CITATION: Wolkenhauer, O. et al. 2014. Enabling multiscale modeling in systems medicine. Genome Medicine, 6:21, doi:10.1186/gm538.The original publication is available at http://genomemedicine.biomedcentral.com[See article for abstract].Publisher's versio

Auffray, Charles

Brass, Olivier

Byrne, Helen

Clairambault, Jean

Deutsch, Andreas

Drasdo, Dirk

Gervasio, Francesco

Kossow, Christina

Kuepfer, Lars

Maini, Philip

Marciniak-Czochra, Anna

Preziosi, Luigi

Ramis-Conde, Ignacio

Rateitschak, Katja

Ribba, Benjamin

Schuppert, Andreas

Smallwood, Rod

Stamatakos, Georgios

Winter, Felix

Wolkenhauer, Olaf

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Olaf Wolkenhauer

Charles Auffray

Jean Clairambault

Andreas Deutsch

Dirk Drasdo

Philip Maini

Christina Kossow

Lars Kuepfer

Katja Rateitschak

Ignacio Ramis-Conde

Benjamin Ribba

Andreas Schuppert

Rod Smallwood

Georgios Stamatakos

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Enabling multiscale modeling in systems medicine

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Wolkenhauer et al. Genome Medicine 2014, 6:21http://genomemedicine.com/content/6/3/21COMMENTEnabling multiscale modeling in systemsmedicineOlaf Wolkenhauer1,2*, Charles Auffray3, Olivier Brass4, Jean Clairambault5,6,7, Andreas Deutsch8, Dirk Drasdo6,7,9,Francesco Gervasio10, Luigi Preziosi11, Philip Maini12, Anna Marciniak-Czochra13, Christina Kossow1,Lars Kuepfer14,15, Katja Rateitschak1, Ignacio Ramis-Conde16, Benjamin Ribba17, Andreas Schuppert18,19,Rod Smallwood20, Georgios Stamatakos21, Felix Winter1,22 and Helen Byrne23From reactions in cells to organ physiologySystems medicine is an interdisciplinary approach thatintegrates data from basic research and clinical practiceto improve our understanding and treatment of diseases.Systems medicine can be seen as a further developmentof systems biology and bioinformatics towards applica-tions of clinical relevance. The term 'systems' refers tosystems approaches, emphasizing a close integration ofdata generation with mathematical modeling [1-3]. The(mal)functioning of the human body is a complex process,characterized by multiple interactions between systemsthat act across multiple levels of structural and functionalorganization - from molecular reactions to cell-cell interac-tions in tissues to the physiology of organs and organsystems. Over the past decade, we have gained detailedinsights into the structure and function of molecular, cellu-lar and organ-level systems, with technologies playing animportant role in the generation of data at these differentscales.Under the umbrella of systems biology, workflows ofdata-driven modeling and model-driven experimentationhave led to the development of computational models thatdescribe processes at all levels, including gene regulatorynetworks, signal transduction pathways and metabolicnetworks, cell populations, structured tissues, pharmaco-kinetic and pharmacodynamic models that analyze drugor vaccine action at the whole organism level, and phar-macogenomic models of disease risk and drug and vaccineexposure at the patient-population level.Major challenges exist for the development of quanti-tative and predictive models that span the gap between* Correspondence: olaf.wolkenhauer@uni-rostock.de1Department of Systems Biology & Bioinformatics, University of Rostock,18051 Rostock, Germany2Stellenbosch Institute for Advanced Study, Wallenberg Research Centre atStellenbosch University, 7600 Stellenbosch, South AfricaFull list of author information is available at the end of the article© 2014 Wolkenhauer et al.; licensee BioMed Cmedium, for [6/12] months following its publiCommons Attribution License (http://creativecreproduction in any medium, provided the orcell-level biochemical models and organism-level phar-macokinetic and pharmacodynamic models. One hurdleis the technical difficulty associated with generatingsufficiently comprehensive quantitative datasets for largenumbers of system variables, across different levels oforganization. Another major challenge is the develop-ment of efficient tools that can handle the wide varietyof available data and identify relevant data subsets fromexisting repositories. Overcoming these hurdles will leadto the development of models that can be used inclinical practice while accounting for the uncertainty inthe data.Another important challenge for systems medicine isthe integration of this knowledge across the relevantlevels of organization. Large, long-term research initia-tives, like the Virtual Physiological Human, the VirtualLiver or the Human Brain Project, are aiming to developcomprehensive, computational representations of organsand organ systems. Here, we focus on opportunities forcomparatively small interdisciplinary collaborations be-tween clinicians and modelers who are targeting specificquestions of clinical relevance. In the projects that weenvisage, modeling is not the final goal; rather, it is a toolthat can be used to advance understanding of the system,to develop more directed experiments in the laboratoryand, ultimately, to generate testable predictions to enableimproved therapies and prophylaxes.Multiscale modelingThe fields of theoretical and mathematical biology havepioneered the development of mathematical and compu-tational models of biological systems. Systems biologyhas contributed workflows for data-driven modeling andmodel-driven experimentation to the life sciences. Takentogether, these provide a considerable body of experi-ence for modeling at different levels (Box 1).entral Ltd. The licensee has exclusive rights to distribute this article, in anycation. After this time, the article is available under the terms of the Creativeommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andiginal work is properly cited.Molecular modeling, for example•Drug/vaccine target interactions•Prediction of pathogenic mutations•Protein-protein interactionsModeling of subcellular processes, for example:•Signaling pathways, gene regulatory networks, metabolicnetworks•Drug target prediction by sensitivity analysis•Linking signaling pathways to phenotypic changes (for example,regression models)Individual-cell or cell-based models, for example:•Behavior of individual cells in their microenvironment•Cell-cell interactions•Control of cell material properties and shape by molecularmechanisms, and the effect of physical properties of cells andtheir environment on cell decisionsTissue/organ level models, for example:•Population dynamics, the formation and maintenance of tissuearchitecture•Cell-stroma interactions, for example epithelial-mesenchymaltransition•Mechanobiology/biomechanical modelsBody systems level models, for example:•Pharmacokinetic/pharmacodynamic modeling•Physiologically based pharmacokinetic modeling for applicationsin systems pharmacology•Modeling environmental influences and long-term clinical events(for example, overall survival).Box 1. Different levels at which modeling of biologicalsystems has been carried outWolkenhauer et al. Genome Medicine 2014, 6:21 Page 2 of 3http://genomemedicine.com/content/6/3/21This list is necessarily incomplete and serves here toindicate the progression of modeling efforts, with an in-creasing diversity of approaches available, a tendency tocombine approaches in modeling workflows, and clearindications of an increasing number of successful casestudies.Multiscale modeling serves as a ‘macroscope’ that inte-grates the evidence available at different levels of a par-ticular system’s structural and functional organizationwhile enabling us to zoom in on the details where neces-sary. For experimental models, it is almost certain thatnot all parameters influencing a disease will be knownand, often, known parameters cannot be influenced and/or measured. This makes it difficult to unambiguouslyidentify the mechanisms at work in diseases. Modelingtherefore makes an epistemic contribution by allowing insilico (simulation) experiments under defined conditions,aiding the exploration of hypotheses by explicitly ac-counting for the uncertainty that arises from biologicalcomplexity.Central to these efforts are data of high quality thatare quantitative, derived from reproducible studies andthat cover multiple levels of functional and structuralorganization, generated from a range of experimentalsystems. A particularly important challenge is to enableclinical validation of models and simulations.Actions required for multiscale modelingOur discussions have identified a number ofrecommendations.Short-term recommended actionsWith respect to data collection and modeling, in theshort-term (that is, the next one to two years), the fol-lowing actions should be given priority:1. Exploitation of existing data as a starting point formultiscale modeling. This will lead to theidentification of gaps in data and, therefore, inunderstanding of underlying mechanisms, and willimprove the targeted generation of new data thatcan be exploited for quantitative models.2. Development of standard operating procedures andquality standards for the systematic collection ofquantitative data and information. This will enablemodels to be built and validated for specificapplications.3. Definition of test scenarios and proof-of-conceptstudies.4. Identification of required standards and ontologies(for example, a markup language and ontology forindividual-based models) for models and data reposi-tories in systems medicine.5. Development of concepts for dedicated modelingworkflows for the integration of data and models.These research actions would benefit from initiativesthat coordinate training and encourage collaborationsbetween clinicians and multiscale modelers; support co-ordinated clinical projects to bring clinicians, modelersand biologists together; and run sandpits for modelersand clinicians to develop proposals to address clinicalquestions, to improve mutual understanding and providerealistic expectations for such collaborations.Medium-term recommended actionsIn the medium-term of two to three years, attentionshould focus on the provision of suitable informationtechnology infrastructure and the development of stan-dards, such as:Wolkenhauer et al. Genome Medicine 2014, 6:21 Page 3 of 3http://genomemedicine.com/content/6/3/211. The development of computational tools andalgorithms for efficient multiscale simulations.2. The development of mathematical