Course syllabus - Biomathematics and Bioinformatics

Objectives

The objective of the course is to give some insight into how mathematics and mathematical statistics appear as a natural component in life sciences and health technology and give a basic introduction to statistical and other mathematical methods and tools in health technology, medical engineering, biology, bioinformatics and other life sciences. Another objective is to develop the student’s ability to formulate problems, identify methods and solving problems using statistical and other mathematical methods, both with and without computer aid.
 

Learning outcomes

After completing the course, the student is expected to be able to

- overview , explain clearly and independently use basic modern mathematical models , methods and tools for health technologies, medical engineering , biology, bioinformatics and life sciences
- demonstrate an ability to independently identify health engineering , medical and biological problems that can be solved with mathematical modeling, and be able to choose an appropriate method
- with proper terminology describe tools and solution methods for modeling problems in medical mathematical image processing, biostatistics and signal processing in a well structured and logically sound fashion
- explain the basics for the relevant statistical and other mathematical methods and problems that are used in bioinformatics , data classification , data clustering and data mining with applications in genetics, biology, evolutionary biology, structural biology, medicine and pharmacology
- describe how statistical and other mathematical tools and methods can be used in health technology and health care, text mining, NLP ( Natural Language Processing ), and the use and development of medical and pharmacological databases as well as describe examples of their application, particularly in medical informatics, bioinformatics and health technology; and decision making within the health care system
- independently apply basic modeling and algorithms to research relevant problems in health technology and life sciences
 

Course content

Mathematical modelling of evolution, population dynamics and growth processes in biological and microbiological systems using difference equations, differential equations, dynamical systems, symmetry, chaos and fractals, mathematical image analysis and signal processing in health technology, medicine and other life sciences, mathematics for ultrasound, x-ray and microwave technology in health science; statistical and other mathematical methods for analysis and classification of genetic and other data from biological and medicinal experiments and databases, methods for information gathering and relevance ranking in medical literature, biological, medicinal and pharmacological databases, ontologies and internet resources; mathematical hypothesis analysis and optimization of decision-making processes in biological and medical research and healthcare; statistical and other computational methods for studying disease, health status, treatments and tools (surgery, heart disease, brain afflictions, dementia, disabling diseases, diabetes, cancer, etc.).
 

Tuition

Lectures and seminars.
 

Specific requirements

Option I: Knowledge of Mathematics / Applied Mathematics equivalent to Single Variable Calculus 7.5 credits, Vector Algebra 7.5 credits and either Discrete Mathematics 7.5 credits or Probability Theory and Statistical Inference 7.5 credits, Option II: 60 credits university studies or qualified working experience in the life sciences.

Examination

Project (PRO1), 4.5 credits, marks Pass (G) or Pass with distinction (VG)
Seminars (SEM1), 3 credits, marks Pass (G) or Pass with distinction (VG)
 

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass with distinction, Pass, Fail