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DTSTAMP:20260312T161440Z
UID:1757336400@ist.ac.at
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DESCRIPTION:Speaker: Natalia Ruzickova\nhosted by Lisa Bugnet\nAbstract: Th
 is thesis comprises two separate pieces of work\, both of which revolve ar
 ound the importance of interaction networks for biological function and ho
 w we can utilise them in mathematical modelling to deepen our understandin
 g of biological systems.In Part I\, we focus on large intracellular networ
 ks of interacting genes\, introducing a method for predicting gene express
 ion levels by integrating information about genotype and gene regulatory n
 etwork topology. The novelty lies in combining classical statistical genom
 ics approaches with the knowledge of regulatory networks from systems biol
 ogy\, thereby creating a model which is not only predictive but also inter
 pretable. The broader goal of developing such interpretable predictive mod
 els is to understand the biological mechanisms underlying complex traits\,
  including diseases. Complex traits are encoded by hundreds to thousands o
 f genetic variants all across the genome\, making it challenging to uncove
 r their causal biological mechanisms. Simultaneously\, complex trait predi
 ctions by purely statistical models are hugely overparametrised\, posing t
 echnical challenges in statistical inference. Structuring predictive model
 s by existing biological knowledge addresses both these challenges. Our Qu
 antitative Omnigenic Model (QOM) is a first step in this direction: the QO
 M has hundreds of times fewer parameters than classical statistical genomi
 cs models\, while its predictive performance remains comparable to that of
  standard methods. Simultaneously\, the QOM extracts candidate causal and 
 quantitative chains of effect propagation through the regulatory network f
 or every individual gene\, making it interpretable.In Part II\, we study s
 maller networks of cell-cell interactions in the pancreatic islets of Lang
 erhans\, which release hormones that regulate blood glucose levels. In col
 laboration with the experimental physiology group of Prof. Rupnik at the M
 edical University of Vienna\, we analyse the strong synchronous behaviour 
 of pancreatic cells observed in experiments. These include waves of activi
 ty spreading across the islet on the timescale of 1 second\, as well as lo
 ng pulses on the timescale of several minutes. This collective behaviour c
 an be observed thanks to our collaborators’ novel approach of imaging in
 tact pancreatic slices rather than isolated islets or islet cells\, as is 
 a common practice in the field. Combined with quantitative analysis and bi
 ophysical modelling\, this presents a unique opportunity to address system
 s-level questions. First\, in chapter 8 we analyse a set of experiments wh
 ere islets were exposed to different types of glucose stimulation. Then\, 
 in Chapter 9\, we utilise insights gained from these observations to const
 ruct a simple cell-resolved biophysical model of the islet\, which reprodu
 ces the collective dynamics observed in experiments. A key ingredient of t
 he model is the strong positive coupling between neighbouring cells\, as w
 ell as the antagonistic interaction between two distinct cell types in the
  islet\, α- and β-cells. Understanding the implications of collective be
 haviour on healthy glucose regulation is key\, since disrupted synchrony i
 s a hallmark of diabetes. The novelty in our approach is taking a systems-
 level perspective and focusing on the connection between cell-cell interac
 tions and the biological function of the islet as a whole.
LOCATION:I22 Lakeside View and Zoom\, ISTA
ORGANIZER:
SUMMARY:Natalia Ruzickova: Thesis Defense: Effect propagation in biological
  networks
URL:https://talks-calendar.ista.ac.at/events/5969
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