Large-Scale Multiobjective Evolutionary Algorithm Guided by Low-Dimensional Surrogates of Scalarization Functions.

Recently, computationally intensive multiobjective optimization problems have been efficiently solved by surrogate-assisted multiobjective evolutionary algorithms. However, most of those algorithms can only handle no more than 200 decision variables. As the number of decision variables increases further, unreliable surrogate models will result in a dramatic deterioration of their performance, which makes large-scale expensive multiobjective optimization challenging. To address this challenge, we develop a large-scale multiobjective evolutionary algorithm guided by low-dimensional surrogate models of scalarization functions. The proposed algorithm (termed LDS-AF) reduces the dimension of the original decision space based on principal component analysis, and then directly approximates the scalarization functions in a decompositionbased multiobjective evolutionary algorithm. With the help of a two-stage modeling strategy and convergence control strategy, LDS-AF can keep a good balance between convergence and diversity, and achieve a promising performance without being trapped in a local optimum prematurely. The experimental results on a set of test instances have demonstrated its superiority over eight state-of-the-art algorithms on multiobjective optimization problems with up to 1000 decision variables using only 500 real function evaluations.

Nanopore sequencing improves construction of customized CRISPR-based gene activation libraries.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based screening has emerged as a powerful tool for identifying new gene targets for desired cellular phenotypes. The construction of guide RNA (gRNA) pools largely determines library quality and is usually performed using Golden Gate assembly or Gibson assembly. To date, library construction methods have not been systematically compared, and the quality check of each batch has been slow. In this study, an in-house nanopore sequencing workflow was established for assessing the current methods of gRNA pool construction. The bias of pool construction was reduced by employing the polymerase-mediated non-amplifying method. Then, a small gRNA pool was utilized to characterize stronger activation domains, specifically MED2 (a subunit of mediator complex) and HAP4 (a heme activator protein), as well as to identify better gRNA choices for dCas12a-based gene activation in Saccharomyces cerevisiae. Furthermore, based on the better CRISPRa tool identified in this study, a custom gRNA pool, which consisted of 99 gRNAs targeting central metabolic pathways, was designed and employed to screen for gene targets that could improve ethanol utilization in S. cerevisiae. The nanopore sequencing-based workflow demonstrated here should provide a cost-effective approach for assessing the quality of customized gRNA library, leading to faster and more efficient genetic and metabolic engineering in S. cerevisiae.

Phase Separation Modulates the Formation and Stabilities of DNA Guanine Quadruplex.

In the presence of monovalent alkali metal ions, G-rich DNA sequences containing four runs of contiguous guanines can fold into G-quadruplex (G4) structures. Recent studies showed that these structures are located in critical regions of the human genome and assume important functions in many essential DNA metabolic processes, including replication, transcription, and repair. However, not all potential G4-forming sequences are actually folded into G4 structures in cells, where G4 structures are known to be dynamic and modulated by G4-binding proteins as well as helicases. It remains unclear whether there are other factors influencing the formation and stability of G4 structures in cells. Herein, we showed that DNA G4s can undergo phase separation in vitro. In addition, immunofluorescence microscopy and ChIP-seq experiments with the use of BG4, a G4 structure-specific antibody, revealed that disruption of phase separation could result in global destabilization of G4 structures in cells. Together, our work revealed phase separation as a new determinant in modulating the formation and stability of G4 structures in human cells.

Performance Indicator-Based Adaptive Model Selection for Offline Data-Driven Multiobjective Evolutionary Optimization.

A number of real-world multiobjective optimization problems (MOPs) are driven by the data from experiments or computational simulations. In some cases, no new data can be sampled during the optimization process and only a certain amount of data can be sampled before optimization starts. Such problems are known as offline data-driven MOPs. Although multiple surrogate models approximating each objective function are able to replace the real fitness evaluations in evolutionary algorithms (EAs), their approximation errors are easily accumulated and therefore, mislead the solution ranking. To mitigate this issue, a new surrogate-assisted indicator-based EA for solving offline data-driven multiobjective problems is proposed. The proposed algorithm adopts an indicator-based selection EA as the baseline optimizer due to its selection robustness to the approximation errors of surrogate models. Both the Kriging models and radial basis function networks (RBFNs) are employed as surrogate models. An adaptive model selection mechanism is designed to choose the right type of models according to a maximum acceptable approximation error that is less likely to mislead the indicator-based search. The main idea is that when the uncertainty of the Kriging models exceeds the acceptable error, the proposed algorithm selects RBFNs as the surrogate models. The results comparing with state-of-the-art algorithms on benchmark problems with up to ten objectives indicate that the proposed algorithm is effective on offline data-driven optimization problems with up to 20 and 30 decision variables.

