Gave that talk during my research visit of Carole Goble's group in Manchster.

1. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S E Ssimulation experiment management system
Evolution of Computational
Models in Systems Biology
what’s the matter – what’s there – what’s next
MARTIN SCHARM
Department of Systems Biology & Bioinformatics, University of Rostock
http://sems.uni-rostock.de
Research Stay in Manchester, 2015
May, 2015 Evolution of Computational Models | Martin Scharm 1

2. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
Improving the Management of Simulation Studies in Computational Biology
Martin Scharm, Vivek Garg, Srijana Kayastha, Martin Peters, Dagmar Waltemath
Events
S E Ssimulation experiment management system
https://sems.uni-rostock.de
de.NBI Infrastructure
We will provide data management and support for systems biol-
ogy projects, with a focus on provenance and reproducibility of
experimental and modelling results. de.NBI:SYSBIO is part of a
large German Network for Bioinformatics Infrastructure.
WE ARE HIRING!
Ø
p-cyclin
cdc2-p
p-cyclin
cdc2
cdc2k
p-cyclin
cdc2k-P
ØcyclinØ
total
cdc2
SBGN-ED
SBGN is a markup language to describe mod-
els and exchange information about biological sys-
tems graphically. We will further develop meth-
ods and tools for SBGN-compliant visualisation of
model-related information. WE ARE HIRING!
CombineArchive Toolkit
Sharing in silico experiments is essential for the advance of research
in computational biology. The COMBINE archive is a digital container
format to easen the management of numerous files and to enable the
exchange of reproducible modelling results. We developed the Combin-
eArchive Toolkit, consisting of a library, a web interface and a desktop
application. It support scientists in creating, exploring, modifying, and
sharing COMBINE archives.
2MT
2MT is our web based platform to
demonstrate the capabilities of SEMS-
related tools. It exemplifies how our
model management solutions can be
used in existing tools.
Models as graphs
The increasing diversity of model-related data that is nec-
essary to perform a simulation study leads to new chal-
lenges in model storage. We developed a concept for
graph-based storage of models and model-related data.
Graphs reflect the models’ structure much better, enable
linking of model-related data on the storage layer, and al-
low for an efficient search.
Masymos
Containing SBML- and CellML models,
linked semantic annotations (e.g., from bio-
ontologies), simulation descriptions, graph-
ical representations and other available
types of model-related data, out graph
database Masymos can now be queried for
complete simulation experiments.
Morre
Our retrieval engine for models applies In-
formation Retrieval techniques to retrieve
relevant models from MASYMOS. The pro-
posed ranking and retrieval techniques fo-
cus on the processing of model meta-
information.
Ontology of Differences
Changes in model versions are manifold
and appear on different layers. We de-
velop an ontology of differences occurring in
model versions. It will support researchers
in analysing differences, discovering typical
changes, summarising major changes and
providing statistics.
Version Control for
Computational Models
With thousands of models available, a framework to track the differences
between models and their versions is essential to compare and combine
models. Focusing on SBML and CellML, we developed an algorithm to
accurately detect and describe differences between versions of a model
with respect to (i) the models’ encoding, (ii) the structure of biological
networks, and (iii) mathematical expressions.
version x-1 version x version x+1
C
D
H E
A
B
C D E
F
G
A
B
D H E
F
G BiVeS
Armed with our method for difference detec-
tion, BiVeS is able to detect and communicate
the differences in computational models. The
differences are exported in several machine-
and human-readable formats, ideally suited to
be integrated in other tools.
BudHat
BudHat showcases how BiVeS improves
the understanding of a model’s changes.
BudHat calls BiVeS for the comparison
two versions of a computational model and
displays the obtained results in the web
browser.
VW Summer School, March 9-13, 2015
During the 2015 Whole Cell summer school we aim to
develop a standard-compliant, open version of the whole-
cell model. Eleven tutors and 48 students will hack and
code, model and simulate, layout and annotate the whole-
cell model using openly available software and COM-
BINE standards. This event is funded by the Volkswagen
Stiftung.
HARMONY, April 19-23, 2015
HARMONY is a hackathon-type meeting of the COMBINE Community,
with a focus on development of the standards, interoperability and infras-
tructure. Instead of general discussions or oral presentations, the time is
devoted to hands-on hacking and interaction between people focused on
practical development of software and standards. The HARMONY 2015
is located at the Leucorea Wittenberg and it is hosted by the groups of
Falk Schreiber and Dagmar Waltemath.
m n
Workshop on Reproducible and Citable Data
and Models, September 14-16, 2015
Computational biologists and experimentalists will learn
about standards, citable data, about how to make scien-
tific results sustainable, available through open reposito-
ries, and about how to find and reuse other people’s works
in a mixture of lectures and hands-on sessions. The work-
shop is funded by the ERASYS-APP program.
Ron Henkel
Dagmar Waltemath
Martin ScharmMartin Peters
Vivek Garg
Srijana Kayastha
-
Ad sponsored by Dagmar Waltemath

