Experimental design for parameter estimation in steady-state linear models of metabolic networks

Metabolic networks are typically large, containing many metabolites and reactions. Dynamical models that aim to simulate such networks will consist of a large number of ordinary differential equations, with many kinetic parameters that must be estimated from experimental data. We assume these data to be metabolomics measurements made under steady-state conditions for different input fluxes. Assuming linear kinetics, analytical criteria for parameter identifiability are provided. For normally distributed error terms, we also calculate the Fisher information matrix analytically to be used in the D-optimality criterion. A test network illustrates the developed tool chain for finding an optimal experimental design. The first stage is to verify global or pointwise parameter identifiability, the second stage to find optimal input fluxes, and finally remove redundant measurements.publishedVersio

Impact and detectability of hypothetical CCS offshore seep scenarios as an aid to storage assurance and risk assessment

Carbon Capture and Storage has the potential to make a significant contribution to the mitigation of climate change, however there is a regulatory and societal obligation to demonstrate storage robustness and minimal local environmental impact. This requires an understanding of environmental impact potential and detectability of a range of hypothetical leak scenarios. In the absence of a significant body of real-world release experiments this study collates the results of 86 modelled scenarios of offshore marine releases derived from five different model systems. This synthesis demonstrates a consistent generalised relationship between leak rate, detectability and impact potential of a wide range of hypothetical releases from CO2 storage, which can be described by a power law. For example a leak of the order of 1 T per day should be detectable at, at least, 60 m distance with an environmental impact restricted to less than a 15 m radius of the release point. Small releases are likely to require bottom mounted (lander) monitoring to ensure detection. In summary this work, when coupled with a quantification of leakage risk can deliver a first order environmental impact assessment as an aid to the consenting process. Further this work demonstrates that non-catastrophic release events can be detected at thresholds well below levels which would undermine storage performance or significantly impact the environment, given an appropriate monitoring strategy

New Conceptual Toxicokinetic Model to Assess Synergistic Mixture Effects between the Aromatic Hydrocarbon β-Naphthoflavone and the Azole Nocodazole on the CYP1A Biomarker in a Fish Cell Line

Toxicokinetic interactions with catabolic cytochrome P450 (CYP) enzymes can inhibit chemical elimination pathways and cause synergistic mixture effects. We have created a mathematical bottom-up model for a synergistic mixture effect where we fit a multidimensional function to a given data set using an auxiliary nonadditive approach. The toxicokinetic model is based on the data from a previous study on a fish cell line, where the CYP1A enzyme activity was measured over time after exposure to various combinations of the aromatic hydrocarbon β-naphthoflavone and the azole nocodazole. To describe the toxicokinetic mechanism in this pathway and how that affects the CYP1A biomarker, the model uses ordinary differential equations. Local sensitivity and identifiability analyses revealed that all the 10 parameters estimated in the model were identified uniquely while fitting the model to the data for measuring the CYP1A enzyme activity. The model has a good prediction power and is a promising tool to test the synergistic toxicokinetic interactions between different chemicals

Integrative omics-analysis of lipid metabolism regulation by peroxisome proliferator-activated receptor a and b agonists in male Atlantic cod

Lipid metabolism is essential in maintaining energy homeostasis in multicellular organisms. In vertebrates, the peroxisome proliferator-activated receptors (PPARs, NR1C) regulate the expression of many genes involved in these processes. Atlantic cod (Gadus morhua) is an important fish species in the North Atlantic ecosystem and in human nutrition, with a highly fatty liver. Here we study the involvement of Atlantic cod Ppar a and b subtypes in systemic regulation of lipid metabolism using two model agonists after in vivo exposure. WY-14,643, a specific PPARA ligand in mammals, activated cod Ppara1 and Ppara2 in vitro. In vivo, WY-14,643 caused a shift in lipid transport both at transcriptional and translational level in cod. However, WY-14,643 induced fewer genes in the fatty acid beta-oxidation pathway compared to that observed in rodents. Although GW501516 serves as a specific PPARB/D ligand in mammals, this compound activated cod Ppara1 and Ppara2 as well as Pparb in vitro. In vivo, it further induced transcription of Ppar target genes and caused changes in lipid composition of liver and plasma. The integrative approach provide a foundation for understanding how Ppars are engaged in regulating lipid metabolism in Atlantic cod physiology. We have shown that WY-14,643 and GW501516 activate Atlantic cod Ppara and Pparb, affect genes in lipid metabolism pathways, and induce changes in the lipid composition in plasma and liver microsomal membranes. Particularly, the combined transcriptomic, proteomics and lipidomics analyses revealed that effects of WY-14,643 on lipid metabolism are similar to what is known in mammalian studies, suggesting conservation of Ppara functions in mediating lipid metabolic processes in fish. The alterations in the lipid profiles observed after Ppar agonist exposure suggest that other chemicals with similar Ppar receptor affinities may cause disturbances in the lipid regulation of fish. Model organism: Atlantic cod (Gadus morhua). LSID: urn:lsid:zoobank.org:act:389BE401-2718-4CF2-BBAE-2E13A97A5E7B. COL Identifier: 6K72F.The study was carried out as part of the project “dCod 1.0: decoding systems toxicology of Atlantic cod” financed by the Norwegian Research Council (project no. 248840) and is part of Centre for Digital Life Norway (DLN), financed by the Research Council of Norway (project no. 248810). This work was also part of the iCod 2.0 project (project no. 244564) financed by the Norwegian Research Council. The UPLC-HRMS analysis was performed in collaboration with another project in DLN, AurOmega (project no. 269432). The Genomics Core Facility (GCF) at the University of Bergen, which is a part of the NorSeq consortium, provided services on RNA sequencing; GCF is supported in part by major grants from the Research Council of Norway (grant no. 245979/F50) and Bergen Research Foundation (BFS) (grant no. BFS2017TMT04 and BFS2017TMT08).Peer reviewe

