA:protein interface, as well as essential catalytic residues in the active site. This enhanced and updated methodology will provide the community with an intuitive and interactive interface, that can be easily applied to large macroolecular complexes. Beyond the study of protein:ligand interactions, in order to analyze specific enzymatic …
author: Cardoso Dos Reis Melo, Marcelo
language: en
keywords: system's biology, qm/mm, protein:trna interaction
date published: 2019-08-23T20:44:27Z
date modified: 2026-07-22T22:24:44Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/105124