系統生物學特論(Special Topic on Systems Biology)

科號：09820EE 641400	科目名稱：系統生物學特論	開課教授：陳博現老師 藍忠昱老師 汪上曉老師 莊永仁老師 謝文萍老師
上課地點：資電館104	時間：星期一 晚上7:00
評分標準：Class participation and discussion 60% Presentation 40%

課程大綱

一、課程說明(Course Description)

本課程將從生科,化工,資訊,電機,統計的整合性角度探討 Systems Biology 的發展與研究. 課程自2003年開始後，已培訓許多來自各種不同教育背景的年輕研究人員，一起挑戰與生物、醫學相關的重要課題。此課程著重於結合理論推演及實驗量化驗證的訓練, 亦是[系統生物學]領域的一門重點課程。師資方面則集合來自生命科學、電機資訊、化工系統、與生物統計等系所的專任教授，以執行中的跨領域研究計畫為例，與學員一起學習探討生物體內的複雜機制及其如何調控因環境變化所誘發的反應與運作。

內容包括：

1. Introduction of Systems Biology

2. Biological Networks and Biodatabases

3. Gene Regulatory Networks

4. Micro organisms

5. Host-pathogen interaction

6. Computational physiology

7. Biostatistics

8. Network representation

9. Analysis modeling and identification

10. Model manipulation and control

11. Robustness control

12. Bio-model simulation

13. Mapping and modeling tools

14. Data integration

15. Database construction and mining

參考書目

None

現任助教：王禹超 Email: lancewang@moti.ee.nthu.edu.tw

生物系統計算(Computation of Biosystems)

科號：09820EE 641600	科目名稱：生物系統計算	開課教授：陳博現老師
上課地點：資電館107	時間：星期三 早上10:00~12:00 星期四 早上 10:00~11:00
評分標準：Homework 70% Presentation 30%

課程大綱

一、課程說明(Course Description)

This course present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in Matlab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.

內容包括：

I. REPRESENTATION

2. Representation in populations of neurons

3. Extending population representation

4. Temporal representation in spiking neurons

5. Population-temporal representation

II. TRANSFORMATION

6. Feed-forward transformations

7. Analyzing representation and transformation

8. Dynamic transformations

9. Statistical inference and learning Appendices

參考書目

Neural Engineering - Computation, Representation, and Dynamics in Neurobiological Systems
Chris Eliasmith and Charles H. Anderson

教學歷史資料