1
|
Teufel F, Refsgaard JC, Kasimova MA, Deibler K, Madsen CT, Stahlhut C, Grønborg M, Winther O, Madsen D. Deorphanizing Peptides Using Structure Prediction. J Chem Inf Model 2023; 63:2651-2655. [PMID: 37092865 DOI: 10.1021/acs.jcim.3c00378] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold's confidence metrics have strong performance for prioritizing true peptide-receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.
Collapse
Affiliation(s)
- Felix Teufel
- Digital Science & Innovation, Novo Nordisk A/S, Måløv 2760, Denmark
- Department of Biology, University of Copenhagen Copenhagen 2200, Denmark
| | - Jan C Refsgaard
- Digital Science & Innovation, Novo Nordisk A/S, Måløv 2760, Denmark
| | | | - Kristine Deibler
- Digital Science & Innovation, Novo Nordisk A/S, Seattle 98109, Washington, United States
| | | | - Carsten Stahlhut
- Digital Science & Innovation, Novo Nordisk A/S, Måløv 2760, Denmark
| | - Mads Grønborg
- Global Translation, Novo Nordisk A/S, Måløv 2760, Denmark
| | - Ole Winther
- Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
- Department of Genomic Medicine, Copenhagen University Hospital/Rigshospitalet, Copenhagen 2100, Denmark
| | - Dennis Madsen
- Digital Science & Innovation, Novo Nordisk A/S, Måløv 2760, Denmark
| |
Collapse
|
2
|
Madsen CT, Refsgaard JC, Teufel FG, Kjærulff SK, Wang Z, Meng G, Jessen C, Heljo P, Jiang Q, Zhao X, Wu B, Zhou X, Tang Y, Jeppesen JF, Kelstrup CD, Buckley ST, Tullin S, Nygaard-Jensen J, Chen X, Zhang F, Olsen JV, Han D, Grønborg M, de Lichtenberg U. Combining mass spectrometry and machine learning to discover bioactive peptides. Nat Commun 2022; 13:6235. [PMID: 36266275 PMCID: PMC9584923 DOI: 10.1038/s41467-022-34031-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 10/10/2022] [Indexed: 12/25/2022] Open
Abstract
Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.
Collapse
Affiliation(s)
- Christian T. Madsen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Jan C. Refsgaard
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,Intomics, Kongens Lyngby, Denmark
| | - Felix G. Teufel
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Sonny K. Kjærulff
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,Intomics, Kongens Lyngby, Denmark
| | - Zhe Wang
- Novo Nordisk Research Centre China, Beijing, China
| | - Guangjun Meng
- Novo Nordisk Research Centre China, Beijing, China ,Pulmongene LTD. Rm 502, Building 2, No. 9, Yike Road, Zhongguancun Life Science Park, Changping District, Beijing, China
| | - Carsten Jessen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Petteri Heljo
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Qunfeng Jiang
- Novo Nordisk Research Centre China, Beijing, China ,Innovent Biologics, Inc. DongPing Jie 168, Suzhou, China
| | - Xin Zhao
- Novo Nordisk Research Centre China, Beijing, China
| | - Bo Wu
- Novo Nordisk Research Centre China, Beijing, China ,QL Biopharmaceutical, Rm 101, Building 7, 20 Life Science Park Road, Beijing, China
| | - Xueping Zhou
- Novo Nordisk Research Centre China, Beijing, China ,grid.421648.d0000 0004 5997 3165Crinetics pharmaceuticals, 10222 Barnes Canyon Rd Building 2, San Diego, CA 92121 USA
| | - Yang Tang
- Novo Nordisk Research Centre China, Beijing, China ,Roche R&D Center (China) Ltd, Building 5, 371 Lishizhen Road, 201203 Pudong, Shanghai China
| | - Jacob F. Jeppesen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Christian D. Kelstrup
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Stephen T. Buckley
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Søren Tullin
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,grid.420061.10000 0001 2171 7500Boehringer Ingelheim GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach, Germany
| | - Jan Nygaard-Jensen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,grid.420061.10000 0001 2171 7500Boehringer Ingelheim GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach, Germany
| | - Xiaoli Chen
- Novo Nordisk Research Centre China, Beijing, China
| | - Fang Zhang
- Novo Nordisk Research Centre China, Beijing, China ,Structure Therapeutics. 701 Gateway Blvd., South San Francisco, CA 94080 USA
| | - Jesper V. Olsen
- grid.5254.60000 0001 0674 042XDepartment of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Dan Han
- Novo Nordisk Research Centre China, Beijing, China
| | - Mads Grønborg
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Ulrik de Lichtenberg
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,grid.487026.f0000 0000 9922 7627The Novo Nordisk Foundation, Tuborg Havnevej 19, 2900 Hellerup, Denmark
| |
Collapse
|
3
|
Abstract
Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics.
