scholarly journals A Microbial Screening in Silico Method for the Fitness Step Evaluation in Evolutionary Algorithms

2020 ◽  
Vol 10 (11) ◽  
pp. 3936
Author(s):  
A. Gargantilla Becerra ◽  
R. Lahoz-Beltra
Keyword(s):  
Synthetic Biology ◽  
In Silico ◽  
Optimization Problems ◽  
Evaluation Function ◽  
Laboratory Methods ◽  
Bacterial Colony ◽  
Fitness Value ◽  
General Rules ◽  
A Cell ◽  
Microbial Screening

One of the most delicate stages of an evolutionary algorithm is the evaluation of the goodness of the solutions by some procedure providing a fitness value. However, although there are general rules, it is not always easy to find an appropriate evaluation function for a given problem. In the biological realm, today, there is a variety of experimental methods under the name of microbial screening to identify and select bacteria from their traits, as well as to obtain their fitness. In this paper, we show how given an optimization problem, a colony of synthetic bacteria or bacterial agents is able to evaluate the fitness of candidate solutions by building an evaluation function. The evaluation function is obtained simulating, in silico, a bacterial colony conducting the laboratory methods used in microbiology, biotechnology and synthetic biology to measure microbial fitness. Once the evaluation function is built, it is included in the code of the genetic algorithm as part of the fitness routine. The practical use of this approach is illustrated in two classic optimization problems. In silico routines have been programmed in Gro, a cell programming language oriented to synthetic biology, and can easily be customized to many other optimization problems.

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

2021 ◽  
Author(s):  
Mohammad Shehab ◽  
Laith Abualigah
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Second Stage ◽  
Opposition Based Learning ◽  
High Fitness ◽  
Fitness Value ◽  
Speed Up ◽  
Disruption Operator ◽  
The Given ◽  
Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

scholarly journals Podophyllotoxin and Quercetin in Silico Derivatives Docking Analysis with Cyclooxygenase-2 and Aminopeptidase-N

2021 ◽  
Author(s):  
A.E. Manukyan ◽  
A.A. Hovhannisyan
Keyword(s):  
In Silico ◽  
Biological Activities ◽  
Three Dimensional ◽  
Aminopeptidase N ◽  
Docking Analysis ◽  
Quercetin Derivatives ◽  
Pubchem Database ◽  
A Cell ◽  
Cox 2 ◽  
High Level

ABSTRACTThe cyclooxygenase (COX) enzymes are tumor markers, the inhibition of which can be used in the prevention and therapy of carcinogenesis. It was found that COX-2 IS considered as targets for tumor inhibition. Aminopeptidase N (APN) is a type II membrane-bound metalloprotease associated with cancer, being identified as a cell marker on the surface of malignant myeloid cells and reached a high level of expression in progressive tumors. In anticancer therapy, plant compounds are considered that can inhibit their activity. Modeling of the COX-2 and APN enzymes was carried out on the basis of molecular models of three-dimensional structures from the PDB database [PDB ID: 5f19, 4fyq] RCSB. For docking analysis, 3D ligand models were created using MarvinSketch based on the PubChem database [CID: 5280343, 5281654]. In silico experiments, for the first time, revealed the possible interaction and inhibition of COX-2 and APN by quercetin and quercetin derivatives. Aspirin and Marimastat were taken to compare the results. Possible biological activities and possible side effects of the ligands have been identified.

scholarly journals Multiple infill criterion-assisted hybrid evolutionary optimization for medium-dimensional computationally expensive problems

2021 ◽  
Author(s):  
Shufen Qin ◽  
Chan Li ◽  
Chaoli Sun ◽  
Guochen Zhang ◽  
Xiaobo Li
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Global Search ◽  
Model Management ◽  
The Other ◽  
Local Searches ◽  
Fitness Value ◽  
Expensive Problems ◽  
Computationally Expensive

AbstractSurrogate-assisted evolutionary algorithms have been paid more and more attention to solve computationally expensive problems. However, model management still plays a significant importance in searching for the optimal solution. In this paper, a new method is proposed to measure the approximation uncertainty, in which the differences between the solution and its neighbour samples in the decision space, and the ruggedness of the objective space in its neighborhood are both considered. The proposed approximation uncertainty will be utilized in the surrogate-assisted global search to find a solution for exact objective evaluation to improve the exploration capability of the global search. On the other hand, the approximated fitness value is adopted as the infill criterion for the surrogate-assisted local search, which is utilized to improve the exploitation capability to find a solution close to the real optimal solution as much as possible. The surrogate-assisted global and local searches are conducted in sequence at each generation to balance the exploration and exploitation capabilities of the method. The performance of the proposed method is evaluated on seven benchmark problems with 10, 20, 30 and 50 dimensions, and one real-world application with 30 and 50 dimensions. The experimental results show that the proposed method is efficient for solving the low- and medium-dimensional expensive optimization problems by compared to the other six state-of-the-art surrogate-assisted evolutionary algorithms.

scholarly journals In silico stress fibre content affects peak strain in cytoplasm and nucleus but not in membrane for uniaxial substrate stretch

2019 ◽  
Author(s):  
Tamer Abdalrahman ◽  
Neil H. Davies ◽  
Thomas Franz
Keyword(s):  
Elastic Modulus ◽  
In Silico ◽  
Focal Adhesions ◽  
Element Model ◽  
Homogenization Method ◽  
Fibre Volume ◽  
Fibre Content ◽  
Peak Strain ◽  
Stress Fibre ◽  
A Cell

