Dataset Information

Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.

ABSTRACT: The ability of microbial species to consume compounds found in the environment to generate commercially-valuable products has long been exploited by humanity. The untapped, staggering diversity of microbial organisms offers a wealth of potential resources for tackling medical, environmental, and energy challenges. Understanding microbial metabolism will be crucial to many of these potential applications. Thermodynamically-feasible metabolic reconstructions can be used, under some conditions, to predict the growth rate of certain microbes using constraint-based methods. While these reconstructions are powerful, they are still cumbersome to build and, because of the complexity of metabolic networks, it is hard for researchers to gain from these reconstructions an understanding of why a certain nutrient yields a given growth rate for a given microbe. Here, we present a simple model of biomass production that accurately reproduces the predictions of thermodynamically-feasible metabolic reconstructions. Our model makes use of only: i) a nutrient's structure and function, ii) the presence of a small number of enzymes in the organism, and iii) the carbon flow in pathways that catabolize nutrients. When applied to test organisms, our model allows us to predict whether a nutrient can be a carbon source with an accuracy of about 90% with respect to in silico experiments. In addition, our model provides excellent predictions of whether a medium will produce more or less growth than another (p<10(-6)) and good predictions of the actual value of the in silico biomass production.

SUBMITTER: Seaver SM

PROVIDER: S-EPMC3486842 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.

Seaver Samuel M D SM Sales-Pardo Marta M Guimerà Roger R Amaral Luís A Nunes LA

PLoS computational biology 20121101 11

The ability of microbial species to consume compounds found in the environment to generate commercially-valuable products has long been exploited by humanity. The untapped, staggering diversity of microbial organisms offers a wealth of potential resources for tackling medical, environmental, and energy challenges. Understanding microbial metabolism will be crucial to many of these potential applications. Thermodynamically-feasible metabolic reconstructions can be used, under some conditions, to ...[more]

PMID: 23133365

Similar Datasets

Project description:A deep understanding of the neural architecture of mental function should enable the accurate prediction of a specific pattern of brain activity for any psychological task, based only on the cognitive functions known to be engaged by that task. Encoding models (EMs), which predict neural responses from known features (e.g., stimulus properties), have succeeded in circumscribed domains (e.g., visual neuroscience), but implementing domain-general EMs that predict brain-wide activity for arbitrary tasks has been limited mainly by availability of datasets that 1) sufficiently span a large space of psychological functions, and 2) are sufficiently annotated with such functions to allow robust EM specification. We examine the use of EMs based on a formal specification of psychological function, to predict cortical activation patterns across a broad range of tasks. We utilized the Multi-Domain Task Battery, a dataset in which 24 subjects completed 32 ten-minute fMRI scans, switching tasks every 35 s and engaging in 44 total conditions of diverse psychological manipulations. Conditions were annotated by a group of experts using the Cognitive Atlas ontology to identify putatively engaged functions, and region-wise cognitive EMs (CEMs) were fit, for individual subjects, on neocortical responses. We found that CEMs predicted cortical activation maps of held-out tasks with high accuracy, outperforming a permutation-based null model while approaching the noise ceiling of the data, without being driven solely by either cognitive or perceptual-motor features. Hierarchical clustering on the similarity structure of CEM generalization errors revealed relationships amongst psychological functions. Spatial distributions of feature importances systematically overlapped with large-scale resting-state functional networks (RSNs), supporting the hypothesis of functional specialization within RSNs while grounding their function in an interpretable data-driven manner. Our implementation and validation of CEMs provides a proof of principle for the utility of formal ontologies in cognitive neuroscience and motivates the use of CEMs in the further testing of cognitive theories.

Project description:INTRODUCTION: Adiponectin has been implicated in the pathogenesis of osteoarthritis (OA). We studied the effects of adiponectin on the OA cartilage homeostasis. METHODS: Immunohistochemical analysis was performed to evaluate differential expression of adiponectin receptors (AdipoRs) in nonlesional and lesional areas of OA cartilage. Cartilage and chondrocytes from the knee joints of primary OA patients were cultured in the presence of adiponectin (0~30 ?g/ml). The levels of total nitric oxide (NO), matrix metalloproteinase (MMP)-1, -3, and -13, and tissue inhibitor of metalloproteinase (TIMP)-1 were measured in the conditioned media. The levels of inducible NO synthase (iNOS) and MMPs were determined with the quantitative real-time reverse transcription-polymerase chain reaction. The concentrations of collagenase-cleaved type II collagen neoepitope (C1-2C) were determined in the supernatant of adiponectin-stimulated OA cartilage explants. The effects of kinase and NOS inhibitors were evaluated in the adiponectin-stimulated chondrocytes. RESULTS: The expression levels of both AdipoR1 and AdipoR2 were significantly higher in lesional than in nonlesional areas of OA cartilage. The increased rate of AdipoR1-positive chondrocytes was twice that of AdipoR2-positive chondrocytes when compared between nonlesional and lesional areas. Adiponectin-stimulated OA chondrocytes showed increased total NO and MMP-1, -3, and -13 levels compared with nonstimulated cells. The TIMP-1 level was not affected. The C1-2C levels were increased by adiponectin in OA cartilage explant culture. AMP-activated protein kinase (AMPK) and c-Jun N-terminal kinase (JNK) inhibitors (compound C and SP600125) significantly suppressed adiponectin-induced production of total NO and MMP-1, -3, and -13. Inducible NOS inhibitors enhanced the expression of the adiponectin-induced MMPs. CONCLUSIONS: Adiponectin causes matrix degradation in OA cartilage and increases MMPs and iNOS expression via the AMPK and JNK pathways in human OA chondrocytes. The catabolic effects of adiponectin may be counteracted by NO.

Dataset Information

Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.

Publications

Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets