Network
research of aging with focus on biological network alignment
Organizing committee
Meeting date, time, and place
September 20, 4:30-6:30pm, Baycliff Room.
Tutorial summary
Genes
(proteins) interact with each other to keep us alive. And this is exactly what
biological networks model. Therefore, biological network research is promising
to revolutionize our biological understanding.
Because susceptibility to diseases increases with age,
studying human aging is important. But
studying human aging experimentally is hard. Hence, aging-related knowledge
needs to be transferred from model species. This transfer has traditionally been
carried out by genomic sequence alignment. But because sequence data and
biological network data can give complementary insights, sequence alignment
alone can limit the knowledge transfer. Thus, biological network alignment can
be used to transfer aging-related knowledge between topologically and
functionally conserved network regions of different species.
Gene expression research has also been
indispensable for investigating aging, but it typically ignores genes'
interconnectivities. Thus, analyzing genes' topologies in biological networks
could contribute to our understanding of aging. However, current methods for
analyzing systems-level biological networks deal with their static
representations, although cells are dynamic. Because of this, and because
different data can give complementary biological insights, current static
biological networks can be integrated with aging-related expression data to form
dynamic, age-specific biological networks. Then, cellular changes with age can
be studied from such biological networks.
With the above motivation in mind, this tutorial will review state-of-the-art biological network research of aging. The tutorial is designed at the introductory level, giving enough background on the proposed topics. It is intended to bring together scientists at all stages of their career with interests or expertise in computational or mathematical analyses of biological networks, or in applications of the methods to practical problems in biology, such as aging, evolution, disease, or therapeutics.
Tutorial description
As
more biological network data is becoming available owing to advances in high-throughput
biotechnologies for interactome detection, meaningful alignments of biological
networks of
different species could be viewed as one of the foremost problems in
computational biology. Since network alignment aims to find regions of
similarities between biological networks of different species, it could guide the transfer of
biological knowledge across species between conserved network regions. This is
important since many nodes in biological networks are currently functionally uncharacterized
even for well-studied model species. In particular, this is important when
studying human aging: since human aging is hard to study experimentally due to
long lifespan as well as ethical constraints, the knowledge about aging needs to
be transferred from model species. Traditionally, the transfer of biological
knowledge between species has been restricted to genomic sequence alignment.
However, since biological networks and sequences can give complementary biological insights,
indicating that biological network data can elucidate function that cannot be extracted from
sequence data by current methods, restricting alignment to sequences may limit
the knowledge transfer.
Since
susceptibility to diseases increases with age, studying molecular causes of
aging gains importance. Hence, after discussing in depth (dis)advantages of
existing network alignment methods, the tutorial will demonstrate how the
methods can be used to infer network-based functional orthologs and transfer
aging-related knowledge from well annotated model species to poorly annotated
human. That is, the use of network alignment in deepening our current knowledge
about (human) aging will be discussed.
In addition to network alignment, the tutorial will introduce alternative network-based computational strategies for studying aging. Namely, it will show how integrating current static biological network data with aging-related gene expression data to construct age-specific networks and then dynamically studying these networks provides a valuable model of cellular functioning that can reveal novel key players in aging, and that can reveal more of the novel aging-related information than static network analysis of individual biological data types, as has traditionally been done in the field of biological network research.
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