Systems Medicine

improving health

Systems Medicine combines systematic assessment of complex biopsychosocial information at multiple scales of life to improve our understanding of health and disease and actually deliver improved, personalised prevention and care.

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Systems Medicine combines systematic assessment of complex biopsychosocial information at multiple scales of life – from social networks to organ imaging and molecular “omics” data – with predictive computational modelling – from “black-box” AI machine learning to mechanistic models – to improve our understanding of health and disease and actually deliver improved, personalised prevention and care.

Systems Medicine provides a paradigm shift from parts-oriented reductionism to process-oriented holism developing a valid causal theory of how ‘bio’, ‘psycho’ and ‘socio’ actually relate. 

Delivering on this conceptual progress for improved prevention and care will require similar paradigm shifts in the organisation of healthcare and public health, a task LabVantage-Biomax is actively involved in in many translational medicine projects (see Research Area “Clinical”).

Our projects

EU and national research projects

LabVantage-Biomax contributes to the Systems Medicine approach in almost 20 previous and ongoing large scale EU and national research projects from cancer to allergic, infectious, mental and respiratory disease.

(see also Research Area “Neurological Disorders”)

We provide secure and sustainable infrastructure that semantically integrates

  • Existing biomedical knowledge from allied consortia and public databases
  • Validated micro-scale and macro-scale computational models
  • Clinical data from different studies and disciplines
  • Imaging and experimental data
  • “omics” data on genetic gene expression, proteomics and metabolomics

Areas and projects

( Click on a project to learn more )

The ERACoSysMed-COPD, AirPROM, Synergy-COPD and BioBridge projects research developed machine learning and mechanistic computational models on clinical, multi-omics (expression, proteomics, metabolomics) and imaging data to answer specific questions such as the effect of Reactive Oxygen Species imbalance on muscle development in training programs, the role of eosinophils in lung wall remodelling or the association between lung rehabilitation outcomes and co-morbidities.

MeDALL looked into the environmental, developmental and life-style factors influencing asthma.

Sys-CLAD focused on the last resort therapeutic intervention of lung transplantation and the reasons for chronic lung allograft dysfunction (CLAD) with the aim to develop a predictive computational model based on omics, including microbiome data.

The Immunomonitor and ADaPT-IT projects develop bioinformatics workflows and machine learning approaches to improve the targeted immunotherapy of tumours. 

In SYS-Stomach computational models and machine learning helped to elucidate patho-mechanisms of gastric cancer and develop evidence-based actionable feedback for diagnosis, prognosis and therapy response. In addition, network analysis approaches were used to associate MALDI-Imaging based proteomics and metabolomics data of tumour heterogeneity with in-vitro omics and genetics. 

Pharmaceutically accessible therapeutic targets were evaluated by cell line drug treatment and RNAi, as well as retrospective patient stratification. 

STATegra provided foundational work on multi-omics integrative analysis workflows focusing on B-cell lymphoma as use case. 

DARIO will provide a Clinical Decision Support System based on artificial intelligence and machine learning driven analysis of clinical and laboratory data to identify currently underdiagnosed heart insufficiency resulting from heart failure with preserved ejection fraction (HEFpEF).

ModulMax in contrast developed a general research infrastructure for cardiovascular disease which covered all aspects, from Biobanking of samples, to clinical and multi-omics data management and machine learning.

DRIVE and PREPARE target Dengue vaccine development and a rapid clinical response to emerging epidemics. The later being deeply involved in the COVID-19 pandemic clinical research response with LabVantage-Biomax providing the AILANI COVID-19 knowledge base and question answering system that contributed to the global COVID-19 Disease Map effort.

Instable Glucose levels are a major cause for complications and mortality in intensive care units. GlukoSyS aims to develop an integrative, individualised bedside monitoring and control system. To this end personalised computational models of metabolism need to be developed and coupled to monitoring and control hardware. LabVantage-Biomax provides the technical framework for data management and device-model coupling.

The results from this study have now been published in the journal Human Brain Mapping and high resolution connectomes in NICARA made it on the cover journal.

METSY focused on schizophrenia and the association with metabolic syndrome by applying network analysis and machine learning to clinical, neuroimaging and metabolomics data. 

DEVoTED developed a clinical decision support system for depression treatment response prediction using clinical data as well as genetic and omics factors.

One of the major cost factors in diagnosing sleep disorders is the manual scoring of several hours of nightly polysomnography data. SPAS aims to develop an adaptive machine learning assistant that can be trained by sleep labs to follow their preferred scoring routine and reduce the manual burden. LabVantage-Biomax provides the management for clinical, diagnostic and polysomnography data as well as the user interface to deliver the machine learning results into the sleep lab routine.

Our contribution

Our contribution

For a clinician the availability of detailed, holistic information about a patient is overwhelming and requires computational support. At the same time research in Systems Medicine requires multi-disciplinary collaboration to accumulate and make sense of the required diversity of information. This accumulation of complex data on multiple levels and the need for integrative analysis, computational modelling and decision supporting delivery into clinical practice brings exciting novel opportunities and challenges for IT-infrastructures.

One challenge on the technological side is to provide semantic, syntactic and technical interoperability of data, data bases, analytical and simulation software. At the same time the results these tools provide need to be combined into a graphical user interfaces that enable scientist, clinical researchers and clinicians to distil the integrated information into actionable knowledge and make use of it.

LabVantage-Biomax provides the BioXM™ Knowledge Management Environment in Systems Medicine projects to integrate existing knowledge as dense graph, identify context specific layers and sub-graphs and map complex clinical, molecular (multi-omics), imaging and environmental data to generate dedicated knowledge bases for analysis and computational modelling.

Publically available examples are the Respiratory Knowledge Base and the Zika Knowledge Base.

In addition to knowledge and data integration BioXM provides the technical interoperability to enable experts for data analysis and computational modelling to programmatically access and use the integrated knowledge and data. Often multiple tools and models require semantic mapping to enable integrated workflows of analysis and simulation. Finally, to reach the purpose of Systems Medicine and provide actionable knowledge for better decisions a graphical user interface needs to be developed which delivers decision support into clinical or public health practice. 

In particular, LabVantage-Biomax contributes to the research projects with the following:

Overall, LabVantage-Biomax harmonised, standardised and integrated data on over 100.000 participants in more than 60 cohorts and clinical studies. 

The BioXM technology provides automatic mapping between different formats, semantic concepts and standard units as well as mapping to Ontologies and rule based data transformations. For complex harmonisation processes we provide a collaborative, secure environment for the clinical experts which enables structured management of variables, their definitions and mappings to each other, ensures transparency of discussions and decisions and traces changes over time. 

Data formats provided by clinical partners, e.g. various Excel structures or statistical software formats are syntactically mapped by LabVantage-Biomax. When integrating data from multiple sites or follow-ups a number of data quality issues are frequently observed such as missing values or inconsistencies over time. LabVantage-Biomax provides automatic data quality assessments based on metadata including but not restricted to: completeness; availability of required data; data type check, e.g. float, thesaurus; data ranges based on realistic expected values.

Any type of omics data, from genetic variants, to DNase-seq, Meth-seq, ChIP-seq, gene expression (array and RNA-seq), proteomics and metabolomics and imaging derived information for several thousand participants were integrated by LabVantage-Biomax in the diverse research projects.

Prior knowledge includes, among others, relevant molecular elements (genes, proteins, metabolites, etc.), functional information (GO, OMIM, etc.), functional interactions (e.g. protein-protein interaction, transcriptional regulation such as the mouse TF-regulatory network, miRNA network, etc.) and information about gene homologs (mouse, rat, human).These can be derived from structured databases, ontologies and mined from the literature.

LabVantage-Biomax has generated huge networks on project relevant prior knowledge, connecting >100 data sources and >50 different ontologies by millions of relations.

A disease map is a knowledge sub-network, based on capturing, condensing and structuring the available information about disease facts, from symptoms to co-morbidities, affected or involved organs, tissues and cell types to molecular processes, pathways and single molecules. The disease map thus makes this information available for research management decisions, integrative data analysis or computational modelling.

LabVantage-Biomax is ISO 9001 and ISO 27001 certified and applies the corresponding procedures of quality management and ITsecurity. The BioXMTM Knowledge Management Environment provides encrypted data transfer, authentication, detailed access control based on resources and roles and full audit. All technological and organisational measures required by the GDPR are implemented. GDPR compatible data processing agreement templates are available and corresponding contracts have been approved by company and clinical data protection officers in major European countries.

The ISO 9001 based quality management ensures consistence between specification and reported result, including reviews of requirements and technical specifications, software implementation and documentation. Quality assessment according to ISO 29119 includes unit, integration, regression and acceptance testing.

LabVantage-Biomax provides the BioXM™ Knowledge Management Environment as technological framework and its data scientist semantic modelling expertise to generate the interoperable Knowledge Base that provides access to data sources, data analysis tools and computational modelling. In addition we provide data harmonisation, literature mining and data quality services.

Knowledge management

Standards and certifications

Supporting clinical research privacy tasks
(GDPR, ISO 9001, ISO 27001)

LabVantage-Biomax is ISO 9001 and ISO 27001 certified and applies the corresponding procedures of quality management and IT-security. The NeuroXMTM Brain Science Suite provides encrypted data transfer, authentication, detailed access control based on resources and roles and full audit.

All technological and organisational measures required by the GDPR are implemented.

GDPR compatible data processing agreement templates are available and corresponding contracts have been approved by company and clinical data protection officers in major European countries.

The ISO 9001 based quality management ensures consistence between specification and reported result, including reviews of requirements and technical specifications, software implementation and documentation.

Quality assessment according to ISO 29119 includes unit, integration, regression and acceptance testing.

Fair data

Ensuring FAIR data
from experimental to computational

Scientific background

Scientific Background

In Systems Medicine negative effects on health are seen as result of the interaction type and dynamic of psycho, socio and somatic networks.

Therefore diagnosis and treatment need to systematically take into account factors on all these levels, from molecular omics to physiome and diseasome, which requires the active participation of patients, their close social environment and a multi-disciplinary healthcare team.

As changes to interactions within complex networks give rise to non-deterministic overall effects with typical chaotic effects such as attractors, boundary effects, phase transitions and tipping points, the formalisation of mechanistic hypotheses into computational predictive models.

From asthma and atopic dermatitis through cancer, COPD, depression, infectious disease, intensive care, lung transplantation and rhinitis to schizophrenia and sleep disorders a multitude of diseases and indications have been tackled in the Systems Medicine projects LabVantage-Biomax is or has been involved. 

