|
|
|||||||||||||||||
Course Catalog 2013-2014
SGN-56006 Laboratory course in Information Technology for Health and Biology, 5 cr |
Additional information
Course Webpage with additional information:
http://www.cs.tut.fi/~sanchesr/SGN-56006/index.htm
Suitable for postgraduate studies
Person responsible
Ilkka Korhonen, Andre Sanches Ribeiro, Olli Yli-Harja
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
|
|
|
|
|
|
|
|
Requirements
There are 4 projects. The first two projects are mandatory. Afterwards, the student has to select 2 of the 4 remaining projects. To complete the course, the student is required to perform these projects, and present a written report and an oral presentation. The grade, from 0 to 5, will be the average of the grades of each project.
Completion parts must belong to the same implementation
Learning Outcomes
After this course, the student will be able to: - implement computational methods to solve problems involving measurement data. - perform data acquisition from raw data. - independently search for information and available methods to solve practical problems. - present results, methods and conclusions in written and oral reports.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Project 1: Modeling and Simulation of Genetic Circuits. (Mandatory) | The aim of this project is to construct stochastic models of gene circuits, study their behavior in various conditions and evolve them in a given environment using a simple evolutionary algorithm. | |
| 2. | Project 2: Physiological signal project (filtering, detection, classification of physiological signal). (Mandatory) | Details of the project will be announced later. | |
| 3. | Project 3: Cell to Cell Phenotypic Diversity in Escherichia Coli. | The aim of this project is to, from time lapse confocal microscopy images of Escherichia coli cells, strain DH5-alpha Pro, analyze various phenotypic traits at the single cell level as well as at the population level, temporally. | |
| 4. | Project 4: Analysis of gene expression data from qPCR using a mathematical model. | The aim of this project is to, analyze gene expression pattern from the data coming from quantitative PCR using different mathematical models. For this exercise, raw data obtained by a qPCR instrument will be provided. | |
| 5. | Project 5: Physiological signal analysis project (pre-processing and analysis in frequency domain, time-frequency representations). | Details of the project will be announced later. | |
| 6. | Project 6. Medical image analysis project. | Details of the project will be announced later. |
Instructions for students on how to achieve the learning outcomes
The grade, from 0 to 5, will be the average of the grades of each project. The grade of a project is assessed from the written report and the oral presentation. The first two projects are mandatory. Afterwards, the student has to select 2 of the 4 remaining projects.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Other online content | A.S. Ribeiro | Detailed description of each project and the necessary tasks to be performed. | No | English |
Additional information about prerequisites
Basic knowledge of biology/systems biology and processing of biological signals are recommended. Skills to use Matlab are required to complete some project works.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
There is no equivalence with any other courses
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| In this course, the student will learn to: - implement computational methods to solve problems involving measurement data. - perform data acquisition from raw data. - independently search for information and available methods to solve practical problems. - present results, methods and conclusions in written and oral reports. For important information, please visit the course webpage (http://www.cs.tut.fi/~sanchesr/SGN-56006/index.htm) |