In laboratory for quantitative biology @IIS, UTokyo, we are working on understanding and prediction of various biological information processing by combining quantitative data analysis and mathematical modeling.
The successful candidate(s) of this opportunity are supposed to engage in the following project:
(1) Understanding and prediction of molecular recognition mechanisms in mammalian olfaction, adaptive immunity, and other biological systems
By receiving, recognizing and processing extremely complex and diverse chemical information from the environment, an organism or a cell selects and induce complex responses or differentiation pathways (signalling systems), which are prominent in odor recognition (olfactory system) and antigen recognitions (immune system). In addition, we now know that intracellular reactions and signalling pathways may be more promiscuous and less specific than what people presumed, which makes us reconsider the idea that reaction networks are tightly wired like electric circuits.,
We have been theoretically working on the optimality of odor sensing and exploratory behavior based on optimal filtering and optimal control theory, and also on the understanding of adaptive immune learning by network-based reinforcement learning.
Concurrently, we are developing data-scientific methods to reveal the diversity of immune receptors and the homeostasis of immune cell populations based on next-generation sequencing data and quantitative measurements of immune cell numbers.
In this project, by extending these works, we aim to understand the information processing in molecular recognition where information is conveyed by a mixture of various types of chemicals (odors, antigens, and signaling molecules).
We are going to construct theories and data analysis methods for predicting multidimensional input-output relationships of chemical mixtures. The olfactory and immune systems project distributed information acquired by a huge variety of receptors onto a high-dimensional space using random projections, from which chemical substances are classified. We are trying to understand the roles of the network structure specific to chemical substance recognition and to develop new methods for data analysis.
These theoretical predictions will be validated using simultaneous imaging of parallel signaling systems, comprehensive olfactory receptor response data, and sequence data on immune receptor repertoire.
As for other applications, we also use our methods to design new sensory array systems or odor mixtures for food tech.
This research will be conducted as part of JST CREST project and of a joint research project with Ajinomoto Co.
keywords: chemical reaction theory, chemical reaction network theory, multi-terminal information theory, compressed sensing, active sensing, chemoinformatics, graph neural network, network analysis, time-series analysis, information bottleneck, next-generation sequencing, GPCR, olfaction, immuno-repertoire, sensor array, computational algebra.
Learn more about laboratory for quantitative biology @IIS, UTokyo from https://research.crmind.net.
Informal enquiries may be made to Tetsuya J. Kobayashi (tetsuya ### sat.t.u-tokyo.ac.jp. replace ### with @)