Algorithms for Network-based Bioinformatics

The course will provide a brief overview on molecular biology, the advent of high throughput measurement techniques and large databases of biological knowledge. With this given background, the course will dive into the importance of various network approaches to model biological knowledge.

We will cover topics such as complex network models to characterise and analyse biological systems, approaches to infer network structures from given biological measurements, strategies of network enhancement through network integration, predictions based on network structures, and graph generation (molecular design).

Specifically, the course contains the following topics:

• Background on molecular data: systems biology, data-driven approach, high throughput measurements, sequencing, mass-spectrometry, immunoprecipitation, single cell measurements, molecular properties, molecular descriptors, databases.

• Molecular interactions: transcription factors, protein complexes, metabolic reactions, gene-protein-metabolite interactions, interaction models.

• Frameworks to work with complex systems: graphs with nodes/edges, directionality, layers, heterogeneous graphs, topological graph metrics, graph characteristics (erdos-renyi/scale-free/hierarchical networks), hypergraphs, topological hypergraph metrics, simplicial complexes, simplicial complex metrics

• Network inference and simulation: rate-law modelling of RNA regulation model, dynamic system/steady state, reduce, association networks, linear networks, design matrix, bayesian networks, boolean networks, regularisation, evaluation

• Network enhancement: heterogenous networks, reliability of networks, bias/variance, evaluation strategies, early/intermediate/late integration, machine learning approach, decision trees, classifier combining, probabilistic/dissimilarity/kernel-based representations, network fusion, random walks.

• Network-based predictions on the node-level: guilt-by-association, maximise coherency, Markov random field, clustering based on graph properties, Markov clustering, spectral clustering, Louvain method, active modules, validation.

• Network-based predictions on the graph level: graph neural networks, graph convolution, node message passing, graph attention, molecular property prediction, structure-based function prediction.

• Graph generation for molecular design: generative AI for molecular design, graph variational autoencoders, graph diffusion models