Targeted Proteomic Analysis of Small GTPases in Radioresistant Breast Cancer Cells.

Radiation therapy benefits more than 50% of all cancer patients and cures 40% of them, where ionizing radiation (IR) deposits energy to cells and tissues, thereby eliciting DNA damage and resulting in cell death. Small GTPases are a superfamily of proteins that play critical roles in cell signaling. Several small GTPases, including RAC1, RHOB, and RALA, were previously shown to modulate radioresistance in cancer cells. However, there is no systematic proteomic study on small GTPases that regulate radioresistance in cancer cells. Herein, we applied a high-throughput scheduled multiple-reaction monitoring (MRM) method, along with the use of synthetic stable isotope-labeled (SIL) peptides, to identify differentially expressed small GTPase proteins in two pairs of breast cancer cell lines, MDA-MB-231 and MCF7, and their corresponding radioresistant cell lines. We identified 7 commonly altered small GTPase proteins with over 1.5-fold changes in the two pairs of cell lines. We also discovered ARFRP1 as a novel regulator of radioresistance, where its downregulation promotes radioresistance in breast cancer cells. Together, this represents the first comprehensive investigation about the differential expression of the small GTPase proteome associated with the development of radioresistance in breast cancer cells. Our work also uncovered ARFRP1 as a new target for enhancing radiation sensitivity in breast cancer.

Improved Functional Expression of Cytochrome P450s in Saccharomyces cerevisiae Through Screening a cDNA Library From Arabidopsis thaliana.

Cytochrome P450 enzymes (P450s) are a superfamily of heme-thiolate proteins widely existing in various organisms and play a key role in the metabolic network and secondary metabolism. However, the low expression levels and activities have become the biggest challenge for P450s studies. To improve the functional expression of P450s in Saccharomyces cerevisiae, an Arabidopsis thaliana cDNA library was expressed in the betaxanthin-producing yeast strain, which functioned as a biosensor for high throughput screening. Three new target genes AtGRP7, AtMSBP1, and AtCOL4 were identified to improve the functional expression of CYP76AD1 in yeast, with accordingly the accumulation of betaxanthin increased for 1.32-, 1.86-, and 1.10-fold, respectively. In addition, these three targets worked synergistically/additively to improve the production of betaxanthin, representing a total of 2.36-fold improvement when compared with the parent strain. More importantly, these genes were also determined to effectively increase the activity of another P450 enzyme (CYP736A167), catalyzing the hydroxylation of α-santalene to produce Z-α-santalol. Simultaneous overexpression of AtGRP7, AtMSBP1, and AtCOL4 increased α-santalene to Z-α-santalol conversion rate for more than 2.97-fold. The present study reported a novel strategy to improve the functional expression of P450s in S. cerevisiae and promises the construction of platform yeast strains for the production of natural products.

A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems.

Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are available for evaluating candidate solutions. In this paper, we address a class of expensive data-driven constrained multiobjective combinatorial optimization problems, where the objectives and constraints can be calculated only on the basis of a large amount of data. To solve this class of problems, we propose using random forests (RFs) and radial basis function networks as surrogates to approximate both objective and constraint functions. In addition, logistic regression models are introduced to rectify the surrogate-assisted fitness evaluations and a stochastic ranking selection is adopted to further reduce the influences of the approximated constraint functions. Three variants of the proposed algorithm are empirically evaluated on multiobjective knapsack benchmark problems and two real-world trauma system design problems. Experimental results demonstrate that the variant using RF models as the surrogates is effective and efficient in solving data-driven constrained multiobjective combinatorial optimization problems.

Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization.

