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89 results for “metabolic scaling”
Genome-scale metabolic model of Quercus suber
<p>Genome-scale metabolic model of Quercus suber in SBML Level 3 Version 2 format. This model was reconstructed using <em>merlin</em> (https://merlin-sysbio.org), an open-source software.</p>
Metabolic scaling, growth, and reproduction data for animals
<p>Data for the scaling of metabolic rate, rates of growth, and annual reproduction for animals. Data and R scripts associated with "Metabolic scaling is the product of life history optimization"</p>
Genome-scale community modelling reveals key metabolic cross-feedings in epipelagic bacterioplankton communities (Supplementary Materials)
<p>A comprehensive catalog of 19,791 marine prokaryotic isolates (WGS), single-amplified genomes (SAGs) and metagenomic-assembled genomes (MAGs) compiled from MarRef v4.0 (N=943, mostly high-quality WGS), MarDB v4.0 (N=12,963), and the aquatic representative genomes from the ProGenomes database v1.0 (N=566). This collection of well-documented genomes was complemented by 5,319 MAGs assembled from four distinct studies, namely: Parks et al. 2017 (<a href="https://doi.org/10.1038/s41564-017-0012-7">DOI</a>; N=1,765; downloaded from EBI), Tully et al. 2017/2018 (<a href="https://doi.org/10.7717/peerj.3558">DOI</a> and <a href="https://10.1038/sdata.2017.203">DOI</a>; N=2,597; downloaded from EBI), and Delmont et al. 2018 (<a href="https://doi.org/10.1038/s41564-018-0176-9">DOI</a>; N=957; downloaded from FIGSHARE). The Parks et al. study contained genomes reconstructed from non-marine biomes. Thus, a selection of 1,765 genomes was extracted by searching for specific keywords: “tara|marine|sea|ocean|mediterranean” (case insensitive). Note that depending on their study of origin, included MAGs may have been reconstructed using different assembling and binning methods.</p> <p>The archive includes:</p> <ul> <li>a metadata file describing the quality and redundancy of the genomes named `EcoSysMic_metadata.tsv`</li> <li>sequences of the 19,791 (redundant) genomes in `All/WGS`</li> <li>companion files in `All/Data` and `dRep95/Data` (see Methods in the associated paper), including <ul> <li>predicted CDS and EggNOG functional annotations</li> <li>predicted GTDB taxonomy</li> <li>CarveMe reconstructed metabolic models and their MEMOTE quality</li> </ul> </li> </ul> <p>The 7,658 non-redundant species-level genomes (delineated by a 95% ANI threshold over 60% of genome length) that were used in the associated paper are defined by the column `is_drep95` in the metadata file.</p>
Computed results for Bayesian genome scale modelling temperature effect on yeast metabolism
<p>This repository contains the computed results for reproducing the figures in the manuscript "Li G., et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism". The scripts can be found in Github (<a href="https://github.com/Gangl2016/BayesianGEM">https://github.com/Gangl2016/BayesianGEM</a>)</p>
Dataset for the paper "Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943
<p>This repository contains the data needed to reproduce the results in: </p> <p>Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943</p> <p>The dataset was also used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of data are specified in the dataset (under tab "References")</p>
Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
<p>Unraveling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. To understand the response of individual animals to environmental change, physiology and individual metabolism are often studied. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behavior in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.</p>
Larval nutrition impacts survival to adulthood, body size, and the allometric scaling of metabolic rate in adult honeybees
<p>Resting metabolic rate (RMR) is a fundamental physiological measure linked to numerous aspects of organismal function, including lifespan. Although dietary restriction in insects during larval growth/development affects adult RMR, the impact of larval diet <i>quality</i> on adult RMR has not been studied. Using <i>in vitro</i> rearing to control larval diet quality, we determined the effect of dietary protein and carbohydrate on honeybee survival-to-adulthood, time-to-eclosion, body mass/size and adult RMR. High carbohydrate larval diets increased survival-to-adulthood and time-to-eclosion compared to both low carbohydrate and high protein diets. Upon emergence, bees reared on the