The Multiple Alignment Algorithm for Metabolic Pathways without Abstraction

Chen, Wenbin; Rocha, Andrea M.; Hendrix, William; Schmidt, Matthew C.; Samatova, Nagiza F.

doi:10.1109/icdmw.2010.21

Cited by 4 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If they are defined, then the system could be modeled as a complex network, and multiple network alignment approaches [3,4] that look for subgraphs that co-occur across multiple network instances for the same system’s phenotype are putative candidates for the target component interplays. The key limitation of this strategy is that such approaches aim to identify the component groups that are present in all or most of a given set of network instances and would likely miss those that are only common to a subset of the instances.…”

Section: Introductionmentioning

confidence: 99%

Spice: discovery of phenotype-determining component interplays

et al. 2012

View full text Add to dashboard Cite

BackgroundA latent behavior of a biological cell is complex. Deriving the underlying simplicity, or the fundamental rules governing this behavior has been the Holy Grail of systems biology. Data-driven prediction of the system components and their component interplays that are responsible for the target system’s phenotype is a key and challenging step in this endeavor.ResultsThe proposed approach, which we call System Phenotype-related Interplaying Components Enumerator (Spice), iteratively enumerates statistically significant system components that are hypothesized (1) to play an important role in defining the specificity of the target system’s phenotype(s); (2) to exhibit a functionally coherent behavior, namely, act in a coordinated manner to perform the phenotype-specific function; and (3) to improve the predictive skill of the system’s phenotype(s) when used collectively in the ensemble of predictive models. Spice can be applied to both instance-based data and network-based data. When validated, Spice effectively identified system components related to three target phenotypes: biohydrogen production, motility, and cancer. Manual results curation agreed with the known phenotype-related system components reported in literature. Additionally, using the identified system components as discriminatory features improved the prediction accuracy by 10% on the phenotype-classification task when compared to a number of state-of-the-art methods applied to eight benchmark microarray data sets.ConclusionWe formulate a problem—enumeration of phenotype-determining system component interplays—and propose an effective methodology (Spice) to address this problem. Spice improved identification of cancer-related groups of genes from various microarray data sets and detected groups of genes associated with microbial biohydrogen production and motility, many of which were reported in literature. Spice also improved the predictive skill of the system’s phenotype determination compared to individual classifiers and/or other ensemble methods, such as bagging, boosting, random forest, nearest shrunken centroid, and random forest variable selection method.

show abstract

Section: Introductionmentioning

confidence: 99%

Spice: discovery of phenotype-determining component interplays

et al. 2012

View full text Add to dashboard Cite

show abstract

“…This is the motivation behind network alignment and phylogenetic profiling approaches proposed to-date. The former [9] – [13] look for subgraphs that exist in metabolic networks of multiple organisms. The latter [4] , [5] , [14] seek to find genes or enzymes that are more likely to be present in phenotype-expressing organisms than in phenotype-non-expressing organisms due to an evolutionary pressure to conserve the phenotype-related enzymes [14] .…”

Section: Introductionmentioning

confidence: 99%

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Schmidt

Rocha²,

Padmanabhan³

et al. 2012

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

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.

show abstract

The Application of the Weighted k-Partite Graph Problem to the Multiple Alignment for Metabolic Pathways

Chen

Hendrix

Samatova

2017

Journal of Computational Biology

View full text Add to dashboard Cite

The problem of aligning multiple metabolic pathways is one of very challenging problems in computational biology. A metabolic pathway consists of three types of entities: reactions, compounds, and enzymes. Based on similarities between enzymes, Tohsato et al. gave an algorithm for aligning multiple metabolic pathways. However, the algorithm given by Tohsato et al. neglects the similarities among reactions, compounds, enzymes, and pathway topology. How to design algorithms for the alignment problem of multiple metabolic pathways based on the similarity of reactions, compounds, and enzymes? It is a difficult computational problem. In this article, we propose an algorithm for the problem of aligning multiple metabolic pathways based on the similarities among reactions, compounds, enzymes, and pathway topology. First, we compute a weight between each pair of like entities in different input pathways based on the entities' similarity score and topological structure using Ay et al.'s methods. We then construct a weighted k-partite graph for the reactions, compounds, and enzymes. We extract a mapping between these entities by solving the maximum-weighted k-partite matching problem by applying a novel heuristic algorithm. By analyzing the alignment results of multiple pathways in different organisms, we show that the alignments found by our algorithm correctly identify common subnetworks among multiple pathways.

show abstract

The Multiple Alignment Algorithm for Metabolic Pathways without Abstraction

Cited by 4 publications

References 26 publications

Spice: discovery of phenotype-determining component interplays

Spice: discovery of phenotype-determining component interplays

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

The Application of the Weighted k-Partite Graph Problem to the Multiple Alignment for Metabolic Pathways

Contact Info

Product

Resources

About