formalism toanalyze and compare multiscale models such asparameter estimation, sensitivity analysis,identifiability analysis and image analysis.3. Support for the development of workflows formodeling, including computational tools thatsupport data management, model construction andanalysis.4. Methods to integrate different physical phenomena,including those of mechanotransduction, electrical,mechanical and chemical origin.5. Methods to investigate the interplay between theenvironment, cell behavior and cell fate.In the medium term, the goal is to develop multiscalemodels of normal physiology and disease. The develop-ment of these models should be driven by clinical ques-tions to provide the basis for clinical validation in thelonger term. This will require technologies that promoteand facilitate the collaboration of multidisciplinaryteams.Longer-term recommended actionsOn a timescale beyond four years, the focus should beon the application and validation of multiscale models inthe clinic. To this end, it is desirable to:1. Enhance the formation of small-scale networksfocused on specific clinical needs, possibly clusteredin a larger integrated project (longer timescale).2. Have a funding model in place for small groups oftwo to three partners (one to two modelingpostdocs, one to two experimental postdocs plusconsumables and travel between laboratories). Sucha funding model has been applied in systems biologyto good effect, with smaller groups being integratedthrough networks.The main priority for systems medicine is the develop-ment of computational models that integrate data andknowledge from the clinics and basic science (in vitroand animal model experiments) and are applicable toindividual patients. The aim is to derive a mechanisticunderstanding of pathologies, prophylaxy and support oftherapy optimization. This requires the development ofconcepts, methods and tools that support the integrationof organizational levels to develop interfaces betweenthe computational and mathematical frameworks usedin systems medicine.Competing interestsThe authors declare that they have no competing interests.AcknowledgementsThe workshop that launched the collaborative writing of this paper wasfunded through CASyM, a Coordinating Action for the Implementation ofSystems Medicine across Europe (www.casym.eu). CASyM is an initiative ofthe European Commission, Health Directorate DG Research & Innovation,under grant agreement #305033.Author details1Department of Systems Biology & Bioinformatics, University of Rostock,18051 Rostock, Germany. 2Stellenbosch Institute for Advanced Study,Wallenberg Research Centre at Stellenbosch University, 7600 Stellenbosch,South Africa. 3European Institute for Systems Biology & Medicine, CNRSInstitute of Biological Sciences, Claude Bernard University, Université de Lyon,69100 Villeurbanne, France. 4Sanofi Pasteur, 69280 Marcy l’Etoile, France.5INRIA Paris-Rocquencourt, Laboratoire Jacques-Louis Lions and Institut Uni-versitaire du Cancer, UPMC, 75005 Paris, France. 6INRIA Paris - Rocquencourt,Domaine de Voluceau-Rocquencourt, 78153 Le Chesnay, France. 7UPMCUniversity of Paris 06, CNRS UMR 7598, Laboratoire Jacques-Louis Lions,75005 Paris, France. 8Centre for Information Services & High PerformanceComputing, Technical University Dresden, 01062 Dresden, Germany.9Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, 04109Leipzig, Germany. 10Department of Chemistry, University College London,London WC1H 0AJ, UK. 11Department of Mathematical Sciences, Politecnicodi Torino, 10129 Torino, Italy. 12Wolfson Centre for Mathematical Biology,University of Oxford, Oxford OX2 6GGUK, UK. 13Institute of AppliedMathematics, Interdisciplinary Centre for Scientific Computing andBIOQUANT, University of Heidelberg, 69120 Heidelberg, Germany.14Computational Systems Biology, Bayer Technology Services GmbH, 51368Leverkusen, Germany. 15Institute of Applied Microbiology, RWTH Aachen,52056 Aachen, Germany. 16IMACI, Instituto de Matemática Aplicada a laCiencia y la Ingeniería, Universidad de Castilla la Mancha, 13071 Ciudad Real,Spain. 17NuMed, Ecole Normale Supérieure de Lyon, 69342 Lyon, France.18Technology Development, Bayer Technology Services GmbH, 51368Leverkusen, Germany. 19Joint Research Centre for ComputationalBiomedicine, RWTH Aachen University, 52056 Aachen, Germany. 20KrotoResearch Institute, University of Sheffield, Sheffield S10 2TN, UK. 21In SilicoOncology Group, Institute of Communication and Computer Systems,National Technical University of Athens, 15773 Zografou, Greece. 22ASDGmbH Rostock, 18059 Rostock, Germany. 23School of Mathematical Sciencesand Department of Computer Science, University of Oxford, Oxford OX13QD, UK.Published: 21 March 2014References1. Coordinating Action Systems Medicine (CASyM) [https://www.casym.eu/]2. Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O: The roadfrom systems biology to systems medicine. Pediatr Res 2013, 73:502–507.3. Hood L, Balling R, Auffray C: Revolutionizing medicine in the 21st centurythrough systems approaches. Biotechnol J 2012, 7:992–1001.doi:10.1186/gm538Cite this article as: Wolkenhauer et al.: Enabling multiscale modeling insystems medicine. Genome Medicine 2014 6:21.

Olivier Brass

Francesco Gervasio

Luigi Preziosi

Anna Marciniak-Czochra

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Wolkenhauer, O

Auffray, C

Brass, O

Clairambault, J

Deutsch, A

Drasdo, D

Gervasion, F

Preziosi, L.

Marciniak-Czochra, A

Kossow, C

Kuepfer, L

Rateitschak, K

Ramis-Conde, I

Ribba, B.

Schuppert, A

Smallwood, R

Stamatakos, G

Winter, F

Byrne, H M

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Wolkenhauer et al. Genome Medicine 2014, 6:21
http://genomemedicine.com/content/6/3/21COMMENTEnabling multiscale modeling in systems
medicine
Olaf Wolkenhauer1,2*, Charles Auffray3, Olivier Brass4, Jean Clairambault5,6,7, Andreas Deutsch8, Dirk Drasdo6,7,9,
Francesco Gervasio10, Luigi Preziosi11, Philip Maini12, Anna Marciniak-Czochra13, Christina Kossow1,
Lars Kuepfer14,15, Katja Rateitschak1, Ignacio Ramis-Conde16, Benjamin Ribba17, Andreas Schuppert18,19,
Rod Smallwood20, Georgios Stamatakos21, Felix Winter1,22 and Helen Byrne23From reactions in cells to organ physiology
Systems medicine is an interdisciplinary approach that
integrates data from basic research and clinical practice
to improve our understanding and treatment of diseases.
Systems medicine can be seen as a further development
of systems biology and bioinformatics towards applica-
tions of clinical relevance. The term 'systems' refers to
systems approaches, emphasizing a close integration of
data generation with mathematical modeling [1-3]. The
(mal)functioning of the human body is a complex process,
characterized by multiple interactions between systems
that act across multiple levels of structural and functional
organization - from molecular reactions to cell-cell interac-
tions in tissues to the physiology of organs and organ
systems. Over the past decade, we have gained detailed
insights into the structure and function of molecular, cellu-
lar and organ-level systems, with technologies playing an
important role in the generation of data at these different
scales.
Under the umbrella of systems biology, workflows of
data-driven modeling and model-driven experimentation
have led to the development of computational models that
describe processes at all levels, including gene regulatory
networks, signal transduction pathways and metabolic
networks, cell populations, structured tissues, pharmaco-
kinetic and pharmacodynamic models that analyze drug
or vaccine action at the whole organism level, and phar-
macogenomic models of disease risk and drug and vaccine
exposure at the patient-population level.
Major challenges exist for the development of quanti-
tative and predictive models that span the gap between* Correspondence: olaf.wolkenhauer@uni-rostock.de
1Department of Systems Biology & Bioinformatics, University of Rostock,
18051 Rostock, Germany
2Stellenbosch Institute for Advanced Study, Wallenberg Research Centre at
Stellenbosch University, 7600 Stellenbosch, South Africa
Full list of author information is available at the end of the article
© 2014 Wolkenhauer et al.; licensee BioMed C
medium, for [6/12] months following its publi
Commons Attribution License (http://creativec
reproduction in any medium, provided the orcell-level biochemical models and organism-level phar-
macokinetic and pharmacodynamic models. One hurdle
is the technical difficulty associated with generating
sufficiently comprehensive quantitative datasets for large
numbers of system variables, across different levels of
organization. Another major challenge is the develop-
ment of efficient tools that can handle the wide variety
of available data and identify relevant data subsets from
existing repositories. Overcoming these hurdles will lead
to the development of models that can be used in
clinical practice while accounting for the uncertainty in
the data.
Another important challenge for systems medicine is
the integration of this knowledge across the relevant
levels of organization. Large, long-term research initia-
tives, like the Virtual Physiological Human, the Virtual
Liver or the Human Brain Project, are aiming to develop
comprehensive, computational representations of organs
and organ systems. Here, we focus on opportunities for
comparatively small interdisciplinary collaborations be-
tween clinicians and modelers who are targeting specific
questions of clinical relevance. In the projects that we
envisage, modeling is not the final goal; rather, it is a tool
that can be used to advance understanding of the system,
to develop more directed experiments in the laboratory
and, ultimately, to generate testable predictions to enable
improved therapies and prophylaxes.Multiscale modeling
The fields of theoretical and mathematical biology have
pioneered the development of mathematical and compu-
tational models of biological systems. Systems biology
has contributed workflows for data-driven modeling and
model-driven experimentation to the life sciences. Taken
together, these provide a considerable body of experi-
ence for modeling at different levels (Box 1).entral Ltd. The licensee has exclusive rights to distribute this article, in any
