(199d) A Systems Biology Approach for Mechanistic Understanding of Glycan Structure and Function

Introduction: Glycosylation is an important post-translational modification that alters more than 50% of mammalian secreted and cell surface proteins. Glycosylated proteins play structural and functional roles in diverse biological processes including inflammation, cancer and development. The recent development of advanced analytical tools has heralded the emergence of Glycomics, a field where entire glycomes of cell systems are characterized [1]. It is often challenging to understand glycosylation mechanisms using statistical analysis method alone. Herein, we developed a systems biology approach to unravel the complexity of glycosylation reaction network underlying complex glycan structures and to predict the structure and composition of the glycans when the cellular systems are perturbed.

Methods:  Our approach uses the object-oriented concepts and graph theory to model the cellular glycosylation networks including the glycans, enzymes, reactions, pathways and compartments. With the aid of such definitions, we defines a streamlined modeling procedure to couple the mathematical model with the glycan structures. The mathematical model developed are linked to informatics databases [2]. Thus, information from these large databases are used to inform the model, and the model built can query the databases in a mechanistic manner.  

Master pathway construction is implemented as the first step to construct a "maximum-size" glycosylation pathway, followed by subset modeling, hierarchical clustering and  principal component analysis method to refine the network. Lastly, sensitivity analysis is applied to identify key regulating enzymes and to generate experimental testable hypothesis. All these procedures can be readily implemented in a MATLAB based toolbox called Glycosylation Network Analysis Toolbox (GNAT).  In addition, this toolbox enables the description of glycan structures in Systems Biology Markup Language (SBML) files and it allows high-quality visualization of the glycosylation reaction network.

Results and Conclusions: Our modeling approach has been tested with two examples that simulate:1) N-glycan initiation and branching; 2) O-glycosylation networks. These tests demonstrate that our modeling approach can be used to construct the "feasible" glycosylation reaction network from the experimentally measured mass spectrometry data. In addition, the distribution of the glycans can be computed using our reconstructed models and this enables fitting and comparison with experimental data. In summary, we present a novel, structured approach for the generation of systems-based models that enhance our understanding of cellular glycosylation pathways.

References:

1.Neelamegham, S., et al. Glycobiology, 2011. 21(12): p. 1541-53.
2.Ranzinger, R., et al. Nucleic Acids Res, 2011. 39(Database issue): p. D373-6.