Cells receive signaling molecules by receptors and relay the information via sensory networks so that they can respond properly depending on the type of signals. Recent studies show that , along with static concentration and identity, cells can extract  information from dynamical concentration patterns of signaling molecules. Here we study how cells generally and optimally process multi-dimensional information embedded in dynamical patterns through biochemical networks. Considering a deterministic limit, we model the decoding networks by linear response functions, and optimize the functions with a calculus of variation to maximize the mutual information between patterns and output. We find that optimal decoders are realized with multiple distinct non-monotonic response functions and that such optimal decoders can extract information much  efficiently than typical single layer linear decoders. We also consider the decorrelation of information embedded in the dynamical patterns and show that decorrelating decoders converges to the upper bound of the mutual information for weak noise limit. We explore the biochemical implementation of these decoders using the control theory and show that they can be implemented biochemically through modification of cascade-type networks, which are prevalent in actual signaling pathways.
Cells receive signaling molecules by receptors and relay the information via sensory networks so that they can respond properly depending on the type of signals. Recent studies show that cells can extract multi-dimensional information from dynamical concentration patterns of signaling molecules. Here we study how cells generally and optimally process multi-dimensional information embedded in dynamical patterns through biochemical networks. Considering a deterministic limit, we model the decoding networks by linear response functions, and optimize the functions with the calculus of variations to maximize the mutual information between patterns and output. We find that optimal decoders are realized with multiple distinct non-monotonic response functions and that such optimal decoders can extract information much more efficiently than typical single-layer linear decoders. We also consider the decorrelation of information embedded in the dynamical patterns and show that decorrelating decoders converge to the upper bound of the mutual information in the weak noise limit. We explore the biochemical implementations of these decoders using control theory and demonstrate that they can be implemented biochemically through modification of cascade-type networks, which are prevalent in actual signaling pathways.
D
, along with static concentration and identity,
A
multi-dimensional
R
a calculus of variation
the calculus of variations
A
more
R
single layer
single-layer
R
converges
converge
R
for
in the
R
implementation
implementations
D
the
R
show
demonstrate