Cooperative coevolutionary (CC) algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes CC algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into different subcomponents and thereby a loss of important information about the topology of the overall fitness landscape. In this paper, we suggest an idea that concurrently searches for multiple optima and uses them as informative representatives to be exchanged among subcomponents for compensation. To this end, we incorporate a multimodal optimization procedure into each subcomponent, which is adaptively triggered by the status of subcomponent optimizers. In addition, a nondominance-based selection scheme is proposed to adaptively select one complete solution for evaluation from the ones that are constructed by combining informative representatives from each subcomponent with a given solution. The performance of the proposed algorithm has been demonstrated by comparing five popular CC algorithms on a set of selected problems that are recognized to be hard for traditional CC algorithms. The superior performance of the proposed algorithm is further confirmed by a comprehensive study that compares 17 state-of-the-art CC algorithms and other metaheuristic algorithms on 20 1000-dimensional benchmark functions.

Maximizing geographical efficiency: An analysis of the configuration of Colorado's trauma system.

BACKGROUND: Trauma center designation in excess of need risks dilution of experience, reduction in research and training opportunities, and increased costs. The objective of this study was to evaluate the use of a novel data-driven approach (whole-system mathematical modeling of patient flow) to compare the configuration of an existing trauma system with a mathematically optimized design, using the State of Colorado as a case study. METHODS: Geographical network analysis and multiobjective optimization, 105,448 patients injured in the State of Colorado between 2009 and 2013, who met the criteria for inclusion in the state-mandated trauma registry maintained by the Colorado Department of Public Health and Environment were included. We used the Nondominant Sorting Genetic Algorithm II to conduct a multiobjective optimization of possible trauma system configurations, with the objectives of minimizing total system access time, and the number of casualties who could not reach the desired level of care. RESULTS: Modeling suggested that system configurations with high-volume Level I trauma centers could be mathematically optimized with two centers rather than the current three (with an estimated annual volume of 970-1,020 and 715-722 severely injured patients per year), four to five Level II centers, and 12 to 13 Level III centers. Configurations with moderate volume Level I centers could be optimized with three such centers (with estimated institutional volumes of 439-502, 699-947, and 520-726 severely injured patients per year), two to five Level II centers, and eight to ten Level III centers. CONCLUSION: The modeling suggested that the configuration of Colorado's trauma system could be mathematically optimized with fewer trauma centers than currently designated. Consideration should be given to the role of optimization modeling to inform decisions about the ongoing efficiency of trauma systems. However, modeling on its own cannot guarantee improved patient outcome; thus, the use of model results for decision making should take into account wider contextual information. LEVEL OF EVIDENCE: Epidemiological, Level IV.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

Diversity Assessment in Many-Objective Optimization.

Maintaining diversity is one important aim of multiobjective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multiobjective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for many-objective optimization, which is an accumulation of the dissimilarity in the population, where an L p -norm-based ( ) distance is adopted to measure the dissimilarity of solutions. Empirical results demonstrate our proposed metric can more accurately assess the diversity of solutions in various situations. We compare the diversity of the solutions obtained by four popular many-objective evolutionary algorithms using the proposed diversity metric on a large number of benchmark problems with two to ten objectives. The behaviors of different diversity maintenance methodologies in those algorithms are discussed in depth based on the experimental results. Finally, we show that the proposed diversity measure can also be employed for enhancing diversity maintenance or reference set generation in many-objective optimization.

Regularity Model for Noisy Multiobjective Optimization.

Regularity models have been used in dealing with noise-free multiobjective optimization problems. This paper studies the behavior of a regularity model in noisy environments and argues that it is very suitable for noisy multiobjective optimization. We propose to embed the regularity model in an existing multiobjective evolutionary algorithm for tackling noises. The proposed algorithm works well in terms of both convergence and diversity. In our experimental studies, we have compared several state-of-the-art of algorithms with our proposed algorithm on benchmark problems with different levels of noises. The experimental results showed the effectiveness of the regularity model on noisy problems, but a degenerated performance on some noisy-free problems.

Access to specialist care: Optimizing the geographic configuration of trauma systems.

