430730 A System Identification Enhanced Phenotype Phase Plane Analysis

Tuesday, November 10, 2015: 12:48 PM
Salon E (Salt Lake Marriott Downtown at City Creek)
Kyle Stone, Chemical Engineering, Auburn University, Auburn, AL, Matthew Hilliard, Chemical Engineering, Auburn Unviversity, Auburn, AL, Q. Peter He, Chemical Engineering, Tuskegee University, Tuskegee, AL and Jin Wang, Auburn University, Auburn, AL

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:

Agren R, Liu L, Shoaie S, Vongsangnak W, Nookaew I, Nielsen J. 2013. The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput. Biol. 9. http://dx.plos.org/10.1371/journal.pcbi.1002980.

Balagurunathan B, Jonnalagadda S, Tan L, Srinivasan R. 2012. Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis. Microb Cell Fact 11. http://www.biomedcentral.com/content/pdf/1475-2859-11-27.pdf.

Caspeta L, Shoaie S, Agren R, Nookaew I, Nielsen J. 2012. Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials. BMC Syst. Biol. 6. http://www.biomedcentral.com/1752-0509/6/24/.

Damiani AL, He QP, Jeffries TW, Wang J. 2015. Comprehensive Evaluation of Two Genome-scale Metabolic Network Models for Scheffersomyces stipitis. Biotechnol. Bioeng. http://onlinelibrary.wiley.com/doi/10.1002/bit.25535/abstract.

Edwards JS, Ramakrishna R, Palsson BO. 2001. Characterizing the metabolic phenotype: A phenotype phase plane analysis. Biotechnol. Bioeng. 77:27–36. http://dx.doi.org/10.1002/bit.10047.

Orth JD, Thiele I, Palsson BØ. 2010. What is flux balance analysis? Nat Biotechnol 28:245–248. http://dx.doi.org/10.1038/nbt.1614.

Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S. 2011. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2. 0. Nat. Protoc. 6:1290–1307. http://www.nature.com/nprot/journal/v6/n9/abs/nprot.2011.308.html.

 


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