Efficient Information Coding Over Living Organisms

Background: Encoding information over the genome of living organisms is a fundamental problem in synthetic biology with various applications. This topic is specifically challenging due the fact that living organisms constantly undergo evolution that can cause the deletion of the encoded information. The aim of this research is to study the problem of information coding over living organisms.

Methods: We start by modeling the living organism as a discrete communication channel, in a way that captures the major biological phenomena, and derive the channel capacity for a simplified model. Next we present a coding scheme which allows reliable communication over this channel, and analyse its performance. To evaluate our approach we performed numerical simulations over real genomic data based on some micro organism models. The simulations include random channel behavior based on parameters estimations from evolutional models and genomic data. Following work will include in vivo experiments of the approach.

Results: Channel capacity is analytically given for the simplified channel model. Numerical simulation results indicate that under the simplified channel model, our code achieves the capacity. Also the achievable data rates under the channel model is strictly given for a wide set of parameters.

Conclusion: In this work we have set the foundations for information coding over living organisms, both in presenting a channel model capturing the major biological phenomena, and in presenting a coding scheme.