Ensuring efficient and robust offshore storage – The role of marine system modelling

This paper describes the utility of developing marine system models to aid the efficient and regulatory compliant development of offshore carbon storage, maximising containment assurance by well-planned monitoring strategies. Using examples from several model systems, we show that marine models allow us to characterize the chemical perturbations arising from hypothetical release scenarios whilst concurrently quantifying the natural variability of the system with respect to the same chemical signatures. Consequently models can identify a range of potential leakage anomaly detection criteria, identifying the most sensitive discriminators applicable to a given site or season. Further, using models as in-silico testbeds we can devise the most cost-efficient deployment of sensors to maximise detection of CO2 leakage. Modelling studies can also contribute to the required risk assessments, by quantifying potential impact from hypothetical release scenarios. Finally, given this demonstrable potential we discuss the challenges to ensuring model systems are available, fit for purpose and transferable to CCS operations across the globe

Assuring the integrity of offshore carbon dioxide storage

Carbon capture and storage is a key mitigation strategy proposed for keeping the global temperature rise below 1.5 °C. Offshore storage can provide up to 13% of the global CO2 reduction required to achieve the Intergovernmental Panel on Climate Change goals. The public must be assured that potential leakages from storage reservoirs can be detected and that therefore the CO2 is safely contained. We conducted a controlled release of 675 kg CO2 within sediments at 120 m water depth, to simulate a leak and test novel detection, quantification and attribution approaches. We show that even at a very low release rate (6 kg day−1), CO2 can be detected within sediments and in the water column. Alongside detection we show the fluxes of both dissolved and gaseous CO2 can be quantified. The CO2 source was verified using natural and added tracers. The experiment demonstrates that existing technologies and techniques can detect, attribute and quantify any escape of CO2 from sub-seabed reservoirs as required for public assurance, regulatory oversight and emissions trading schemes

Towards improved monitoring of offshore carbon storage: A real-world field experiment detecting a controlled sub-seafloor CO2 release

Carbon capture and storage (CCS) is a key technology to reduce carbon dioxide (CO2) emissions from industrial processes in a feasible, substantial, and timely manner. For geological CO2 storage to be safe, reliable, and accepted by society, robust strategies for CO2 leakage detection, quantification and management are crucial. The STEMM-CCS (Strategies for Environmental Monitoring of Marine Carbon Capture and Storage) project aimed to provide techniques and understanding to enable and inform cost-effective monitoring of CCS sites in the marine environment. A controlled CO2 release experiment was carried out in the central North Sea, designed to mimic an unintended emission of CO2 from a subsurface CO2 storage site to the seafloor. A total of 675 kg of CO2 were released into the shallow sediments (∼3 m below seafloor), at flow rates between 6 and 143 kg/d. A combination of novel techniques, adapted versions of existing techniques, and well-proven standard techniques were used to detect, characterise and quantify gaseous and dissolved CO2 in the sediments and the overlying seawater. This paper provides an overview of this ambitious field experiment. We describe the preparatory work prior to the release experiment, the experimental layout and procedures, the methods tested, and summarise the main results and the lessons learnt

Terminal velocities of pure and hydrate coated CO<sub>2<\sub> droplets and CH<sub>4<\sub> bubbles rising in a simulated oceanic environment

Understanding the upward motion of CO2 droplets or CH4 bubbles in oceanic waters is prerequisite to predict the vertical distribution of the two most important greenhouse gases in the water column after these have been released from the seabed. One of the key parameters governing the fate of droplets or bubbles dissolving into the surrounding seawater as they rise, is the terminal velocity, uT. The latter is strongly influenced by the ability of both compounds to form skins of gas hydrate, if pressure and temperature satisfy thermodynamic framework conditions. Experimental efforts aiming to elucidate the rise properties of CO2 droplets and CH4 bubbles and specifically the influence of hydrate skins open the possibility to obtain a parameterization of uT applicable to both hydrate-coated and pure fluid particles of CH4 and CO2. With the present study, we report on experimentally determined terminal velocities of single CH4 bubbles released to pressurized and temperature-regulated seawater. Hydrate skins were identified by high bubble sphericities and changed motion characteristics. Based on these experiments as well as published data on the rise of hydrate-coated and pure liquid CO2 droplets and physical principles previously successfully used for clean bubbles near atmospheric pressures, a new parameterization of uT is proposed. Model predictions show a good agreement with the data base established from the laboratory-based measurements