Extracorporeal membrane oxygenation system is used for rescue treatment strategies for temporary cardiopulmonary function support to facilitate adequately oxygenated blood to return into the systemic and pulmonary circulation systems. Therefore, a servo flow regulator is used to adjust the roller motor speed, while support blood flow can match the sweep gas flow (GF) in a membrane oxygenator. A generalised regression neural network is designed as an estimator to automatically estimate the desired roller pump speed and control parameters. Then, the proportional–integral–derivative controller with tuning control parameters showed good performance to achieve speed regulation and speed tracking in the desired operating point. Given the pressure of carbon dioxide, drainage blood flow, and cannula size, the proposed predictable capability control scheme can be validated to meet the intended uses in clinical applications.

In this study, the authors first discuss the existence of Bogdanov–Takens and triple zero singularity of a five neurons neutral bidirectional associative memory neural networks model with two delays. Then, by utilising the centre manifold reduction and choosing suitable bifurcation parameters, the second-order and the third-order normal forms of the Bogdanov–Takens bifurcation for the system are obtained. Finally, the obtained normal form and numerical simulations show some interesting phenomena such as the existence of a stable fixed point, a pair of stable non-trivial equilibria, a stable limit cycles, heteroclinic orbits, homoclinic orbits, coexistence of two stable non-trivial equilibria and a stable limit cycles in the neighbourhood of the Bogdanov–Takens bifurcation critical point.

Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators. Specifically, the authors modify the dictionary learning model, k-SVD, according to the sparse property of GRNs for mining the regulatory signals. The recovered regulatory signals are then used as a pool of regulatory factors to calculate a confidence score for a given transcription factor regulating a target gene. The capability of recovering hidden regulatory signals was verified on simulated data. Comparative experiments for GRN inference between the proposed algorithm (OURM) and some state-of-the-art algorithms, e.g. GENIE3 and ARACNE, on real-world data sets show the superior performance of OURM in inferring GRNs: higher area under the receiver operating characteristic curves and area under the precision–recall curves.

In electronic systems, dynamic random access memory (DRAM) is one of the core modules in the modern silicon computer. As for a bio-computer, one would need a mechanism for storage of bio-information named ‘data’, which, in binary logic, has two levels, logical high and logical low, or in the normalised form, ‘1’ and ‘0’. This study proposes a possible genetic DRAM based on the modified electronic configuration, which uses the biological reaction to fulfil an equivalent RC circuit constituting a memory cell. The authors implement fundamental functions of the genetic DRAM by incorporating a genetic toggle switch for data hold. The results of simulation verify that the basic function can be used on a bio-storage module for the future bio-computer.