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PRODID:-//Memento EPFL//
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SUMMARY:Path integral approaches to mean field approximation and subnetwor
 k description
DTSTART:20161208T141500
DTEND:20161208T151500
DTSTAMP:20260414T080502Z
UID:fbae4678a0b75acf3c85c05d4f615ba22c218af13cc682680fc67173
CATEGORIES:Miscellaneous
DESCRIPTION:Barbara Bravi\nPath integral formalism is a powerful tool borr
 owed from theoretical physics to build dynamical descriptions\, yet its po
 tential is largely unexplored in the context of complex networks\, such as
  the ones common in systems biology. In talk\, I present two mathematical
  frameworks based on path integrals to capture the time evolution of inter
 acting continuous degrees of freedom\, e.g. biochemical concentrations. W
 e first develop a novel mean field approximation\, the Extended Plefka Exp
 ansion\, for stochastic differential equations exhibiting generic nonlinea
 rities. The key element is the definition of “effective” fields which 
 map an interacting dynamics into the “most similar” non-interacting pi
 cture\, i.e. the one producing the same average observables. In the result
 ing picture\, couplings between variables are replaced by a memory and a c
 oloured noise. The Extended Plefka Expansion is expected to become exact i
 n the limit of infinite size networks with couplings of mean field type\, 
 i.e. weak and long-ranged. We show this explicitly for a linear dynamics b
 y comparison with other methods relying on Random Matrix Theory. We finall
 y appeal to path integrals to design “reduced” models\, where equation
 s are referred solely to some selected variables (subnetwork) but still ca
 rry information on the whole network. This model reduction strategy leads 
 to substantially higher quantitative accuracy in the prediction of subnetw
 ork dynamics\, as we demonstrate with an example from the protein interact
 ion network around the Epidermal Growth Factor Receptor.
LOCATION:BSP 727 https://plan.epfl.ch/?room==BSP%20727
STATUS:CONFIRMED
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