high protein diet were smaller and lighter than those reared on other diets, whilst those raised on the high carbohydrate diet varied more in body mass. Newly emerged adult bees' reared on the high carbohydrate diet showed a significantly steeper increase in allometric scaling of RMR compared to those reared on other diets. This suggests that diet quality influences survival-to-adulthood, time-to-eclosion, and the allometric scaling of RMR. Given that agricultural intensification and increasing urbanisation have led to a decrease in both forage availability and dietary diversity for bees, our results are critical to improving understanding of the impacts of poor developmental nutrition on bee growth/development and physiology.</p>
Supplementary material for Metabolic constraints on the body size scaling of extreme population densities
<p>Data for the analysis of the paper entitled</p> <p>Metabolic constraints on the body size scaling of extreme population densities. Ecology letters.</p> <p><a href="https://zenodo.org/api/files/a60c335d-e6ca-4bd9-a17f-fc98ca9703e2/air_sea_outputs_L4_nsw.txt?versionId=1416dceb-47d0-45b1-99a8-baa833df8f76">air_sea_outputs_L4_nsw.txt </a></p> <p>This data set contains 8693 rows and 13 variables</p> <p>From colum 1 to 13 the following names apply:</p> <p>Julian- Day of the year (1rst January =1)</p> <p>day- Day number</p> <p>month- Month number</p> <p>year- Year number</p> <p>lat- Latitude</p> <p>lon- Longitude</p> <p>wind_speed_m_s- 1m wind speed in meters per second </p> <p>presure_mB- Air pressure in milibars</p> <p>Temp_°C- Air temperature in degrees Celsius </p> <p>Temp_algo- Other temperature</p> <p>Cloud_cover%- Percentage of cloud cover</p> <p>SST_°C- sea surface temperature in degree Celsius</p> <p>SW_Radiation_W_m2- Short wave radiation in Watts per square meter.</p> <p><a href="https://zenodo.org/api/files/a60c335d-e6ca-4bd9-a17f-fc98ca9703e2/SurfaceTemperature_L4_Merged.csv?versionId=9738e9d6-53ed-49c4-ab0d-136b74ddb16e">SurfaceTemperature_L4_Merged.csv </a></p> <p>This data set has 1064 rows and two columns</p> <p>Fechas_tempAll is the date of measurement formated as (Year-Month-Day Hour)</p> <p>e.g. 2013-05-20 09:00:00</p> <p>TEMP_EC- Surface water Temperature in degree Celsius</p> <p><a href="https://zenodo.org/record/7374166/files/Nutrients.csv?download=1">Nutrients.csv</a></p> <p>The data set sas 445 rows and 8 Variables</p> <p>Sample.Date- Date when surface water sample was taken.</p> <p>NITRITE.µM, - Nitrite (NO2) concentration in micromolar</p> <p>NITRATE.NITRITE.µM"- Nitrate (NO3) concentration in micromolar</p> <p>AMMONIA.µM, - Ammonia (NH4) concentration in micromolar</p> <p>SILICATE.µM, - Silica (Si) concentration in micromolar</p> <p>PHOSPHATE.µM- - Posphate (PO4) concentration in micromolar<br> FLAG- Quality flag (TRUE/FALSE).</p> <p> </p> <p> </p>
Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
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Larval nutrition impacts survival to adulthood, body size, and the allometric scaling of metabolic rate in adult honeybees
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Local-scale thermal history influences metabolic response of marine invertebrates
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Data from: Why does offspring size affect performance? Integrating metabolic scaling with life-history theory
Within species, larger offspring typically outperform smaller offspring. While the relationship between offspring size and performance is ubiquitous, the cause of this relationship remains elusive. By linking metabolic and life-history theory, we provide a general explanation for why larger offspring perform better than smaller offspring. Using high-throughput respirometry arrays, we link metabolic rate to offspring size in two species of marine bryozoan. We found that metabolism scales allometrically with offspring size in both species: while larger offspring utilise absolutely more energy than smaller offspring, larger offspring use proportionally less of their maternally-derived energy throughout the dependent, non-feeding phase. The increased metabolic efficiency of larger offspring while dependent on maternal investment may explain offspring size effects –larger offspring reach nutritional independence (feed for themselves) with a higher proportion of energy relative to structure than smaller offspring. These findings offer a potentially universal explanation for why larger offspring tend to perform better than smaller offspring but studies on other taxa are needed.