AbstractExisting in silico models for single cell mechanics feature limited representations of cytoskeletal structures that contribute substantially to the mechanics of a cell. We propose a micromechanical hierarchical approach to capture the mechanical contribution of actin stress fibres. For a cell-specific fibroblast geometry with membrane, cytoplasm and nucleus, the Mori-Tanaka homogenization method was employed to describe cytoplasmic inhomogeneities and constitutive contribution of actin stress fibres. The homogenization was implemented in a finite element model of the fibroblast attached to a substrate through focal adhesions. Strain in cell membrane, cytoplasm and nucleus due to uniaxial substrate stretch was assessed for different stress fibre volume fractions and different elastic modulus of the substrate. A considerable decrease of the peak strain with increasing stress fibre content was observed in cytoplasm and nucleus but not the membrane, whereas the peak strain in cytoplasm, nucleus and membrane increased for increasing elastic modulus of the substrate.

The link between the AMPK/SIRT1 axis and a flavonoid‐rich extract of Citrus bergamia juice: A cell‐free, in silico, and in vitro study

2019 ◽  
Vol 33 (7) ◽  
pp. 1805-1814 ◽  
Author(s):  
Alessandro Maugeri ◽  
Nadia Ferlazzo ◽  
Laura De Luca ◽  
Rosaria Gitto ◽  
Michele Navarra
Keyword(s):  
In Silico ◽  
In Vitro Study ◽  
Vitro Study ◽  
A Cell ◽  
Citrus Bergamia

scholarly journals The in silico human surfaceome

2018 ◽  
Vol 115 (46) ◽  
pp. E10988-E10997 ◽  
Author(s):  
Damaris Bausch-Fluck ◽  
Ulrich Goldmann ◽  
Sebastian Müller ◽  
Marc van Oostrum ◽  
Maik Müller ◽  
...  
Keyword(s):  
Cell Surface ◽  
In Silico ◽  
Embryonic Stem ◽  
Surface Protein ◽  
Surface Proteins ◽  
Cell Surface Protein ◽  
Cell Surface Proteins ◽  
A Cell ◽  
Visualization Tools ◽  
Protein Classes

Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.

scholarly journals A Multiobjective Optimization Algorithm Based on Discrete Bacterial Colony Chemotaxis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhigang Lu ◽  
Tao Feng ◽  
Zhaozheng Liu
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Population Diversity ◽  
Transfer Mechanism ◽  
Bacterial Colony ◽  
Continuous Space ◽  
Pareto Optimal Set ◽  
Crowding Distance ◽  
Multiobjective Optimization Problems ◽  
Definition Of

Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC) algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.

Optimization of Road Networks Using Evolutionary Strategies

1997 ◽  
Vol 5 (4) ◽  
pp. 419-438 ◽  
Author(s):  
Frank Schweitzer ◽  
Werner Ebeling ◽  
Helge Rosé ◽  
Olaf Weiss
Keyword(s):  
Road Network ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Road Construction ◽  
Stationary Regime ◽  
Analytical Approximation ◽  
Evolutionary Strategies ◽  
The Road ◽  
Total Length ◽  
Fitness Value

A road network usually has to fulfill two requirements: (i) it should as far as possible provide direct connections between nodes to avoid large detours; and (ii) the costs for road construction and maintenance, which are assumed proportional to the total length of the roads, should be low. The optimal solution is a compromise between these contradictory demands, which in our model can be weighted by a parameter. The road optimization problem belongs to the class of frustrated optimization problems. In this paper, a special class of evolutionary strategies, such as the Boltzmann and Darwin and mixed strategies, are applied to find differently optimized solutions (graphs of varying density) for the road network, depending on the degree of frustration. We show that the optimization process occurs on two different time scales. In the asymptotic limit, a fixed relation between the mean connection distance (detour) and the total length (costs) of the network exists that defines a range of possible compromises. Furthermore, we investigate the density of states, which describes the number of solutions with a certain fitness value in the stationary regime. We find that the network problem belongs to a class of optimization problems in which more effort in optimization certainly yields better solutions. An analytical approximation for the relation between effort and improvement is derived.

scholarly journals Improvement Analysis and Application of Real-Coded Genetic Algorithm for Solving Constrained Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jiquan Wang ◽  
Zhiwen Cheng ◽  
Okan K. Ersoy ◽  
Panli Zhang ◽  
Weiting Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Global Search ◽  
Single Mutation ◽  
Constrained Optimization Problems ◽  
Fitness Value ◽  
Steering Mechanism ◽  
Real Coded Genetic Algorithm

An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization problems. First, a sorting grouping selection method is given with the advantage of easy realization and not needing to calculate the fitness value. Secondly, a heuristic normal distribution crossover (HNDX) operator is proposed. It can guarantee the cross-generated offsprings to locate closer to the better one among the two parents and the crossover direction to be very close to the optimal crossover direction or to be consistent with the optimal crossover direction. In this way, HNDX can ensure that there is a great chance of generating better offsprings. Thirdly, since the GA in the existing literature has many iterations, the same individuals are likely to appear in the population, thereby making the diversity of the population worse. In IRCGA, substitution operation is added after the crossover operation so that the population does not have the same individuals, and the diversity of the population is rich, thereby helping avoid premature convergence. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, this paper proposes a combinational mutation method, which makes the mutation operation take into account both local search and global search. The computational results with nine examples show that the IRCGA has fast convergence speed. As an example application, the optimization model of the steering mechanism of vehicles is formulated and the IRCGA is used to optimize the parameters of the steering trapezoidal mechanism of three vehicle types, with better results than the other methods used.

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