3. Models evolve over time.
Example: C. acetobutylicum
metabolic and gene regulation network model in C. acetobutylicum
Haus et. al. 2011
May, 2015 Evolution of Computational Models | Martin Scharm 3

4. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Equations and calculations for fermentations of butyric acid bacteria
1984 in Biotechnology and bioengineering
May, 2015 Evolution of Computational Models | Martin Scharm 4

5. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Shinto
Kinetic modeling and sensitivity analysis of acetone–butanol–ethanol production
2007 in Journal of biotechnology
May, 2015 Evolution of Computational Models | Martin Scharm 4

6. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Shinto
COSMIC I
A systems biology approach to investigate the effect of pH-induced gene regulation
on solvent production by Clostridium acetobutylicum in continuous culture
2011 in BMC systems biology
May, 2015 Evolution of Computational Models | Martin Scharm 4

7. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Shinto
COSMIC I
COSMIC II
A shift in the dominant phenotype governs
the pH-induced switch in C. acetobutylicum
2013 in Applied Microbiology and Biotechnology
May, 2015 Evolution of Computational Models | Martin Scharm 4

8. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Shinto
COSMIC I
COSMIC II
???
May, 2015 Evolution of Computational Models | Martin Scharm 4

9. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Shinto
COSMIC I
COSMIC II
???
some title
20?? in some journal
May, 2015 Evolution of Computational Models | Martin Scharm 4

10. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Shinto
COSMIC I
COSMIC II
???
May, 2015 Evolution of Computational Models | Martin Scharm 4

11. Models evolve over time.
Example: C. acetobutylicum
time
internal Version Release
Papoutsakis
Shinto
COSMIC I
COSMIC II
???
May, 2015 Evolution of Computational Models | Martin Scharm 4

12. Model Evolution
Case Study: Cell Cycle
Romond1999 Goldbeter1991 Tyson1991
Novak1993 Marlovits1998
Novak1995Novak1997
Moriya2011
Calzone2007
Novak1998
Tyson2001 Chen2000
Chen2004 Queralt2006 Vinod2011
Novak2001
Sriram2007
Csikasz-Nagy2006
Hatzimanikatis1999
Swat2004
Qu2003
Ciliberto2003
mammalian R-point
(G1/S-transition)
Srividyha2006
Mitotic exit
Budding Yeast
Not in biomodels database
minimal oscillator
Novak1995
Gardner1998
Ibrahim2008a Ibrahim2008b
Ibrahim2009
Mitosis
Obeyesekere1997
Conradie2010
Obeyesekere1999
Bai2003
Aguda&Tang1999
Novak2004
Haberichter2007
(G-Phase)
May, 2015 Evolution of Computational Models | Martin Scharm 5

