Computing with viruses

In recent years, different computing models have emerged within the area of Unconven-tional Computation, and more specifically within Natural Computing, getting inspiration from mechanisms present in Nature. In this work, we incorporate concepts in virology and theoretical computer science to propose a novel computational model, called Virus Ma-chine. Inspired by the manner in which viruses transmit from one host to another, a virus machine is a computational paradigm represented as a heterogeneous network that con-sists of three subnetworks: virus transmission, instruction transfer, and instruction-channel control networks. Virus machines provide non-deterministic sequential devices. As num-ber computing devices, virus machines are proved to be computationally complete, that is, equivalent in power to Turing machines. Nevertheless, when some limitations are imposed with respect to the number of viruses present in the system, then a characterization for semi-linear sets is obtained

Molecular Logic Computation with Debugging Method

Seesaw gate concept, which is based on a reversible DNA strand branch process, has been found to have the potential to be used in the construction of various computing devices. In this study, we consider constructing full adder and serial binary adder, using the new concept of seesaw gate. Our simulation of the full adder preformed properly as designed; however unexpected exception is noted in the simulation of the serial binary adder. To identify and address the exception, we propose a new method for debugging the molecular circuit. The main idea for this method is to add fan-outs to monitor the circuit in a reverse stepwise manner. These fan-outs are fluorescent signals that can obtain the real-time concentration of the target molecule. By analyzing the monitoring result, the exception can be identified and located. In this paper, examples of XOR and serial binary adder circuits are described to prove the practicability and validity of the molecular circuit debugging method

Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy

Background: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Our study proposed an automatic approach for identifying TATA-binding proteins efficiently, accurately, and conveniently. This method would guide for the special protein identification with computational intelligence strategies. Results: Firstly, we proposed novel fingerprint features for TBP based on pseudo amino acid composition, physicochemical properties, and secondary structure. Secondly, hierarchical features dimensionality reduction strategies were employed to improve the performance furthermore. Currently, Pretata achieves 92.92% TATA- binding protein prediction accuracy, which is better than all other existing methods. Conclusions: The experiments demonstrate that our method could greatly improve the prediction accuracy and speed, thus allowing large-scale NGS data prediction to be practical. A web server is developed to facilitate the other researchers, which can be accessed at http://server.malab.cn/preTata/

When Matrices Meet Brains

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, a discrete structure representation of SN P systems is proposed. Specifically, matrices are used to represent SN P systems. In order to represent the computations of SN P systems by matrices, configuration vectors are defined to monitor the number of spikes in each neuron at any given configuration; transition net gain vectors are also introduced to quantify the total amount of spikes consumed and produced after the chosen rules are applied. Nondeterminism of the systems is assured by a set of spiking transition vectors that could be used at any given time during the computation. With such matrix representation, it is quite convenient to determine the next configuration from a given configuration, since it involves only multiplying vectors to a matrix and adding vectors

Deterministic Solutions to QSAT and Q3SAT by Spiking Neural P Systems with Pre-Computed Resources

In this paper we continue previous studies on the computational effciency of spiking neural P systems, under the assumption that some pre-computed resources of exponential size are given in advance. Specifically, we give a deterministic solution for each of two well known PSPACE-complete problems: QSAT and Q3SAT. In the case of QSAT, the answer to any instance of the problem is computed in a time which is linear with respect to both the number n of Boolean variables and the number m of clauses that compose the instance. As for Q3SAT, the answer is computed in a time which is at most cubic in the number n of Boolean variables

Embedded Based Miniaturized Universal Electrochemical Sensing Platform

We created an embedded sensing platform based on STM32 embedded system, with integrated carbon-electrode ionic sensor by using a self-made plug. Given ration of concentration-unknown nitrate liquid samples, this platform is able to measure the nitrate concentration in neutral environment. Response signals which were transmitted by the sensor can be displayed via a serial port to the computer screen or via Bluetooth to the smartphone. Processed by a fitting function, signals are transformed into related concentration. Through repeating the experiment many times, the accuracy and repeatability turned out to be excellent. The results can be automatically stored on smartphone via Bluetooth. We created this embedded sensing platform for field water quality measurement. This platform also can be applied for other micro sensors’ signal acquisition and data processing

LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat