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Martin Scharm

The document discusses the evolution of computational models over time. It provides the example of models of the cell cycle control network in the bacterium Clostridium acetobutylicum that have been developed and expanded on since 1984. The models have become more detailed over time, incorporating new experimental findings and insights. The document also discusses how models of the core cell cycle oscillator have evolved since 1991 with the addition of new molecular components and regulatory interactions based on studies in different organisms.

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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
RO vs CA Clear the “mess” Research Object -vs- Combine Archive May, 2015 Evolution of Computational Models | Martin Scharm 14
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
RO – CA Clear the “mess” Research Object Combine Archive May, 2015 Evolution of Computational Models | Martin Scharm 16
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
Research Object What are already doing. May, 2015 Evolution of Computational Models | Martin Scharm 18
RO – CA All singing, all dancing graphics taken from openclipart.org May, 2015 Evolution of Computational Models | Martin Scharm 19
BiVeS identifies differences in versions of computational models What? × Who? When? Why? How? ... ?? May, 2015 Evolution of Computational Models | Martin Scharm 20
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
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
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
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
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
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

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Group meeting in Manchester.

  • 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