You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Efficient Reconstruction of Predictive Consensus Metabolic Network Models}
Article:
Abstract:
Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models( GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.
Introduction:
Genome-scale constraint-based metabolic models( GSMs) are curated organism-specific knowledge repositories[1]. They integrate many distinct( bio) chemical entities and typically account for thousands of metabolites, reactions and genes. When assuming that metabolism is in a steady state, GSMs also enable metabolic simulations with applications in genome annotation[2, 3], analysis of omics data[4–6], phenotype predictions[7–9], organism comparison[9–12], drug discovery[7, 13, 14], and metabolic engineering[8, 15]. GSMs thereby quantitatively reconstruct the internal metabolic and transport wiring of the modeled organism and thus increase our systems level understanding. Genome-scale metabolic reconstructions consist of metabolites, metabolic reactions( including boundary reactions and a biomass reaction), cellular compartments, and genes[1, 16]. The reactions are organized according to the cellular compartments in which they are active. Enzyme-driven( as opposed to spontaneous) reactions are associated with Gene-protein-reaction rules( GPR), which include one or more genes. For multiple genes, the GPR indicates whether alternative isozymes or enzyme complexes catalyze the reaction[17]. A reaction’s equation consists of substrates and products with their corresponding stoichiometries. A reaction’s reversibility describes whether the reaction operates forward, backward, or bi-directionally. The reaction flux bounds specify the reaction’s capacity, that is, the absolute upper and lower bounds of the reaction flux. Transport reactions transfer metabolites between cellular compartments, whereas boundary reactions define nutrient uptake and secretion. The biomass reaction, finally, reflects the molecular composition of a cell or organism and represents cell or organism growth. Together, these entities and their encoding in a GSM aim to represent the current knowledge of the organism’s metabolism. However, even for well-studied organisms such as Saccharomyces cerevisiae or Bacillus subtilis, many uncertainties remain during GSM construction. These uncertainties are typically manually addressed based on expert knowledge and scientific literature, which involves a laborious iterative process that can take several years, for example, for eukaryotes[1]. The main sources of uncertainties are:( i) incomplete and erroneous information from heterogeneous and potentially contradictory data sources such as insufficiently curated and inconsistent gene annotations[18], alternative naming and spelling variants of metabolites( different namespaces)[18–21], and conflicting reaction reversibilities[2, 22];( ii) subjectivity in interpreting literature sources;( iii) integration of qualitative and quantitative data( e. g., inconsistent growth data); and( iv) incompatible levels of detail between and among( reference) databases; for example, databases may represent metabolic pathways by detailed individual reactions or by a single lumped reaction[18], and they may use varying structural definitions for metabolite classes such as lipids and polymers[21, 23]. As a consequence, when several GSMs for the same organism are developed independently, they are complementary and only partially overlapping[24, 25]. The extent of variation between models for the same organism can be dramatic. For example, the well-established human and yeast GSMs agree only on 3%[18] and 35%[20] of their reactions, respectively, when ignoring electron, proton, and water imbalances. Differences between GSMs resulting from different modeling frameworks and model authors can even be more substantial than biological differences between organisms[26]. Any GSM-driven analysis, which needs to( somewhat arbitrarily) select one GSM when several are available, thus, only operates on a subset of the available information. To represent metabolism more comprehensively, and thereby improve our understanding of a target organism, alternative GSMs of a target organism can be integrated into a so-called consensus model of the respective organism, one per organism. Consensus models have an increased scope( by combining unique parts of initial GSMs) and they are more consolidated( by identifying shared parts of initial GSMs that are likely to be reliable). When discrepancies exist between GSMs, these must be carefully examined to select the most appropriate modeling alternative. However, while consensus models have been generated successfully for several( model) organisms such as budding yeast and human, this required extensive manual curation by communities of domain experts[10, 20, 24, 25, 27]. To alleviate this bottleneck and render GSMs truly useful for the understanding of cellular function and evolution, community function, and host-pathogen interactions, semi-automatic consensus model generation approaches have been proposed. It has been shown that the combination of complementary GSMs of the same organism reduces existing gaps in individually reconstructed GSMs[28, 29]. These approaches focused mainly on reconciling namespaces( a particularly important challenge for matching metabolites) or on curating the underlying databases[18, 21]. Thereby, existing methods address only a small subset of the problems in consensus model generation described above. For example, they do not identify and curate cases when two initial GSMs represent the same metabolic process at different levels of granularity[30]. Here, we present COMMGEN, a tool for COnsensus Metabolic Model GENeration that reconciles two or more distinct GSMs of the same organism beyond a common namespace. COMMGEN automatically identifies similarities, dissimilarities, and complements of the metabolic networks based on an extensive classification of problems that typically arise during GSM integration and on novel algorithms to resolve these problem classes. For several model organisms, we show that semi-automatically created consensus GSMs in a standardized namespace[31] are substantially more consolidated than achievable by a common namespace alone, and that they retain or even improve on the initial GSMs’ predictive capabilities. Because the consensus GSMs contain the information from each initial GSM, they comprehensively represent our best understanding of the organisms’ metabolic networks.
