טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
Ph.D Thesis
Ph.D StudentVitkin Edward
SubjectComputational Aspects of Metabolic Processes:
Modeling, Analysis and Applications
DepartmentDepartment of Computer Science
Supervisor Professor Zohar Yakhini


Abstract

This PhD dissertation addresses various aspects of simulating the metabolic behavior of living organisms. The main topics include modeling and optimizing bio-industrial fermentation processes, the refinement of metabolic network models leveraging high-throughput knockout data and the normalization and visualization of data from clinical metabolite measurements.

We start with presenting BioLego, which is a freely downloadable webservice, ready for installation in the Microsoft Azure Cloud environment. BioLego provides a friendly and intuitive interface that enables the simulation and the optimization of single and two-step fermentation processes. The BioLego fermentation simulator is a scalable distributed framework, providing means to the process designers for analyzing, predicting and comparing the expected efficiency of several fermentation scenarios. It is based on a novel flexible modular modelling approach, enabling smooth generation of different multi-organism fermentation configurations consisting of independent encapsulated modules, representing individual organisms.

We continue with genome-scale single organism cellular metabolic models. Gene-fitness assays, measuring organism behavior in different growth conditions after genetic modifications, lead to newly generated data that are relevant for metabolic modelling. We developed a methodology to leverage these data to improve gene-to-reaction associations in existing genome-scale models. Specifically, we use these data to assign genes to orphan reactions. We also show how to integrate these data with statistics gathered from other types of functional-genomic data, such as gene-expression profiles, to facilitate gene-to-reaction assignments.

Finally, we present an approach for measurement-based analysis of human metabolism using the example of the steroidogenesis pathway. We describe a novel method for normalizing highly biased data, such as metabolomic hormonal profiles of children. We also describe a novel method for visualizing the analysis results both for patient-based and population-based perspectives.

The major contributions of this PhD research are as follows:

         BioLego project, including

o    Novel modular modelling approach, enabling smooth generation of different multi-organism fermentation configurations

o    Distributed framework based on Microsoft Azure Cloud environment, enabling high-scale simulations and optimization of single and two-step fermentation processes, including multiple knockouts in each organism.

o    Intuitive interface, requiring no programming skills from potential users.

o    Source code and installation instructions, freely downloadable and ready to install in the Microsoft Azure Cloud environment.

o    Biological predictions for various fermentation scenarios supporting several published studies.

         Gene-to-reaction assignment methodology for fitness assays, including:

o    A novel methodology to leverage fitness assays for genes and promoters to improve existing gene-to-reaction assignment techniques

o    An integration of fitness assays statistics with other statistics based on gene-expression profiles

o    Biological predictions of candidate genes potentially encoding proteins that catalyze 107 orphan reactions in iJO1366 metabolic model of E. coli.

         Analysis of clinical metabolomics profiles, including:

o    A systems approach for analyzing steroid metabolomics data to identify a steroid-related disease signatures and patterns

o    A novel peer-group normalization approach, clearly overperforming current methods in the field

o    A novel visualization approach, addressing both patient-based and population-based studies

o    Analysis of data from Congenital Adrenal Hyperplasia due to 21-hydroxylase deficiency and from childhood obesity subjects.