Difference between revisions of "User:Boris/Temp/APB"
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<td class="sc">Jan. 9 - 14</td> | <td class="sc">Jan. 9 - 14</td> | ||
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− | Computational Biology aims to bring biology from its descriptive beginnings to a truly predictive science, based on consistent and well understood principles. In the systems biology field of computational biology, we deal primarily with large-scale, cross-sectional data, its relationships and hierarchies. | + | <div class="table-intro-text">Computational Biology aims to bring biology from its descriptive beginnings to a truly predictive science, based on consistent and well understood principles. In the systems biology field of computational biology, we deal primarily with large-scale, cross-sectional data, its relationships and hierarchies.</div> |
* Course Organisation | * Course Organisation | ||
* [[CSB_Introduction|Introduction to Computational Systems Biology]] | * [[CSB_Introduction|Introduction to Computational Systems Biology]] | ||
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<td class="sc">Jan. 16 - 21</td> | <td class="sc">Jan. 16 - 21</td> | ||
<td class="sc"> | <td class="sc"> | ||
− | In principle, most of the data of interest to us is freely available on the Web, in public repositories. However, the number of databases and associated Web services is large and in constant flux and integrating the data has its own issues. The most important issue is the question of how to define ''function'' and make this concept computable; from this arises the central role that ontologies play in the field. | + | <div class="table-intro-text">In principle, most of the data of interest to us is freely available on the Web, in public repositories. However, the number of databases and associated Web services is large and in constant flux and integrating the data has its own issues. The most important issue is the question of how to define ''function'' and make this concept computable; from this arises the central role that ontologies play in the field.</div> |
* [[CSB_Web_tools|CSB on the Web: Databases and services]] | * [[CSB_Web_tools|CSB on the Web: Databases and services]] | ||
* [[Data_integration|Data integration]] | * [[Data_integration|Data integration]] | ||
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<td class="sc">Jan. 23 - 28</td> | <td class="sc">Jan. 23 - 28</td> | ||
<td class="sc"> | <td class="sc"> | ||
− | The large volume of data in any given systems biology experiment basically precludes the manual, gene-by-gene analysis of results. Questions arise regarding computational strategies for ''gene lists'', especially the statistical tools and strategies we have at our disposal, and a minimum set of programming and automation skills. | + | <div class="table-intro-text">The large volume of data in any given systems biology experiment basically precludes the manual, gene-by-gene analysis of results. Questions arise regarding computational strategies for ''gene lists'', especially the statistical tools and strategies we have at our disposal, and a minimum set of programming and automation skills.</div> |
− | * | + | * Lists of genes - a common outcome of high-throughput experiments |
** [[CSB Gene lists|Gene lists]] | ** [[CSB Gene lists|Gene lists]] | ||
** [[Enrichment|Enrichment analysis]] | ** [[Enrichment|Enrichment analysis]] | ||
** [[GSEA|The GSEA approach]] | ** [[GSEA|The GSEA approach]] | ||
− | + | * Statistics | |
− | + | ** [[Statistics|Principles of Statistics in molecular biology]] | |
− | + | ** [[R]] | |
− | + | ** [[Bioconductor]] | |
− | + | ** [[Clustering|Clustering and Classification]] | |
− | + | ** [[EDA|Exploratory Data Analysis]] | |
− | + | ** [[Data_mining|Data mining]] | |
− | + | * Programming | |
*** [[Informal_programming|Informal programming]] | *** [[Informal_programming|Informal programming]] | ||
*** [[IDE|Using an IDE (Integrated Development Environments)]] | *** [[IDE|Using an IDE (Integrated Development Environments)]] |
Revision as of 17:41, 27 January 2012
Timetable and syllabus
I n t r o d u c t i o n | ||||
Week | Date | Topics | Activities | Assignment |
1 | Jan. 9 - 14 |
Computational Biology aims to bring biology from its descriptive beginnings to a truly predictive science, based on consistent and well understood principles. In the systems biology field of computational biology, we deal primarily with large-scale, cross-sectional data, its relationships and hierarchies.
|
— | Assignment 1 |
D a t a | ||||
Week | Date | Topics | Activities | Assignment |
2 | Jan. 16 - 21 |
In principle, most of the data of interest to us is freely available on the Web, in public repositories. However, the number of databases and associated Web services is large and in constant flux and integrating the data has its own issues. The most important issue is the question of how to define function and make this concept computable; from this arises the central role that ontologies play in the field.
|
Quiz 1 | Assignment 2 |
3 | Jan. 23 - 28 |
The large volume of data in any given systems biology experiment basically precludes the manual, gene-by-gene analysis of results. Questions arise regarding computational strategies for gene lists, especially the statistical tools and strategies we have at our disposal, and a minimum set of programming and automation skills.
|
Quiz 2, project concept due | Assignment 3 |
"-omics" | ||||
Week | Date | Topics | Activities | Assignment |
4 | Jan. 30 - Feb. 3 |
Genome sequencing brought the first complete overview of the information underlying life; the associated concept of the transcriptome - a description of which portion of the genome is expressed at what time - is the most basic functional description of this information. |
Quiz 3 | Assignment 4 |
5 | Feb. 6 - 10 |
Many more holistic, or cross-sectional descriptions of the molecular construction of the cell are being worked on. |
Quiz 4, project outline due | Assignment 5 |
I n t e r a c t i o n s , P a t h w a y s a n d N e t w o r k s | ||||
Week | Date | Theme | Activities | Assignment |
6 | Feb. 13 - 17 |
Fundamentally, -omics descriptions provide us with lists of components. However, to understand how things work, we need to address the relationships of the components - how things are put together: the molecular blueprints. At its most basic level, this is the question of molecular interactions in the cell. A quantitative description of molecular interactions relies heavily on the mathematical discipline of graph theory. Cytoscape is a visualization and analysis platform for molecular interactions. |
Quiz 5 | Assignment 6 |
Feb. 20 - 24 | Reading Week - School closed | |||
7 | Feb. 27 - Mar. 2 |
Here we focus on the biological objects of "Interaction Science". |
Quiz 6 | Assignment 7 |
S y s t e m s | ||||
Week | Date | Topics | Activities | Assignment |
8 | Mar. 5 - 9 |
Several key networked systems exist in the cell. Here we discuss their paradigms, how they are constructed from experimental data and examples of how they can be organized in databases.
|
Quiz 7 | Assignment 8 |
9 | Mar. 12 - 16 |
Information theory has proven to be one of the cornerstones of biological analysis. A good example of its power and utility is the definition of systems in large-scale biological datasets. |
Quiz 8 | Assignment 9 |
10 | Mar. 19 - 23 |
Life is not static, only death is. Yet, the dynamic nature of biological systems is often overlooked. |
Quiz 9, project final submission due | Assignment 10 |
M o d e l s | ||||
Week | Date | Theme | Activities | Assignment |
11 | Mar. 26 - 30 |
The notion that computational biology will at some time become redictive is tied to the idea that we will be able to model the cell's systems. Many approaches exist, each with its own strengths and weaknesses; how to integrate such models, preferrably
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— | Assignment 11 |
12 | Apr. 2 - 6 | — | Assignment 12 |