Collapse
Affiliation(s)
- Ufuk Kirik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Science , University of Copenhagen , Blegdamsvej 3B , DK-2200 Copenhagen , Denmark
| | - Jan C Refsgaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Science , University of Copenhagen , Blegdamsvej 3B , DK-2200 Copenhagen , Denmark.,Intomics A/S , Lottenborgvej 26 , DK-2800 Kongens Lyngby , Denmark
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Science , University of Copenhagen , Blegdamsvej 3B , DK-2200 Copenhagen , Denmark
| |
Collapse
|
4
|
Hansen AL, Refsgaard JC, Olesen JE, Børgesen CD. Potential benefits of a spatially targeted regulation based on detailed N-reduction maps to decrease N-load from agriculture in a small groundwater dominated catchment. Sci Total Environ 2017; 595:325-336. [PMID: 28388450 DOI: 10.1016/j.scitotenv.2017.03.114] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 03/03/2017] [Accepted: 03/11/2017] [Indexed: 05/12/2023]
Abstract
Denmark must further decrease the N-load to coastal waters from agricultural areas to comply with the Baltic Sea Action Plan and the EU Water Framework Directive. A new spatially targeted regulation is under development that focuses on locating N-mitigation measures in areas with low natural reduction of nitrate (N-reduction). A key tool in this respect is N-reduction maps showing how much N is removed by natural reduction processes, i.e. the ratio between the N-load out of the catchment and the N-leaching from the root zone for each spatial unit within the catchment. For the 85 km2 groundwater dominated Norsminde catchment in Denmark we have analysed the potential benefits of a spatially targeted regulation and how its efficiency is affected by uncertainty in the N-reduction map. Our results suggest that there are potential benefits of implementing a spatially targeted regulation compared to a spatially uniform regulation. The total N-load at the catchment outlet can be decreased up to 8% by relocating the existing agricultural practice according to the N-reduction map and thus without decrease fertilization inputs. A further decrease in N-load can be obtained by identifying target areas with low N-reduction where N-mitigation measures must be applied. Uncertainty on the N-reduction map is found to lower the efficiency of spatially targeted regulation. This uncertainty can be lowered substantially by using the mean of an ensemble of N-reduction maps. The uncertainty decreases with coarser spatial resolution of the N-reduction map, but this will at the same time decrease the benefit from spatially targeted regulation.
Collapse
Affiliation(s)
- A L Hansen
- Department of Hydrology, Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen K, Denmark.
| | - J C Refsgaard
- Department of Hydrology, Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen K, Denmark
| | - J E Olesen
- Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - C D Børgesen
- Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| |
Collapse
|
5
|
Junge A, Refsgaard JC, Garde C, Pan X, Santos A, Alkan F, Anthon C, von Mering C, Workman CT, Jensen LJ, Gorodkin J. RAIN: RNA-protein Association and Interaction Networks. Database (Oxford) 2017; 2017:baw167. [PMID: 28077569 PMCID: PMC5225963 DOI: 10.1093/database/baw167] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 11/18/2016] [Accepted: 12/05/2016] [Indexed: 12/11/2022]
Abstract
Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded.Database URL: http://rth.dk/resources/rain.
Collapse
Affiliation(s)
- Alexander Junge
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark
| | - Jan C Refsgaard
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Building: 06-2-26, Blegdamsvej 3B, DK-2200 Copenhagen N, Denmark
| | - Christian Garde
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Center for Biological Sequence Analysis, Technical University of Denmark, Kemitorvet, Building 208, DK-2800 Lyngby, Denmark
| | - Xiaoyong Pan
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Building: 06-2-26, Blegdamsvej 3B, DK-2200 Copenhagen N, Denmark
| | - Alberto Santos
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Building: 06-2-26, Blegdamsvej 3B, DK-2200 Copenhagen N, Denmark
| | - Ferhat Alkan
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark
| | - Christian Anthon
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Christopher T Workman
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Center for Biological Sequence Analysis, Technical University of Denmark, Kemitorvet, Building 208, DK-2800 Lyngby, Denmark
| | - Lars Juhl Jensen
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Building: 06-2-26, Blegdamsvej 3B, DK-2200 Copenhagen N, Denmark
| | - Jan Gorodkin
- Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.,Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark
| |
Collapse
|
6
|
Abstract
Advances in mass spectrometric instrumentation in the past 15 years have resulted in an explosion in the raw data yield from typical phosphoproteomics workflows. This poses the challenge of confidently identifying peptide sequences, localizing phosphosites to proteins and quantifying these from the vast amounts of raw data. This task is tackled by computational tools implementing algorithms that match the experimental data to databases, providing the user with lists for downstream analysis. Several platforms for such automated interpretation of mass spectrometric data have been developed, each having strengths and weaknesses that must be considered for the individual needs. These are reviewed in this chapter. Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here.
Collapse
Affiliation(s)
- Jan C Refsgaard
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.,Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark
| | - Stephanie Munk
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark
| | - Lars J Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.
| |
Collapse
|
7
|
Schölz C, Lyon D, Refsgaard JC, Jensen LJ, Choudhary C, Weinert BT. Avoiding abundance bias in the functional annotation of post-translationally modified proteins. Nat Methods 2016; 12:1003-4. [PMID: 26513550 DOI: 10.1038/nmeth.3621] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Christian Schölz
- Department of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - David Lyon
- Department of Disease Systems Biology, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Jan C Refsgaard
- Department of Disease Systems Biology, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Lars Juhl Jensen
- Department of Disease Systems Biology, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Chunaram Choudhary
- Department of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Brian T Weinert
- Department of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
8
|
Abstract
Global phosphoproteomics investigations yield overwhelming datasets with up to tens of thousands of quantified phosphosites. The main challenge after acquiring such large-scale data is to extract the biological meaning and relate this to the experimental question at hand. Systems level analysis provides the best means for extracting functional insights from such types of datasets, and this has primed a rapid development of bioinformatics tools and resources over the last decade. Many of these tools are specialized databases that can be mined for annotation and pathway enrichment, whereas others provide a platform to generate functional protein networks and explore the relations between proteins of interest. The use of these tools requires careful consideration with regard to the input data, and the interpretation demands a critical approach. This chapter provides a summary of the most appropriate tools for systems analysis of phosphoproteomics datasets, and the considerations that are critical for acquiring meaningful output.
Collapse
Affiliation(s)
- Stephanie Munk
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.1, 2200, Copenhagen, Denmark
| | - Jan C Refsgaard
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.1, 2200, Copenhagen, Denmark
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark
| | - Jesper V Olsen
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.1, 2200, Copenhagen, Denmark.
| |
Collapse
|
9
|
Abstract
Kinases play a pivotal role in propagating the phosphorylation-mediated signaling networks in living cells. With the overwhelming quantities of phosphoproteomics data being generated, the number of identified phosphorylation sites (phosphosites) is ever increasing. Often, proteomics investigations aim to understand the global signaling modulation that takes place in different biological conditions investigated. For phosphoproteomics data, identifying the kinases central to mediating this response is key. This has prompted several efforts to catalogue the immense amounts of phosphorylation data and known or predicted kinases responsible for the modifications. However, barely 20 % of the known phosphosites are assigned to a kinase, initiating various bioinformatics efforts that attempt to predict the responsible kinases. These algorithms employ different approaches to predict kinase consensus sequence motifs, mostly based on large scale in vivo and in vitro experiments. The context of the kinase and the phosphorylated proteins in a biological system is equally important for predicting association between the enzymes and substrates, an aspect that is also being tackled with available bioinformatics tools. This chapter summarizes the use of the larger phosphorylation databases, and approaches that can be applied to predict kinases that phosphorylate individual sites or that are globally modulated in phosphoproteomics datasets.
Collapse
Affiliation(s)
- Stephanie Munk
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark
| | - Jan C Refsgaard
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.,Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark
| | - Jesper V Olsen
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark
| | - Lars J Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.
| |
Collapse
|
10
|
Abstract
Key sources of uncertainty of importance for water resources management are (1) uncertainty in data; (2) uncertainty related to hydrological models (parameter values, model technique, model structure); and (3) uncertainty related to the context and the framing of the decision-making process. The European funded project 'Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)' has resulted in a range of tools and methods to assess such uncertainties, focusing on items (1) and (2). The project also engaged in a number of discussions surrounding uncertainty and risk assessment in support of decision-making in water management. Based on the project's results and experiences, and on the subsequent discussions a number of conclusions can be drawn on the future needs for successful adoption of uncertainty analysis in decision support. These conclusions range from additional scientific research on specific uncertainties, dedicated guidelines for operational use to capacity building at all levels. The purpose of this paper is to elaborate on these conclusions and anchoring them in the broad objective of making uncertainty and risk assessment an essential and natural part in future decision-making processes.
Collapse
Affiliation(s)
- M W Blind
- Ministry of Transport, Public Works and Water Management, Institute for Inland Water Management and Waste Water Treatment/RIZA, P.O. Box 17, 8200 AA, Lelystad, The Netherlands.
| | | |
Collapse
|
11
|
Jørgensen LF, Refsgaard JC, Højberg AL. Joint use of monitoring and modelling. Water Sci Technol 2007; 56:21-29. [PMID: 17978429 DOI: 10.2166/wst.2007.596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
There is much to gain in joining monitoring and modelling efforts, especially in the present process of implementing the European Water Framework Directive. Nevertheless, it is rare to see forces combined in these two disciplines. To bring the monitoring and the modelling communities together, a number of workshops have been arranged with discussions on benefits and constraints in joint use of monitoring and modelling. The workshops have been attended by scientists, water managers, policy makers as well as stakeholders and consultants. Emphasis has been put on data availability and accessibility, remote sensing and data assimilation techniques, monitoring programmes and modelling support to the design or optimisation of these as well as potential benefits of using supporting modelling tools in the process of designing Programmes of Measures by impact assessment etc. The way models can support in extrapolation in time and space, in data analysis, in process understanding (conceptual models), in accessing correct interaction between pressures and impacts etc. have also been elaborated. Although practitioners have been open-minded to the presented ideas, they are somewhat reluctant towards how to implement this in their daily work. This paper presents some experiences from the workshops.
Collapse
Affiliation(s)
- L F Jørgensen
- Department of Hydrology, Geological Survey of Denmark and Greenland, Øster Voldgade 10, DK-1350 Copenhagen K. Denmark.
| | | | | |
Collapse
|
12
|
Brown JD, Heuvelink GB, Refsgaard JC. An integrated methodology for recording uncertainties about environmental data. Water Sci Technol 2005; 52:153-60. [PMID: 16304947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Understanding the limitations of environmental data is essential both for managing environmental systems effectively and for encouraging the responsible use of scientific research when knowledge is limited and priorities are varied. Using a combination of quantitative and qualitative techniques for assessing probabilities, and acknowledging the importance of scenarios where probabilities cannot be determined, an integrated methodology is presented for handling uncertainties about environmental data. The methodology is based on a fourfold distinction between the empirical quality of data (and the ancillary information, such as 'scale', required to interpret this), the sources of uncertainty in data, the 'fitness for use' of the data, and the quality or 'goodness' of an uncertainty model.
Collapse
Affiliation(s)
- J D Brown
- Institute for Biodiversity and Ecosystem Dynamics, Universiteit van Amsterdam, The Netherlands.
| | | | | |
Collapse
|
13
|
Højberg AL, Refsgaard JC. Model uncertainty--parameter uncertainty versus conceptual models. Water Sci Technol 2005; 52:177-86. [PMID: 16304950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Uncertainties in model structures have been recognised often to be the main source of uncertainty in predictive model simulations. Despite this knowledge, uncertainty studies are traditionally limited to a single deterministic model and the uncertainty addressed by a parameter uncertainty study. The extent to which a parameter uncertainty study may encompass model structure errors in a groundwater model is studied in a case study. Three groundwater models were constructed on the basis of three different hydrogeological interpretations. Each of the models was calibrated inversely against groundwater heads and streamflows. A parameter uncertainty analysis was carried out for each of the three conceptual models by Monte Carlo simulations. A comparison of the predictive uncertainties for the three conceptual models showed large differences between the uncertainty intervals. Most discrepancies were observed for data types not used in the model calibration. Thus uncertainties in the conceptual models become of increasing importance when predictive simulations consider data types that are extrapolates from the data types used for calibration.
Collapse
Affiliation(s)
- A L Højberg
- Geological Survey of Denmark and Greenland (GEUS), Copenhagen.
| | | |
Collapse
|