Python Programming for Biological Problems

Solve more than 30 exercises and 4 Biology projects using Python programming language! Step by step implementations!

Biologists, biology students, and professionals in related fields generally have little or no contact with computer programming. However, the growing of data in genomic, protein and organism databases can be used to model the solution for some problems, such as the discovery of medicines and insecticides. It leads biologists to benefit from computer programming knowledge, so that they can develop useful applications in molecular biology, ecology, research on diseases, among others.

This course was developed with the purpose of introducing biologists, students of biology, biomedicine, ecology, pharmacy and professionals in related areas to programming using Python, which is nowadays one of the most used programming languages. It has a clear syntax and is easy to learn especially if you are a professional who are not familiar with technology. Many tools used in the field of biology were written in Python, which makes it a great option for establishing your first contact with computer programming. You will learn the following topics:

1. Python installation and main tools (IDEs)

2. Variables, constants and strings

3. Math operations

4. Logical, relational and conditional operators

5. Loops (for and while)

6. Functions

7. Lists, dictionaries, tuples, sets and arrays

8. Manipulation of text files

9. Error and exception handling

10. Regular expressions

11. Object oriented

After learning the basic concepts of Python, you will be able to apply the concepts in exercises, challenges and practical projects related to ​​Biology. Below are some of the case studies that we will implement step by step:

1. Prediction of the mass of a peptide sequence according to its amino acid composition

2. Schedule a biology test that calculates the grade and whether the user got each question right or wrong

3. Creating classes related to objects in the biological world

4. .fasta gene sequence analysis

5. Analysis of gene frequencies according to the Hardy-Weinberg Theorem

6. Creating functions for population ecology calculations

7. Discover patterns in RNA sequences

8. Estimation of gene distances

9. Basic species identification

10. Troubleshooting gene frequencies

11. Creating scripts for parsing .pdb-type protein sequence files

12. Transcription of DNA sequences into RNA

There are more than 80 classes, concepts, code demonstration, and exercises with solutions! More than 30 proposed challenges and 4 small projects applying the concepts learned in each section in a biological context, with step-by-step resolution.