Abstract
Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.
Highlights
Certain industrial processes, such as the production of hydrogen and ethanol, benefit from using prokaryotic or eukaryotic organisms to produce, reduce, and convert important chemical compounds [1,2]
Genetic engineers often seek to modify the physical traits of microorganisms used in industrial processes in order to improve the efficiency of the overall process
In the last few years, it has become understood that the physical traits of an organism are often the result of a coordinated set of interactions between multiple genes that make up a biological subsystem
Summary
Certain industrial processes, such as the production of hydrogen and ethanol, benefit from using prokaryotic or eukaryotic organisms to produce, reduce, and convert important chemical compounds [1,2]. In order to obtain the desired changes in the organism’s phenotype, engineers require a deciphering of which genes are related to the expression of the given phenotype, known as genotype-phenotype associations [4,5]. Such an understanding has not kept pace with the rate at which genes are discovered [6]. Uncovering genotype-phenotype associations could be greatly improved if organism’s metabolic systems involved in the phenotype expression were understood [7]. It is imperative to be able to identify all of the enzymes that make up a phenotype-related metabolic system
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