Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

Abstract

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

“…The first version of this framework created models with an average accuracy of 66% before optimization and 87% after optimization, as determined using experimental validation [56]. However, recent advances in automated metabolic modeling have expanded the ModelSEED algorithm to include a likelihood maximization approach to gap filling that resulted in greater accuracy and the identification of 5–30% additional reactions compared to the original parsimony-based approach[51]. Of the reactions identified by this new approach, 5 to 30% are not identified using the original parsimony-based approach.…”

“…This results in a final, optimized GEM. The process shown here is based on the ProbModelSEED pipeline [51], but the same essential process is used to create all GEMs.…”

Genome-scale modeling for metabolic engineering

“…This process is data intensive and involves gathering species-specific information from genome annotations, high-throughput experiments, the literature and/or publically available databases, such as KEGG [41], EcoCyc [42], BKM-react [46], or BRENDA [84]. Gap-filling methodologies are subsequently applied [13, 75] to improve connectivity to the point where the model can simulate phenotypes. As labor intensive as manual reconstruction is, the process has been well developed and described [95].…”

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

“…Prioritization of the removal of reactions without sequence similarity minimizes the inclusion of locally (enzymes with an associated sequence that is not present in the target genome) and globally (reactions without sequence association) orphaned reactions. Very recently, a bottom-up MILP approach also used sequence similarity as a likelihood metric for the existence of a gap-filling reaction in the target genome (31). Gap analysis itself has been used to identify knowledge gaps in human metabolism (32) and to leverage contextual information of networks to hypothesize gene function (33).…”