Publications

  1. Haffner I, Schierle K, Raimúndez E, Geier B, Maier D, Hasenauer J, Luber B, Walch A, Kolbe K, Riera Knorrenschild J, Kretzschmar A, Rau B, Fischer von Weikersthal L, Ahlborn M, Siegler G, Fuxius S, Decker T, Wittekind C, Lordick F. HER2 Expression, Test Deviations, and Their Impact on Survival in Metastatic Gastric Cancer: Results From the Prospective Multicenter VARIANZ Study. J Clin Oncol. 2021 Mar 25;JCO2002761. PMID: 33764808
  2. Raimúndez E, Keller S, Ebert K, Hug S, Theis FJ, Maier D, Luber B, Hasenauer J. Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines. PLoS Comput Biol. 2020 Mar 2;16(3):e1007147. PMID: 32119655
  3. Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta-Resendiz A, Singh V, Aghamiri SS, Acencio ML, Glaab E, Ruepp A, Fobo G, Montrone C, Brauner B, Frischman G, Gómez LCM, Somers J, Hoch M, Gupta SK, Scheel J, Borlinghaus H, Czauderna T, Schreiber F, Montagud A, Leon MP de, Funahashi A, Hiki Y, Hiroi N, Yamada TG, Dräger A, Renz A, Naveez M, Bocskei Z, Messina F, Börnigen D, Fergusson L, Conti M, Rameil M, Nakonecnij V, Vanhoefer J, Schmiester L, Wang M, Ackerman EE, Shoemaker J, Zucker J, Oxford K, Teuton J, Kocakaya E, Summak GY, Hanspers K, Kutmon M, Coort S, Eijssen L, Ehrhart F, Rex D a. B, Slenter D, Martens M, Haw R, Jassal B, Matthews L, Orlic-Milacic M, Ribeiro AS, Rothfels K, Shamovsky V, Stephan R, Sevilla C, Varusai T, Ravel J-M, Fraser R, Ortseifen V, Marchesi S, Gawron P, Smula E, Heirendt L, Satagopam V, Wu G, Riutta A, Golebiewski M, Owen S, Goble C, Hu X, Overall RW, Maier D, Bauch A, Gyori BM, Bachman JA, Vega C, Grouès V, Vazquez M, Porras P, Licata L, Iannuccelli M, Sacco F, Nesterova A, Yuryev A, Waard A de, Turei D, Luna A, Babur O, Soliman S, Valdeolivas A, Esteban-Medina M, Peña-Chilet M, Helikar T, Puniya BL, Modos D, Treveil A, Olbei M, Meulder BD, Dugourd A, Naldi A, Noel V, Calzone L, Sander C, Demir E, Korcsmaros T, Freeman TC, Augé F, Beckmann JS, Hasenauer J, Wolkenhauer O, Wilighagen EL, Pico AR, Evelo CT, Gillespie ME, Stein LD, Hermjakob H, D’Eustachio P, Saez-Rodriguez J, Dopazo J, Valencia A, Kitano H, Barillot E, Auffray C, Balling R, Schneider R, Community the C-19 DM. COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms. bioRxiv. Cold Spring Harbor Laboratory; 2020 Oct 28;2020.10.26.356014.
  4. Fuertes E, Sunyer J, Gehring U, Porta D, Forastiere F, Cesaroni G, Vrijheid M, Guxens M, Annesi-Maesano I, Slama R, Maier D, Kogevinas M, Bousquet J, Chatzi L, Lertxundi A, Basterrechea M, Esplugues A, Ferrero A, Wright J, Mason D, McEachan R, Garcia-Aymerich J, Jacquemin B. Associations between air pollution and pediatric eczema, rhinoconjunctivitis and asthma: A meta-analysis of European birth cohorts. Environ Int. 2020 Mar;136:105474. PMID: 31962272
  5. Bédard A, Antó JM, Fonseca JA, Arnavielhe S, Bachert C, Bedbrook A, Bindslev-Jensen C, Bosnic-Anticevich S, Cardona V, Cruz AA, Fokkens WJ, Garcia-Aymerich J, Hellings PW, Ivancevich JC, Klimek L, Kuna P, Kvedariene V, Larenas-Linnemann D, Melén E, Monti R, Mösges R, Mullol J, Papadopoulos NG, Pham-Thi N, Samolinski B, V Tomazic P, Toppila-Salmi S, Ventura MT, Yorgancioglu A, Bousquet J, Pfaar O, Basagaña X, MASK study group. Correlation between work impairment, scores of rhinitis severity and asthma using the MASK-air® App. Allergy. 2020 Jan 29; PMID: 31995656
  6. Bauch A, Pellet J, Schleicher T, Yu X, Gelemanović A, Cristella C, Fraaij PL, Polasek O, Auffray C, Maier D, Koopmans M, de Jong MD. Informing epidemic (research) responses in a timely fashion by knowledge management – a Zika virus use case. Biol Open [Internet]. 2020 Nov 4; Available from: https://bio.biologists.org/content/early/2020/11/03/bio.053934 PMID: 33148605
