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SUMMARY:Mathematical and computational analysis of biological networks unc
 overs evolution\, disease\, and gene functions
DTSTART;VALUE=DATE-TIME:20101229T160000Z
DTEND;VALUE=DATE-TIME:20101229T170000Z
DTSTAMP;VALUE=DATE-TIME:20261010T001303Z
UID:indico-event-61@indico.ipb.ac.rs
DESCRIPTION:Genes produce thousands of different protein types that intera
 ct in complex networked ways and make cells work. It is possible that inte
 raction network data will be as useful as the sequence data in uncovering 
 new biology. Given the abundance of interaction data\, systems-level compa
 risons of networks of pathogenic and non-pathogenic species could play a v
 ital role in understanding mechanisms of pathogenicity. Also\, comparing n
 etworks of healthy and disease-affected cells could deepen our understandi
 ng of disease and lead to identification of cellular parts that are candid
 ates for therapeutic intervention. Furthermore\, biological network compar
 ison and alignment could enable transfer of knowledge between species\, si
 nce we may know a lot about bio-molecules in one species and almost nothin
 g about aligned bio-molecules in another species.Existing network alignmen
 t methods use information external to network topology\, e.g.\, sequence d
 ata.  Since network topology provides a new and independent source of bio
 logical information\, it is important to understand how much biology we ca
 n learn from it independently from any other data source.  Hence\, we dev
 elop mathematically rigorous ways for aligning networks based solely on th
 eir topology. Our network aligners produce by far the most complete alignm
 ents of biological networks to date\, exposing large and contiguous region
 s of network similarity even for as distant species as yeast and human thu
 s suggesting broad similarities in internal cellular wiring across all lif
 e on Earth.  Moreover\, they demonstrate that protein function and specie
 s phylogeny can be extracted solely from network topology. Analogous to re
 construction of phylogenetic trees using sequence similarities\, we use ou
 r network alignment similarities to successfully reconstruct the phylogeni
 es of protists\, fungi\, and herpesviruses.In addition\, we show that netw
 ork topology around cancer and non-cancer genes is different and use this 
 to predict new cancer genes. Our predictions are phenotypically validated.
  We present evidence that topology-based analyses of biological networks p
 rovide new biological and phylogenetic insights and that they can help ide
 ntify novel drug targets\, hence aiding therapeutics and health care.\n\nh
 ttps://events.saifa.rs/event/61/
LOCATION:Računarski Fakultet 1
URL:https://events.saifa.rs/event/61/
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