Objective

Statistical mechanical informatics (SMI) is an approach that applies statistical-mechanics techniques developed for many-body problems in natural science to information sciences. SMI has been extensively applied to various research fields of information sciences, including in particular information and communication theory, probabilistic inference, and combinatorial optimization problems, and has brought about novel advances in these fields.

From a formal point of view, the most significant advantage of the SMI approach would be that it facilitates our look at hierarchical organization of a complex system, on the basis of which reduction of many `microscopic' degrees of freedom is performed to obtain a `macroscopic' description of the system with a small number of parameters, which is in turn utilized to analyze the system microscopically. Hierarchy, reduction, and macroscopic description should also be essential in order to understand biological systems. These observations imply that the conceptual framework of SMI should be extensible to various research fields in bioinformatics, and new theoretical frameworks for bioinformatics will be established from the system science viewpoint. To advance research activities in these directions, however, interactions between SMI and bioinformatics are needed.

We organize this workshop in order to explore such new directions for SMI and bioinformatics through interactions between various research disciplines, and to incubate joint research initiatives. To accomplish these objectives, we invite leading researchers in the fields of DNA microarray analysis, brain science, medical engineering and mathematical biology, to exchange advanced findings on and to detail achievements in bioinformatics and statistical mechanics.

Date

September 13-16, 2009

Venue

Mielparque-Kyoto (map)
676-13, Higashi-no-Touindoori-Nanajyousagaru-Higashi-Shiokouji-machi, Shimogyou-ku, Kyoto　600-8216, Japan
#Just close to JR Kyoto station.

Registration

There is no registration fee.
We encourage prospective attendees to the workshop to register (although the registration is not mandatory).
Please send an e-mail to iw-smi2009-reg [- atmark -] sp.dis.titech.ac.jp with the following information:

• Name
• Affiliation
  
• E-mail address
• Whether or not to attend the Banquet on September 15 (Fee: 7,000 Japanese yen, to be paid on-site)

Banquet

Banquet will be held at "Hotel New Hankyu Kyoto" from 19:30 on September 15, 2009. Banquet fee is 7,000 Japanese yen (To be paid on site).

Programme

List of Speakers

To speakers: Information about paper submission
Organized session only: we do not accept submissions from non-invited speakers.

• Toru Aonishi (Tokyo Institute of Technology, Japan): Is the Langevin phase equation an efficient model for oscillating neurons?
• ACC Coolen (King's College London, UK): Generating functional analysis of complex formation and dissociation in large protein interaction networks
• Koji Hukushima (University of Tokyo, Japan): A statistical-mechanical study of evolution of robustness in noisy environment
• Shiro Ikeda (Institute of Statistical Mathematics, Japan): Capacity of a single spiking neuron
• Masato Inoue (Waseda University, Japan): Grouping preprocess for haplotype inference from SNP and CNV data
  
• Naoyuki Kamatani (RIKEN, Japan): Statistical challenges to genome-wide association studies
• Michael Laessig (University of Cologne, Germany): From nonequilibrium statistical mechanics to bioinformatics of evolutionary processes
• Faming Liang (Texas A&M University, USA): Monte Carlo dynamically weighted importance sampling for spatial models with intractable normalizing constants
• Yoichi Miyawaki (ATR, Japan): Visual image reconstruction from human brain activity using a combination of multi-scale local image decoders
• Remi Monasson (ENS, France): Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods
• Shigeyuki Oba (Kyoto University, Japan): Differential gene detection incorporating common expression patterns
• Masafumi Oizumi (University of Tokyo, Japan): A general framework for investigating how far the decoding process in the brain can be simplified
• Masato Okada (University of Tokyo, Japan): Statistical mechanics of attractor neural network models with synaptic depression
• Arno Onken (Technische Universität Berlin, Germany): A Frank mixture copula family for modeling higher-order correlations of neural spike counts
• Stefano Panzeri (Italian Institute of Technology, Italy): On the presence of high-order interactions among somatosensory neurons and their effect on information transmission
• Magnus Rattray (University of Manchester, UK):  Inference algorithms and learning theory for Bayesian sparse factor analysis
• Elad Schneidman (Weizmann Institute of Science, Israel): How fast can we learn maximum entropy models of neural populations?
• Matthias Seeger (MPI for Informatics and Saarbruecken University, Germany): Sparse Linear Models: Variational Approximate Inference and Bayesian Experimental Design
• Jun Sese (Ochanomizu University, Japan): Discovering large network motifs from complex biological network
• Nigel G. Stocks (University of Warwick, UK): The role of stochasticity in an information-optimal neural population code
• Ichiro Takeuchi (Nagoya Institute of Technology, Japan): Metric learning for DNA microarray data analysis
• Koji Tsuda (National Institute of Advanced Industrial Science and Technology, Japan): Dense module enumeration in biological Networks
• Si Wu (Institute of NeuroScience, Shanghai, China): Tracking dynamics of two-dimensional continuous attractor neural networks
• Ryo Yamada (Kyoto University, Japan): How to measure genetic heterogeneity
  

Organizing Committee

Masato Inoue (Waseda University)
Shin Ishii (Kyoto University) (Program Chair)
Yoshiyuki Kabashima (Tokyo Institute of Technology)(Vice-General Chair, DEX-SMI Head Investigator)
Masato Okada (University of Tokyo)(General Chair)
Kazuyuki Tanaka (Tohoku University)
Toshiyuki Tanaka (Kyoto University)

Sponsor

Grant-in-Aid for Scientific Research on Priority Area "Deepening and Expansion of Statistical Mechanical Informatics" (DEX-SMI)

In collaboration with

	The Physical Society of Japan (JPS)
	IEICE Engineering Sciences Society
	Japanese Neural Network Society
	Japanese Society for Bioinformatics