Computacional Methods in Biology

Recommended Prerequisites

Basic knowledge of Real Analysis, Linear Algebra and Analytic Geometry.

Teaching Methods

Theoretical classes with detailed exposure of the syllabus contents highlighting the strong interaction between theoretical concepts and their concrete application. The teaching of the different topics follows from the particular to the general, from the concrete to the progressively abstract, appealing to the participation of the students, promoting continuous learning, in order to develop their critical spirit.

Theoretical-practical classes, using programming to illustrate concepts, with particular emphasis on visualization, and implementation of projects related to problems in the field of Biology.

The assessment of the theoretical component consists of a final exam or, alternatively, an intermediate midterm exams. The evaluation of the theoretical-practical component is done through tests or computational projects carried out during the semester. The final mark will be a weighted average of the marks obtained in each of the components.

Learning Outcomes

Introduce students to Python programming language and its use in the resolution of frequent scientific problems. Present the main concepts, theories and techniques of numerical methods in a perspective that values the intuition underlying the concepts and their application to problems in Biology. Illustrate the need of numerical calculation in the development of creativity, critical thinking and adaptability to new problems. Develop effective and pratical numerical skills in problem solving in Biology using programming techniques.

Work Placement(s)

No

Syllabus

1. Introduction to Python:

1.1 Variable types.

1.2. Flow control instructions

1.3. Functions and modules

1.4. Files

 

2. Numerical methods:

2.1. Approximation of functions.

2.2 Numerical differentiation and integration.

2.3. Equations and systems: direct and iterative methods for linear systems, non-linear equations, non-linear systems.

2.4. Ordinary differential equations with boundary conditions and initial values.

2.5. Partial differential equations.

Head Lecturer(s)

Gonçalo Nuno Travassos Borges Alves Pena

Assessment Methods

Continuous assessment
Frequency: 100.0%

Final assessment
Exam: 100.0%

Bibliography

1. Alfio Quarteroni, Fausto Saleri e Paola Gervasio, Scientific Computing with MATLAB and Octave, Springer, 2014.

2. Cleve Moler, Numerical Computing with MATLAB, SIAM, 2004.

3. Heitor Pina, Métodos Numéricos, Mcgraw Hill, 1995.

4. Richard L. Burden e J. Douglas Faires, Numerical Analysis, CENGAGE Learning, 2015.

5. Stanley M. Dunn, Alkis Constantinides e Prabhas V. Moghe, Numerical Methods in Biomedical Engineering, Academic Press, 2005.

6. Jaan Kiusalaas, Numerical Methods in Engineering with Python, Cambridge University Press (2010)