BACKGROUND: The optimal geographic configuration of health care systems is key to maximizing accessibility while promoting the efficient use of resources. This article reports the use of a novel approach to inform the optimal configuration of a national trauma system. METHODS: This is a prospective cohort study of all trauma patients, 15 years and older, attended to by the Scottish Ambulance Service, between July 1, 2013, and June 30, 2014. Patients underwent notional triage to one of three levels of care (major trauma center [MTC], trauma unit, or local emergency hospital). We used geographic information systems software to calculate access times, by road and air, from all incident locations to all candidate hospitals. We then modeled the performance of all mathematically possible network configurations and used multiobjective optimization to determine geospatially optimized configurations. RESULTS: A total of 80,391 casualties were included. A network with only high- or moderate-volume MTCs (admitting at least 650 or 400 severely injured patients per year, respectively) would be optimally configured with a single MTC. A network accepting lower-volume MTCs (at least 240 severely injured patients per year) would be optimally configured with two MTCs. Both configurations would necessitate an increase in the number of helicopter retrievals. CONCLUSION: This study has shown that a novel combination of notional triage, network analysis, and mathematical optimization can be used to inform the planning of a national clinical network. Scotland's trauma system could be optimized with one or two MTCs. LEVEL OF EVIDENCE: Care management study, level IV.

Feasibility and utility of population-level geospatial injury profiling: prospective, national cohort study.

BACKGROUND: Geospatial analysis is increasingly being used to evaluate the design and effectiveness of trauma systems, but there are no metrics to describe the geographic distribution of incidents. The aim of this study, therefore, was to evaluate the feasibility and utility of using spatial analysis to characterize, at scale, the geospatial profile of an injured population. METHODS: This is a prospective national cohort study of all trauma patients attended to by the Scottish Ambulance Service in a complete year (between July 1, 2013, and June 30, 2014). Incident location and severity were collected at source. Incident distribution was evaluated using geostatistical techniques. RESULTS: There were 80,391 recorded incidents involving traumatic injury. Incident density was highest in the central Southern part of the country and along the East coast, broadly following the population distribution and road network. The overall distribution was highly clustered, and centered on the central Southern and Eastern parts of the country. When analyzed by triage category, the distribution of incidents triaged to major trauma center care was slightly less clustered than that of incidents triaged to trauma unit or local emergency hospital care, but the spread was similar. When analyzed by type of injury, assaults and falls were more clustered than incidents relating to traffic and transportation. CONCLUSION: This study demonstrates the feasibility and power of describing the geographic distribution of a group of injured patients. The methodology described has potential application for injury surveillance and trauma system design and evaluation.

A memetic optimization strategy based on dimension reduction in decision space.

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

Optimizing trauma system design: the GEOS (Geospatial Evaluation of Systems of Trauma Care) approach.

BACKGROUND: Trauma systems have been shown to reduce death and disability from injury but must be appropriately configured. A systematic approach to trauma system design can help maximize geospatial effectiveness and reassure stakeholders that the best configuration has been chosen. METHODS: This article describes the GEOS [Geospatial Evaluation of Systems of Trauma Care] methodology, a mathematical modeling of a population-based data set, which aims to derive geospatially optimized trauma system configurations for a geographically defined setting. GEOS considers a region's spatial injury profile and the available resources and uses a combination of travel time analysis and multiobjective optimization. The methodology is described in general and with regard to its application to our case study of Scotland. RESULTS: The primary outcome will be trauma system configuration. CONCLUSION: GEOS will contribute to the design of a trauma system for Scotland. The methodology is flexible and inherently transferable to other settings and could also be used to provide assurance that the configuration of existing trauma systems is fit for purpose.

Corner Sort for Pareto-Based Many-Objective Optimization.

Nondominated sorting plays an important role in Pareto-based multiobjective evolutionary algorithms (MOEAs). When faced with many-objective optimization problems multiobjective optimization problems (MOPs) with more than three objectives, the number of comparisons needed in nondominated sorting becomes very large. In view of this, a new corner sort is proposed in this paper. Corner sort first adopts a fast and simple method to obtain a nondominated solution from the corner solutions, and then uses the nondominated solution to ignore the solutions dominated by it to save comparisons. Obtaining the nondominated solutions requires much fewer objective comparisons in corner sort. In order to evaluate its performance, several state-of-the-art nondominated sorts are compared with our corner sort on three kinds of artificial solution sets of MOPs and the solution sets generated from MOEAs on benchmark problems. On one hand, the experiments on artificial solution sets show the performance on the solution sets with different distributions. On the other hand, the experiments on the solution sets generated from MOEAs show the influence that different sorts bring to MOEAs. The results show that corner sort performs well, especially on many-objective optimization problems. Corner sort uses fewer comparisons than others.