Larval nutrition impacts the scaling of adult metabolic rate with body mass in honeybees
<p>Resting metabolic rate (RMR) is a fundamental physiological measure linked to numerous aspects of organismal function, including lifespan. Although dietary restriction in insects during larval growth/development affects adult RMR, the impact of larval diet quality on adult RMR has not been studied. Using in vitro rearing to control larval diet quality, we determined the effect of dietary protein and carbohydrate on honeybee survival-to-adulthood, time-to-eclosion, body mass/size and adult RMR. High carbohydrate larval diets increased survival-to-adulthood and time-to-eclosion compared to both low carbohydrate and high protein diets. Upon emergence, bees reared on the high protein diet were smaller and lighter than those reared on other diets, whilst those raised on the high carbohydrate diet varied more in body mass. Newly-emerged adult bees' reared on the high carbohydrate diet showed a significantly steeper increase in allometric scaling of RMR compared to those reared on other diets. This suggests that diet quality influences survival-to-adulthood, time-to-eclosion, and the allometric scaling of RMR. Given that agricultural intensification and increasing urbanisation have led to a decrease in both forage availability and dietary diversity for bees, our results are critical to improving understanding of the impacts of poor developmental nutrition on bee growth/development and physiology.</p>
Data and code to reproduce analyses in Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites"
<p>This datasets archives the GitHub version found at https://github.com/ThieleLab/CodeBase to reproduce simulations for the article Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites", Cell Systems, in press.</p>
Deriving population scaling rules from individual-level metabolism and life history traits - Code and Data
<p>Individual metabolism generally scales with body mass with an exponent around 3/4. From dimensional arguments it follows that maximum population growth rate (rmax) scales with a -1/4 exponent. However, the dimensional argument implicitly assumes that offspring size is proportional to adult size. Here we calculate rmax from metabolic scaling at the level of individuals within size-structured populations while explicitly accounting for offspring size. We identify four general patterns of how rmax scales with adult mass based on four empirical life-history patterns employed by groups of species. These life-history patterns are determined by how traits of somatic growth rate and/or offspring mass relate to adult mass. One life-history pattern -- constant adult:offspring mass ratio and somatic growth rate independent of adult mass -- leads to the classic -1/4 scaling of rmax. The other three life-history patterns lead either to non-metabolic population growth scaling with adult mass or do not follow a power-law relationship at all. Using life-history data of five marine taxa and terrestrial mammals, we identify species groups that belong to one of each case. We predict that elasmobranchs, copepods, and mammals follow standard -1/4 power-law scaling, whereas teleost fish and bivalves do not have a pure power-law scaling. Our work highlights how taxa may deviate from the classic -1/4 metabolic scaling pattern of maximum population growth. The approach is generic and can be applied to any taxa.</p>
Strain specific genome scale metabolic models for 1011 Saccharomyces cerevisiae
<p>This is generated strain-specific genome scale metabolic models for 1011 S.cerevisiae. This is linked with the paper: Lu, H. et al. <em>A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.</em> Nature Communications 10, 3586 (2019). <a href="https://doi.org/10.1038/s41467-019-11581-3">doi:10.1038/s41467-019-11581-3</a></p>
Comprehensive Context-specific Genome-scale Metabolic Models for Mus Musculus
<p>Comprehensive Context-specific Genome-scale Metabolic Models for Mus Musculus. The data consists of 28 models for the combination 2 mouse strains (WT and Ob/Ob), 2 diets (WT and HFD) and 7 tissues (Aorta, Heart, Liver, Skeletal Muscle, Hippocampus, Hypothalamus and Epididymal fat).</p>
Scaling of metabolic rate in hibernators: Translation of literature data: For a comment to Nespolo et al. (2022)