cation. After this time, the article is available under the terms of the Creative
ommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
iginal work is properly cited.
Molecular modeling, for example
•Drug/vaccine target interactions
•Prediction of pathogenic mutations
•Protein-protein interactions
Modeling of subcellular processes, for example:
•Signaling pathways, gene regulatory networks, metabolic
networks
•Drug target prediction by sensitivity analysis
•Linking signaling pathways to phenotypic changes (for example,
regression models)
Individual-cell or cell-based models, for example:
•Behavior of individual cells in their microenvironment
•Cell-cell interactions
•Control of cell material properties and shape by molecular
mechanisms, and the effect of physical properties of cells and
their environment on cell decisions
Tissue/organ level models, for example:
•Population dynamics, the formation and maintenance of tissue
architecture
•Cell-stroma interactions, for example epithelial-mesenchymal
transition
•Mechanobiology/biomechanical models
Body systems level models, for example:
•Pharmacokinetic/pharmacodynamic modeling
•Physiologically based pharmacokinetic modeling for applications
in systems pharmacology
•Modeling environmental influences and long-term clinical events
(for example, overall survival).
Box 1. Different levels at which modeling of biological
systems has been carried out
Wolkenhauer et al. Genome Medicine 2014, 6:21 Page 2 of 3
http://genomemedicine.com/content/6/3/21This list is necessarily incomplete and serves here to
indicate the progression of modeling efforts, with an in-
creasing diversity of approaches available, a tendency to
combine approaches in modeling workflows, and clear
indications of an increasing number of successful case
studies.
Multiscale modeling serves as a ‘macroscope’ that inte-
grates the evidence available at different levels of a par-
ticular system’s structural and functional organization
while enabling us to zoom in on the details where neces-
sary. For experimental models, it is almost certain that
not all parameters influencing a disease will be known
and, often, known parameters cannot be influenced and/
or measured. This makes it difficult to unambiguously
identify the mechanisms at work in diseases. Modeling
therefore makes an epistemic contribution by allowing in
silico (simulation) experiments under defined conditions,aiding the exploration of hypotheses by explicitly ac-
counting for the uncertainty that arises from biological
complexity.
Central to these efforts are data of high quality that
are quantitative, derived from reproducible studies and
that cover multiple levels of functional and structural
organization, generated from a range of experimental
systems. A particularly important challenge is to enable
clinical validation of models and simulations.
Actions required for multiscale modeling
Our discussions have identified a number of
recommendations.
Short-term recommended actions
With respect to data collection and modeling, in the
short-term (that is, the next one to two years), the fol-
lowing actions should be given priority:
1. Exploitation of existing data as a starting point for
multiscale modeling. This will lead to the
identification of gaps in data and, therefore, in
understanding of underlying mechanisms, and will
improve the targeted generation of new data that
can be exploited for quantitative models.
2. Development of standard operating procedures and
quality standards for the systematic collection of
quantitative data and information. This will enable
models to be built and validated for specific
applications.
3. Definition of test scenarios and proof-of-concept
studies.
4. Identification of required standards and ontologies
(for example, a markup language and ontology for
individual-based models) for models and data reposi-
tories in systems medicine.
5. Development of concepts for dedicated modeling
workflows for the integration of data and models.
These research actions would benefit from initiatives
that coordinate training and encourage collaborations
between clinicians and multiscale modelers; support co-
ordinated clinical projects to bring clinicians, modelers
and biologists together; and run sandpits for modelers
and clinicians to develop proposals to address clinical
questions, to improve mutual understanding and provide
realistic expectations for such collaborations.
Medium-term recommended actions
In the medium-term of two to three years, attention
should focus on the provision of suitable information
technology infrastructure and the development of stan-
dards, such as:
Wolkenhauer et al. Genome Medicine 2014, 6:21 Page 3 of 3
http://genomemedicine.com/content/6/3/211. The development of computational tools and
algorithms for efficient multiscale simulations.
2. The development of mathematical formalism to
analyze and compare multiscale models such as
parameter estimation, sensitivity analysis,
identifiability analysis and image analysis.
3. Support for the development of workflows for
modeling, including computational tools that