Discussion:
Genome-scale constraint-based metabolic models are both integrated knowledge repositories and predictive mathematical models. In terms of knowledge representation, a consensus model should be more consolidated than individual GSMs due to shared parts, more comprehensive due to unique parts, and more accurate due to reconciliation of inconsistencies in similar parts. A consensus model, however, can propagate errors in the initial models’ unique parts, and it may be less consistent than the initial models, especially when inconsistencies in similar model parts were not identified or reconciled. Inconsistencies in GSMs are typically nested, not mutually exclusive, and therefore difficult to address, which so far prevented the development of methods for the automated generation of consensus models[30]. Manual network reconciliation, the predominant approach applied today, is difficult and cumbersome because the number of inconsistencies between just two or three IGSMs already runs in the thousands. Based on a systematic classification of inconsistencies, COMMGEN automatically identifies and semi-automatically reconciles inconsistencies between and within two or more IGSMs. The inconsistencies could theoretically be reconciled fully automatically, but automated resolution depends on the used reference databases, which vary to a large extent[18]. Therefore, COMMGEN does not entirely remove the need for manual inspection and curation. For example, our framework relies on network similarity between alternative realizations of metabolites and reactions in order to match them. Because the reactions surrounding biomass formation are often implemented very differently in different GSMs, they are not matched. While our implementation lets the user choose one of the IGSM biomass reactions, a manual update seems necessary as long as COMMGEN does not automatically fetch external information that would enable an automatic reconciliation of the biomass reaction. In addition, there exists a trade-off between sensitivity and specificity for the identification of inconsistent reactions, which limits the detection of lumped and non-lumped pathway representations with a different net reaction. Also, the identification of similar or identical reactions in different cellular compartments is difficult to achieve automatically( but an extension of the current framework could progress in this direction by combining the information from metabolite instances in different compartments prior to metabolite matching). COMMGEN thus forms a necessary bridge between full automation and high-quality manual curation for consensus metabolic model generation. Regarding a GSM’s predictive mathematical model character, it is important to note that remaining inconsistencies in a consensus model can have severe effects, for example, when inconsistencies resulting from model merging are not adequately addressed. As a consequence, individual GSMs may outperform a consensus model in terms of predictive ability even though the latter is more representative of the available information. COMMGEN’s aim( and design) is to compare and reconcile IGSMs in order to obtain a high-quality representation of the IGSMs’ combined information. In contrast to model optimization methods such as GrowMatch[34], COMMGEN does not create a model optimized for predictive ability, and it does not use corresponding experimental information. However, our example applications also demonstrated that automatically generated consensus models almost always have higher predictive power than the manually curated IGSMs and that these models can be comparable to manually constructed consensus models as shown for yeast. COMMGEN increases coherence with the actual biological system while maintaining predictive power. This balance is of utmost importance for the usability and reliability of GSMs to elucidate cell function interactions. As demonstrated by our case study for P. putida, we argue that( semi-) automatically generated consensus models provide the basis for additional improvements due to their comprehensiveness and standardized naming system. Gap-filling methods[2, 39] may be able to close gaps that are not apparent in the IGSMs. One can use existing methods[2, 40] to re-evaluate reaction directionalities, especially for reactions that differed in the IGSMs. Compartment assignment methods[41] can resolve remaining compartmentalization issues and optimization methods[34, 42] may alter the model to increase its predictive ability. Finally, a good consensus model is a solid foundation for new models by providing a basis for GSMs of similar organisms, and via its integration into multi-scale whole-cell or tissue models[36]. More generally, the systematic integration of heterogeneous information is an essentially unsolved challenge in( post-) genomic biology. For metabolism, consensus GSMs are formalized means for complementing incomplete information, and for identifying and addressing errors through the comparison of independently generated GSMs for the same organism. COMMGEN automatically identifies and semi-automatically resolves widespread and highly interlinked inconsistencies between initial GSMs, thereby moving beyond existing approaches for manual and computer-aided consensus model generation. It can therefore facilitate the construction of new models by comparing and combining information from automatic model construction tools such as the modelSEED[43] and manual model construction efforts, and facilitate GSM updates using a reference—both tasks are analogous to consensus GSM generation. While we focus here on the reconciliation of multiple GSMs for the same species, we argue that COMMGEN’s methods and standardization are more widely applicable. The identification of similar, yet distinct, biochemical entities can help to compare metabolic capabilities of organisms in detail via their GSMs, or even to compare entire pathway databases. However, dealing with different species will require new, systematic preprocessing steps to map gene sets in different organisms functionally to each other( e. g., via orthology or enzyme classification numbers), which is a topic of future research. In addition, COMMGEN’s methods for identifying redundancies and hierarchical relationships in networks can be used to further advance standardization of terms and ontologies. We therefore expect COMMGEN to be of substantial aid in future integration of knowledge for metabolic networks, to greatly accelerate model-building processes and to thereby improve subsequent high-throughput model-based network analyses. Although COMMGEN will not directly address the domain-specific problems, these capabilities will lay a solid foundation for the systematic, genome-scale comparison of metabolic spaces within and across genera and will have substantial impact for large-scale evolutionary analyses, design of microbial communities, and understanding of host-microbe( pathogen, microbiome) interactions.
Many large-scale mathematical models describe metabolism to understand how microbes and other organisms ( including humans ) function and interact with each other and with their environment . Making these models is extremely time- and effort-intensive; it requires gathering and combining information from many sources , including the organism’s genome sequence , biological databases , scientific literature , and expert advice . The exact procedure and resources used depend on the model creators’ expertise and research interests , such that independently created models for the same organism are often very different and can hardly be compared . However , each model typically contains unique information that is ‘lost’ when working with a different model . To integrate the available knowledge , we developed a computational tool to build consensus metabolic models . Our tool—COMMGEN- combines independently generated models by matching identical parts and resolving differences between inconsistent parts . We apply our tool to four sets of models of different organisms . In all these sets , COMMGEN identified and resolved hundreds of inconsistencies . COMMGEN can be generally applied to standardize and improve models of metabolism , in particular for complex scenarios , such as those involving microbial communities and host-pathogen interactions .