| 研究課題/領域番号 |
20KK0160
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| 研究種目 |
国際共同研究加速基金(国際共同研究強化(B))
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| 配分区分 | 基金 |
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| 審査区分 |
中区分44:細胞レベルから個体レベルの生物学およびその関連分野
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| 研究機関 | 京都大学 |
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研究代表者 |
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| 研究分担者 |
安井 孝介 京都大学, 高等研究院, 特定助教 (10877640)
ABDALKADER Rodi 立命館大学, 立命館グローバル・イノベーション研究機構, 准教授 (20839964)
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| 研究期間 (年度) |
2020-10-27 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
18,850千円 (直接経費: 14,500千円、間接経費: 4,350千円)
2023年度: 3,770千円 (直接経費: 2,900千円、間接経費: 870千円)
2022年度: 7,280千円 (直接経費: 5,600千円、間接経費: 1,680千円)
2021年度: 7,280千円 (直接経費: 5,600千円、間接経費: 1,680千円)
2020年度: 520千円 (直接経費: 400千円、間接経費: 120千円)
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| キーワード | stem cell / cardiomyocyte / data / machine learning / regression / descriptors / iPS / cardiomyocite / statistics / small molecule / Stem cell / Cardiomyocite / Screening / Machine learning / DFT / Data-driven / ベイズ最適化 / 材料情報 / 機械学習 / 幹細胞分化増殖 / 再生医療 |
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| 研究開始時の研究の概要 |
In order to generate tissues for clinical purposes, cells of a specific type must be created. Such cells are usually generated by adding nutrients to a stem cell culture medium. This project will develop an AI-based platform for efficient identification nutrients for inducing the target cell state.
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| 研究実績の概要 |
This project aims to predict compounds for inducing cardiac differentiation of stem cells using data science. During FY2022, we created a series of regression models which can predict whether small organic molecules can be used as WNT inhibitors in a cardiac differentiation protocol. In FY2023, we extended this work as follows: (i) minor tweaking of model performance by introducing thermodynamically averaged descriptors; (ii) development of a new random sensitivity analysis technique for assessing model robustness to overtraining; (iii) prediction of several new compounds for use as WNT inhibitors; (iv) synthesis and experimental verification of one of these compounds when used in a real iPS stem cell differentiation protocol.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The project was delayed during FY2020 and FY2021 due to pandemic-related travel barriers, as well as the loss of a stem cell laboratory at our institute, which prevented us from acquiring data early in the project. However, after acquiring an existing data set in early FY2021 we could swiftly progress through the research plan, meeting all of the research goals at roughly the targeted times.
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| 今後の研究の推進方策 |
The project has been extended by one year due to a large amount of unused budget from FY2023 (around 1M yen). The additional year will be used write a paper and attend international conferences to share the results.
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