  7. Ammar A, Bonaretti S, Winckers L, Quik J, Bakker M, Maier D, Lynch I, van Rijn J, Willighagen E. A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences. Nanomaterials. Multidisciplinary Digital Publishing Institute; 2020 Oct;10(10):2068.
  8. Hohmann C, Keller T, Gehring U, Wijga A, Standl M, Kull I, Bergstrom A, Lehmann I, von Berg A, Heinrich J, Lau S, Wahn U, Maier D, Anto J, Bousquet J, Smit H, Keil T, Roll S. Sex-specific incidence of asthma, rhinitis and respiratory multimorbidity before and after puberty onset: individual participant meta-analysis of five birth cohorts collaborating in MeDALL. BMJ Open Respir Res. 2019;6(1):e000460. PMCID: PMC6797252
  9. Gomez-Cabrero D, Tarazona S, Ferreirós-Vidal I, Ramirez RN, Company C, Schmidt A, Reijmers T, Paul V von S, Marabita F, Rodríguez-Ubreva J, Garcia-Gomez A, Carroll T, Cooper L, Liang Z, Dharmalingam G, van der Kloet F, Harms AC, Balzano-Nogueira L, Lagani V, Tsamardinos I, Lappe M, Maier D, Westerhuis JA, Hankemeier T, Imhof A, Ballestar E, Mortazavi A, Merkenschlager M, Tegner J, Conesa A. STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Sci Data. 2019 31;6(1):256. PMCID: PMC6823427
  10. Franssen FM, Alter P, Bar N, Benedikter BJ, Iurato S, Maier D, Maxheim M, Roessler FK, Spruit MA, Vogelmeier CF, Wouters EF, Schmeck B. Personalized medicine for patients with COPD: where are we? International Journal of Chronic Obstructive Pulmonary Disease. 2019 Jul 9;(14):1465–1484.
  11. Brosseau C, Danger R, Durand M, Durand E, Foureau A, Lacoste P, Tissot A, Roux A, Reynaud-Gaubert M, Kessler R, Mussot S, Dromer C, Brugière O, Mornex JF, Guillemain R, Claustre J, Magnan A, Brouard S, COLT and SysCLAD Consortia. Blood CD9+ B cell, a biomarker of bronchiolitis obliterans syndrome after lung transplantation. Am J Transplant. 2019 Nov;19(11):3162–3175. PMID: 31305014
  12. Thacher JD, Gehring U, Gruzieva O, Standl M, Pershagen G, Bauer C-P, Berdel D, Keller T, Koletzko S, Koppelman GH, Kull I, Lau S, Lehmann I, Maier D, Schikowski T, Wahn U, Wijga AH, Heinrich J, Bousquet J, Anto JM, von Berg A, Melén E, Smit HA, Keil T, Bergström A. Maternal Smoking during Pregnancy and Early Childhood and Development of Asthma and Rhinoconjunctivitis – a MeDALL Project. Environ Health Perspect. 2018 12;126(4):047005. PMID: 29664587
  13. Mouraux S, Bernasconi E, Pattaroni C, Koutsokera A, Aubert J-D, Claustre J, Pison C, Royer P-J, Magnan A, Kessler R, Benden C, Soccal PM, Marsland BJ, Nicod LP, Jougon J, Velly J-F, Rozé H, Blanchard E, Dromer C, Antoine M, Cappello M, Ruiz M, Sokolow Y, Vanden Eynden F, Van Nooten G, Barvais L, Berré J, Brimioulle S, De Backer D, Créteur J, Engelman E, Huybrechts I, Ickx B, Preiser TJC, Tuna T, Van Obberghe L, Vancutsem N, Vincent J-L, De Vuyst P, Etienne I, Féry F, Jacobs F, Knoop C, Vachiéry JL, Van den Borne P, Wellemans I, Amand G, Collignon L, Giroux M, Angelescu D, Chavanon O, Hacini R, Pirvu A, Porcu P, Albaladejo P, Allègre C, Bataillard A, Bedague D, Briot E, Casez-Brasseur M, Colas D, Dessertaine G, Durand M, Francony G, Hebrard A, Marino MR, Oummahan B, Protar D, Rehm D, Robin S, Rossi-Blancher M, Augier C, Bedouch P, Boignard A, Bouvaist H, Briault A, Camara B, Claustre J, Chanoine S, Dubuc M, Quétant S, Maurizi J, Pavèse P, Pison C, Saint-Raymond C, Wion N, Chérion C, Grima R, Jegaden O, Maury J-M, Tronc F, Flamens C, Paulus S, Mornex