Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared ...to other constraint-based modeling methods.
We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.
Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa ...and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.
An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic ...functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.
Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, ...with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Inflammatory bowel diseases, such as Crohn's Disease, are characterised by an altered blood and faecal metabolome, and changes in gut microbiome composition. Here, we present an efficient, scalable, ...tractable systems biology framework to mechanistically link microbial strains and faecal metabolites. We retrieve strain-level relative abundances from metagenomics data from a cohort of paediatric Crohn's Disease patients with and without dysbiosis and healthy control children and construct and interrogate a personalised microbiome model for each sample. Predicted faecal secretion profiles and strain-level contributions to each metabolite vary broadly between healthy, dysbiotic, and non-dysbiotic microbiomes. The reduced microbial diversity in IBD results in reduced numbers of secreted metabolites, especially in sulfur metabolism. We demonstrate that increased potential to synthesise amino acids is linked to Proteobacteria contributions, in agreement with experimental observations. The established modelling framework yields testable hypotheses that may result in novel therapeutic and dietary interventions targeting the host-gut microbiome-diet axis.
Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured ...knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
This comprehensive review elucidates the pivotal role of microbes in drug metabolism, synthesizing insights from an exhaustive analysis of over two hundred papers. Employing a structural ...classification system grounded in drug atom involvement, the review categorizes the microbiome-mediated drug-metabolizing capabilities of over 80 drugs. Additionally, it compiles pharmacodynamic and enzymatic details related to these reactions, striving to include information on encoding genes and specific involved microorganisms. Bridging biochemistry, pharmacology, genetics, and microbiology, this review not only serves to consolidate diverse research fields but also highlights the potential impact of microbial drug metabolism on future drug design and in silico studies. With a visionary outlook, it also lays the groundwork for personalized medicine interventions, emphasizing the importance of interdisciplinary collaboration for advancing drug development and enhancing therapeutic strategies.This comprehensive review elucidates the pivotal role of microbes in drug metabolism, synthesizing insights from an exhaustive analysis of over two hundred papers. Employing a structural classification system grounded in drug atom involvement, the review categorizes the microbiome-mediated drug-metabolizing capabilities of over 80 drugs. Additionally, it compiles pharmacodynamic and enzymatic details related to these reactions, striving to include information on encoding genes and specific involved microorganisms. Bridging biochemistry, pharmacology, genetics, and microbiology, this review not only serves to consolidate diverse research fields but also highlights the potential impact of microbial drug metabolism on future drug design and in silico studies. With a visionary outlook, it also lays the groundwork for personalized medicine interventions, emphasizing the importance of interdisciplinary collaboration for advancing drug development and enhancing therapeutic strategies.
Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA ...(assembly of gut organisms through reconstruction and analysis), a resource of genome-scale metabolic reconstructions semi-automatically generated for 773 human gut bacteria. Using this resource, we identified a defined growth medium for Bacteroides caccae ATCC 34185. We also showed that interactions among modeled species depend on both the metabolic potential of each species and the nutrients available. AGORA reconstructions can integrate either metagenomic or 16S rRNA sequencing data sets to infer the metabolic diversity of microbial communities. AGORA reconstructions could provide a starting point for the generation of high-quality, manually curated metabolic reconstructions. AGORA is fully compatible with Recon 2, a comprehensive metabolic reconstruction of human metabolism, which will facilitate studies of host-microbiome interactions.
Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological ...approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.
Synopsis
Sex‐specific, whole‐body human metabolic models were developed and constrained with physiological, dietary, and metabolomic data. They recapitulate known whole‐body metabolic functions and enable mechanistic exploration of host‐microbiome co‐metabolism.
Sex‐specific whole‐body metabolic reconstructions represent the integrated function of 26 organs and six blood cell types.
Stoichiometric reconstructions of metabolism can be constrained with whole‐body physiological and metabolomic data to generate personalized models.
Whole‐body metabolic models recapitulate known inter‐organ metabolic cycles and energy use, successfully predict known biomarkers of inherited metabolic diseases, and explore potential host‐microbiome co‐metabolism.
Sex‐specific, whole‐body human metabolic models were developed and constrained with physiological, dietary, and metabolomic data. They recapitulate known whole‐body metabolic functions and enable mechanistic exploration of host‐microbiome co‐metabolism.
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