Info

Network Science in Bioinformatics MSc Program, University of Crete

BC204 Course of Spring 2026:

Material

Workshop

Wednesday 20/05 13:00–17:00

Part 0 · Data Structures and Complexity Theory

• data structures in R and networks (matrices, edge lists, adjacency)
• complicated vs complex systems
• logistic map, deterministic chaos, Turing reaction-diffusion
• reductionism, holism, emergence — where network science fits

Part 1 · Graph Models

• Erdős–Rényi random graphs, phase transition
• Barabási–Albert scale-free networks
• Watts–Strogatz small-world networks

Part 2 · C. elegans Connectome

• degree distribution and power-law fitting
• centralities: degree, betweenness, PageRank
• spatial layout of the frontal connectome

(45-minute break)

Part 3 · Community Detection

• Leiden algorithm
• resolution sensitivity
• assortativity

Part 4 · E. coli PPI and Robustness

• BioGRID protein–protein interactions
• random vs targeted attack robustness
• network inference from correlation thresholds

Part 5 · Networks as Computation

• message passing on the C. elegans network
• graph neural networks and AlphaFold
• open data and reproducibility (MIxS, Turing Way)

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BC204 Course of Spring 2025:

Material

Presentation

Workshop

Wednesday 21/05 11:00-14:00 - 7Α-01

Background: Network basics

• what are networks?
• complexity science
• networks in biology
• Large Language Models
• types of networks
• networks as graphs - basics
• network distributions

On hands Workshop

• workspace and workflow introduction
• graphs as mathematical objects and their representation
• models for network creation (random, scale free, small world)
• introduction to dynamic systems on networks
• network descriptives
• mesoscale analysis of networks
• importance of nodes - centralities

Wednesday 28/05 11:00-14:00 - 7Α-01

From -omics data to Networks

• types of omics data
• molecular ecology
• large scale expeditions
• data standards
• network inference
• case study: soil metagenomics

On hands Workshop

• import data from database
• network inference from data
• hairballs
• percolation theory - robustness

Schedule - Course of Fall 2018:

The course contains 3 lectures - computer labs and one last meeting for student presentations.

1. Thursday 29/11 14:00-17:00 : Lecture 1 - PC-room (E110)
2. Tuesday 4/12 15:00-18:00 : Lecture 2 - PC-room (E110)
3. Thursday 6/12 14:00-17:00 : Lecture 3 - PC-room (E110)
4. Tuesday 11/12 14:00-17:00 : Q&A - Multimedia Room (E141)
5. Thursday 13/12 14:00-17:00 : Presentations - Multimedia Room (E141)

Lecture 1 : Introduction

Material created by Dr Christoforos Nikolaou, Group Leader of CG² Lab

• what are networks?
• networks in biology
• types of networks
• graph theory basics
• random networks
• scale-free networks

HTML

Lecture 2 : Network descriptives

• workspace and workflow introduction
• graphs as mathematical objects and their representation
• import data from database
• network descriptives
• mesoscale analysis of networks
• importance of nodes - centralities

HTML – Rmarkdown file

Lecture 3 : Network construction from Models and Data

• models for network creation (random, scale free, small world)
• percolation theory - robustness
• network inference from data
• introduction to dynamic systems on networks
• gene ontology annotation
• neural networks vs networks
• state of the art in network science

HTML – Rmarkdown file

Presentations

For the final examination of the course all students, individually, will present their assignments. They can choose a specific topic they are intersted in or choose from the list below. Students can present their assignments with slides or using markdown with their code. Duration of presentations will be 20 minutes.

Structure

Chapter 1

1. Research question
2. Why it is important?
3. Who did what so far? Review of literature
4. What remains to be done? Why the biologist is not satisfied?

Chapter 2

1. Methodology for addressing the problem
2. Necessary theoretical concepts and results

Chapter 3

1. Data (origin, reliability, size)
2. Computations Results
3. Mathematical meaning of the results

Chapter 4

Biological significance of the results

Chapter 5

Conclusions:

1. Mathematical comparison with available results (what is new)
2. Biological comparison with available results (what is new)
3. General comparison with available results (what is new)
4. Perspectives for future work

Featured topics

1. Generalize assortativity for every centrality measure and redefine it using information theory
2. Compare network community detection methods (Modularity vs Information vs Link-based) and their applications in biology
3. Centrality lethality rule using Gene Ontology centralities
4. Spreading phenomena - dynamics
5. Analysis of neural networks as networks (strongly connected components)
6. Multiplex networks robustness
7. Network analysis of gene ontology as bipartite, as association networks based on terms from real data annotation
8. Inference network from gene expression data and perform the network analysis and gene ontology annotation
9. Integrating genetic and protein–protein interaction networks maps a functional wiring diagram of a cell
10. Analyzing complex networks evolution through Information Theory quantifiers

Online material

• Networks Book by Mark Newman
• Network Science Book by Albert-László Barabási
• Nice network analysis and visualization introduction tutorial Katherine Ognyanova
• Brief introduction Introduction to Network Analysis with R
• Introduction to Tidygraph
• An awesome list of resources to construct, analyze and visualize network data
• Experiments with igraph
• Introduction to Bioconductor here
• Epidemic modeling in R, packages, tutorials and workshops : EpiModel
• Simulating network diffusion with R Tutorial
• Duke Network Analysis Center Diffusion simulations