NIHBA: a network interdiction approach for metabolic engineering design.

MOTIVATION: Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. RESULTS: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article). AVAILABILITY AND IMPLEMENTATION: Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. CONTACT: math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Neural modeling of antisaccade performance of healthy controls and early Huntington's disease patients.

Huntington's disease (HD), a genetically determined neurodegenerative disease, is positively correlated with eye movement abnormalities in decision making. The antisaccade conflict paradigm has been widely used to study response inhibition in eye movements, and reliable performance deficits in HD subjects have been observed, including a greater number and timing of direction errors. We recorded the error rates and response latencies of early HD patients and healthy age-matched controls performing the mirror antisaccade task. HD participants displayed slower and more variable antisaccade latencies and increased error rates relative to healthy controls. A competitive accumulator-to-threshold neural model was then employed to quantitatively simulate the controls' and patients' reaction latencies and error rates and uncover the mechanisms giving rise to the observed HD antisaccade deficits. Our simulations showed that (1) a more gradual and noisy rate of accumulation of evidence by HD patients is responsible for the observed prolonged and more variable antisaccade latencies in early HD; (2) the confidence level of early HD patients making a decision is unaffected by the disease; and (3) the antisaccade performance of healthy controls and early HD patients is the end product of a neural lateral competition (inhibition) between a correct and an erroneous decision process, and not the end product of a third top-down stop signal suppressing the erroneous decision process as many have speculated.

Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN.

Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses' environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing-temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.

A novel and sensitive electrochemical sensor based on nanoporous gold for determination of As(III).

Three-dimensional porous gold nanoparticles (NPG) were synthesized in situ on indium-doped tin oxide (ITO) substrates by a green and convenient one-step electrodeposition method to achieve super-sensitive As(III) detection. The introduction of NPG method not only greatly improves the electron transfer capacity and surface area of sensor interface but provides more active sites for As(III) enrichment, thus boosting sensitivity and selectivity. The sensor was characterized by scanning electron microscopy, energy dispersion spectroscopy, differential pulse anode stripping voltammetry (DPASV), and electrochemical impedance to evaluate its morphology, composition, and electrochemical performance. The wall thickness of NPG was customized by optimizing the concentration of electroplating solution, dissolved electrolyte, deposition potential, and reaction time. Under optimal conditions, the electrochemical sensor showed a wide linear range from 0.1 to 50 µg/L As(III), with a detection limit (LOD) of 0.054 µg/L (S/N = 3). The LOD is far below 10 µg/L, the recommended maximum value by the world health organization for drinking water. Stability, reproducibility, and repeatability of NGP/ITO were determined to be 2.77%, 4.9%, and 4.1%, respectively. Additionally, the constructed sensor has been successfully applied to determine As(III) in three actual samples, and the results are in good agreement with that of hydride generation atomic fluorescence spectrometry (AFS). Graphical abstract.

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance.

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap-filling technique to increase population diversity. SDM further guides rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence in every generation of evolution regardless of environmental changes. The proposed algorithm has been extensively studied through comparison with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with DMO problems.

CaR: A Cutting and Repulsion-Based Evolutionary Framework for Mixed-Integer Programming Problems.

A mixed-integer programming (MIP) problem contains both constraints and integer restrictions. Integer restrictions divide the feasible region defined by constraints into multiple discontinuous feasible parts. In particular, the number of discontinuous feasible parts will drastically increase with the increase of the number of integer decision variables and/or the size of the candidate set of each integer decision variable. Due to the fact that the optimal solution is located in one of the discontinuous feasible parts, it is a challenging task to solve a MIP problem. This article presents a cutting and repulsion-based evolutionary framework (called CaR) to solve MIP problems. CaR includes two main strategies: 1) the cutting strategy and 2) the repulsion strategy. In the cutting strategy, an additional constraint is constructed based on the objective function value of the best individual found so far, the aim of which is to continuously cut unpromising discontinuous feasible parts. As a result, the probability of the population entering a wrong discontinuous feasible part can be decreased. In addition, in the repulsion strategy, once it has been detected that the population has converged to a discontinuous feasible part, the population will be reinitialized. Moreover, a repulsion function is designed to repulse the previously explored discontinuous feasible parts. Overall, the cutting strategy can significantly reduce the number of discontinuous feasible parts and the repulsion strategy can probe the remaining discontinuous feasible parts. Sixteen test problems developed in this article and two real-world cases are used to verify the effectiveness of CaR. The results demonstrate that CaR performs well in solving MIP problems.

OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production.

Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximization of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout), leading to high biochemical production. The usefulness and capabilities of OptDesign are demonstrated for the production of three biochemicals in Escherichia coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. The source code is available at https://github.com/chang88ye/OptDesign.

Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization.

Dynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more important, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features is then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite is more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot.

A novel electrochemical sensor for determination of hydroxyl radicals in living cells by coupling nanoporous gold layer with self-assembled 6-(Ferrocenyl) hexanethiol.

The detection of hydroxyl radicals (•OH) in live cells is significant to study its physiological and pathological roles, while it is full of challenge due to the extremely low concentration and short lifetime of •OH. Herein, we have developed a novel electrochemical sensor based on 6-(Ferrocenyl) hexanethiol (6-FcHT) self-assembled nanoporous gold layer (NPGL) modified GE (6-FcHT/NPGL/GE), which can detect the release of •OH from living cells with high sensitivity and selectivity. The superior sensitivity can stem from the unique porous architecture of NPGL, which enlarged electrode surface area and expedited electron transportation during electrochemical reactions. Additionally, NPGL provides more active binding sites for the assembly of capture agent (6-FcHT) of •OH, thus ensuring high selectivity. For comparison, 6-FcHT/GE was applied to detect •OH, and the obtained sensitivity was 0.0305 mA nM-1 and detection limit was 0.133 nM in the linear range of 0.4 nM-70 nM. After modification of NPGL, the sensitivity of 6-FcHT/NPGL/GE to the •OH response was increased to 0.1364 mA nM-1, detection limit was reduced to 0.316 pM and the linear range was extended from 1 pM to 100 nM. It is worth mentioning that a plenty of extra merits has also been validated like reproducibility, repeatability and stability, enabling to direct electrochemical detection of •OH in HepG2 cells.

Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons.

Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for DMO, in this paper, we propose a new benchmark generator that is able to tune a number of challenging characteristics, including mixed Pareto-optimal front (convexity-concavity), nonmonotonic and time-varying variable-linkages, mixed types of changes, and randomness in type change, which have rarely or not been considered or tested in the literature. A test suite of ten instances with different dynamic features is produced from the generator in this paper. Additionally, a few new performance measures are proposed to evaluate algorithms for DMOPs with different characteristics. Six representative multiobjective evolutionary algorithms from the literature are investigated based on the proposed DMO test suite and performance measures. The experimental results facilitate a better understanding of strengths and weaknesses of these compared algorithms for DMOPs.

An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.