J-F, Philit F, Senechal A, Glérant J-C, Turquier S, Gamondes D, Chalabresse L, Thivolet-Bejui F, Barnel C, Dubois C, Tiberghien A, Le Pimpec-Barthes F, Bel A, Mordant P, Achouh P, Boussaud V, Guillemain R, Méléard D, Bricourt MO, Cholley B, Pezella V, Brioude G, D’Journo XB, Doddoli C, Thomas P, Trousse D, Dizier S, Leone M, Papazian L, Bregeon F, Basire A, Coltey B, Dufeu N, Dutau H, Garcia S, Gaubert JY, Gomez C, Laroumagne S, Nieves A, Picard LC, Reynaud-Gaubert M, Secq V, Mouton G, Baron O, Lacoste P, Perigaud C, Roussel JC, Danner I, Haloun A, Magnan A, Tissot A, Lepoivre T, Treilhaud M, Botturi-Cavaillès K, Brouard S, Danger R, Loy J, Morisset M, Pain M, Pares S, Reboulleau D, Royer P-J, Fabre D, Fadel E, Mercier O, Mussot S, Stephan F, Viard P, Cerrina J, Dorfmuller P, Ghigna SM, Hervén Ph, Le Roy Ladurie F, Le Pavec J, Thomas de Montpreville V, Lamrani L, Castier Y, Mordant P, Cerceau P, Augustin P, Jean-Baptiste S, Boudinet S, Montravers P, Brugière O, Dauriat G, Jébrak G, Mal H, Marceau A, Métivier A-C, Thabut G, Lhuillier E, Dupin C, Bunel V, Falcoz P, Massard G, Santelmo N, Ajob G, Collange O, Helms O, Hentz J, Roche A, Bakouboula B, Degot T, Dory A, Hirschi S, Ohlmann-Caillard S, Kessler L, Kessler R, Schuller A, Bennedif K, Vargas S, Stauder J, Ali-Azouaou S, Bonnette P, Chapelier A, Puyo P, Sage E, Bresson J, Caille V, Cerf C, Devaquet J, Dumans-Nizard V, Felten M-L, Fischler M, Si Larbi A-G, Leguen M, Ley L, Liu N, Trebbia G, De Miranda S, Douvry B, Gonin F, Grenet D, Hamid AM, Neveu H, Parquin F, Picard C, Roux A, Stern M, Bouillioud F, Cahen P, Colombat M, Dautricourt C, Delahousse M, D’Urso B, Gravisse J, Guth A, Hillaire S, Honderlick P, Lequintrec M, Longchampt E, Mellot F, Scherrer A, Temagoult L, Tricot L, Vasse M, Veyrie C, Zemoura L, Berjaud J, Brouchet L, Dahan M, Mathe FO, Benahoua H, DaCosta M, Serres I, Merlet-Dupuy V, Grigoli M, Didier A, Murris M, Crognier L, Fourcade O, Jougon J, Velly J-F, Rozé H, Blanchard E, Dromer C, Antoine M, Cappello M, Ruiz M, Sokolow Y, Vanden Eynden F, Van Nooten G, Barvais L, Berré J, Brimioulle S, De Backer D, Créteur J, Engelman E, Huybrechts I, Ickx B, Preiser TJC, Tuna T, Van Obberghe L, Vancutsem N, Vincent J-L, De Vuyst P, Etienne I, Féry F, Jacobs F, Knoop C, Vachiéry JL, Van den Borne P, Wellemans I, Amand G, Collignon L, Giroux M, Angelescu D, Chavanon O, Hacini R, Pirvu A, Porcu P, Albaladejo P, Allègre C, Bataillard A, Bedague D, Briot E, Casez-Brasseur M, Colas D, Dessertaine G, Durand M, Francony G, Hebrard A, Marino MR, Oummahan B, Protar D, Rehm D, Robin S, Rossi-Blancher M, Augier C, Bedouch P, Boignard A, Bouvaist H, Briault A, Camara B, Claustre J, Chanoine S, Dubuc M, Quétant S, Maurizi J, Pavèse P, Pison C, Saint-Raymond C, Wion N, Chérion C, Grima R, Jegaden O, Maury J-M, Tronc F, Flamens C, Paulus S, Mornex J-F, Philit F, Senechal A, Glérant J-C, Turquier S, Gamondes D, Chalabresse L, Thivolet-Bejui F, Barnel C, Dubois C, Tiberghien A, Le Pimpec-Barthes F, Bel A, Mordant P, Achouh P, Boussaud V, Guillemain R, Méléard D, Bricourt MO, Cholley B, Pezella V, Brioude G, D’Journo XB, Doddoli C, Thomas P, Trousse D, Dizier S, Leone M, Papazian L, Bregeon F, Basire A, Coltey B, Dufeu N, Dutau H, Garcia S, Gaubert JY, Gomez C, Laroumagne S, Nieves A, Picard LC, Reynaud-Gaubert M, Secq V, Mouton G, Baron O, Lacoste P, Perigaud C, Roussel JC, Danner I, Haloun A, Magnan A, Tissot A, Lepoivre T, Treilhaud M, Botturi-Cavaillès K, Brouard S, Danger R, Loy J, Morisset M, Pain M, Pares S, Reboulleau D, Royer P-J, Fabre D, Fadel