13. Model Evolution
Case Study: Cell Cycle
Romond1999 Goldbeter1991 Tyson1991
Novak1993 Marlovits1998
Novak1995Novak1997
Moriya2011
Calzone2007
Novak1998
Tyson2001 Chen2000
Chen2004 Queralt2006 Vinod2011
Novak2001
Sriram2007
Csikasz-Nagy2006
Hatzimanikatis1999
Swat2004
Qu2003
Ciliberto2003
mammalian R-point
(G1/S-transition)
Srividyha2006
Mitotic exit
Budding Yeast
Not in biomodels database
minimal oscillator
Novak1995
Gardner1998
Ibrahim2008a Ibrahim2008b
Ibrahim2009
Mitosis
Obeyesekere1997
Conradie2010
Obeyesekere1999
Bai2003
Aguda&Tang1999
Novak2004
Haberichter2007
(G-Phase)
May, 2015 Evolution of Computational Models | Martin Scharm 5

14. Model Evolution
Case Study: Cell Cycle
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Modeling the cell division...
John J Tyson, 1991
May, 2015 Evolution of Computational Models | Martin Scharm 6

15. Model Evolution
Case Study: Cell Cycle
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Cdc25Cdc25∗
Wee1 Wee1∗
Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte
Bela Novak and John J Tyson, 1993
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Modeling the cell division...
John J Tyson, 1991
May, 2015 Evolution of Computational Models | Martin Scharm 6

16. Model Evolution
Case Study: Cell Cycle
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Cdc25Cdc25∗
Wee1 Wee1∗
Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte
Bela Novak and John J Tyson, 1993
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Cdc25Cdc25∗
Mik1 Mik1∗
Wee1 Wee1∗
Quantitative analysis of a molecular model of mitotic control in Fission yeast
Bela Novak and John J Tyson, 1995
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Modeling the cell division...
John J Tyson, 1991
May, 2015 Evolution of Computational Models | Martin Scharm 6

17. Model Evolution
Case Study: Cell Cycle
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Cdc25Cdc25∗
Wee1 Wee1∗
Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte
Bela Novak and John J Tyson, 1993
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Cdc25Cdc25∗
Mik1 Mik1∗
Wee1 Wee1∗
Quantitative analysis of a molecular model of mitotic control in Fission yeast
Bela Novak and John J Tyson, 1995
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Modeling the cell division...
John J Tyson, 1991
Cyclin
Cdc2 P
Cyclin
Cdc2 P
Cdc25Cdc25∗
Mik1 Mik1∗
Wee1 Wee1∗
Cyclin
Cdc2 P
Rum1
Modeling the control of DNA replication in fission yeast
Bela Novak and John J Tyson, 1997
May, 2015 Evolution of Computational Models | Martin Scharm 6

18. BiVeS
Difference Detection
A r C
B
D
cycE/cdk2
RB/E2F
RB-Hypo
free E2F
A r
B
C
D
E s
RB/E2F
RB-Hypo
free E2F
cycE/cdk2
RB-Phos
A
r
B
C
D
A
r
B
C
D
E
s
Biochemical Model Version Control System
• compares models encoded in standadised
formats (currently: and )
• maps hierarchically structured content
• constructs a diff (in XML format)
• is able to interprete this diff
<XML>
Diff
moves
product of r: C
deletes
product of r: B
inserts
species: E
product of r: E
reaction s
</XML>
mapping
diff construction
May, 2015 Evolution of Computational Models | Martin Scharm 7

19. BiVeS identifies differences
in versions of computational models
C
D
H E
communicationpb
A
B
C D E
F
G
A
B
D H E
F
G
evaluationpb
A
B
C D E
F
G
A
B
D H E
F
G
propagationpb
A
B
C D E
F
G
A
B
D H E
F
G
id=“species1” id=“species1”
initialmappingpb
A
B
C D E
F
G
A
B
D H E
F
G
model version 1
model version 2
list of species list of reactions
C + D E D + H E
pre-processingpb
Identifying, Interpreting, and Communicating
Changes in XML-encoded Models of Biological
Systems
Scharm et. al. 2015, under revision at BIOINFORMATICS
May, 2015 Evolution of Computational Models | Martin Scharm 8