support data management, model construction and
analysis.
4. Methods to integrate different physical phenomena,
including those of mechanotransduction, electrical,
mechanical and chemical origin.
5. Methods to investigate the interplay between the
environment, cell behavior and cell fate.
In the medium term, the goal is to develop multiscale
models of normal physiology and disease. The develop-
ment of these models should be driven by clinical ques-
tions to provide the basis for clinical validation in the
longer term. This will require technologies that promote
and facilitate the collaboration of multidisciplinary
teams.
Longer-term recommended actions
On a timescale beyond four years, the focus should be
on the application and validation of multiscale models in
the clinic. To this end, it is desirable to:
1. Enhance the formation of small-scale networks
focused on specific clinical needs, possibly clustered
in a larger integrated project (longer timescale).
2. Have a funding model in place for small groups of
two to three partners (one to two modeling
postdocs, one to two experimental postdocs plus
consumables and travel between laboratories). Such
a funding model has been applied in systems biology
to good effect, with smaller groups being integrated
through networks.
The main priority for systems medicine is the develop-
ment of computational models that integrate data and
knowledge from the clinics and basic science (in vitro
and animal model experiments) and are applicable to
individual patients. The aim is to derive a mechanistic
understanding of pathologies, prophylaxy and support of
therapy optimization. This requires the development of
concepts, methods and tools that support the integration
of organizational levels to develop interfaces between
the computational and mathematical frameworks used
in systems medicine.
Competing interests
The authors declare that they have no competing interests.Acknowledgements
The workshop that launched the collaborative writing of this paper was
funded through CASyM, a Coordinating Action for the Implementation of
Systems Medicine across Europe (www.casym.eu). CASyM is an initiative of
the European Commission, Health Directorate DG Research & Innovation,
under grant agreement #305033.
Author details
1Department of Systems Biology & Bioinformatics, University of Rostock,
18051 Rostock, Germany. 2Stellenbosch Institute for Advanced Study,
Wallenberg Research Centre at Stellenbosch University, 7600 Stellenbosch,
South Africa. 3European Institute for Systems Biology & Medicine, CNRS
Institute of Biological Sciences, Claude Bernard University, Université de Lyon,
69100 Villeurbanne, France. 4Sanofi Pasteur, 69280 Marcy l’Etoile, France.
5INRIA Paris-Rocquencourt, Laboratoire Jacques-Louis Lions and Institut Uni-
versitaire du Cancer, UPMC, 75005 Paris, France. 6INRIA Paris - Rocquencourt,
Domaine de Voluceau-Rocquencourt, 78153 Le Chesnay, France. 7UPMC
University of Paris 06, CNRS UMR 7598, Laboratoire Jacques-Louis Lions,
75005 Paris, France. 8Centre for Information Services & High Performance
Computing, Technical University Dresden, 01062 Dresden, Germany.
9Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, 04109
Leipzig, Germany. 10Department of Chemistry, University College London,
London WC1H 0AJ, UK. 11Department of Mathematical Sciences, Politecnico
di Torino, 10129 Torino, Italy. 12Wolfson Centre for Mathematical Biology,
University of Oxford, Oxford OX2 6GGUK, UK. 13Institute of Applied
Mathematics, Interdisciplinary Centre for Scientific Computing and
BIOQUANT, University of Heidelberg, 69120 Heidelberg, Germany.
14Computational Systems Biology, Bayer Technology Services GmbH, 51368
Leverkusen, Germany. 15Institute of Applied Microbiology, RWTH Aachen,
52056 Aachen, Germany. 16IMACI, Instituto de Matemática Aplicada a la
Ciencia y la Ingeniería, Universidad de Castilla la Mancha, 13071 Ciudad Real,
Spain. 17NuMed, Ecole Normale Supérieure de Lyon, 69342 Lyon, France.
18Technology Development, Bayer Technology Services GmbH, 51368
Leverkusen, Germany. 19Joint Research Centre for Computational
Biomedicine, RWTH Aachen University, 52056 Aachen, Germany. 20Kroto
Research Institute, University of Sheffield, Sheffield S10 2TN, UK. 21In Silico
Oncology Group, Institute of Communication and Computer Systems,
National Technical University of Athens, 15773 Zografou, Greece. 22ASD
GmbH Rostock, 18059 Rostock, Germany. 23School of Mathematical Sciences
and Department of Computer Science, University of Oxford, Oxford OX1
3QD, UK.
Published: 21 March 2014
References
1. Coordinating Action Systems Medicine (CASyM) [https://www.casym.eu/]
2. Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O: The road
from systems biology to systems medicine. Pediatr Res 2013, 73:502–507.
3. Hood L, Balling R, Auffray C: Revolutionizing medicine in the 21st century
through systems approaches. Biotechnol J 2012, 7:992–1001.
doi:10.1186/gm538
Cite this article as: Wolkenhauer et al.: Enabling multiscale modeling in
systems medicine. Genome Medicine 2014 6:21.


