Metabolism in silico

Code:

MBE04

Acronym:

MIS

Keywords
Classification	Keyword
CNAEF	Medicine

Instance: 2022/2023 - 1S

Active?	Yes
Responsible unit:	Departamento de Biomedicina
Course/CS Responsible:	Metabolism - Biopathology and Experimental

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MBE	12	Current Studies Plan	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

A) To acquire theoretical knowledge on bioinformatic tools and procedures, commonly used in the Life and Health Sciences, for analyzing molecular and experimental data;

B) To develop the autonomy and practical skills in both bioinformatics (accessing and obtaining molecular data, analysis and integration of biological data) and biostatistics (analysis of data using appropriate statistical software);

C) To obtain competences to critically interpret the results, understanding the capabilities and limitations of the algorithms/methods of analysis.

The acquisition of these competences is necessary for the design and development of research projects in order to prepare the Master's thesis. In addition, they allow the understanding of the work performed by other researchers and promote the establishment of collaborations.

Learning outcomes and competences

In the molecular bioinformatics module, the syllabus aims at filling gaps in student learning.  These gaps are often due to the unawareness of bioinformatics tools, which are constantly being updated and developed, or the lack of knowledge in its practical use.  The program contents begin with the presentation of several sequence databases/repositories, followed by sequence comparison and analysis for prediction of biological properties and ending with the search for gene ontology and integration of biological data, following a sequence from basic to the more complex, allowing the student to integrate knowledge.  Together, this module makes the student able to use with confidence a great diversity of bioinformatics tools necessary for research in the framework of this Master Course.

 

In the Biostatistics module, the essential data analysis techniques and procedures will be taught, considering the following organization of the contents:

• Definition of the clinical research question/aim of the researcher;
• Identify which data analyses procedures will be required to provide an answer to the previously defined research question;
• Implement the defined data analysis procedures in a statistical software and provide an adequate interpretation of the obtained results;
• Define and implement a communication strategy of the produced evidence.

This sequence of steps will be used as a teaching guide during classes. To promote an interactive environment the teacher will use real databases and clinical problems, which will be presented and discussed with students (problem-based learning). These challenges will be solved using data analysis software. This teaching-learning methodology ensures that the learning objective A), B), C) are fulfilled.

Additionally, to ensure that pedagogic objectives B) and C) are fully covered, the teacher will ask students to autonomously conduct some data analysis and perform the corresponding data reporting.

In all classes, the teacher will discuss previously published scientific articles, to illustrate the different communication strategies that may be used to promote the diffusion of the resulting evidence after data analysis.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowing how to work with a computer in the user's perspective.

Program

Molecular Bioinformatics module

• Sequence databases: introduction, examples, data retrieval;
• Sequence comparison: alignment methods, similarity searches, homology, protein motifs and domains;
• Sequence analysis: prediction of biological properties from DNA, RNA and protein sequences. Primer design for cloning and gene expression analyses and siRNA design. Restriction maps;
• Gene ontology: retrieving biological insight from high throughput experiments. Functional analysis of sequence variants and associated databases;
• Integration of biological data: comparison of gene data sets and multiple databases query.

Biostatistics module

• The SPSS® software environment – overall organization and main menus;
• Definition of a SPSS® data base;
• Implementation of the most common data analysis procedures in SPSS®;
• Descriptive analysis – absolute and relative frequencies, central tendency measures, dispersion measures;
• The statistical procedures used to check the Normal distribution of a continuous variable: visual inspection of the histogram, skewness and kurtosis of the histogram, Kolmogorov-Smirnov/Shapiro-Wilk tests;
• Tests used to compare proportions: qui-squared and Fisher exact test;
• Tests used to test the correlation between two continuous variables: Pearson and Spearman correlation tests;
• Tests used to compare a continuous variable between two or more groups:

          - Parametric tests: one-sample Student T test, independent/paired sample Student t test, One-way analysis of variance (ANOVA)
          - Non-parametric tests: Wilcoxon test, Mann-Whitney test, Kruskal Wallis test;

• Measures of association: mean difference, proportion difference, odds ratio, risk ratio;
• Multivariate analysis: regression analysis                     -

           - Types of regressions: linear, binary logistic, Cox, Poisson;
          - Practical examples of Implementation of linear regression.

Mandatory literature

Xiong, J ; Essential Bioinformatics, Cambridge University Press, 2007
Field, A; Discovering statistics using SPSS, SAGE Publications Ltd, 2009
Norman, G. R., & Streiner, D. L. ; Biostatistics: the bare essentials , BC Decker Inc, 2008

Complementary Bibliography

https://www.youtube.com/user/NCBINLM; NCBI Tutorials Video Channel: NCBI YouTube channel featuring short tutorials on the use of many NCBI resources
https://www.youtube.com/user/EBImedia/videos; EMBL-EBI Tutorials Video Channel: EBI YouTube Channel featuring tutorials on Ensembl genome browser

Teaching methods and learning activities

The teaching methodologies of each module are the following:

- The module Molecular Bioinformatics is composed of theoretical and practical classes;

- The module of Biostatistics is composed as follows: 10% theoretical classes; 50% classes with both theoretical and practical components; 40% practical classes. 

Software

GraphPad Prism
SPSS Statistics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	5,00
Exame	30,00
Trabalho prático ou de projeto	20,00
Apresentação/discussão de um trabalho científico	45,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	20,00
Frequência das aulas	42,00
Trabalho escrito	97,00
Apresentação/discussão de um trabalho científico	3,00
Total:	162,00

Eligibility for exams

Frequency to the two modules with minimal approval of seven points (in twenty) in each.

Calculation formula of final grade

To obtain final approval to the curricular unit a minimum classification of 7 points (in 20) in one of the modules is required and the final classification (FC) must be ≥10 points. The FC results from the average of the classifications obtained for each module based on the following formula:

 FC = (Module 1+Mondule 2) / 2

 

The evaluation components of each module are the following:

- In the module Molecular Bioinformatics the evaluation consists of an oral presntation and discussion of an individual miniproject (90%) and daily assessment of the participation and demonstred interest (10%);

-In the module of Biostatistics the final classification is of the student is determined by a written exam (60% of the classification) and four practical exercises (each exercise will correspond to 10% of the final classification).

Classification improvement

It is possible to improve the final grade by frequency of the curricular unit.