Reprogrammed tracrRNAs enable repurposing RNAs as crRNAs and detecting RNAs

In type II CRISPR systems, the guide RNA (gRNA) consists of a CRISPR RNA (crRNA) and a hybridized trans-acting CRISPR RNA (tracrRNA) which interacts directly with Cas9 and is essential to its guided DNA targeting function. Though tracrRNAs are diverse in sequences and structures across type II CRISPR systems, the programmability of crRNA-tracrRNA hybridization for particular Cas9 has not been studied adequately. Here, we revealed the high programmability of crRNA-tracrRNA hybridization for Streptococcus pyogenes Cas9. By reprogramming the crRNA-tracrRNA hybridized sequence, reprogrammed tracrRNAs can repurpose various RNAs as crRNAs to trigger CRISPR function. We showed that the engineered crRNA-tracrRNA pairs enable design of orthogonal cellular computing devices and hijacking of endogenous RNAs as crRNAs. We next designed novel RNA sensors that can monitor the transcriptional activity of specific genes on the host genome and detect SARS-CoV-2 RNA in vitro. The engineering potential of crRNA-tracrRNA interaction has therefore redefined the capabilities of CRISPR/Cas9 system.

Overview of Cellular Computing-Basic Principles and Applications

Computers, due to their raw speed and massive computing power, have been highly used by biologists to expedite life science research whereas several computational algorithms like artificial neural network, genetic algorithm and many similar ones have been inspired by the behaviors of several biological or cellular entities. However till date both these disciplines i.e. life sciences and computer sciences have mostly progressed separately while recent studies are increasingly highlighting the impact of each discipline on the other. The chapter describes several features of biological systems which could be used for further optimizations of computer programs or could be engineered to harness necessary computational capabilities in lieu of traditional silico chip systems. We also highlight underlying challenges and avenues of implementations of cellular computing.

Pathways to cellular supremacy in biocomputing

Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found.

MSB based Cellular Automata for Edge Detection

A vitalcrucial pre-processing phase in image processing, computer vision and machine learning applications is Edge Detection which detects boundaries of foreground and background objects in an image. Discrimination between significant edges and not so important spurious edges highly affects the accuracy of edge detection process. This paper introduces an approach for extraction of significant edges present in images based on cellular automata. Cellular automata is a finite state machine where every cell has a state. Existing edge detection methods are complex to implement so they have large processing time. These methods tend to produce non-satisfactory results for noisy images which have cluttered background. Some methods are so trivial that they miss part of true edges and some methods are so complex that they tend to give spurious edges which are not required. The advantage of using cellular computing approach is to enhance edge detection process by reducing complexity and processing time. Parallel processing makes this method fast and computationally imple. MATLAB results of proposed method performed on images from Mendeley Dataset are compared with results obtained from existing edge detection techniques by evaluation of MSE and PSNR values Results indicate promising performance of the proposed algorithm. Visually compared, the proposed method produces better results to identify edges more clearly and is intelligent enough to discard spurious edges even for cluttered and complex images