Browsing by Subject "Computational biology"

Now showing 1 - 3 of 3
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    Quantitative analysis of gene regulatory networks: from single cells to cell communities
    (2013-06) Biliouris, Konstantinos
    Although the great advances in experimental biology have fueled our ability to explore the behavior of natural and synthetic biological systems, key challenges still exist. A major shortcoming is that, unlike other research areas, biological systems are significantly non-linear with unknown molecular components. In addition, the inherent stochasticity of biological systems forces identical cells to behave dissimilarly even when exposed to the same environmental conditions. These challenges limit in-depth understanding of biological systems using solely experimental techniques. The current research is focused on the joint frontier of mathematical modeling and experimental work in biology. Guided by experimental observations, quantitative modeling analysis of two natural and two synthetic biological systems was carried out. These systems are all gene regulatory networks and range from the single cell level to the population level. The objective of this research is three-fold: 1) The development of detailed mathematical models that capture the relevant biomolecular interactions of the systems of interest. Experimental data are used to inform and validate these models. 2) The use of the models as a means for understanding the complexity underlying biological systems. This allows for explaining the behavior of biological systems by quantifying the molecular interactions involved. 3) The simulation of the behavior of biological systems and the associated molecular parts. This helps to quickly and inexpensively predict the behavior of these systems under various conditions and motivates new sets of experiments.
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    Reconstruction, Reconciliation, and Validation of Metabolic Networks
    (2018-05) Krumholz, Elias
    Metabolic networks are rigorous and computable representations of metabolism that describe the connections between genes, enzymes, reactions, and metabolites. The comprehensive nature of metabolic networks has allowed them to become the first truly “genome-scale” models, and they have served as a foundational framework for the broader effort of systems biology, which aims to model all aspects of cellular function. A more thorough and accurate understanding of metabolism has the potential to improve the synthesis of important biological compounds, better model metabolic diseases, and progress towards simulations of entire cells. The thesis research presented here focuses on the reconstruction of organism-specific metabolic networks from genome annotations and methods for improving metabolic networks by reconciling them with observed phenotypes, specifically the synthesis of essential cellular metabolites such as DNA, amino acids, and other small molecules. Gene sequence similarity and estimations of thermodynamic reaction parameters are used to guide network reconciliation through the use of numerical optimization algorithms. Particular attention is devoted to the validation of metabolic networks using experimental data, such as gene essentiality, and the development of computational controls using parameter randomization.
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    Reverse engineering biological networks: computational approaches for modeling biological systems from perturbation data
    (2013-09) Kim, Yungil
    A fundamental goal of systems biology is to construct molecule level models that explain and predict cellular or organism level properties. A popular approach to this problem, enabled by recent developments in genomic technologies, is to make precise perturbations of an organism's genome, take measurements of some phenotype of interest, and use these data to "reverse engineer" a model of the underlying network. Even with increasingly massive datasets produced by such approaches, this task is challenging because of the complexity of biological systems, our limited knowledge of them, and the fact that the collected data are often noisy and biased. In this thesis, we developed computational approaches for making inferences about biological systems from perturbation data in two different settings: (1) in yeast where a genome-wide approach was taken to make second-order perturbations across millions of mutants, covering most of the genome, but with measurement of only a gross cellular phenotype (cell fitness), and (2) in a model plant system where a focused approach was used to generate up to fourth-order perturbations over a small number of genes and more detailed phenotypic and dynamic state measurements were collected. These two settings demand different computational strategies, but we demonstrate that in both cases, we were able to gain specific, mechanistic insights about the biological systems through modeling. More specifically, in the yeast setting, we developed statistical approaches for integrating data from double perturbation experiments with data capturing physical interactions between proteins. This method revealed the highly organized, modular structure of the yeast genome, and uncovered surprising patterns of genetic suppression, which challenge the existing dogma in the genetic interaction community. In the model plant setting, we developed both a Bayesian network approach and a regularized regression strategy for integrating perturbations, dynamic gene expression levels, and measurements of plant immunity against bacterial pathogens after genetic perturbation. The models resulting from both methods successfully predicted dynamic gene expression and immune response to perturbations and captured similar biological mechanisms and network properties. The models also highlighted specific network motifs responsible for the emergent properties of robustness and tunability of the plant immune system, which are the basis for plants' ability to withstand attacks from diverse and fast-evolving pathogens. More broadly, our studies provide several guidelines regarding both experimental design and computational approaches necessary for inferring models of complex systems from combinatorial mutant analysis.