21 research outputs found
Capacity of a Simple Intercellular Signal Transduction Channel
We model the ligand-receptor molecular communication channel with a
discrete-time Markov model, and show how to obtain the capacity of this
channel. We show that the capacity-achieving input distribution is iid;
further, unusually for a channel with memory, we show that feedback does not
increase the capacity of this channel.Comment: 5 pages, 1 figure. To appear in the 2013 IEEE International Symposium
on Information Theor
Capacity of a Simple Intercellular Signal Transduction Channel
We model biochemical signal transduction, based on a ligand-receptor binding
mechanism, as a discrete-time finite-state Markov channel, which we call the
BIND channel. We show how to obtain the capacity of this channel, for the case
of binary output, binary channel state, and arbitrary finite input alphabets.
We show that the capacity-achieving input distribution is IID. Further, we show
that feedback does not increase the capacity of this channel. We show how the
capacity of the discrete-time channel approaches the capacity of Kabanov's
Poisson channel, in the limit of short time steps and rapid ligand release.Comment: Accepted for publication in IEEE Transactions on Information Theor
Finite-State Channel Models for Signal Transduction in Neural Systems
Information theory provides powerful tools for understanding communication
systems. This analysis can be applied to intercellular signal transduction,
which is a means of chemical communication among cells and microbes. We discuss
how to apply information-theoretic analysis to ligand-receptor systems, which
form the signal carrier and receiver in intercellular signal transduction
channels. We also discuss the applications of these results to neuroscience.Comment: Accepted for publication in 2016 IEEE International Conference on
Acoustics, Speech, and Signal Processing, Shanghai, Chin