One of the most important emerging ICT application domain is biology, biotechnology and genetics.
The goal of this course is to introduce students into the subject of bioinformatics and simulation of biological systems by providing students with a clear picture of recent developments in this area and eliciting the need for innovative application development on topics such as drug discovery, personalized treatment, prevention and response to biological risks, etc.
The first part of the course includes an introduction to modern biology and genetics as well as recent developments in this area. Biological data types are presented, existing representation standards for their coding are described and their management requirements are elicited. Subsequently a detailed description of a range of bioinformatics algorithms for the analysis of such types of data (microarray or proteomic data) is introduced.
The second part of the course will focus on issues of modeling physiological systems. The objective is to enable students to gain a better understanding of how the principles of control theory, systems analysis, and model identification are used in physiological regulation. Several MATLAB examples will provide students with a hands-on approach for exploring modeling and analysis of biological control systems.
Discussions on the latest developments in system identification and nonlinear dynamical analysis will keep students up-to-date with recent bioengineering advances.
Have an understanding of the key features of this highly significant field, namely bioinformatics and its applications.
Upon successful completion of this course the student will:
- Have knowledge of tools and techniques for managing large volumes of multilevel biological data.
- Understand the role of algorithmic bioinformatics analysis or visualization of such data and become familiar with relevant tools, eg the BLAST (Basic Local Alignment Search Tool) algorithm and known variants, but also to develop other such biological data analysis algorithms.
- Understand the basic modeling principles and will be able to choose the most suitable methods for modeling specific physiological systems.
- Apply artificial intelligence methods for modeling physiological systems (eg cardiovascular, respiratory, nervous, metabolic system).
- Be able to co-operate with fellow students to create and present smart solutions to selected problems of modern biology and bioengineering.
Detailed Course Syllabus
The course consists of two main sections: a) Introduction to Bioinformatics and b) simulation of physiological systems.
The main areas that the course will cover in relation to the first section are:
- Introduction to Bioinformatics: Basic concepts, applications
- Bases, tools, open source software.
- Similarities between sequences, the algorithm BLAST (Basic Local Alignment Search Tool) and known variants, other matching algorithms
- The protein structure, calculation of dimensional structure.
- Microarrays, image analysis, data analysis.
- Systems biology, Information models of cells, etc
- Overview of boinformatics applications in drug discovery and personalized treatment.
The second main section of the course will focus on:
- Basic modeling principles.
- Applications of mathematical & computer principles for the modeling of physiological systems.
- Linear control theory and non - linear systems.
- Analysis and identification of normal open and closed loop systems.
- Modeling of tumor growth and response to radiotherapy.
- Methods of artificial intelligence for modeling and control of physiological systems. Examples of the cardiovascular, respiratory, nervous, metabolic system.
- Using special software for the analysis and simulation of physiological systems. Computational requirements and architectures.