whole cell volkswagen summer school - a sems project

http://sems.uni-rostock.de

Markus Wolfien [email protected]

Standardising Karr’s Whole Cell Model

24.02.2014 Research Seminar

http://sems.uni-rostock.de

Karr‘s Whole Cell Model of Mycoplasma genitalium

2

• First in silico Whole Cell Model

• Why M.genitalium ? Bacterium with the smallest known genome – 525 genes

European consortium determined the transcriptome (Guell et al., 2009),

proteome (Kuhner et al.,2009), and metabolome (Yus et al., 2009)

• Benefits ?

(1) Describes the life cycle of a single cell at molecular level

(2) Accounts the specific function of every annotated gene product

(3) Predicts a wide range of observable cellular behaviors

• Effort ? Based on 900 publications, 116 m.files and more than 1,900

experimentally observed parameters

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http://sems.uni-rostock.de 3


• Workflow and Model overview




Figure 1: Whole cell model architecture (Karr et al. 2012a)




16 state variables

that drive the

model (m)

28 submodules

processes that occur in

the cell (n)

Figure 2: Whole cell model architecture and summary of state variables and submodules(Karr et al. 2012a)



• Challenge:

• Whole model is encoded in Matlab-file format, and is therefore not accessible

to the broad modeler community

• To run, edit and work with the model you have to be comfortable with Matlab

language and the complex structure of the model – thousands of source code

lines more or less sufficiently described

• Limited reusability



• Challenge:







• Solution:



• Challenge:







• Solution:

Standardisation of the Whole Cell Model


SEMS is getting involved

• Aim:

Encoding the Whole Cell Model into the current xml standards formats

• Free and open standard with widespread software support and a

community of users and developers

• Can represent many different classes of biological phenomena


SEMS is getting involved

• Aim:

Encoding the Whole Cell Model into the current xml standards formats

• Free and open standard with widespread software support and a

community of users and developers

• Can represent many different classes of biological phenomena

SBML – Systems Biology Markup Language (Hucka et al. 2003)

SED-ML – Simulation Experiment Description Markup

Language (Waltemath et al. 2011)

GO – Gene Ontology and other bio-ontologies

(Ashburner et al. 2000)


Volkswagen Summer School

“Combining standards for today’s models”


Volkswagen Summer School

“Combining standards for today’s models”

• Funding application sent in January 2014

• Anticipated date for summer school in late September 2014 for one week in

Rostock

• Around 60 participants

• STANDARD DEVELOPERS: Experts from the standard-developing

COMBINE community and tool developers

• COORDINATORS: Scientific research staff who is familiar with the

COMBINE standards and infrastructure

• RESEARCH TEAMS: Modelers from different bioinformatics and

systems biology areas, young researchers


Volkswagen Summer School – Goals

(1) Providing a reusable, standards-compliant version of the Whole Cell Model

to the community

(2) Teaching young researchers how to take advantage of scientific results

encoded in standard formats

(3) Demonstrating the power of standard formats

(4) Establishing a roadmap for standard development in systems biology based

on the gaps and needs identified during the summer school


Volkswagen Summer School – Involved Researchers

Dagmar Waltemath

University of Rostock

Jonathan Karr

Stanford University

Falk Schreiber

IPK Gatersleben

Frank Bergmann

University of Heidelberg

Nicolas Le Novère

Babraham Institute

BioModels Database Team

EMBL-EBI

Wolfram Liebermeister

Humboldt University


Volkswagen Summer School – Involved Researchers

Dagmar Waltemath

University of Rostock

Jonathan Karr

Stanford University

Falk Schreiber

IPK Gatersleben

Frank Bergmann

University of Heidelberg

Nicolas Le Novère

Babraham Institute

BioModels Database Team

EMBL-EBI

Wolfram Liebermeister

Humboldt University

… and you?


Volkswagen Summer School – Scientific Gain

• For the community:

• Open access to a reusable version of the modules that form the Whole

Cell Model

• Visibility of the COMBINE community, promotion of the use of

standards, better understanding of needs and aims of the different partners

(modellers, developers)

• Training of young modelers who will carry this knowledge into the

different labs and thereby increase the acceptance of standard formats

• For the standard developers:

• Evaluation of current standards

• Promotion of standard development to modelers, in particular young

researchers


Volkswagen Summer School – Further Information

• SEMS – Volkswagen Summer School Project

https://sems.uni-rostock.de/workshops/volkwagen-summer-school-

project/

• Google group for information about the current status of the Summer school

https://groups.google.com/forum/#!forum/wholecell-symposium

• Google Project to share source codes of the Whole Cell Model pre Summer

School – GIT repository

http://code.google.com/p/wholecell/

https://sems.uni-rostock.de/workshops/volkwagen-summer-school-project/









https://groups.google.com/forum/



http://code.google.com/p/wholecell/


Volkswagen Summer School – Further Information

Figure 3: Example of whole cell visualisation by http://wholecellviz.stanford.edu/

http://wholecellviz.stanford.edu/


References

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight,

S.S., Eppig, J.T., et al. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology

Consortium. Nat Genet 25, 25-29.

Guell, M., van Noort, V., Yus, E., Chen, W.H., Leigh-Bell, J., Michalodimitrakis, K., Yamada, T., Arumugam, M.,

Doerks, T., Kuhner, S., et al. (2009). Transcriptome complexity in a genome-reduced bacterium. Science 326,

1268-1271.

Hucka, M., Finney, A., Sauro, H.M., Bolouri, H., Doyle, J.C., Kitano, H., Arkin, A.P., Bornstein, B.J., Bray, D.,

Cornish-Bowden, A., et al. (2003). The systems biology markup language (SBML): a medium for

representation and exchange of biochemical network models. Bioinformatics 19, 524-531.

Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B., Jr., Assad-Garcia, N., Glass,

J.I., and Covert, M.W. (2012). A whole-cell computational model predicts phenotype from genotype. Cell

150, 389-401.

Kuhner, S., van Noort, V., Betts, M.J., Leo-Macias, A., Batisse, C., Rode, M., Yamada, T., Maier, T., Bader, S.,

Beltran-Alvarez, P., et al. (2009). Proteome organization in a genome-reduced bacterium. Science 326, 1235-

1240.

Waltemath, D., Adams, R., Bergmann, F.T., Hucka, M., Kolpakov, F., Miller, A.K., Moraru, II, Nickerson, D.,

Sahle, S., Snoep, J.L., et al. (2011). Reproducible computational biology experiments with SED-ML--the

Simulation Experiment Description Markup Language. BMC Syst Biol 5, 198.

Yus, E., Maier, T., Michalodimitrakis, K., van Noort, V., Yamada, T., Chen, W.H., Wodke, J.A., Guell, M., Martinez,

S., Bourgeois, R., et al. (2009). Impact of genome reduction on bacterial metabolism and its regulation.

Science 326, 1263-1268.