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
Plan:
Any observed biological phenomenon is the result of interactions between extensive numbers ofunknown elements. Systems biology describes the process of identifying these elements and piecingthem together into a model which fully reconstructs the targeted observed phenomenon. There are
two major philosophies for building such models, mechanism-driven physical models and data-driven
statistical models. Both of these approaches have their own specific advantages. Physical models canaccurately represent the dynamics of a system in order to simulate real world observations, andstatistical models make explicit use of the available experimental data to uncover previously unknown
relationships.
Historically there has always been a clear separation in the applicability of these two approaches. For
example, physical models are used to model reaction systems because their known chemical
properties provide an ideal starting point for such analyses. In contrast, statistical models are betterused to represent protein interaction relationships whose specific physical properties are often
unknown, but their potential existence can be experimentally predicted with high throughput
experimentation.
Recently, separate advances in each field have had the effect of bringing these two distinct fields
closer together. These advancements have been prompted by the requirement to overcome theshortcomings of each individual approach. For example, numerous heuristic approaches now exist tooverlay experimental data into the standard physical model approach of flux balance analysis.
Additionally in an effort to elucidate a more detailed physical description from statistical models,
methods based on the stochastic differential equation representation of protein and kinetic activity arebeing investigated. These combined approaches are being complemented by rapid advances inexperimental procedures which are now capable of simultaneous observations over multiple domains,such as transcriptomic and metabolomic data measurements over the same time course.
However, the concept of combing physical and statistical methodologies remains relatively new.
Furthermore, many proposed methods are simple heuristics which make dramatic simplifications
within one domain to enable the imposition of new information within the previously establishedmethodologies. One common example of this simplification is the discretization of experimental data
to define an active set of reactions for input into a physical model. Whilst these heuristic approaches
provide a glimpse into the potential advantages that could be gained by combining these twomethodologies, their heuristic nature raises several concerns about the validity of the biology theyseek to represent. An ideal combination of these two philosophies is clearly one which does not
reduce the information within one domain in order to force it into the established methodology of theother. One methodology, stochastic differential equation modeling, provides such flexibility byallowing for a compact representation of the relationships that exist across both domains.

Research Objectives
The goal of this project is to investigate the potential of stochastic differential equation modeling toaccurately determine the physical mechanics of metabolic processes from the information contained
within high-throughput experimental data. Specifically, we aim to express a metabolic network as aset of three coupled differential equations. These equations model the metabolite, enzyme and mRNA
expression levels of a specified network. These equations are defined such that the concentration of a
specific metabolite is assumed to be dependent on the enzyme concentration of the producing andconsuming reactions. The enzyme concentration is subsequently assumed to be dependent on themRNA expression of the genes which encode the enzyme which catalyzes these reactions. From thesethree equations that define our network model, observations from two sources, metabolite
concentration and mRNA expression can be sourced by utilizing the data from simultaneoustranscriptomic and metabolomic experiments. Then the missing enzyme concentration function canbe inferred.
Our aim is to investigate the possibility of inferring the unknown enzyme concentration function byassuming it is a latent factor which transforms the expression signal of the reaction genes intometabolite concentration levels. Latent feature models are probabilistic methods which try to infer anunobservable underlying process which generates the observed data. For metabolic networks it isclear that one potential unobserved process involves the enzyme concentration levels which catalyze
the reactions. A latent factor model which can infer the enzyme concentration levels fromexperimental observation of real metabolic networks could potentially overcome the limitations
inherent in both physical and statistical approaches. This model would not only be of great interest tosystems and synthetic biologists but also to the broader biological community.
The proposed research objectives falls strongly in line with the large scale objectives of theTheoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin under the guidance ofProfessor Edda Klipp. Within the Theoretical Biophysics laboratory there is a wide variety ofresearch spanning disciplines from experimental biology, theoretical physics to applied computersciences. A unifying theme amongst these researchers is to combine their respective skills to systems
biology. As the goal of this research is to combine ideas from machine learning and physical networkmodels with an overall systems biology objective, a research visit to the Theoretical Biophysics
Laboratory would clearly be beneficial. There are three clear objectives to the research plan:
1. To learn the current ways research from computer science and biology are combining theirvarious skills and ideas to meet a systems biology objectives. Specifically, as my backgroundis in computer science my goals will be focused on the understanding the biological objectivesof the projects and understanding how machine learning algorithms are altered to meet theseobjectives.
2. To observe and participate in this collaborative process. This will be achieved by attending
seminars and presenting my own research. The overall goal is to receive input on my current
research and get new direction ideas such that future research better resembles the systems
biological objectives.
3. To investigate the possibility of future Japanese-German collaborative efforts. To achieve thisI will be discussing my ideas with the researchers in Professor Klipp's laboratory in an effort togarner interest in a combined effort towards the possible future research objectives.