| || Models ||Description|
Boolean network-based analysis of the apoptosis network (Mai and Liu 2009)
||"To understand the design principles of the molecular interaction network associated with the
irreversibility of cell apoptosis and the stability of cell surviving, we constructed a Boolean network integrating both the intrinsic and extrinsic pro-apoptotic pathways with pro-survival signal transduction pathways.
We performed statistical analyses of the dependences of cell fate on initial
states and on input signals.
The analyses reproduced the well-known pro- and anti-apoptotic effects of
key external signals and network components. We found that the external GF signal by itself did not change the apoptotic ratio from randomly chosen initial states when there is no external TNF signal, but can significantly offset apoptosis induced by the TNF signal. ..."
Olfactory bulb network: neurogenetic restructuring and odor decorrelation (Chow et al. 2012)
||Adult neurogenesis in the olfactory bulb has been shown experimentally
to contribute to perceptual learning. Using a computational network
model we show that fundamental aspects of the adult neurogenesis
observed in the olfactory bulb -- the persistent addition of new
inhibitory granule cells to the network, their activity-dependent
survival, and the reciprocal character of their synapses with the
principal mitral cells -- are sufficient to restructure the network
and to alter its encoding of odor stimuli adaptively so as to reduce
the correlations between the bulbar representations of similar
stimuli. The model captures the experimentally observed
role of neurogenesis in perceptual learning and the enhanced response
of young granule cells to novel stimuli. Moreover, it makes specific
predictions for the type of odor enrichment that should be effective
in enhancing the ability of animals to discriminate similar odor
mixtures. NSF grant DMS-0719944.