<p>This spreadsheet provides a translation of data from the literature for the manuscript: Do bears hibernate in the woods? Comment on 'Why bears hibernate? Redefining the scaling energetics of hibernation', and includes both the background data for Figure 1 which is presented in the manuscript.</p> <p>Full reference for article it is commented on: Nespolo, R. F., Mejias, C., and Bozinovic, F. Why bears hibernate? Redefining the scaling energetics of hibernation. Proc. R. Soc. B. 2022; 289(1973):20220456. We have made corrections to the data in the original article's Table S1, and translated empirical BMR data from literature instead of calculating a regression line from White, C.R. & Seymour, R.S. (2003). This changes the body mass where regression lines for Log (DDE<sub>HIB</sub>) and Log(BMR) cross, and thus where no savings from hibernation can be expected, from 75 kg to over 2,250 kg and shows that bears hibernate to save energy.</p>
The scaling of metabolic traits differs among larvae and juvenile colonies of scleractinian corals
<p>Body size profoundly affects organism fitness and ecosystem dynamics through the scaling of physiological traits. This study tests for variation in metabolic scaling and its potential drivers among corals differing in life history strategies and taxonomic identity. Data were compiled from published sources and augmented with empirical measurements of corals in Moorea, French Polynesia. The data compilation revealed metabolic isometry in broadcasted larvae, but size-independent metabolism in brooded larvae; empirical measures of <em>Pocillopora acuta</em> larvae also supported size-independent metabolism in brooded coral larvae. In contrast, for juvenile colonies (i.e., 1–4 cm diameter), metabolic scaling was isometric for <em>Pocillopora</em> spp. and negatively allometric for <em>Porites</em> spp. The scaling of biomass with surface area was isometric for <em>Pocillopora</em> spp., but positively allometric for <em>Porites</em> spp., suggesting the surface area:biomass ratio mediates metabolic scaling in these corals. The scaling of tissue biomass and metabolism was not affected by light treatment (i.e., either natural photoperiods or constant darkness) in both juvenile taxa. However, biomass was reduced by 9–15% in the juvenile corals from the light treatments and this coincided with higher metabolic scaling exponents, thus supporting the causal role of biomass in driving variation in scaling. This study shows that metabolic scaling is plastic in the early life stages of corals, with intrinsic differences between life history strategy (i.e., brooded and broadcasted larvae) and taxa (i.e., <em>Pocillopora </em>spp<em>. </em>and <em>Porites </em>spp<em>.</em>), and acquired differences attributed to changes in area-normalized biomass.</p>
Data from: Scaling of leaf area with biomass in trees reconsidered: Constant metabolically active sapwood volume per unit leaf area with height growth
<p>Hypoallometric (slope<1) scaling between metabolic rate and body mass is often regarded as near-universal across organisms. However, there are compelling reasons to question hypoallometric scaling in woody plants, where metabolic rate=leaf area. This leaf area must provide carbon to the metabolically active sapwood volume (VMASW). Within populations of a species, variants in which VMASW increases per unit leaf area with height growth (e.g. ⅔ or ¾ scaling) would have proportionally less carbon for growth and reproduction as they grow taller. Therefore, selection should favor individuals in which, as they grow taller, leaf area scales isometrically with shoot VMASW (slope=1). Using tetrazolium staining, we measured total VMASW and total leaf area (LAtot) across 22 individuals of Ricinus communis and confirmed that leaf area scales isometrically with VMASW, and that VMASW is much smaller than total sapwood volume. With the potential of the LAtot-VMASW relationship to shape factors as diverse as the crown area-stem diameter relationship, conduit diameter scaling, reproductive output, and drought-induced mortality, our work suggests that the notion that sapwood increases per unit leaf area with height growth requires revision.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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