Optimization of cDNA microarray experimental designs using an evolutionary algorithm

The cDNA microarray is an important tool for generating large datasets of gene expression measurements.An efficient design is critical to ensure that the experiment will be able to address relevant biologicalquestions. Microarray experimental design can be treated as a multicriterion optimization problem. For thisclass of problems evolutionary algorithms (EAs) are well suited, as they can search the solution space andevolve a design that optimizes the parameters of interest based on their relative value to the researcher undera given set of constraints. This paper introduces the use of EAs for optimization of experimental designs ofspotted microarrays using a weighted objective function. The EA and the various criteria relevant to designoptimization are discussed. Evolved designs are compared with designs obtained through exhaustive searchwith results suggesting that the EA can find just as efficient optimal or near-optimal designs within atractable timeframe.     

Automated neuron model optimization techniques: a review

The increase in complexity of computational neuron models makes the hand tuning of model parameters more difficult than ever. Fortunately, the parallel increase in computer power allows scientists to automate this tuning. Optimization algorithms need two essential components. The first one is a function that measures the difference between the output of the model with a given set of parameter and the data. This error function or fitness function makes the ranking of different parameter sets possible. The second component is a search algorithm that explores the parameter space to find the best parameter set in a minimal amount of time. In this review we distinguish three types of error functions: feature-based ones, point-by-point comparison of voltage traces and multi-objective functions. We then detail several popular search algorithms, including brute-force methods, simulated annealing, genetic algorithms, evolution strategies, differential evolution and particle-swarm optimization. Last, we shortly describe Neurofitter, a free software package that combines a phase–plane trajectory density fitness function with several search algorithms.     

Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids

The docking of ligands to proteins can be formulated as a computational problem where the task is to find the most favorable energetic conformation among the large space of possible protein-ligand complexes. Stochastic search methods such as evolutionary algorithms (EAs) can be used to sample large search spaces effectively and is one of the commonly used methods for flexible ligand docking. During the last decade, several EAs using different variation operators have been introduced, such as the ones provided with the AutoDock program. In this paper we evaluate the performance of different EA settings such as choice of variation operators, population size, and usage of local search. The comparison is performed on a suite of six docking problems previously used to evaluate the performance of search algorithms provided with the AutoDock program package. The results from our investigation confirm that the choice of variation operators has an impact on the search-capabilities of EAs. The introduced DockEA using the best settings found obtained the overall best docking solutions compared to the Lamarckian GA (LGA) provided with AutoDock. Furthermore, the DockEA proved to be more robust than the LGA (in terms of reproducing the results in several runs) on the more difficult problems with a high number of flexible torsion angles.     

Molecular variability elicits a tunable switch with discrete neuromodulatory response phenotypes

Conductance-based compartment modeling requires tuning of many parameters to fit the neuron model to target electrophysiological data. Automated parameter optimization via evolutionary algorithms (EAs) is a common approach to accomplish this task, using error functions to quantify differences between model and target. We present a three-stage EA optimization protocol for tuning ion channel conductances and kinetics in a generic neuron model with minimal manual intervention. We use the technique of Latin hypercube sampling in a new way, to choose weights for error functions automatically so that each function influences the parameter search to a similar degree. This protocol requires no specialized physiological data collection and is applicable to commonly-collected current clamp data and either single- or multi-objective optimization. We applied the protocol to two representative pyramidal neurons from layer 3 of the prefrontal cortex of rhesus monkeys, in which action potential firing rates are significantly higher in aged compared to young animals. Using an idealized dendritic topology and models with either 4 or 8 ion channels (10 or 23 free parameters respectively), we produced populations of parameter combinations fitting the target datasets in less than 80 hours of optimization each. Passive parameter differences between young and aged models were consistent with our prior results using simpler models and hand tuning. We analyzed parameter values among fits to a single neuron to facilitate refinement of the underlying model, and across fits to multiple neurons to show how our protocol will lead to predictions of parameter differences with aging in these neurons.     

A new MapReduce associative classifier based on a new storage format for large-scale imbalanced data