Anna Marciniak Czochra

Ignacio Ramis Conde

PREZIOSI, LUIGI

PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)

RWTH Publications

Heidelberger Dokumentenserver

COMMENTEnabling multiscale modeling in systemsmedicineOlaf Wolkenhauer1,2*, Charles Auffray3, Olivier Brass4, Jean Clairambault5,6,7, Andreas Deutsch8, Dirk Drasdo6,7,9,Francesco Gervasio10, Luigi Preziosi11, Philip Maini12, Anna Marciniak-Czochra13, Christina Kossow1,Lars Kuepfer14,15, Katja Rateitschak1, Ignacio Ramis-Conde16, Benjamin Ribba17, Andreas Schuppert18,19,Rod Smallwood20, Georgios Stamatakos21, Felix Winter1,22 and Helen Byrne23From reactions in cells to organ physiologySystems medicine is an interdisciplinary approach thatintegrates data from basic research and clinical practiceto improve our understanding and treatment of diseases.Systems medicine can be seen as a further developmentof systems biology and bioinformatics towards applica-tions of clinical relevance. The term 'systems' refers tosystems approaches, emphasizing a close integration ofdata generation with mathematical modeling [1-3]. The(mal)functioning of the human body is a complex process,characterized by multiple interactions between systemsthat act across multiple levels of structural and functionalorganization - from molecular reactions to cell-cell interac-tions in tissues to the physiology of organs and organsystems. Over the past decade, we have gained detailedinsights into the structure and function of molecular, cellu-lar and organ-level systems, with technologies playing animportant role in the generation of data at these differentscales.Under the umbrella of systems biology, workflows ofdata-driven modeling and model-driven experimentationhave led to the development of computational models thatdescribe processes at all levels, including gene regulatorynetworks, signal transduction pathways and metabolicnetworks, cell populations, structured tissues, pharmaco-kinetic and pharmacodynamic models that analyze drugor vaccine action at the whole organism level, and phar-macogenomic models of disease risk and drug and vaccineexposure at the patient-population level.Major challenges exist for the development of quanti-tative and predictive models that span the gap betweencell-level biochemical models and organism-level phar-macokinetic and pharmacodynamic models. One hurdleis the technical difficulty associated with generatingsufficiently comprehensive quantitative datasets for largenumbers of system variables, across different levels oforganization. Another major challenge is the develop-ment of efficient tools that can handle the wide varietyof available data and identify relevant data subsets fromexisting repositories. Overcoming these hurdles will leadto the development of models that can be used inclinical practice while accounting for the uncertainty inthe data.Another important challenge for systems medicine isthe integration of this knowledge across the relevantlevels of organization. Large, long-term research initia-tives, like the Virtual Physiological Human, the VirtualLiver or the Human Brain Project, are aiming to developcomprehensive, computational representations of organsand organ systems. Here, we focus on opportunities forcomparatively small interdisciplinary collaborations be-tween clinicians and modelers who are targeting specificquestions of clinical relevance. In the projects that weenvisage, modeling is not the final goal; rather, it is a toolthat can be used to advance understanding of the system,to develop more directed experiments in the laboratoryand, ultimately, to generate testable predictions to enableimproved therapies and prophylaxes.Multiscale modelingThe fields of theoretical and mathematical biology havepioneered the development of mathematical and compu-tational models of biological systems. Systems biologyhas contributed workflows for data-driven modeling andmodel-driven experimentation to the life sciences. Takentogether, these provide a considerable body of experi-ence for modeling at different levels (Box 1).* Correspondence: olaf.wolkenhauer@uni-rostock.de1Department of Systems Biology & Bioinformatics, University of Rostock,18051 Rostock, Germany2Stellenbosch Institute for Advanced Study, Wallenberg Research Centre atStellenbosch University, 7600 Stellenbosch, South AfricaFull list of author information is available at the end of the article© 2014 Wolkenhauer et al.; licensee BioMed Central Ltd. The licensee has exclusive rights to distribute this article, in anymedium, for [6/12] months following its publication. After this time, the article is available under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.Wolkenhauer et al. Genome Medicine 2014, 6:21http://genomemedicine.com/content/6/3/21This list is necessarily incomplete and serves here toindicate the progression of modeling efforts, with an in-creasing diversity of approaches available, a tendency tocombine approaches in modeling workflows, and clearindications of an increasing number of successful casestudies.Multiscale modeling serves as a ‘macroscope’ that inte-grates the evidence available at different levels of a par-ticular system’s structural and functional organizationwhile enabling us to zoom in on the details where neces-sary. For experimental models, it is almost certain thatnot all parameters influencing a disease will be knownand, often, known parameters cannot be influenced and/or measured. This makes it difficult to unambiguouslyidentify the mechanisms at work in diseases. Modelingtherefore makes an epistemic contribution by allowing insilico (simulation) experiments under defined conditions,aiding the exploration of hypotheses by explicitly ac-counting for the uncertainty that arises from biologicalcomplexity.Central to these efforts are data of high quality thatare quantitative, derived from reproducible studies andthat cover multiple levels of functional and structuralorganization, generated from a range of experimentalsystems. A particularly important challenge is to enableclinical validation of models and simulations.Actions required for multiscale modelingOur discussions have identified a number ofrecommendations.Short-term recommended actionsWith respect to data collection and modeling, in theshort-term (that is, the next one to two years), the fol-lowing actions should be given priority:1. Exploitation of existing data as a starting point formultiscale modeling. This will lead to theidentification of gaps in data and, therefore, inunderstanding of underlying mechanisms, and willimprove the targeted generation of new data thatcan be exploited for quantitative models.2. Development of standard operating procedures andquality standards for the systematic collection ofquantitative data and information. This will enablemodels to be built and validated for specificapplications.3. Definition of test scenarios and proof-of-conceptstudies.4. Identification of required standards and ontologies(for example, a markup language and ontology forindividual-based models) for models and data reposi-tories in systems medicine.5. Development of concepts for dedicated modelingworkflows for the integration of data and models.These research actions would benefit from initiativesthat coordinate training and encourage collaborationsbetween