**Prepared by:** Gaston K. Mazandu

**Module name:** Computational Systems Biology

**Contact hours** (to be used as a guide)**:** Total (40 hrs), Theory (62.5%), Practical (37.5%)

**SPECIFIC OUTCOMES ADDRESSED**

On completion of this module, students should be able to:

1. Study whole systems of biological components with the aid of integrative computational approaches.

2. Analyze the structure and understand dynamics of the system, both quantitative and qualitative, and build predictive models.

3. Understand protein-protein interaction networks and biological pathways in the context of health and disease.

4. Map high-throughput biological datasets to functional knowledge.

5. Perform post-analyses of results from high-throughput computational approaches.

**BACKGROUND KNOWLEDGE REQUIRED**

**H3ABioNet bioinformatics modules as pre-requisites: **Biostatistics I (basic arithmetic skills and basic statistics), Programming I

**H3ABioNet bioinformatics modules recommended **(especially for the last chapter of the Computational Systems Biology module)**:** Population Genetics and GWAS, High-Throughput Sequencing

**BOOKS AND OTHER SOURCES**

1. Hefferon J. Linear algebra. 2017, http://joshua.smcvt.edu/linearalgebra

2. Schroder BSW. A Workbook for Differential Equaquations. Wiley Publisher, 2010.

3. Mazandu GK, Chimusa ER, Rutherford K, Zekeng EG, Gebremariam ZZ, Onifade MY,Mulder NJ. Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets. Brief Bioinform 2017,doi: 10.1093/bib/bbx052.

4. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28(1):27-30. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC102409/

5. Fell DA. Understanding the control of metabolism. Portland Press, London 1996.

6. Fell DA, Sauro HM. Metabolic control and its analysis: additional relationships between elasticities and control coefficients. Eur. J. Biochem. 1985, 148:555-561.

7. Mazandu GK, Chimusa ER, Mulder NJ. Gene ontology semantic similarity tools: survey on features and challenges for biological knowledge discovery. Brief Bioinform 2016, DOI:10.1093/bib/bbw067.

**COURSE CONTENT**

**A) Theory lectures**

**1. An introduction to basics mathematical functions, linear algebra and ordinary differential equations** (5 hours)

- Mathematical sets and functions
- Vector space, linear functions, matrix and Jacobian matrix
- Concepts of ordinary differential equations

**2. Biological networks** (8 hours)

- Mathematical concept of graph or network
- Network centrality measures and modularity
- Type of biological networks: Protein-protein interaction (PPI) networks, Gene regulatory networks, Gene expression networks, metabolic networks (biological pathways) and signalling networks
- Computational approaches for predicting PPI networks
- Bioinformatics tools for visualizing PPI networks.

**3. Metabolic control analysis** (6 hours)

- Kinetic models for chemical reactions and networks of coupled reactions
- Enzyme kinetics, response and flux control coefficients
- Algebra of metabolic control analysis
- Metabolic regulation: Supply-demand analysis

**4. Biological high-throughput post-analysis** (6 hours)

- Gene ontology and semantic similarity measures
- Enrichment analysis: identification of expressed proteins, enriched biological processes and pathways, functionally related proteins
- Post-analysis related to genomics for human variation, including Genome wide association studies (GWAS) and Next genera on sequencing (NGS) post-analyses.
- Application to pharmacogenomics.

** B) Practical Component**

1. Exploring PPI online databases, Building human PPI network, identifying key proteins and modules (follows lecture 2)

2. Practial topic2 (follows lecture 4)

**ASSESSMENT ACTIVITIES AND THEIR WEIGHTS **

Practical group work (50% weight)

Seminar and oral presentation (30% weight)

Homework (Individual work, 20% weight)