A realization of Aqueous Computing by using Droplet Microfluidic Devices and Molecular Reinforcement Learning.
| Project/Area Number |
14380157
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | TOKYO INSTITUTE OF TECHNOLOGY |
|---|
Principal Investigator |
YAMAMURA Masayuki Tokyo Institute of Technology, Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Professor, 大学院・総合理工学研究科, 教授 (00220442)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
FUJII Teruo University of Tokyo, Institute of Industrial Science, Associate professor, 生産技術研究所, 助教授 (30251474)
YAMAMOTO Takatoki University of Tokyo, Institute of Industrial Science, Assistant, 生産技術研究所, 助手 (20322688)
|
|---|
| Project Period (FY) |
2002 – 2004
|
| Project Status |
Completed (Fiscal Year 2004)
|
| Budget Amount *help |
¥16,900,000 (Direct Cost: ¥16,900,000)
Fiscal Year 2004: ¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2003: ¥5,700,000 (Direct Cost: ¥5,700,000)
Fiscal Year 2002: ¥7,900,000 (Direct Cost: ¥7,900,000)
|
|---|
| Keywords | DNA Computing / Molecular Memory / Reinforcement Learning / Droplet / Microfluidic Device / Aqueous Computing / NP-complete / PNA / マイクロリアクター / パプチド核酸 / 電気泳動 / アクエリアスコンピューティング |
|---|
| Research Abstract |
DNA computing has three issues to lead to breakthrough ; (a)achievement of a general purpose framework, (b)improvement of experimentally operational protocols, and (c)killer applications. The purpose of this research is to solve these issues by using droplet micro-fluidic devices ; (a)realizes a general purpose computational scheme ‘Aqueous computing', (b)automates its experimental protocols, and (c)proposes an algorithm for molecular reinforcement learning as a candidate of killer applications. As the result of research, we achieved to realize a micro-fluidic device to write PNAs replacing into DNA molecular memory by high efficiency, and also proposed an algorithm for a part of molecular reinforcement learning with LAMP method.
|
Report
(4 results)
Research Products
(39 results)