E, Mercier O, Mussot S, Stephan F, Viard P, Cerrina J, Dorfmuller P, Ghigna SM, Hervén Ph, Le Roy Ladurie F, Le Pavec J, Thomas de Montpreville V, Lamrani L, Castier Y, Mordant P, Cerceau P, Augustin P, Jean-Baptiste S, Boudinet S, Montravers P, Brugière O, Dauriat G, Jébrak G, Mal H, Marceau A, Métivier A-C, Thabut G, Lhuillier E, Dupin C, Bunel V, Falcoz P, Massard G, Santelmo N, Ajob G, Collange O, Helms O, Hentz J, Roche A, Bakouboula B, Degot T, Dory A, Hirschi S, Ohlmann-Caillard S, Kessler L, Kessler R, Schuller A, Bennedif K, Vargas S, Stauder J, Ali-Azouaou S, Bonnette P, Chapelier A, Puyo P, Sage E, Bresson J, Caille V, Cerf C, Devaquet J, Dumans-Nizard V, Felten M-L, Fischler M, Si Larbi A-G, Leguen M, Ley L, Liu N, Trebbia G, De Miranda S, Douvry B, Gonin F, Grenet D, Hamid AM, Neveu H, Parquin F, Picard C, Roux A, Stern M, Bouillioud F, Cahen P, Colombat M, Dautricourt C, Delahousse M, D’Urso B, Gravisse J, Guth A, Hillaire S, Honderlick P, Lequintrec M, Longchampt E, Mellot F, Scherrer A, Temagoult L, Tricot L, Vasse M, Veyrie C, Zemoura L, Berjaud J, Brouchet L, Dahan M, Mathe FO, Benahoua H, DaCosta M, Serres I, Merlet-Dupuy V, Grigoli M, Didier A, Murris M, Crognier L, Fourcade O, Krueger T, Ris HB, Gonzalez M, Jolliet Ph, Marcucci C, Chollet M, Gronchi F, Courbon C, Berutto C, Manuel O, Koutsokera A, Aubert J-D, Nicod LP, Mouraux S, Bernasconi E, Pattaroni C, Marsland BJ, Soccal PM, Rochat T, Lücker LM, Hillinger S, Inci I, Weder W, Schuepbach R, Zalunardo M, Benden C, Schuurmans MM, Gaspert A, Holzmann D, Müller N, Schmid C, Vrugt B, Fritz A, Maier D, Deplanche K, Koubi D, Ernst F, Paprotka T, Schmitt M, Wahl B, Boissel J-P, Olivera-Botello G, Trocmé C, Toussaint B, Bourgoin-Voillard S, Sève M, Benmerad M, Siroux V, Slama R, Auffray C, Charron D, Lefaudeux D, Pellet J. Airway microbiota signals anabolic and catabolic remodeling in the transplanted lung. Journal of Allergy and Clinical Immunology. 2018 Feb 1;141(2):718-729.e7.
  14. Keller S, Zwingenberger G, Ebert K, Hasenauer J, Wasmuth J, Maier D, Haffner I, Schierle K, Weirich G, Luber B. Effects of trastuzumab and afatinib on kinase activity in gastric cancer cell lines. Mol Oncol. 2018 Jan 11; PMID: 29325228
  15. Frank E, Maier D, Pajula J, Suvitaival T, Borgan F, Butz-Ostendorf M, Fischer A, Hietala J, Howes O, Hyötyläinen T, Janssen J, Laurikainen H, Moreno C, Suvisaari J, Van Gils M, Orešič M. Platform for systems medicine research and diagnostic applications in psychotic disorders-The METSY project. Eur Psychiatry. 2018 Jan 17; PMID: 29361398
  16. Contreras ZA, Chen Z, Roumeliotaki T, Annesi-Maesano I, Baïz N, Berg A von, Bergström A, Crozier S, Duijts L, Ekström S, Eller E, Fantini MP, Kjaer HF, Forastiere F, Gerhard B, Gori D, Ginkel MWH, Heinrich J, Iñiguez C, Inskip H, Keil T, Kogevinas M, Lau S, Lehmann I, Maier D, Meel ER van, Mommers M, Murcia M, Porta D, Smit HA, Standl M, Stratakis N, Sunyer J, Thijs C, Torrent M, Vrijkotte TGM, Wijga AH, Berhane K, Gilliland F, Chatzi L. Does early onset asthma increase childhood obesity risk? A pooled analysis of 16 European cohorts. European Respiratory Journal. 2018 Sep 1;52(3):1800504. PMID: 30209194
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  49. Jan Brugard, Daniel Hedberg, Marta Cascante, Gunnar Cedersund,Alex Gomez-Garrido, Dieter Maier, Elin Nyman, Vitaly Selivanov and Peter Stralfors. Creating a Bridge between Modelica and the Systems Biology Community. Proceedings 7th Modelica Conference Como. 2009;

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