The process of knowledge discovery from big and high dimensional datasets has become a popular research topic. The classification problem is a key task in bioinformatics, business intelligence, decision science, astronomy, physics, etc. Building associative classifiers has been a notable research interest in recent years because of their superior accuracy. In associative classifiers, using under-sampling or over-sampling methods for imbalanced big datasets reduces accuracy or increases running time, respectively. Hence, there is a significant need to create efficient associative classifiers for imbalanced big data problems. These classifiers should be able to handle challenges such as memory usage, running time and efficiently exploring the search space. To this end, efficient calculation of measures is a primary objective for associative classifiers. In this paper, we propose a new efficient associative classifier for big imbalanced datasets. The proposed method is based on Rare-PEARs (a multi-objective evolutionary algorithm that efficiently discovers rare and reliable association rules) and is able to evaluate rules in a distributed manner by using a new storing data format. This format simplifies measures calculation and is fully compatible with the MapReduce programming model. We have applied the proposed method (RPII) on a well-known big dataset (ECBDL’14) and have compared our results with seven other learning methods. The experimental results show that RPII outperform other methods in sensitivity and final score measures (the values of sensitivity and final score measures were approximately 0.74 and 0.54 respectively). The results demonstrate that the proposed method is a good candidate for large-scale classification problems; furthermore, it achieves reasonable execution time when the target platform is a typical computer clusters.     

MRSD: a web server for metabolic route search and design

SUMMARY: We present a tool called MRSD (Metabolic Route Search and Design) to search and design routes based on the weighted compound transform diagraph. The search submodule returns routes between a source and product compound within seconds in the network of one or multiple organisms based on data from KEGG. The design submodule designs a route from an appointed compound in an interactive mode. The two complementary functions, Metabolic Route Search and Design, can be broadly used in biosynthesis, bio-pharmaceuticals and the other related fields. AVAILABILITY: bioinfo.ustc.edu.cn/softwares/MRSD/.     

Randomized,replicated, staggered clonal-row (R2SCR) seed orchard design

Spatial randomization of clones across a seed orchard’s grid is commonly applied to promote cross-fertilization and minimize selfing. The high selection differential attained from advanced-generation breeding programs sets high premier on the genetic gain and diversity delivery from seed orchards, thus clonal allocation is important and even more challenging when clones share common ancestry. Evidences of low selfing in many conifers’ seed orchards, as a result of their high genetic load, inbreeding depression, and polyembryony are abundant and call for orchards’ design re-evaluation, specifically when randomization is associated with added managerial burden. Clonal-rows represent a viable option for simplifying orchards management; however, they are often associated with elevated correlated matings between adjacent clones. Here, we propose a modified clonal-row design that replicates, staggers, and randomizes the rows, thus doubling the number of adjacent clones and providing different set of neighboring clones at each replication, thus allowing accommodating related parents more readily than any single-tree arrangement. We present a novel algorithm packaged in user-friendly software for executing various seed orchards’ designs. The developed program is interactive and suitable for any orchard size and configuration, accommodates any number of clones that are allocated to rows with variable length (ranging from a single tree to any even number) and pre-set separation zone between ramets of the same clone. The program offers three deployment modes (equal, linear, and custom) each with multiple layouts determined by the number of iterations requested. The resulting layouts are ranked based on four criteria including: (1) the number of empty positions, (2) deviation between expected and observed clone size, (3) minimum inbreeding, and (4) a neighborhood index that expresses the efficiency of clonal distribution.     

First hitting time analysis of continuous evolutionary algorithms based on average gain

Runtime analysis of continuous evolutionary algorithms (EAs) is a hard topic in the theoretical research of evolutionary computation, relatively few results have been obtained compared to the discrete EAs. In this paper, we introduce the martingale and stopping time theory to establish a general average gain model to estimate the upper bound for the expected first hitting time. The proposed model is established on a non-negative stochastic process and does not need the assumption of Markov property, thus is more general. Afterwards, we demonstrate how the proposed model can be applied to runtime analysis of continuous EAs. In the end, as a case study, we analyze the runtime of (1, \(\lambda )\)ES with adaptive step-size on Sphere function using the proposed approach, and derive a closed-form expression of the time upper bound for 3-dimensional case. We also discuss the relationship between the step size and the offspring size \(\lambda \) to ensure convergence. The experimental results show that the proposed approach helps to derive tight upper bound for the expected first hitting time of continuous EAs.     

Differential drying rates of recalcitrant Trichilia dregeana embryonic axes: a study of survival and oxidative stress metabolism

Studies to elucidate the biochemical basis of survival of excised embryonic axes (EAs) of recalcitrant seeds of Trichilia dregeana at different drying rates revealed significant differences between slow and rapid drying. Rapid drying allowed these EAs to survive dehydration to much lower water contents (WCs; ca. 0.31 g g^?1 dry mass basis with 73% germination) compared with slow drying, where 90% of the EAs lost viability at a WC of ca. 0.79 g g^?1. In EAs slowly dried within seeds, the levels of hydroxyl radical (three‐ to fivefold at WCs >0.5 g g^?1) and lipid peroxidation (50% at similar WC) were significantly higher compared with those dried rapidly to comparable WCs. When EAs were dried slowly, enzymic antioxidant levels were not sustained and declined significantly with prolonged storage. In contrast, sustained activity of enzymic antioxidants was detected in rapidly dried EAs even at relatively low WCs. Furthermore, the greater decline in glutathione (GSH)/GSH disulphide ratio in EAs slowly dried within seeds compared with rapidly dried EAs and a shift in GSH redox potential to relatively more positive values in the EAs slowly dried within seeds was correlated with considerable viability loss. It is apparent from this study that greater retention of viability to lower WCs in rapidly dried EAs from recalcitrant seeds may at least be partly explained by the retention of functional antioxidant status. It is also suggested that the reduction of viability in rapidly dried EAs at very low WCs appears to be a non‐oxidative process.     