20. BiVeS identifies differences
in versions of computational models
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<bives type="fullDiff">
<update/>
<delete>
[...]
<node id="6" oldChildNo="1"
oldParent="../listOfModifiers[1]"
oldPath="../listOfModifiers[1]/modifierSpeciesReference[1]"
oldTag="modifierSpeciesReference" triggeredBy="5"/>
<attribute id="7" name="species"
oldPath="../modifierSpeciesReference[1]"
oldValue="cdc2" triggeredBy="6"/>
</delete>
<insert>
[...]
<node id="12" newChildNo="2"
newParent="../listOfReactants[1]"
newPath="../listOfReactants[1]/speciesReference[2]"
newTag="speciesReference"/>
<attribute id="13" name="species"
newPath="../speciesReference[2]"
newValue="cdc2" triggeredBy="12"/>
<attribute id="14" name="metaid"
newPath="../speciesReference[2]"
newValue="_818337" triggeredBy="12"/>
</insert>
[...]
</bives>
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Scharm et. al. 2015, under revision at BIOINFORMATICS
May, 2015 Evolution of Computational Models | Martin Scharm 9

21. Indeed!
models change over time
BIOMD0000000001
BIOMD0000000021
BIOMD0000000041
BIOMD0000000061
BIOMD0000000081
BIOMD0000000101
BIOMD0000000121
BIOMD0000000141
BIOMD0000000161
BIOMD0000000182
BIOMD0000000202
BIOMD0000000222
BIOMD0000000242
BIOMD0000000262
BIOMD0000000282
BIOMD0000000302
BIOMD0000000322
BIOMD0000000342
BIOMD0000000362
BIOMD0000000382
BIOMD0000000402
BIOMD0000000422
BIOMD0000000442
BIOMD0000000462
Apr 05
Jun 05
Jul 05
Jan 06
Jun 06
Oct 06
Jan 07
Jun 07
Sep 07
Mar 08
Aug 08
Dec 08
Mar 09
Jun 09
Sep 09
Jan 10
Apr 10
Sep 10
Apr 11
Sep 11
Feb 12
May 12
Aug 12
Dec 12
Jun 13
Nov 13
0
5
10
50
100
500
1000
5000
14157
Novak
1993
12 updates 20 moves 80 inserts 20 deletes
q q
q
q q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q q q
q
q
q
020004000600080001000012000
AveragenumberofnodesintheXMLdocument
Apr05
Jun05
Jul05
Jan06
Jun06
Oct06
Jan07
Jun07
Sep07
Mar08
Aug08
Dec08
Mar09
Jun09
Sep09
Jan10
Apr10
Sep10
Apr11
Sep11
Feb12
May12
Aug12
Dec12
Jun13
Nov13
02004006008001000
Numberofmodels;Avgnumberofspecies/reactions/parameters/rulespermodel
q
q
q
q
q
q
avg number of nodes per model
number of models per release
avg number of species per model
avg number of reactions per model
avg number of parameters per model
avg number of rules per model
q
q
q
q
q
q
q
Scharm et. al. 2015, under revision at BIOINFORMATICS
May, 2015 Evolution of Computational Models | Martin Scharm 10

22. COMBINE 2015
In Utah
COMBINE 2015: October 12-16 in Salt Lake City
Day 1: invited talks by Fred Adler, Mike Hucka, Richard Normann, Sharon
Crook, Miriah Meyer, Huaiyu Mi, Tara Deans, and Anil Wipat
Days 2-5: contributed talks and discussions.
Chris J. Myers (University of Utah) COMBINE 2015 October 12-16
Ad sponsored by COMBINE initiative
May, 2015 Evolution of Computational Models | Martin Scharm 11

23. Why am I here?
The HERMES program
Provenance for models of biological systems
Woche
1 2 3 4 5 6 7 8 9 10
Setup / Aligning Visions
Learning & evaluating tools/workflows
Identifying research gap
Doing a Case Study
Developing a concept and a schedule
Start writing a proposal
Planning future
May, 2015 Evolution of Computational Models | Martin Scharm 12

24. Why am I here?
The unofficial goal
Linked Data
ResearchObjects
Workflows
Networking
Software
Engineering
Project
Managment
Bookmaking Provenance
Proposal
Coordination
Beer
May, 2015 Evolution of Computational Models | Martin Scharm 13

25. RO vs CA
Clear the “mess”
Research Object
-vs-
Combine Archive
May, 2015 Evolution of Computational Models | Martin Scharm 14

26. Comparison of Archives
Means to transfer simulation studies
ZIP Docker VBox CombineArchive ResearchObject
Fancy Icon? ×
Aspect 2 × × ~ ~
Aspect 3 × ~ × ~
Aspect 4 × ~ ×
Aspect 5 ? ? ? ? ?
May, 2015 Evolution of Computational Models | Martin Scharm 15

27. RO – CA
Clear the “mess”
Research Object Combine Archive
May, 2015 Evolution of Computational Models | Martin Scharm 16

28. Combine Archive
What are already doing.
internet
internet
SEARCHubiquitin
internet
RESULTS
EXPORT
EXPORT
EXPORT
EXPORT
Query database
for annotations, persons,
simulation descriptions
Retrieve information
about models, simulations,
figures, documentation
Export simulation study
as COMBINE archive
Download archive
and open the study
with your favourite
simulation tool
Open archive in CAT
to modify its contents and
to share it with others
internet
API Commincations
enrich your studies
with simulation results
Simulate a Study
with just a single click
Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit.
Scharm et. al., DM4LS @ BTW 2015, Hamburg, GER
May, 2015 Evolution of Computational Models | Martin Scharm 17

29. Research Object
What are already doing.
May, 2015 Evolution of Computational Models | Martin Scharm 18

30. RO – CA
All singing, all dancing
graphics taken from openclipart.org
May, 2015 Evolution of Computational Models | Martin Scharm 19

31. BiVeS identifies differences
in versions of computational models
What?
× Who? When? Why? How? ... ??
May, 2015 Evolution of Computational Models | Martin Scharm 20

32. Functional Curation Project
The WebLab
A call for virtual experiments: Accelerating the scientific process.
Cooper et. al., Progress in biophysics and molecular biology (2014).
The Cardiac Electrophysiology Web Lab.
Cooper et. al., submitted to Circulation: Arrhythmia and Electrophysiology
May, 2015 Evolution of Computational Models | Martin Scharm 21

33. Functional Curation Project
The WebLab
A call for virtual experiments: Accelerating the scientific process.
Cooper et. al., Progress in biophysics and molecular biology (2014).
The Cardiac Electrophysiology Web Lab.
Cooper et. al., submitted to Circulation: Arrhythmia and Electrophysiology
Workshop on the Web Lab
10th & 11th September 2015
Department of Computer Science, University of Oxford
http://s.binfalse.de/fcworkshop
Ad sponsored by Jonathan Cooper
May, 2015 Evolution of Computational Models | Martin Scharm 21

34. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S E Ssimulation experiment management system
Thank you!
Dagmar Waltemath, Ron Henkel, Martin Peters, Olaf Wolkenhauer
@SemsProject
http://sems.uni-rostock.de
An initiative of:
THE
SYSTEMS MEDICINE
WEB HUB
Pictures: Wavebreakmedia Ltd
www.systemsmedicine.net
@sysmednet
sysmednet
The Systems Medicine Web Hub
systemsmedicine.net
www.systemsmedicine.net/feed
SYSTEMS
MEDICINE NET
Promote your research
Find jobs and experts
Disseminate your news
Increase your visibility
Beinformed
EVENTS
REPORTS
RESOURCES
PROJECTSPOSITIONS
Ad sponsored by Olaf Wolkenhauer
May, 2015 Evolution of Computational Models | Martin Scharm 22

35. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S E Ssimulation experiment management system
References
• Sylvia Haus, Sara Jabbari, Thomas Millat, Holger Janssen, Ralf-Jorg Fischer, Hubert Bahl, John
King, Olaf Wolkenhauer: A systems biology approach to investigate the effect of pH-induced gene
regulation on solvent production by Clostridium acetobutylicum in continuous culture. BMC
Systems Biology, 2011, 5:10.
• E.T. Papoutsakis: Equations and calculations for fermentations of butyric acid bacteria.
Biotechnology and bioengineering, 1984, 26:174–187.
• H. Shinto, Y. Tashiro, M. Yamashita, G. Kobayashi, T. Sekiguchi, T. Hanai, Y. Kuriya, M. Okamoto,
K. Sonomoto: Kinetic modeling and sensitivity analysis of acetone–butanol–ethanol production.
Journal of biotechnology, 2007, 131:45–56.
• Thomas Millat, Holger Janssen, Graeme J. Thorn, John R. King, Hubert Bahl, Ralf-Jörg Fischer,
Olaf Wolkenhauer: A shift in the dominant phenotype governs the pH-induced metabolic switch of
Clostridium acetobutylicumin phosphate-limited continuous cultures. Applied Microbiology and
Biotechnology, 2013, 97:6451-6466.
• John J Tyson: Modeling the cell division cycle : cdc2 and cyclin interactions. Proceedings of the
National Academy of Sciences, 1991, 88:7328–7332.
May, 2015 Evolution of Computational Models | Martin Scharm 23

36. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S E Ssimulation experiment management system
• B Novak, J J Tyson: Numerical analysis of a comprehensive model of M-phase control in
Xenopus oocyte extracts and intact embryos. Journal of Cell Science, 1993, 106:1153-1168.
• Bela Novak, John J. Tyson: Quantitative analysis of a molecular model of mitotic control in fission
yeast. Journal of Theoretical Biology, 1995, 173:283–305.
• B Novak, J J Tyson: Modeling the control of DNA replication in fission yeast. Proceedings of the
National Academy of Sciences of the United States of America, 1997, 94:9147–52.
• M Scharm, O Wolkenhauer, D Waltemath: Identifying, Interpreting, and Communicating Changes
in XML-encoded Models of Biological Systems. Under review at BIOINFORMATICS.
• M Scharm, D Waltemath: Extracting reproducible simulation studies from model repositories
using the CombineArchive Toolkit. In proceedings of the Workshop on Data Management for Life
Sciences (DMforLS 2015) at BTW 2015, Hamburg, GER.
• J Cooper, M Scharm, G Mirams: The Cardiac Electrophysiology Web Lab. Submitted to
Circulation: Arrhythmia and Electrophysiology.
• J Cooper, JO Vik, D Waltemath: A call for virtual experiments: Accelerating the scientific process.
Progress in biophysics and molecular biology, 2014, 117:1, 99–106.
May, 2015 Evolution of Computational Models | Martin Scharm 24

37. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S E Ssimulation experiment management system
Further Literature
• S Bechhofer, D De Roure, M Gamble, C Goble, I Buchan: Research objects: Towards exchange
and reuse of digital knowledge. In: The Future of the Web for Collaborative Science (FWCS
2010); 26 Apr 2010-26 Apr 2010; Raleigh, NC, USA.
• S Bechhofer, I Buchan, D De Roure, P Missier, J Ainsworth, J Bhagat, P Couch, D Cruickshank,
M Delderfield, I Dunlop, M Gamble, D Michaelides, S Owen, D Newman, S Sufi, C Goble: Why
linked data is not enough for scientists. Future Generation Computer Systems, 2013, 29(2),
599-611.
• FT Bergmann, R Adams, S Moodie, J Cooper, M Glont, M Golebiewski, M Hucka, C Laibe, AK
Miller, DP Nickerson, BG Olivier, N Rodriguez, HM Sauro, M Scharm, S Soiland-Reyes, D
Waltemath, F Yvon, NL Novère: COMBINE archive and OMEX format: one file to share all
information to reproduce a modeling project. BMC bioinformatics, 2014, 15(1), 369.
May, 2015 Evolution of Computational Models | Martin Scharm 25