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