Common vitamin D pathway gene variants reveal contrasting effects on serum vitamin D levels in African Americans and European Americans

Vitamin D deficiency is more common among African Americans (AAs) than among European Americans (EAs), and epidemiologic evidence links vitamin D status to many health outcomes. Two genome-wide association studies (GWAS) in European populations identified vitamin D pathway gene single-nucleotide polymorphisms (SNPs) associated with serum vitamin D [25(OH)D] levels, but a few of these SNPs have been replicated in AAs. Here, we investigated the associations of 39 SNPs in vitamin D pathway genes, including 19 GWAS-identified SNPs, with serum 25(OH)D concentrations in 652 AAs and 405 EAs. Linear and logistic regression analyses were performed adjusting for relevant environmental and biological factors. The pattern of SNP associations was distinct between AAs and EAs. In AAs, six GWAS-identified SNPs in GC, CYP2R1, and DHCR7/NADSYN1 were replicated, while nine GWAS SNPs in GC and CYP2R1 were replicated in EAs. A CYP2R1 SNP, rs12794714, exhibited the strongest signal of association in AAs. In EAs, however, a different CYP2R1 SNP, rs1993116, was the most strongly associated. Our models, which take into account genetic and environmental variables, accounted for 20 and 28 % of the variance in serum vitamin D levels in AAs and EAs, respectively.     

Racial differences in genome-wide methylation profiling and gene expression in breast tissues from healthy women

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.     

Adiponectin multimers and metabolic syndrome traits: relative adiponectin resistance in African Americans

African Americans (AAs) tend to have lower total adiponectin levels compared to European Americans (EA); however, it is not known whether race affects adiponectin multimer distribution and their relationships to metabolic traits. We measured total adiponectin, high molecular weight (HMW), low molecular weight (LMW) (i.e., hexamer), and trimer adiponectin in 132 normoglycemic premenopausal women (75 AAs, 57 EAs), together with measures of total and abdominal fat, plasma lipids, insulin sensitivity (S(i)), and genetic admixture estimates. We found that lower total adiponectin in AAs was explained by reduced LMW, and trimer forms because levels of HMW did not differ between races. In EAs, HMW was highly correlated with multiple metabolic syndrome traits. In contrast, the LMW and trimer forms were most highly correlated with metabolic traits in AAs, including abdominal adiposity, lipids, and S(i). At similar levels of visceral adiposity, AAs exhibited significantly lower LMW adiponectin than EAs. Similarly, at comparable levels of HMW and LMW adiponectin, AAs were more insulin resistant than their EA counterparts. In conclusion, (i) serum adiponectin is lower in AAs predominantly as a result of reduced concentrations of LMW and trimers multimeric forms; (ii) LMW and trimer, not HMW, are most broadly correlated with metabolic traits in AAs. Thus, HMW adiponectin may exert less bioactivity in explaining the metabolic syndrome trait cluster in populations of predominant African genetic background.     

Trypanosoma cruzi extracellular amastigotes engage Rac1 and Cdc42 to invade RAW macrophages

Cell invasion by Trypanosoma cruzi extracellular amastigotes (EAs) relies significantly upon the host cell actin cytoskeleton. In past decades EAs have been established as a reliable model for phagocytosis inducer in non-phagocytic cells. Our current hypothesis is that EAs engage a phagocytosis-like mechanism in non-professional phagocytic cells; however, the molecular mechanisms in professional phagocytes still remain unexplored. In this work, we evaluated the involvement of Rac1 and Cdc42 in the actin-dependent internalization of EAs in RAW 264.7 macrophages. Kinetic assays showed similar internalization of EAs in unstimulated RAW and non-phagocytic HeLa cells but increased in LPS/IFN-γ stimulated RAW cells. However, depletion of Rac1, Cdc42 or RhoA inhibited EA internalization similarly in both unstimulated and stimulated RAW cells. Overexpression of active, but not the dominant-negative, construct of Rac1 increased EA internalization. Remarkably, for Cdc42, both the active and the inactive mutants decreased EA internalization when compared to wild type groups. Despite that, both Rac1 and Cdc42 activation mutants were similarly recruited to and colocalized with actin at the EA-macrophage contact sites when compared to their native isoforms. Altogether, these results corroborate that EAs engage phagocytic processes to invade both professional and non-professional phagocytic cells providing evidences of converging actin mediated mechanisms induced by intracellular pathogens in both cell types.     

