Mark Pogson

Introducing Spatial Information into Predictive NF-κB Modelling – An Agent-Based Approach

Nature is governed by local interactions among lower-level sub-units, whether at the cell, organ, organism, or colony level. Adaptive system behaviour emerges via these interactions, which integrate the activity of the sub-units. To understand the system level it is necessary to understand the underlying local interactions. Successful models of local interactions at different levels of biological organisation, including epithelial tissue and ant colonies, have demonstrated the benefits of such ‘agent-based’ modelling [1]–[4]. Here we present an agent-based approach to modelling a crucial biological system – the intracellular NF-κB signalling pathway. The pathway is vital to immune response regulation, and is fundamental to basic survival in a range of species [5]–[7]. Alterations in pathway regulation underlie a variety of diseases, including atherosclerosis and arthritis. Our modelling of individual molecules, receptors and genes provides a more comprehensive outline of regulatory network mechanisms than previously possible with equation-based approaches [8]. The method also permits consideration of structural parameters in pathway regulation; here we predict that inhibition of NF-κB is directly affected by actin filaments of the cytoskeleton sequestering excess inhibitors, therefore regulating steady-state and feedback behaviour.

Economic and greenhouse gas costs of Miscanthus supply chains in the United Kingdom

Miscanthus has been identified as one of the most promising perennial grasses for renewable energy generation in Europe and the United States [Mitigation and Adaptation Strategies for Global Change 9 (2004) 433]. However, the decision to use Miscanthus depends to a considerable degree on its economic and environmental performance [Soil Use and Management 24 (2008) 235; Renewable and Sustainable Energy Reviews 13 (2009) 1230]. This article assessed the spatial distribution of the economic and greenhouse gas (GHG) costs of producing and supplying Miscanthus in the UK. The average farm‐gate production cost of Miscanthus in the UK is estimated to be 40 £ per oven‐dried tonne (£ odt−1), and the average GHG emissions from the production of Miscanthus are 1.72 kg carbon equivalent per oven‐dried tonnes per year (kg CE odt−1 yr−1). The production cost of Miscanthus varies from 35 to 55 £ odt−1 with the lowest production costs in England, Wales and Northern Ireland, and the highest costs in Scotland. Sensitivity analysis shows that yield of Miscanthus is the most influential factor in its production cost, with precipitation the most crucial input in determining yield. GHG emissions from the production of Miscanthus range from 1.24 to 2.11 kg CE odt−1 yr−1. To maximize the GHG benefit, Miscanthus should be established preferentially on croplands, though other considerations obviously arise concerning suitability and value of the land for food production.

Global change synergies and trade-offs between renewable energy and biodiversity

Reliance on fossil fuels is causing unprecedented climate change and is accelerating environmental degradation and global biodiversity loss. Together, climate change and biodiversity loss, if not averted urgently, may inflict severe damage on ecosystem processes, functions and services that support the welfare of modern societies. Increasing renewable energy deployment and expanding the current protected area network represent key solutions to these challenges, but conflicts may arise over the use of limited land for energy production as opposed to biodiversity conservation. Here, we compare recently identified core areas for the expansion of the global protected area network with the renewable energy potential available from land‐based solar photovoltaic, wind energy and bioenergy (in the form of Miscanthus × giganteus). We show that these energy sources have very different biodiversity impacts and net energy contributions. The extent of risks and opportunities deriving from renewable energy development is highly dependent on the type of renewable source harvested, the restrictions imposed on energy harvest and the region considered, with Central America appearing at particularly high potential risk from renewable energy expansion. Without restrictions on power generation due to factors such as production and transport costs, we show that bioenergy production is a major potential threat to biodiversity, while the potential impact of wind and solar appears smaller than that of bioenergy. However, these differences become reduced when energy potential is restricted by external factors including local energy demand. Overall, we found that areas of opportunity for developing solar and wind energy with little harm to biodiversity could exist in several regions of the world, with the magnitude of potential impact being particularly dependent on restrictions imposed by local energy demand. The evidence provided here helps guide sustainable development of renewable energy and contributes to the targeting of global efforts in climate mitigation and biodiversity conservation.

How does bioenergy compare with other land‐based renewable energy sources globally?

