Informatics in Life Sciences

The subheadings 1-3 cover the fundamental topics of the interdisciplinary fields of bioinformatics and computational systems biology. The subheadings 4-5 focus on recent state-of-the-art in the research targeting computational methods and machine learning in life sciences.

1) Bioinformatics – Sequence Analysis and Genomics

Annotation:
The candidate will familiarize themselves with algorithmic foundations of biological sequence analysis (motif recognition, alignment, mapping, assembly) and practical problems of NGS data processing, genome annotation, or phylogenetic analysis. Thereafter they should concentrate on issues of interest or directly applicable to their doctoral thesis.

Warp:
Sources and types of sequence data. Online and offline algorithms for sequence searching and alignment. Alignment, sequence mapping and phylogenetic tree reconstruction. Hidden Markov Models and machine learning methods in sequence analysis. Genomics, metagenomics, sequencing technology and genome assembly. Genome annotation. Popular workflows and software tools in bioinformatics and genomics.

Basic study material:

Stuart M. Brown. Next-generation DNA sequencing informatics. Cold Spring Harbor Laboratory Press, 2015.
Naiara Rodríguez-Ezpeleta et al. Bioinformatics for high throughput sequencing. Springer, 2012.

Examiner: Ing. Matej Lexa, Ph.D, doc. RNDr. David Šafránek, Ph.D.

Other recommended literature:

Phillip Compeau and Pavel Pevzner. Bioinformatics algorithms : an active learning approach. Active Learning Publishers, 2014.
A. Malcolm Campbell and Laurie J. Heyer. Discovering genomics, proteomics, and bioinformatics. CSHL Press, c2007.
David W. Mount. Bioinformatics: sequence and genome analysis. Cold Spring Harbor Laboratory Press, 2001.

2) Bioinformatics – Protein Structure and Function

Annotation:
The candidate will familiarize themselves with algorithmic foundations of protein sequence (motif searching, alignment, sequence profiles) and structure (structure alignment and analysis, structure and function prediction) analysis. Thereafter they should concentrate on issues of interest or directly applicable to their doctoral thesis.

Warp:
Sources and types of protein sequence and structural data. Hidden Markov Models and methods of machine learning for protein study. Physical and chemical foundations of protein structure, evolutionary analysis of protein sequences and structures. 3-D structure prediction from sequence (with and without homology information). Protein structure visualization and annotation. Popular workflows and software tools in structural and evolutionary bioinformatics.

Basic study material:

Jenny Gu and Philip E. Bourne. Structural bioinformatics. Wiley-Blackwell, 2009.

Examiner: Ing. Matej Lexa, Ph.D, doc. RNDr. Barbora Kozlíková, Ph.D.

Other recommended literature:

John M. Hancock and Marketa J. Zvelebil. Concise encyclopaedia of bioinformatics and computational biology. Wiley Blackwell, 2014.
A. Malcolm Campbell and Laurie J. Heyer. Discovering genomics, proteomics, and bioinformatics. CSHL Press, 2007.
David W. Mount. Bioinformatics: sequence and genome analysis. Cold Spring Harbor Laboratory Press, 2001.

3) Digital Systems Biology

Annotation:
The student will get acquainted with the basic methods and techniques in the use of computational methods for modeling and analysis of biological systems and corresponding applications. The specific emphasis is given to understanding living matter as a complex dynamical system. The candidate can focus on the specific topics applicable to their thesis.

Warp:
Basic concepts of systems approach to biological systems, specification of biological systems, dynamics of chemical reaction networks, stochastic modeling and simulation, signalling pathways, regulatory networks, modeling of cellular communication networks and pathways, model validation, algorithms for automated model analysis.

Basic study material:

Olaf Wolkenhauer: Systems Biology - Dynamic Pathways Modeling, Rostock University, 2012 (available with the author's permission as an electronic copy from the examiner)
Marco Bernardo, Erik de Vink, Alessandra Di Pierro , Herbert Wiklicky (Eds.): Formal Methods for Dynamical Systems, SFM 2013. Springer. Bertinoro, Italy, June 17-22, 2013 Bernardo, Marco; Degano, Pierpaolo; Zavattaro, Gianluigi (Eds.): Formal Methods for Computational Systems Biology. SFM 2008 Bertinoro, Italy, June 2-7, 2008. Springer. Chapters will be specified for the student individually.

Examiner: prof. RNDr. Luboš Brim, CSc., doc. RNDr. David Šafránek, Ph.D.

Other recommended literature:

Uri Alon: An Introduction to Systems Biology - Design Principles of Biological Circuits. Chapman & Hall / CRC, 2007.
Eda Klip et al. Systems biology: a textbook. Wiley-VCH Verlag, 2009.
Darren James Wilkinson. Stochastic modelling for systems biology. Chapman & Hall / CRC, 2006.

4) Computational Methods in Systems Biology

Annotation:
The student will get acquainted at the expert level with the latest scientific knowledge in the interdisciplinary expert field of the use of computational methods for modeling and analysis of biological systems and corresponding applications.

Warp:
Formalisms and languages for modeling biological processes: rule-based languages, Boolean networks, Petri nets; algorithms and tools for verification, validation, analysis, simulation, and control of biological systems; parallel and high-performance computational methods in systems biology.

Basic study material:
3-4 articles from the last years of the conference Computational Methods in Systems Biology, journals Briefings in Bioinformatics, Bioinformatics, PLoS Computational Biology, BMC Systems Biology and possibly others according to the examiner's recommendations. Articles will be specified for the student individually.

Examiner: doc. RNDr. David Šafránek, Ph.D., prof. RNDr. Luboš Brim, CSc.

5) Machine Learning Applications in Life Sciences

Annotation:
In recent years, advanced machine learning techniques such as deep, representation and relational learning, have been instrumental in a number of life science breakthroughs (to name just two such success stories, one can mention the Stanford's dermatologist-level model for predicting melanoma in 2017, or Google Deep Mind's leap in accuracy of predicting protein structure in 2020). This has been enabled by growing maturity of the applied machine learning methods, and by vast volumes and numbers of life science datasets becoming available for machine processing, as reviewed in the two surveys that form the basic study material.

Using the surveys, the candidate will get acquainted with the overall principles of deep and relational learning applications in biomedicine. Furthermore, the candidate will choose specific dataset(s) and technique(s) that correspond to the focus of their doctoral thesis, and get acquainted in detail with those.

Warp:
Biomedical deep learning applications; Representation learning for life sciences; Relational learning for life sciences; Network biology; Network medicine; Biomedical knowledge graphs; Biomedical knowledge graph embedding; Protein structure, interaction and function prediction; Gene-disease association prediction; Drug discovery and pharmacovigilance; Precision medicine; Clinical decision support

Basic study material:

Ching, Travers, et al. Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface 15.141 (2018): 20170387.
Li, Michelle M., Kexin Huang, and Marinka Zitnik. Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities. arXiv preprint arXiv:2104.04883 (2021).

Examiner: Mgr. Vít Nováček, Ph.D., doc. RNDr. Tomáš Brázdil, Ph.D.

Other recommended literature:

Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
Bengio Y, Goodfellow I, Courville A. Deep learning. MIT press, 2017.
Ji, Shaoxiong, et al. A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems (2021).