图 书 馆
在合成生物学中使用序贯蒙特卡罗方法作为设计工具Using sequential Monte Carlo approaches as a design tool in synthetic biology |
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| 课程网址: | http://videolectures.net/licsb2010_barnes_usm/ |
| 主讲教师: | Chris Barnes |
| 开课单位: | 伦敦帝国学院 |
| 开课时间: | 2010-05-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In many engineering contexts it is easy to state what we want but hard to achieve our desired outcomes. The more potential solutions exist, the harder it becomes to identify optimal solutions. Here we show how this problem can be approached in an approximate Bayesian computation framework. Our approach has the advantage that it builds on the powerful Bayesian model selection formalism, includes sensitivity and robustness analysis at no extra cost, and flexibly incorporates diverse design objectives. We illustrate the performance of this approach in the context of bacterial two-component systems (TCS). These systems enable prokaryotes (and some simple eukaryotes and plants) to sense their environments and adapt their internal state to changing circumstances. We present a detailed analysis of orthodox and unorthodox TCSs and show how we can rationally construct TCS that show robust and optimal response characteristics to different stimuli encountered during bacterial infections or in biotechnological (e.g. biofuels production and bioremediation) applications. We conclude by elaborating on the connections between our approach and maximum-entropy procedures and the advantages over traditional engineering strategies. |
| 关 键 词: | 机器学习; 蒙特卡罗方法; 计算机科学; 计算生物学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-04：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 57 |