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: Timothy Hancock
Title: Machine Learning Approaches for Computation Systems Biology
Institute: 京都大学化学研究所バイオインフォマティクスセンター
Partner Institute of your choice: Theoretical Biophysics Laboratory, Institute of Biology, HumboldtUniversität zu Berlin
Duration of your choice: 21st October 2011 to 8th January 2012
Report:The Theoretical Biophysics Laboratory is located within Humboldt-Universität in the center of Berlin.
The group of Professor Edda Klipp is quite large with approximately 30 regular members (6 staff, 10
post-doctoral fellows, ~ 16-20 students) and many visitors at all times of the year. Within thelaboratory each person is attached to one or many projects with its own source of funding. The
projects objectives range from the experimental “wet lab” biological approaches, through to combined
experimental and computational methodologies and to finally pure theoretical and computational
systems biology model creation and validation.
The laboratory has many events during theweek beginning with the weekly progressmeeting where everybody over all projects
provides a quick five minute summary of
their progress. Following, every week one
member of laboratory is chosen a one hourseminar providing a more detailed progressof their's and the projects progress. In
conjunction with this laboratory specific
events, there are separate discipline specificeducational interest groups, such as“ComputationalSystemsBiology”,“Information Theory in Systems Biology”
and “Molecular Systems Biology”, which Group photo of everyone at the Theoretical Biophysics
hold regular weekly seminars where guest
Laboratory in November 2011.
speakers present a tutorial style review of
their current research. Through the attendance of these weekly events I was able to learn the researchobjectives currently being studied in Professor Klipp's laboratory and the processes which they follow
to meet these objections.
During my stay I presented an overview of my research twice two different laboratories both related tothe ITP project. Firstly to Professor Klipp's laboratory in the first week of my arrival. In thispresentation I reviewed the topics related to my pathway mining through metabolic networks. This
project constitutes the major part of my research, of which the most recent contribution was in part
inspired by papers published with Professor Klipp's laboratory by Dr. Thomas Handorf (Basler et al.
2008, Christian 2009). From my presentation I received many useful comments from Dr. Handorf andothers. The comments focused on the applicability of my proposed pathway mining approach to their
specific networks and experimental purposes. These comments raised some interesting avenues forfuture research in extending my current approach to more accurately capture the complexities inherent
within biological networks, such as the action of protein complex, phosphorylation processes andinhibition reactions.
1. Basler G., Nikoloski, Z., Ebenhöh, O. & Handorf, T., “Biosynthetic potentials from species-specificmetabolic networks.” (2008), Genome Informatics 20, 135-48.
2. Christian, N., May, P., Kempa, S., Handorf, T. & Ebenhöh, O. “An integrative approach towards
completing genome-scale metabolic networks.” (2009), Molecular BioSystems, 5, 1889-1903.
The second presentation I gave to the laboratory of Professor Ernst-Walter Knapp in the Freie
Universität of Berlin. The focus of Professor Knapp's laboratory is more in the informatics direction,
specifically chemoinformatics, than Professor Klipp's laboratory and as such I presented topics related
to constructing network classification models. The goal of this work is to use a combination of aknown network structure and experimental observations in order to identify network features whichstrongly relate to specific experimental conditions. From my presentation I was able have an
informative discussion with Professor Knapp and his students on topics related constructing classifiers
which directly encode the known structure of the biological or chemical problems. It was interesting
to observe that many of the same problems we faced in our network classification project were also
encountered within Professor Knapp's laboratory, although different solutions were employed toovercome these problems.
My research during my stay in Professor Klipps laboratory focused on two related application toGaussian Process (GP) latent factor models for microarray data. The first model is for multi-task GP
regression and the second was for latent protein profile estimation. I selected these two models after
discussions with members of Professor Klipp's laboratory and realizing the need for structured
predictive models for unobservable network features. Whilst in Berlin I derived and implementedthese two approaches and applied them in a biological setting. During this process I discovered key
limitations to these benchmark methods which have to be overcome before they are applicable to large
scale systems biological modeling. These are two major problems which I quickly describe here:
1. Convergence: both algorithms employ a simple gradient decent optimization strategy and assuch often gets caught in sub-optimal solutions.
2. Multi co-linearity: there is likely to large amounts of highly correlated variables in biologicalnetworks. In fact these are the targets of our latent factor approach. However when thecorrelation get too strong the methods fail.
There are well known methods within the machine learning literature to overcome these problems.
However this requires customization of these algorithms which in turn offers an interesting avenue forfuture research. In addition to these more technical modifications, I discussed with members ofProfessor Klipp's Laboratory ways to better represent the biology within these models. Thesediscussions centered on current methods of modeling known biophysical properties within differential
equation models. During my stay I read several papers on these topics with an overall research
objective to encoding enzyme kinetic equations into GP kernels which I can then include in GP
regression and latent factor models.
On the non-research side Berlin is a famous old European city with a very interesting history.
Additionally during my stay the annual Christmas markets were held which are a German tradition
and allowed me to try traditional German food. In the photographs below there is a selection of someof my favorite Berlin tourist attractions.

The Brandenburg Gate

The road to Schloss Charlottenburg in fall.

Lions guarding the Babylonian Ishtar Gate in the Ice skating rink at the Alexanderplatz Christmas
Pergamon museum.
markets during the night.

Alexanderplatz Christmas markets during the day. The last remaining stretch of the Berlin Wall.