The potential power generation from land‐based bioenergy is predicted globally using a computer model. Simultaneous consideration of land use, cost and carbon restrictions enables practical evaluation of net power output. Comparisons are made with wind and solar power, and a sensitivity analysis is used to explore the effects of different policy assumptions. Biomass is shown to offer only moderate power‐generating potential, and would satisfy less than half of current demand even if all suitable existing arable land were used to grow bioenergy crops. However, bioenergy can be cheap to generate given current economics, and is able to remove atmospheric carbon in some cases if coupled with carbon capture and storage. Wind turbines are able to provide more power globally, but photovoltaic solar panels are the only source considered with the potential to satisfy existing demand. Since land‐based bioenergy is also restricted by the need to grow food for an expanding population, and technological developments are likely to greatly increase the viability of other renewable sources, the role of land‐based bioenergy appears relatively limited and short‐term.

Systems approaches in global change and biogeochemistry research.

Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.

Dynamic proteome profiling of individual proteins in human skeletal muscle after a high-fat diet and resistance exercise

It is generally accepted that muscle adaptation to resistance exercise (REX) training is underpinned by contraction‐induced, increased rates of protein synthesis and dietary protein availability. By using dynamic proteome profiling (DPP), we investigated the contribution of both synthesis and breakdown to changes in abundance on a protein‐by‐protein basis in human skeletal muscle. Age‐matched, overweight males consumed 9 d of a high‐fat, low‐carbohydrate diet during which time they either undertook 3 sessions of REX or performed no exercise. Precursor enrichment and the rate of incorporation of deuterium oxide into newly synthesized muscle proteins were determined by mass spectrometry. Ninety proteins were included in the DPP, with 28 proteins exhibiting significant responses to REX. The most common pattern of response was an increase in turnover, followed by an increase in abundance with no detectable increase in protein synthesis. Here, we provide novel evidence that demonstrates that the contribution of synthesis and breakdown to changes in protein abundance induced by REX differ on a protein‐by‐protein basis. We also highlight the importance of the degradation of individual muscle proteins after exercise in human skeletal muscle.—Camera, D. M., Burniston, J. G., Pogson, M. A., Smiles, W. J., Hawley, J. A. Dynamic proteome profiling of individual proteins in human skeletal muscle after a high‐fat diet and resistance exercise. FASEB J. 31, 5478–5494 (2017). www.fasebj.org

Simulation of greenhouse gases following land-use change to bioenergy crops using the ECOSSE model. A comparison between site measurements and model predictions

This article evaluates the suitability of the ECOSSE model to estimate soil greenhouse gas (GHG) fluxes from short rotation coppice willow (SRC‐Willow), short rotation forestry (SRF‐Scots Pine) and Miscanthus after land‐use change from conventional systems (grassland and arable). We simulate heterotrophic respiration (Rh), nitrous oxide (N2O) and methane (CH4) fluxes at four paired sites in the UK and compare them to estimates of Rh derived from the ecosystem respiration estimated from eddy covariance (EC) and Rh estimated from chamber (IRGA) measurements, as well as direct measurements of N2O and CH4 fluxes. Significant association between modelled and EC‐derived Rh was found under Miscanthus, with correlation coefficient (r) ranging between 0.54 and 0.70. Association between IRGA‐derived Rh and modelled outputs was statistically significant at the Aberystwyth site (r = 0.64), but not significant at the Lincolnshire site (r = 0.29). At all SRC‐Willow sites, significant association was found between modelled and measurement‐derived Rh (0.44 ≤ r ≤ 0.77); significant error was found only for the EC‐derived Rh at the Lincolnshire site. Significant association and no significant error were also found for SRF‐Scots Pine and perennial grass. For the arable fields, the modelled CO2 correlated well just with the IRGA‐derived Rh at one site (r = 0.75). No bias in the model was found at any site, regardless of the measurement type used for the model evaluation. Across all land uses, fluxes of CH4 and N2O were shown to represent a small proportion of the total GHG balance; these fluxes have been modelled adequately on a monthly time‐step. This study provides confidence in using ECOSSE for predicting the impacts of future land use on GHG balance, at site level as well as at national level.

