Noise Tolerance Analysis for Reliable Analog and Digital Computation in Living Cells
Rizik L, Ram Y and Danial R*
Biomedical Engineering Department, Technion – Israel Institute of Technology, Haifa, Israel
- *Corresponding Author:
- Danial R
Biomedical Engineering Department
Technion– Israel Institute of Technology, Haifa, Israel
Tel: 972 77-887-5555
E-mail: [email protected]
Received: April 05, 2016; Accepted: April 20, 2016; Published: April 27, 2016
Citation: Rizik L, Ram Y, Danial R (2016) Noise Tolerance Analysis for Reliable Analog and Digital Computation in Living Cells. J Bioengineer & Biomedical Sci 6:186. doi:10.4172/2155-9538.1000186
Copyright: © 2016 Rizik L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Biomolecular computing, encompassing computations performed by molecules, proteins and DNA, is a central area of focus in Synthetic Biology research and development, which attempt to apply engineering design principles in living cells. Two major computation paradigms have been implemented so far in living cells - analog paradigm that computes with a continuous set of numbers and digital paradigm that computes with two-discreet set of numbers. Here, we analyze the biophysical and technological limits of large-scale gene networks created based on analog and digital computation in living cells. More specifically, we calculate the precision of analog systems and the noise margin of digital systems in living cells. We conclude that both systems are challenging to operate with low protein levels. To overcome this challenge, we show that analog systems should operate with a Hill coefficient smaller than 1 and digital systems should be buffered. Furthermore, an analytical description of a biophysical model recently developed for positive feedback linearization circuits and used in analog synthetic biology, is presented. Finally, we suggest new directions for engineering biological circuits capable of computation.