[institut] [Bioinformatika] Seminar Bioinformatika

[Jovana Kovacevic]

Poštovane koleginice i kolege,

Prvi sastanak seminara Bioinformatika u 2021. godini biće održan online u
sredu, 10.2. u 18:15. Izlagaće prof. dr Nataša Pržulj na temu "Between
viral targets and differentially expressed genes in COVID-19: the sweet
spot for therapeutic intervention". Kratku biografiju predavača i apstrakt
predavanja možete pronaći u nastavku.

Link za predavanje:
https://matf.webex.com/matf/j.php?MTID=m51343e8204a534f2ff8a39ae8c84b293

Link će biti aktivan od 18h.

Srdačan pozdrav,
rukovodioci Seminara
Gordana Pavlović-Lažetić
Nenad Mitić
Anđela Rodić

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Biography

Professor Natasa Przulj is an ICREA Research Professor and a Group Leader
at Barcelona Supercomputing Center. She is a leader in network science and
AI algorithms for biomedical data fusion applied to precision medicine. Her
research has been cited around 10,000 times, h-index=43, i10-index=70
(Google Scholar) and supported by over €15 million in competitive funding.
Notably, she received three prestigious, single PI, European Research
Council (ERC) grants: Consolidator (2018-2023), Proof of Concept
(2020-2022) and Starting (2012-2017). She has been elected into: The
Serbian Royal Academy of Scientists and Artists in 2019; Academia Europaea,
The Academy of Europe, in 2017; Fellow of the British Computer Society
(BCS) Academy of Computing, in 2013. In 2014, she received a BCS Roger
Needham Award, sponsored by Microsoft Research, in recognition of the
potential her research has to revolutionize health and pharmaceutics. She
obtained a PhD in Computer Science from the University of Toronto.

Abstract

The COVID-19 pandemic is raging. It revealed the importance of rapid
scientific advancement towards understanding and treating new diseases. To
address this challenge, we build onto our previous methods for extracting
new biomedical knowledge from the wiring patterns of systems-level,
heterogeneous biomedical networks.  These methods are needed due to the
flood of molecular and clinical data, measuring interactions between
various bio-molecules in and around a cell that form large, complex
systems. These systems-level network data provide heterogeneous, but
complementary information about cells, tissues and diseases. The challenge
is how to mine them collectively to answer fundamental biological and
medical questions.  This is nontrivial, because of computational
intractability of many underlying problems on networks (also called
graphs), necessitating the development of approximate algorithms (heuristic
methods) for finding approximate solutions.

We adapt an explainable artificial intelligence algorithm for data fusion
and utilize it on new omics data on viral-host interactions, human protein
interactions, and drugs to better understand SARS-CoV-2 infection
mechanisms and predict new drug-target interactions for COVID-19. We
discover that in the human interactome, the human proteins targeted by
SARS-CoV-2 proteins and the genes that are differentially expressed after
the infection have common neighbors central in the interactome that may be
key to the disease mechanisms. We uncover 185 new drug-target interactions
targeting 49 of these key genes and suggest re-purposing of 149
FDA-approved drugs, including drugs targeting VEGF and nitric oxide
signaling, whose pathways coincide with the observed COVID-19 symptoms. Our
integrative methodology is universal and can enable insight into this and
other serious diseases.

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