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DTSTART:20180325T030000
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DTSTAMP:20260405T192622Z
UID:577e1574efc83911169338@ist.ac.at
DTSTART:20180430T160000
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DESCRIPTION:Speaker: Walter Fontana\nhosted by Calin Guet\nAbstract: As mec
 hanistic facts about the many diverse molecular interactions in cells beco
 me more numerous and detailed\, computational models suitable for explorin
 g the dynamical consequences of this information are more needed than ever
 . Rule-based approaches allow for models that constitute a transparent\, e
 ditable\, formal\, and executable representation of the facts they rests u
 pon. The downside is that we replace a world we don’t understand with a 
 model we don’t understand—at least temporarily. The challenge\, then\,
  is to develop mathematical techniques and a sound software infrastructure
  for analyzing\, visualizing\, manipulating\, simplifying—in short\, rea
 soning with—models that are like empirical objects.Rules are a key organ
 izing principle in chemistry. The point of a rule is to distinguish betwee
 n the transformation of molecular parts and the reaction instance resultin
 g from that transformation in the context of specific molecular entities. 
 Since a particular interaction between proteins often appears to depend on
  some but not all aspects of their state\, rule-based languages adapt the 
 chemical perspective to molecular systems biology by viewing proteins as h
 igher-order atoms and non-covalent associations between proteins as higher
 -order molecules. Rules provide compactness\, transparency\, and a handle 
 on combinatorial complexity\; but perhaps most significantly\, rule system
 s constitute a more appropriate level for causal analysis than reaction ne
 tworks\, because reasoning at the level of rules avoids contamination with
  context that defines a reaction\, yet is irrelevant to the application of
  the underlying rule. In this presentation I will frame our current state 
 of mechanistic causal analysis in rule-based systems. In particular\, I wi
 ll sketch two formal concepts\, which we call "causal compression" and "co
 unterfactual resimulation"\, that use the detailed event trace of a model 
 towards reconstructing an "explanation" of how an event of interest occurr
 ed.This is joint work with Jonathan Laurent (CMU)\, Pierre Boutillier (HMS
 )\, Jean Krivine (Paris 7)\, Jerome Feret (ENS Paris)\, Ioana Cristescu (R
 ennes)\, and Jean Yang (CMU).
LOCATION:Raiffeisen Lecture Hall\, Central Building\, ISTA
ORGANIZER:kzaruba@ist.ac.at
SUMMARY:Walter Fontana: How did that happen?
URL:https://talks-calendar.ista.ac.at/events/47
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