Computational methods for integrating metabolomics data with metabolic engineering strain design

Dromms, Robert A.

Title:

Computational methods for integrating metabolomics data with metabolic engineering strain design

dc.contributor.advisor	Styczynski, Mark P.
dc.contributor.author	Dromms, Robert A.
dc.contributor.committeeMember	Kemp, Melissa
dc.contributor.committeeMember	Realff, Matthew
dc.contributor.committeeMember	Chen, Rachel
dc.contributor.committeeMember	Peralta-Yahya, Pamela
dc.contributor.department	Chemical and Biomolecular Engineering
dc.date.accessioned	2019-08-21T13:47:26Z
dc.date.available	2019-08-21T13:47:26Z
dc.date.created	2017-08
dc.date.issued	2017-05-18
dc.date.submitted	August 2017
dc.date.updated	2019-08-21T13:47:26Z
dc.description.abstract	The genome-scale analysis of cellular metabolites, “metabolomics”, provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA) and were developed using assumptions that preclude direct integration of metabolomics data into the underlying models. To improve their accuracy, we have focused on developing strategies to account for metabolite levels and metabolite-dependent regulation in these tools and models. We demonstrated the competitiveness of a biologically-inspired “Impulse” function from the transcriptional profiling literature against previously described fitting schemas to show that it may serve as an effective single option for data smoothing in metabolic flux estimation applications. We also developed a resampling-based approach to buffer out sensitivity to specific data sets and allow for more accurate fitting of noisy data. We designed, implemented, and characterized a modeling framework based on dynamic FBA (DFBA) to add strictly linear constraints describing the kinetics and regulation of metabolism. We identified model parameters using both regression from the flux distribution calculated with Dynamic Flux Estimation and global parameter optimization to produce models that performed comparable to or better than Ordinary Differential Equation models fitted by regression to generalized-mass-action rate laws. We demonstrated the efficacy of our framework in a larger, biologically relevant model, assessed the consequences and benefits of two different parameterization structures, and explored the impact of regulatory structure on model behavior to determine its robustness and the viability of using a greedy search method to identify regulatory interactions. The work described has led to the development of a modeling framework that allows widely-used tools for metabolic engineering strain design to directly account for and integrate metabolomics data, metabolite dynamics, and metabolite-dependent regulation.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/61595
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Metabolomics
dc.subject	Systems biology
dc.subject	Metabolic engineering
dc.subject	Flux balance analysis
dc.subject	Data smoothing
dc.subject	Metabolic modeling
dc.subject	Computational modeling
dc.subject	Linear programming
dc.title	Computational methods for integrating metabolomics data with metabolic engineering strain design
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Styczynski, Mark P.
local.contributor.corporatename	School of Chemical and Biomolecular Engineering
local.contributor.corporatename	College of Engineering
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relation.isOrgUnitOfPublication	6cfa2dc6-c5bf-4f6b-99a2-57105d8f7a6f
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral