Welcome from the Chairs
|15–30 November 2018|
Cost: No registration fee; no need to travel
The 3rd International Electronic Conference on Metabolomics will be held from 15-30 November 2018 in an online environment. The objective of this event is to bring researchers working in the field onto a common platform where they can present and discuss their recent research without the need to travel.
This year, Metabolites would like to award the Best Presentation as selected by an evaluation committee. The winner will be offered publication of the extended full manuscript FREE of charge in the Special Issue of Metabolites together with a certificate. After the conference, the authors are also welcome to submit an extended version of the proceeding papers to the Special Issue of Journal Metabolites and receive a 20% discount off the Article Processing Charge.
- Conference Open: 15 - 30 Nov. 2018
- Section A: Advanced Metabolomics and Data Analysis Approaches
- Section B: Identification of Unknowns
- Section C: Precision Nutrition and Food Specific Profiles
- Section D: Microbiota and Metabolomics
- Section E: Pathway Mapping and Fluxomics
Monash University, Australia
Biography: Dr. Darren Creek is a Senior Lecturer and NHMRC Career Development Fellow at the Monash Institute of Pharmaceutical Sciences, and is Director of the Metabolomics Node of the Monash Proteomics and Metabolomics Facility. He completed his PhD at Monash University in 2007, and performed post-doctoral research in Uganda, Scotland and Australia working on antimalarial drug discovery and clinical trials, before focusing on metabolomics studies of tropical parasites. He developed several novel analytical methods and software tools for the metabolomics field, and discovered novel pathways and drug mechanisms in protozoan parasites. Dr Creek’s laboratory currently uses metabolomics and proteomics to understand mechanisms of drug action and resistance for infectious diseases, with a major focus on African trypanosomiasis and malaria. In 10 years since completion of his PhD, Darren has over 75 publications, 10 grants and fellowships from NHMRC, ARC and NIH, and was recently a Director of the International Metabolomics Society.
University of Cambridge, UK
Biography: Dr. Christian Frezza is an MRC Programme Leader at the MRC Cancer Unit, University of Cambridge. He studied Medicinal Chemistry at the University of Padova, Italy, and gained his MSc in 2002, after a period of research on mitochondrial toxicity induced by photoactivable anticancer drugs. Christian then joined the laboratory of Luca Scorrano in Padova to start a PhD on mitochondrial dynamics and apoptosis. In 2008, he moved to the Beatson Institute of Cancer Research in Glasgow as recipient of an EMBO Long Term Fellowship, where he investigated the role of mitochondrial defects in tumorigenesis. He moved to the MRC Cancer Unit in 2012, to take up his current position. Christian’s research is focused on understanding the role of altered metabolism in cancer, particularly investigating how small molecule metabolites affect the process of tumorigenesis. The major goal of his team is to exploit this knowledge to pioneer novel tools for cancer diagnosis and therapy.
Topic: Pathway Mapping Operations in BioCyc
SRI International, USA
Biography: Dr. Peter D. Karp is the director of the Bioinformatics Research Group within the Artificial Intelligence Center at SRI International. Dr. Karp has authored more than 160 publications in bioinformatics and computer science in areas including metabolic pathway bioinformatics, computational genomics, scientific visualization, and scientific databases. He is a Fellow of the American Association for the Advancement of Science and of the International Society for Computational Biology. He received the Ph.D. degree in Computer Science from Stanford University in 1989, and was a postdoctoral fellow at the NIH National Center for Biotechnology Information.
University of Alberta, Canada
Biography: Oliver Jones is an associate professor of analytical chemistry based at RMIT University in Melbourne. He obtained his MSc and PhD from Imperial College London in 2005 and then held a postdoctoral fellowship in biochemistry at the University of Cambridge until 2009. Oliver then worked at the University of Durham before moving to RMIT in 2012. Oliver’s group conducts research in chromatography and NMR, for a range of applications, predominantly metabolomics and the trace analysis of environmental pollutants. At RMIT he is Deputy Director of both the Water: Effective Technologies & Tools Research Centre and the Environmental Sustainability & Remediation Centre. He also recently developed a mobile app “Chirality-2” to help teach Chemistry. He is currently (2015-date) a member of the Australian Academy of Science National Committee for Chemistry. He is president of the Australian and New Zealand Metabolomics Network; secretary and board member of the Australian and New Zealand Society for Magnetic Resonance and a board member of the Metabolomics Society. Oliver has won several awards including the 2015 ANZMAG Sir Paul Callaghan Medal and the RMIT College of Science Engineering and Health Media Star Award. He has been an invited speaker at several conferences and have helped organise many others (he was a member of the organising committee of the 2017 Metabolomics Conference). He has over 86 peer-reviewed publications, with 3987 total citations and an h-index of 27.
Yale University, USA
Biography: Caroline H. Johnson, PhD, is Assistant Professor of Epidemiology in the Department of Environmental Health Sciences at Yale School of Public Health. She graduated from Imperial College London in 2009 with a PhD in Analytical Chemistry. Since then she has held postdoctoral and staff appointments at the National Cancer Institute and The Scripps Research Institute. Dr. Johnson's research uses mass spectrometry-based metabolomics to understand the role of metabolites in human health. Her primary research interest is to investigate the relationship between genetic and environmental influences (diet, hormones and microbiome) in colon cancer. She is also examining exposures during pregnancy.
By Oliver Jones
List of accepted submissions (20)
|sciforum-022629||A Mass Spectrometry-based Lipidomics Study for Early Diagnosis of clear cell Renal Cell Carcinoma||Malena Manzi, Martín Palazzo, Nicolás Zabalegui, María Knott, Patricio Yankilevich, María Giménez, Lydia Puricelli, María Monge||N/A||
Kidney cancer is fundamentally a metabolic disease.1 Renal cell carcinoma (RCC) is among the 10 most common cancers worldwide.2, 3 More than 30% of patients, often incidentally diagnosed by imaging procedures, exhibit locally advanced or metastatic RCC at the time of diagnosis.4, 5 The disease is inherently resistant to chemotherapy6 and radiotherapy.7 Clear cell RCC (ccRCC) is the most common (75%) lethal subtype, and is considered a glycolytic and lipogenic tumor.8, 9 The present work consists on a lipid profiling study of serum samples from a cohort that included patients with different ccRCC stages (stage I, II, III and, IV; n=112) and healthy individuals (n=52). A discovery-based lipidomics approach using reverse phase ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry was implemented to investigate the potential role of lipids in sample classification. Multivariate statistical analysis was conducted on a 386-feature matrix by means of machine learning algorithms using support vector machines (SVM) coupled with the least absolute shrinkage and selection operator (Lasso) variable selection method. This analysis provided a panel of 18 features that allowed discriminating healthy individuals from ccRCC patients with 96% accuracy, 93% specificity, and 100% sensitivity in a training set under cross-validation, and 79% accuracy, 100% specificity, and 79% sensitivity in an independent test set with an AUC of 0.89. A second multivariate model trained to discriminate early stages (I and II) from late stages (III and IV) ccRCC, yielded a panel of 26 features that allowed sample classification with 84% accuracy in the training set under cross-validation, and 82% accuracy in the classification of stage I ccRCC patients from an independent test set. Preliminary putative identification of discriminant lipids was based on exact mass, isotopic pattern and database search. Significant changes in lipid levels were evaluated after correcting for multiple testing between sample classes. Phosphatidylethanolamine levels were significantly decreased (p<0.001) in serum samples from ccRCC patients relative to controls. Significantly (p<0.02) decreased levels of fatty acids were detected in serum samples from ccRCC patients compared to healthy individuals, and along disease progression from early to late ccRCC stages. Current work involves the identification of the discriminant lipid panels by tandem MS experiments and chemical standards. Serum samples were provided by the Public Oncologic Serum Bank from Instituto de Oncología “Ángel H. Roffo” and Hospital Italiano de Buenos Aires.
(1) Linehan, W. M.; Srinivasan, R.; Schmidt, L. S., The genetic basis of kidney cancer: a metabolic disease. Nat. Rev. Urol. 2010, 7, 277-85.
(2) Linehan, W. M.; Bratslavsky, G.; Pinto, P. A.; Schmidt, L. S.; Neckers, L.; Bottaro, D. P.; Srinivasan, R., Molecular Diagnosis and Therapy of Kidney Cancer. Annu. Rev. Med. 2010, 61, 329-343.
(3) IARC Globocan 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012. http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx?cancer=prostate
(4) Hu, B.; Lara, P. N., Jr.; Evans, C. P., Defining an Individualized Treatment Strategy for Metastatic Renal Cancer. Urol. Clin. North Am. 2012, 39, 233-249.
(5) Graves, A.; Hessamodini, H.; Wong, G.; Lim, W. H., Metastatic renal cell carcinoma: update on epidemiology, genetics, and therapeutic modalities. Immunotargets Ther. 2013, 2, 73-90.
(6) Diamond, E.; Molina, A. M.; Carbonaro, M.; Akhtar, N. H.; Giannakakou, P.; Tagawa, S. T.; Nanus, D. M., Cytotoxic chemotherapy in the treatment of advanced renal cell carcinoma in the era of targeted therapy. Crit. Rev. Oncol. Hematol. 2015, 96, 518-526.
(7) De Meerleer, G.; Khoo, V.; Escudier, B.; Joniau, S.; Bossi, A.; Ost, P.; Briganti, A.; Fonteyne, V.; Van Vulpen, M.; Lumen, N.; Spahn, M.; Mareel, M., Radiotherapy for renal-cell carcinoma. Lancet Oncol. 2014, 15, e170-e177.
(8) Hsieh, J. J.; Purdue, M. P.; Signoretti, S.; Swanton, C.; Albiges, L.; Schmidinger, M.; Heng, D. Y.; Larkin, J.; Ficarra, V., Renal cell carcinoma. Nature Reviews Disease Primers 2017, 3, 17009.
(9) Hakimi, A. A.; Pham, C. G.; Hsieh, J. J., A clear picture of renal cell carcinoma. Nat. Genet. 2013, 45, 849-850.
|sciforum-022015||A Metabolic Pattern of Influenza A Virus Infected Sus scrofa: Perturbations on Eicosanoids and Gut Metabolism||Daniel Schultz, Karen Methling, Michael Lalk||N/A||
Introduction: Virus infections of the upper respiratory tract in combination with secondary bacterial infections can lead to severe lung infections. The aim of the current project KoInfekt is to elucidate the host-pathogen interactions establishing the pig as an animal infection model due to high genetic and physiological similarities to human beings.
Material and Methods: Animal experiments were done on the Federal Research Institute for Animal Health (Isle of Riems, Germany). A group of 25 pigs were infected with Influenza A virus (H1N1, Germany) and samples were collected over 31 days. For metabolic analysis tissues samples (lung, spleen), biofluids (blood plasma, BALF) and feces were collected and analyzed by a combination of 1H-NMR, GC-MS and LC-MS/MS.
Results: The increased amounts of pro-inflammatory eicosanoids like prostaglandins and thromboxane in the spleen were detected during infection. Furthermore, a specific eicosanoid profile was observed in the different sample types. The analysis of metabolites from the feces reveals a high time-and animal-dependent level for the majority of compounds.
Discussion: Perturbations in the eicosanoid profile of Influenza A virus infected pigs were detected. The occurrence of different pro-and anti-inflammatory lipid mediators gives a hint for the immune status of the analyzed organs from the pig. The analysis of the fecal metabolites enables an overview about the gut microbiota, which is linked to the host immune response and the interplay of the host and the bacteria community. This is the first step for the metabolic analysis of bacto-viral co-infections, which play an important role in human and animal health.
Annotation of phospholipids in mass spectrometry-based metabolomics
Phospholipids play numerous roles in biological systems, including the formation of membrane lipid bilayers and the signaling of multiple biological pathways, so that their dyshomeostasis have been associated with the development of multiple diseases, such as Alzheimer’s disease and cancer. Metabolomics based on mass spectrometry has been largely employed to investigate these disease-related perturbations in the phospholipidome. However, the annotation of discriminant features still remains as a major bottleneck in the metabolomic pipeline. Chemical standards of individual phospholipid species are normally not commercially available due to the large number of isomers, so the knowledge of their characteristic fragmentation patterns upon tandem mass spectrometry is of great utility for their annotation (1). In this work, we provide a simplified guideline for the MS/MS-based identification of the most important phospholipid classes and their fatty acid composition.
(1) R. González-Domínguez. Metabolomic approaches for phospholipid analysis: advances and challenges. Bioanalysis 10 (2018) 1069-1071
Application of targeted and non-targeted approaches to investigate the effect of genotype and growing conditions on the strawberry metabolome
|Raúl González-Domínguez, Ana Sayago, Ángeles Fernández-Recamales||N/A||
Strawberry is composed of numerous primary metabolites (sugars, amino acids, organic acids) and secondary metabolites (anthocyanins, flavan-3-ols, phenolic acids), which play an essential role in fruit quality, organoleptic characteristics and healthy benefits. In this context, metabolomics presents a great potential to get a deep overview of this complex chemical meshwork, which can provide valuable information on the effect of multiple growing factors in the strawberry composition. In this work, we show the utility of different metabolomic approaches to investigate the influence of variety and agronomic conditions in the strawberry metabolome on the basis of data acquired in two published studies conducted in our research group. First, we conducted a GC/MS-based non-targeted metabolomic analysis in strawberries of three varieties with different sensitivity to environmental conditions (Camarosa, Festival and Palomar), which in turn were grown in soilless systems by using various agronomic conditions (electrical conductivity, coverage and substrates) (1). Complementarily, a targeted metabolomic approach based on UHPLC-MS/MS was also applied to identify and quantitate the main polyphenol compounds in these strawberry fruits (2). The most discriminant metabolites were several amino acids, sugars, organic acids, anthocyanins, ellagic acid derivatives, flavan-3-ols, chlorogenic acid and quercetin 3-O-glucuronide, which could be associated with differences in organoleptic characteristics and the biosynthesis of strawberry antioxidants.
(1) I. Akhatou, R. González-Domínguez, A. Fernández-Recamales. Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress. Plant Physiol. Biochem. 101 (2016) 14-22
(2) I. Akhatou, A. Sayago, R. González-Domínguez, Á. Fernández-Recamales. Application of targeted metabolomics to investigate optimum growing conditions to enhance bioactive content of strawberry. J. Agric. Food Chem. 65 (2017) 9559-9567
|sciforum-022617||Applying an untargeted metabolomics approach using two complementary platforms for the discovery and validation of banana intake biomarkers||Natalia Vázquez-Manjarrez, Christopher Weinert, Marynka Ulaszewska, Mélanie Pétéra, Carina Mack, Sabine Kulling, Bub Achim, Pierre Micheau, Delphine Centeno, Charlotte Joly, Stephanie Durand, Estelle Pujos-Guillot, Lars Dragsted, Claudine Manach||N/A||
Background: Accurate assessment of dietary intake is crucial for nutritional and health research. However, the dietary assessment tools currently used, such as dietary records or food frequency questionnaires, are subject to different factors that result in inaccurate information. The use of biomarkers of intake to determine dietary exposure offers more objective and potentially more precise information compared to the currently used dietary assessment tools. The identification of biomarkers of intake for highly consumed foods i.e. banana, may promote further research on their impact on human health. Banana is a widely consumed fruit in different countries. However, it has been widely neglected by the research community. Thus, identifying the biomarkers of intake of this fruit may promote further investigation on its impact on human health.
Objective: To discover and validate urinary intake biomarkers of banana by applying an untargeted metabolomics approach using two different platforms, UPLC-QTOF-MS and GC×GC-MS, to analyze urine samples from two different study designs.
Methods: In order to discover new biomarkers of banana intake, n=12 healthy subjects were recruited for a three arm, crossover, randomized, controlled meal study. The dietary interventions consisted of: 1) 240 g of banana, 2) 300 g of tomato and 3) 250 ml of control drink; each intervention phase was separated by a washout period of 3 days minimum. Urine samples obtained from the meal intervention study were analyzed by UPLC-QTOF-MS and GC×GC-MS. Following data-analysis, the identification of the relevant features was performed with MS/MS experiments in an Orbitrap-LTQ-XL MS instrument. To confirm the identity of the compounds in both systems, standards were acquired and conjugated when needed. In addition, banana samples were analyzed to look for compounds recovered in urine profiles. Finally, to validate the candidate biomarkers of banana, n=78 samples from an observational study, The Karlsruhe Metabolomics and Nutrition Study (KarMeN), were selected based on the volunteers’ declared amount of banana consumption using 24 h dietary recalls. Samples were grouped based on recorded intakes ( 1)high consumers of banana, 2) low consumers of banana and 3) non consumers of banana) and analysed on both platforms.
Results: The discriminating compounds identified by both platforms in the meal intervention were cross-validated in the observational study. Among the highly discriminant compounds biogenic amine metabolites, methoxyphenols as well as tryptophan and carbohydrate metabolites were observed. The combination of two metabolites, methoxyeugenol glucuronide and 6-hydroxy-1-methyl-1,2,3,4-tetrahydro-b-carboline-sulfate, were validated as a parsimonious biomarker of banana intake with excellent ability to predict the intake of banana, exhibiting a ROC curve AUC (CV) of 0.92 (p<0.001). In addition, from the analysis by the GC×GC-MS system three metabolites (5-hydroxyindole-acetic-acid, dopamine and the putatively identified deoxypentitol) were detected in significantly higher concentrations (p <0.001, p= 0.001, p=0.01 respectively) in the urine samples of the high and low-consumers of banana compared to non-consumers.
Conclusion: This collaborative work led to the identification and validation of new candidate biomarkers for the intake of banana. This information may be useful to further investigate the effect of this fruit in human health.
This work was funded by the EU Joint Programming Initiative (JPI) A Healthy diet for Healthy life.
Best Presentation Award
The winner will be offered publication of the extended full manuscript free of change in the Special Issue of Metabolites together with a Certificate.
Terms and Conditions:
This year, as a sponsor, Metabolites would like to award the best presentation as selected by an evaluation committee. The winner will be offered publication of the extended full manuscript free of charge in a Special Issue of Metabolitestogether with a certificate.
We look forward to receiving your contributions.
- Full PPT presentation must be submitted to IECM-3
- Originality / Novelty of the paper
- Significance of Content
- Scientific Soundness
- Interest to the readers
- English language and style
- Each Evaluation Committee member will give an assessment for each applicant in terms of the criteria outlined above.
- Total score for each presentation will be ranked, from highest to lowest.
- If two or more students get the same score, further evaluation will be carried out.
- All decisions made by the Evaluation Committee are final.
Instructions for Authors
Submissions should be done by the authors online by registering with www.sciforum.net, and using the "Start New Submission" function once logged into system.
- Researchers interested in attending the conference must submit the abstract, on this website and no later than 30 October 2018.
- The Conference Committee will pre-evaluate, based on the submitted abstract, whether a contribution from the author fits in the scope of The 3rd International Electronic Conference on Metabolomics. All authors will be notified about the acceptance of their abstract.
- After the abstract is accepted by the Scientific Committee, the authors will be invited to prepare a full description of their work preferably under the form of a PowerPoint and/or video presentation, and to upload it before 5 November 2018 to ensure final check.
- The presentations will be accessible on https://sciforum.net/conference/iecm-3 during the conference time.
- After the conference, the authors are also welcome to submit an extended version of the proceeding papers to the Special Issue of Journal Metabolites with a 20% discount off the Article Processing Charge.
Authors are encouraged to prepare a power point presentation using the template provided by the Conference (see download below). Slides will be displayed directly in the website using Sciforum.net's proprietary slides viewer. They can be prepared in the same way as for any traditional conference where research results can be presented. Slides should be converted to the PDF format before submission so that our process can easily and automatically convert them for online displaying.
Authors are also welcome to submit video presentations. If you are interested in submitting, please contact the conference organizer ([email protected] or [email protected]) to get to know more about the procedure.
Tips for authors: If you would like to prepare a video (15-20 minutes) based on your PPT presentation, you may use the "record slide" function in the PowerPoint. After recording, you can save the file as type: MPEG-4 Viedo (*.mp4).
It is the authors' responsibility to identify and declare any personal circumstances or interests that may be perceived as inappropriately influencing the representation or interpretation of clinical research. If there is no conflict, please state here "The authors declare no conflict of interest." This should be conveyed in a separate "Conflict of Interest" statement preceding the "Acknowledgments" and "References" sections at the end of the manuscript. Financial support for the study must be fully disclosed under "Acknowledgments" section. It is the authors' responsibility to identify and declare any personal circumstances or interests that may be perceived as inappropriately influencing the representation or interpretation of clinical research.
MDPI, the publisher of the Sciforum.net platform, is an open access publisher. We believe that authors should retain the copyright to their research works. Hence, by submitting a contribution to this conference, the authors retain the copyright of their contribution, but they grant MDPI AG the non-exclusive right to publish this contribution online on the Sciforum.net platform. This means the authors can easily submit their contribution to any scientific journal at a later stage and transfer the copyright to its publisher (if required by that publisher).
Call for Papers
We thank you in advance for your presence at this conference and your contribution to its success.
Professor Peter Meikle
Metabolomics Laboratory NHMRC, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
Dr. Thusitha W. Rupasinghe
Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
Professor Susan Sumner
Nutrition Research Institute, Department of Nutrition at the University of North Carolina at Chapel Hill, USA
Dr. Justin van der Hooft
Bioinformatics Group, Department of Plant Sciences, Wageningen University, Wageningen, The Netherlands
Dr. Reza Salek
International Agency for Research on Cancer (IARC), France
Prof. Dr. James E. Cox
University of Utah, 20 South 2030 East, Salt Lake City, UT 84112, USA
Dr. Madhu Basetti
Cancer Research UK Cambridge Institute, University of Cambridge, Robinson way, Cambridge CB2 ORE, UK
Prof. Dr. Michael Lalk
Cellular Biochemistry & Metabolomics, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Strasse 4, 17489 Greifswald, Germany
Dr. Jianguo (Jeff) Xia
McGill University, Montreal, QC H3A 0G4, Canada
Dr. Horst Joachim Schirra
Centre for Advanced Imaging, University of Queensland, Brisbane QLD 4072, Australia
Dr. Maria Fuller
Genetics and Molecular Pathology, SA Pathology (at Women's and Children's Hospital), Adelaide, Australia
Prof. Dr. Arthur S. Edison
University of Georgia, USA
Dr. Chi Chen
Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108, USA
Prof. Dr. Bruno Stefanon
Department of Agrofood, Environmental and Animal Sciences, University of Udine, Udine, Italy
Dr. Shu-Kun Lin, MDPI, Basel, Switzerland
Dr. Franck Vazquez, MDPI , Basel, Switzerland
Ms. Cherise He, MDPI Branch Office, Wuhan, China
Ms. Verna Zheng, MDPI Branch Office, Wuhan, China
A. Advanced Metabolomics and Data Analysis Approaches
- Data processing
- Data analysis
- Data integration
- Data visualization
- Mass spectrometry
- Mass spectrometry fragmentation
- Metabolome mining
- Metabolic modelling
- Metabolic regulation
- Metabolic interactions
- Genome-scale metabolic models
- Computational biology
- Ion mobility spectrometry
- Quantitative metabolomics
- Stable isotope analysis
This Session is sponsored by
Dr. Justin van der Hooft, Bioinformatics Group, Department of Plant Sciences, Wageningen University, Wageningen, The Netherlands
Dr. Reza Salek, International Agency for Research on Cancer (IARC), France
B. Identification of Unknowns
- Systems biology
- Metabolic modelling
- Structure Elucidation
- Metabolite structure identification
- Metabolite identification
This Session is sponsored by
Dr. Horst Joachim Schirra, Centre for Advanced Imaging, University of Queensland, Australia
Dr. Maria Fuller, Genetics and Molecular Pathology, SA Pathology (at Women's and Children's Hospital), Adelaide, Australia
Dr. Arthur S. Edison, University of Georgia, USA
C. Precision Nutrition and Food Specific Profiles
- Mass spectrometry
- Plant science
- Food science
- Nutritional metabolomics
- Food chemistry
- Diet and health
- Metabolomics in nutrition science
- Advanced NMR and mass spectrometry in food science
This Session is sponsored by
Dr. Susan Sumner, Nutrition Research Institute, Department of Nutrition at the University of North Carolina at Chapel Hill, USA
Dr. James E. Cox, University of Utah, Salt Lake City, UT 84112, USA
Dr. Chi Chen, University of Minnesota, USA
D. Microbiota and Metabolomics
- Microbial metabolomics
- Microbial physiology
- Mass spectrometry
- Gas chromatography
- Metabolic engineering
- Microbial metabolomics
- Metabolic networks
- Microbial communities
- Gut microbiota
University of Regensburg, Germany
McGill University, Canada
University of Greifswald, Germany
University of Udine, Udine, Italy
This Session is sponsored by
Dr. Katja Dettmer-Wilde, Institute of Functional Genomics, University of Regensburg, Germany
Dr. Jianguo (Jeff) Xia, McGill University, Montreal, QC H3A 0G4, Canada
Dr. Michael Lalk, Cellular Biochemistry & Metabolomics, Institute of Biochemistry, University of Greifswald, Germany
Dr. Bruno Stefanon, Department of Agrofood, Environmental and Animal Sciences, University of Udine, Udine, Italy
E. Pathway Mapping and Fluxomics
- Metabolic Pathway
- Pathway mapping
- Mass spectrometry
- Metabolic flux analysis
- Flux balance analysis
- Dynamic flux modeling
- System biology
The University of Melbourne, Australia
University of Cambridge, UK
Topic: Mitochondrial Dysfunction and Cancer: Metabolites and Beyond
This Session is sponsored by
Dr. Thusitha W. Rupasinghe, Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
Dr. Madhu Basetti, Cancer Research UK Cambridge Institute, University of Cambridge, Robinson way, Cambridge CB2 ORE, UK