Evaluation of the ECOSSE model for simulating soil carbon under short rotation forestry energy crops in Britain

Understanding and predicting the effects of land‐use change to short rotation forestry (SRF) on soil carbon (C) is an important requirement for fully assessing the C mitigation potential of SRF as a bioenergy crop. There is little current knowledge of SRF in the UK and in particular a lack of consistent measured data sets on the direct impacts of land use change on soil C stocks. The ECOSSE model was developed to simulate soil C dynamics and greenhouse gas (GHG) emissions in mineral and organic soils. The ECOSSE model has already been applied spatially to simulate land‐use change impacts on soil C and GHG emissions. However, it has not been extensively evaluated under SRF. Eleven sites comprising 29 transitions in Britain, representing land‐use change from nonwoodland land uses to SRF, were selected to evaluate the performance of ECOSSE in predicting soil C and soil C change in SRF plantations. The modelled C under SRF showed a strong correlation with the soil C measurements at both 0–30 cm (R = 0.93) and 0–100 cm soil depth (R = 0.82). As for the SRF plots, the soil C at the reference sites have been accurately simulated by the model. The extremely high correlation for the reference fields (R ≥ 0.99) shows a good performance of the model spin‐up. The statistical analysis of the model performance to simulate soil C and soil C changes after land‐use change to SRF highlighted the absence of significant error between modelled and measured values as well as the absence of significant bias in the model. Overall, this evaluation reinforces previous studies on the ability of ECOSSE to simulate soil C and emphasize its accuracy to simulate soil C under SRF plantations.

Volumetric diffusers: Pseudorandom cylinder arrays on a periodic lattice

Most conventional diffusers take the form of a surface based treatment, and as a result can only operate in hemispherical space. Placing a diffuser in the volume of a room might provide greater efficiency by allowing scattering into the whole space. A periodic cylinder array (or sonic crystal) produces periodicity lobes and uneven scattering. Introducing defects into an array, by removing or varying the size of some of the cylinders, can enhance their diffusing abilities. This paper applies number theoretic concepts to create cylinder arrays that have more even scattering. Predictions using a boundary element method are compared to measurements to verify the model, and suitable metrics are adopted to evaluate performance. Arrangements with good aperiodic autocorrelation properties tend to produce the best results. At low frequency power is controlled by object size and at high frequency diffusion is dominated by lattice spacing and structural similarity. Consequently the operational bandwidth is rather small. By using sparse arrays and varying cylinder sizes, a wider bandwidth can be achieved.

High-resolution spatial modelling of greenhouse gas emissions from land-use change to energy crops in the United Kingdom

We implemented a spatial application of a previously evaluated model of soil GHG emissions, ECOSSE, in the United Kingdom to examine the impacts to 2050 of land‐use transitions from existing land use, rotational cropland, permanent grassland or woodland, to six bioenergy crops; three ‘first‐generation’ energy crops: oilseed rape, wheat and sugar beet, and three ‘second‐generation’ energy crops: Miscanthus, short rotation coppice willow (SRC) and short rotation forestry poplar (SRF). Conversion of rotational crops to Miscanthus, SRC and SRF and conversion of permanent grass to SRF show beneficial changes in soil GHG balance over a significant area. Conversion of permanent grass to Miscanthus, permanent grass to SRF and forest to SRF shows detrimental changes in soil GHG balance over a significant area. Conversion of permanent grass to wheat, oilseed rape, sugar beet and SRC and all conversions from forest show large detrimental changes in soil GHG balance over most of the United Kingdom, largely due to moving from uncultivated soil to regular cultivation. Differences in net GHG emissions between climate scenarios to 2050 were not significant. Overall, SRF offers the greatest beneficial impact on soil GHG balance. These results provide one criterion for selection of bioenergy crops and do not consider GHG emission increases/decreases resulting from displaced food production, bio‐physical factors (e.g. the energy density of the crop) and socio‐economic factors (e.g. expenditure on harvesting equipment). Given that the soil GHG balance is dominated by change in soil organic carbon (SOC) with the difference among Miscanthus, SRC and SRF largely determined by yield, a target for management of perennial energy crops is to achieve the best possible yield using the most appropriate energy crop and cultivar for the local situation.