若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 応募書類
Name: Sayaka Mizutani
Title: Analysis of drug targeting protein networks in dynamic models
Institute: Bioinformatics Center, Institute for Chemical Research, Kyoto University
Partner institute of your choice：Humboldt University in Berlin
Duration of your choice: January 7, 2014 – March 16, 2014
Report :Thanks to the financial support from the International Training Program (ITP) I was given
an opportunity to visit the Theoretical Biophysics Laboratory, Humboldt University in Berlin. TheProfessor Edda Klipp’s laboratory studies systems biology with a variety of biological interests
including yeast metabolism. The lab members are dedicated to one or a few topics in groups. There
were two weekly meetings, a group meeting where all members report their progresses, and aseminar where one person is given a whole hour to present his/her research. Several guests alsocame to the seminars. Among them the most interesting was a talk by Professor Dirk Brockmann
from the Institute for Theoretical Biology, Humboldt University in Berlin about his recent
publication on a world-wide epidemic model (Brockmann and Helbing, 2013). The laboratory also
set up special weeks for a selected project, where the project was divided in smaller modules sothat the members in a group work together to set up and archive a goal within the week. Professor
Edda Klipp’s laboratory provided one of the ideal research environments where postdoctoral
fellows and students had close communications through which technical and biological questions
were shared and solved cooperatively.
During my stay I collaborated with Dr. Marcus Krantz and Dr. Max Flöttmann in studying
the dynamic model of cancer-related signal transduction pathways and the effects ofcombinatorial use of molecular targeting drugs. In a bipartite Boolean network modelingframework rxncon, which has been developed in Professor Edda Klipp’s laboratory (Tiger et al.,2012 and Flöttmann et al., 2013), a dynamic network model is defined by update rules based on aset of reactions and a set of contingencies, resulting a bipartite graph representation of thenetwork (Figure 1). A reaction set is a list of reactions occurring between molecules (e.g. Receptor
tyrosine kinase A phosphorylates Enzyme B). A contingency set defines constraints for thereactions to occur (e.g. Receptor tyrosine kinase A phosphorylates Enzyme B constraint to thestate that Receptor tyrosine kinase A is bounded by Ligand C). The framework outperforms
classical Boolean network models in describing signal transduction pathways because it
implements protein states (e.g. phosphorylated) as a result of signaling events in a mechanisticway. Using the framework, it is possible to introduce specific experimental conditions in a network
such as the presence or absence of outside signals.
Regarding the treatment of drug resistance in cancer cells, combinatorial use of moleculartargeting drugs has been gaining more attention. For example, breast cancer cells acquire
resistance to trastuzumab, an ERBB2 inhibitor, by gaining mutation in PI3K or PTEN loss, which若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 応募書類

Figure 1 Regulatory network representation of breast cancer-related PI3K pathway.
can be cured by everolimus, a mTOR/PI3K pathway inhibitor. However, identification of ideal drug
combinations requires computational approaches. The bipartite Boolean network approach may
help to simulate and monitor the dynamic behaviors of network signal processing with or without
alterations in specific proteins. Based on a KEGG cancer pathway map (Kanehisa et al., 2014), weconstructed reaction lists and contingency lists. Final states of the pathway are defined based on
the concepts of “the hallmarks of cancer” defined in Hanahan and Weinberg, 2011). In this study,
three different cell states were examined; (1) normal cell, (2) cancer cell, and (3) cancer cell on
anti-neoplastic molecular targeting drug(s). Breast cancer and non-small cell lung cancer models
were constructed and examined with single and double mutations, and single use andcombinatorial use of the drugs (Table 1). The constructed models successfully described thereasonable outcomes in respect to the conditions given. The combinatorial use of drugs describedthe effect of double mutations resulting in the rescue of anti-cancer cellular responses (Figure 2).

Figure 2 State transitions of breast cancer model in phosphoinositide-3-kinase (PI3K) pathway.

若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 応募書類

Table 1. Types of cancers studied
Type of cancer

Related pathway

MutationDrug

Breast cancer

PI3K pathway

Mut 1 : ERBB2 overexpression

Drug 1 : Trastuzumab

Mut 2 : PI3K mutation/PTEN loss

Drug 2 : Everolimus

Non-small cell lung

PI3K pathway &Mut 1 : KRAS mutation

Drug 1 : Selumetinib

cancer

MAPK pathway

Mut 2 : PKB mutation

Drug 2 : MK2206

Future work will be to extend to a larger network to add more drugs and targets to search for new
combinations. One possibly interesting result would be how the integration of different pathways
reveal the effects of signals which could propagate through multiple pathways.
Acknowledgement :I appreciate Professor Hiroshi Mamitsuka and Associate Professor Susumu Goto for providing me
a great opportunity to participate in the ITP. I also thank Professor Edda Klipp for hosting me andsetting up a collaborative research. Dr. Marcus Krantz and Dr. Max Flöttmann always gave me
advices in technical and biological means. Dr. Mao Tanabe from the KEGG group helped me tointerpret the KEGG cancer pathway.
Reference：
Flöttmann, M., Krause, F., Klipp, E., & Krantz, M. Reaction-contingency based bipartite Boolean modeling, BMC
Syst Biol 7(1), 58 (2013).
Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.; Data, information, knowledge
and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).
Hanahan, D., and Weinberg, R.A., Hallmarks of cancer: the next generation, Cell. 144(5):646-74, (2011).
Tiger, C.F., Krause, F., Cedersund, G., Palmer, R., Klipp, E., Hohmann, S., Kitano, H., and Krants, M., A framework
for mapping, visualization and automatic model creation of signal-transduction networks, Molecular Systems
Biology, 8: 578 (2012).

Picture with Professor Edda Klipp

Presenting my result in a laboratory’s seminar