(341b) A System Identification Enhanced Phenotype Phase Plane Analysis

Authors: 
Stone, K., Auburn University
Hilliard, M., Auburn University
He, Q. P., Auburn University
Wang, J., Auburn University

A
System Identification Enhanced Phenotype Phase Plane Analysis

Kyle
Stone1, Matthew Hilliard1, Q. Peter He2, and
Jin Wang1

1Department
of Chemical Engineering, Auburn University, Auburn, AL, 36849, USA

2Department
of Chemical Engineering, Tuskegee University, Tuskegee, AL, 36088, USA
Abstract:

Genotype-phenotype relationship is fundamental to
biology, and predicting different phenotypes based on the sequenced genome is
one of the main goals for genome-scale metabolic model development. These
models provide a holistic view of the organism's metabolism, and
constraint-based metabolic flux analysis methods have been used extensively to
study genome-wide cellular metabolic networks (Balagurunathan
et al., 2012; Caspeta et al., 2012; Orth et al., 2010).

A prevalent method to dissect genome-scale metabolic
models is phenotype phase plane (PhPP) analysis, which breaks down metabolic
behavior in distinct phases through shadow prices analysis (Edwards
et al., 2001).  The shadow price is defined as the effect of the
metabolite concentration on the objective function.  Two independent variables,
typically the carbon and oxygen source, are varied, and the shadow price is
calculated for each metabolite. Shadow price analysis has the capability to
determine if a substrate was in limited or in excess supply, and whether a
product was expelled from the network.  However, due to the scale and
complexity involved in genome-scale models, many times only knowing whether a
metabolite is limited or excess and whether it is expelled from the network is
not sufficient in characterizing different phenotypes. In order to fully
characterize different phenotypes predicted by a model, it is highly desirable
to figure out how different pathways interact with each other for a given
phenotype, and how such interactions differ from different phenotypes.

To address this challenge, the system identification
based (SID) framework that we developed previously for genome-scale model
validation and refinement is extended to enhance PhPP. In the SID framework, we
first perturb the network through designed input sequences, i.e., designed in
silico
experiments; then apply multivariate statistical analysis tools to
analyze the in silico results in order to extract information on how
such perturbations propagate through the network; finally, we visualize the
extracted knowledge against the network map to provide easy accessibility. The
SID framework has been successfully applied to genome-scale model evaluation,
as well as guiding the development of an improved genome-scale model of Scheffersomyces
stipitis
(Damiani
et al., 2015).

Figure 1 provides an overview of the SID enhanced
PhPP. In the proposed approach, in silico experiments can be designed to
obtain the same findings obtained through shadow price analysis with less
computation. More importantly, the SID framework allows us to obtain further
information to help characterize
the different phenotypes and identify the key differences among them.
Visualization tools provided in Raven toolbox has been modified to visualize
the information extracted from the SID enhanced PhPP (Agren
et al., 2013). The core metabolic network model of
E. coli were used to demonstrate the effectiveness of the proposed
SID-PhPP approach (Schellenberger
et al., 2011).

Figure 1 Overview of SID enhanced PhPP

References:

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11. http://www.biomedcentral.com/content/pdf/1475-2859-11-27.pdf.

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