clinicians and multiscale modelers; support co-ordinated clinical projects to bring clinicians, modelersand biologists together; and run sandpits for modelersand clinicians to develop proposals to address clinicalquestions, to improve mutual understanding and providerealistic expectations for such collaborations.Medium-term recommended actionsIn the medium-term of two to three years, attentionshould focus on the provision of suitable informationtechnology infrastructure and the development of stan-dards, such as:Molecular modeling, for example•Drug/vaccine target interactions•Prediction of pathogenic mutations•Protein-protein interactionsModeling of subcellular processes, for example:•Signaling pathways, gene regulatory networks, metabolicnetworks•Drug target prediction by sensitivity analysis•Linking signaling pathways to phenotypic changes (for example,regression models)Individual-cell or cell-based models, for example:•Behavior of individual cells in their microenvironment•Cell-cell interactions•Control of cell material properties and shape by molecularmechanisms, and the effect of physical properties of cells andtheir environment on cell decisionsTissue/organ level models, for example:•Population dynamics, the formation and maintenance of tissuearchitecture•Cell-stroma interactions, for example epithelial-mesenchymaltransition•Mechanobiology/biomechanical modelsBody systems level models, for example:•Pharmacokinetic/pharmacodynamic modeling•Physiologically based pharmacokinetic modeling for applicationsin systems pharmacology•Modeling environmental influences and long-term clinical events(for example, overall survival).Box 1. Different levels at which modeling of biologicalsystems has been carried outWolkenhauer et al. Genome Medicine 2014, 6:21 Page 2 of 3http://genomemedicine.com/content/6/3/211. The development of computational tools andalgorithms for efficient multiscale simulations.2. The development of mathematical formalism toanalyze and compare multiscale models such asparameter estimation, sensitivity analysis,identifiability analysis and image analysis.3. Support for the development of workflows formodeling, including computational tools thatsupport data management, model construction andanalysis.4. Methods to integrate different physical phenomena,including those of mechanotransduction, electrical,mechanical and chemical origin.5. Methods to investigate the interplay between theenvironment, cell behavior and cell fate.In the medium term, the goal is to develop multiscalemodels of normal physiology and disease. The develop-ment of these models should be driven by clinical ques-tions to provide the basis for clinical validation in thelonger term. This will require technologies that promoteand facilitate the collaboration of multidisciplinaryteams.Longer-term recommended actionsOn a timescale beyond four years, the focus should beon the application and validation of multiscale models inthe clinic. To this end, it is desirable to:1. Enhance the formation of small-scale networksfocused on specific clinical needs, possibly clusteredin a larger integrated project (longer timescale).2. Have a funding model in place for small groups oftwo to three partners (one to two modelingpostdocs, one to two experimental postdocs plusconsumables and travel between laboratories). Sucha funding model has been applied in systems biologyto good effect, with smaller groups being integratedthrough networks.The main priority for systems medicine is the develop-ment of computational models that integrate data andknowledge from the clinics and basic science (in vitroand animal model experiments) and are applicable toindividual patients. The aim is to derive a mechanisticunderstanding of pathologies, prophylaxy and support oftherapy optimization. This requires the development ofconcepts, methods and tools that support the integrationof organizational levels to develop interfaces betweenthe computational and mathematical frameworks usedin systems medicine.Competing interestsThe authors declare that they have no competing interests.AcknowledgementsThe workshop that launched the collaborative writing of this paper wasfunded through CASyM, a Coordinating Action for the Implementation ofSystems Medicine across Europe (www.casym.eu). CASyM is an initiative ofthe European Commission, Health Directorate DG Research & Innovation,under grant agreement #305033.Author details1Department of Systems Biology & Bioinformatics, University of Rostock,18051 Rostock, Germany. 2Stellenbosch Institute for Advanced Study,Wallenberg Research Centre at Stellenbosch University, 7600 Stellenbosch,South Africa. 3European Institute for Systems Biology & Medicine, CNRSInstitute of Biological Sciences, Claude Bernard University, Université de Lyon,69100 Villeurbanne, France. 4Sanofi Pasteur, 69280 Marcy l’Etoile, France.5INRIA Paris-Rocquencourt, Laboratoire Jacques-Louis Lions and Institut Uni-versitaire du Cancer, UPMC, 75005 Paris, France. 6INRIA Paris - Rocquencourt,Domaine de Voluceau-Rocquencourt, 78153 Le Chesnay, France. 7UPMCUniversity of Paris 06, CNRS UMR 7598, Laboratoire Jacques-Louis Lions,75005 Paris, France. 8Centre for Information Services & High PerformanceComputing, Technical University Dresden, 01062 Dresden, Germany.9Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, 04109Leipzig, Germany. 10Department of Chemistry, University College London,London WC1H 0AJ, UK. 11Department of Mathematical Sciences, Politecnicodi Torino, 10129 Torino, Italy. 12Wolfson Centre for Mathematical Biology,University of Oxford, Oxford OX2 6GGUK, UK. 13Institute of AppliedMathematics, Interdisciplinary Centre for Scientific Computing andBIOQUANT, University of Heidelberg, 69120 Heidelberg, Germany.14Computational Systems Biology, Bayer Technology Services GmbH, 51368Leverkusen, Germany. 15Institute of Applied Microbiology, RWTH Aachen,52056 Aachen, Germany. 16IMACI, Instituto de Matemática Aplicada a laCiencia y la Ingeniería, Universidad de Castilla la Mancha, 13071 Ciudad Real,Spain. 17NuMed, Ecole Normale Supérieure de Lyon, 69342 Lyon, France.18Technology Development, Bayer Technology Services GmbH, 51368Leverkusen, Germany. 19Joint Research Centre for ComputationalBiomedicine, RWTH Aachen University, 52056 Aachen, Germany. 20KrotoResearch Institute, University of Sheffield, Sheffield S10 2TN, UK. 21In SilicoOncology Group, Institute of Communication and Computer Systems,National Technical University of Athens, 15773 Zografou, Greece. 22ASDGmbH Rostock, 18059 Rostock, Germany. 23School of Mathematical Sciencesand Department of Computer Science, University of Oxford, Oxford OX13QD, UK.Published: 21 March 2014References1. Coordinating Action Systems Medicine (CASyM) [https://www.casym.eu/]2. Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O: The roadfrom systems biology to systems medicine. Pediatr Res 2013, 73:502–507.3. Hood L, Balling R, Auffray C: Revolutionizing medicine in the 21st centurythrough systems approaches. Biotechnol J 2012, 7:992–1001.doi:10.1186/gm538Cite this article as: Wolkenhauer et al.: Enabling multiscale modeling insystems medicine. Genome Medicine 2014 6:21.Wolkenhauer et al. Genome Medicine 2014, 6:21 Page 3 of 3http://genomemedicine.com/content/6/3/21

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