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG's biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).     

Beyond the Virtual Intracranial Stenting Challenge 2007: Non-Newtonian and flow pulsatility effects

The Virtual Intracranial Stenting Challenge 2007 (VISC’07) is becoming a standard test case in computational minimally invasive cerebrovascular intervention. Following views expressed in the literature and consistent with the recommendations of a report, the effects of non-Newtonian viscosity and pulsatile flow are reported. Three models of stented cerebral aneurysms, originating from VISC’07 are meshed and the flow characteristics simulated using commercial computational fluid dynamics (CFD) software. We conclude that non-Newtonian and pulsatile effects are important to include in order to discriminate more effectively between stent designs.     

Digital ‘faces’ of synthetic biology

In silicio design plays a fundamental role in the endeavour to synthesise biological systems. In particular, computer-aided design software enables users to manage the complexity of biological entities that is connected to their construction and reconfiguration. The software’s graphical user interface bridges the gap between the machine-readable data on the algorithmic subface of the computer and its human-amenable surface represented by standardised diagrammatic elements. Notations like the Systems Biology Graphical Notation (SBGN), together with interactive operations such as drag & drop, allow the user to visually design and simulate synthetic systems as ‘bio-algorithmic signs’. Finally, the digital programming process should be extended to the wet lab to manufacture the designed synthetic biological systems. By exploring the different ‘faces’ of synthetic biology, I argue that in particular computer-aided design (CAD) is pushing the idea to automatically produce de novo objects. Multifaceted software processes serve mutually aesthetic, epistemic and performative purposes by simultaneously black-boxing and bridging different data sources, experimental operations and community-wide standards. So far, synthetic biology is mainly a product of digital media technologies that structurally mimic the epistemological challenge to take both qualitative as well as quantitative aspects of biological systems into account in order to understand and produce new and functional entities.     

ConsNet: new software for the selection of conservation area networks with spatial and multi-criteria analyses

ConsNet is a comprehensive software package for the design of conservation area networks (CANs). The software selects areas to be potentially placed under conservation management for the representation of biodiversity surrogates. Additionally, ConsNet optimizes spatial criteria including compactness, connectivity, replication, and alignment, as well as socio-economic criteria as specified by users. ConsNet uses an advanced tabu search engine to identify efficient alternatives quickly, offering capabilities beyond existing planning software. The ability to perform ongoing interactive analysis with multi-criteria objectives makes ConsNet an ideal decision support tool for large scale planning exercises.     

Robustness analysis of EGFR signaling network with a multi-objective evolutionary algorithm

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is believed to be a necessary property of biological systems. In this paper, we address the issue of robustness in an important signal transduction network--epidermal growth factor receptor (EGFR) network. First, we analyze the robustness in the EGFR signaling network using all rate constants against the Gauss variation which was described as "the reference parameter set" in the previous study [Kholodenko, B.N., Demin, O.V., Moehren, G., Hoek, J.B., 1999. Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274, 30169-30181]. The simulation results show that signal time, signal duration and signal amplitude of the EGRR signaling network are relatively not robust against the simultaneous variation of the reference parameter set. Second, robustness is quantified using some statistical quantities. Finally, a multi-objective evolutionary algorithm (MOEA) is presented to search reaction rate constants which optimize the robustness of network and compared with the NSGA-II, which is a representation of a class of modern multi-objective evolutionary algorithms. Our simulation results demonstrate that signal time, signal duration and signal amplitude of the four key components--the most downstream variable in each of the pathways: R-Sh-G-S, R-PLP, R-G-S and the phosphorylated receptor RP in EGRR signaling network for the optimized parameter sets have better robustness than those for the reference parameter set and the NSGA-II. These results can provide valuable insight into experimental designs and the dynamics of the signal-response relationship between the dimerized and activated EGFR and the activation of downstream proteins.     

SAmBA: An interactive software for optimizing the design of biological macromolecules crystallization experiments

SAmBA is a new software for the design of minimal experimental protocols using the notion of orthogonal arrays of strength 2. The main application of SAmBA is the search of protein crystallization conditions. Given a user input defining the relevant effectors/variables (e.g., pH, temperature, salts) and states (e.g., pH: 5, 6, 7 and 8), this software proposes an optimal set of experiments in which all tested variables and the pairwise interactions between them are symmetrically sampled. No a priori restrictions on the number and range of experimental variables is imposed. SAmBA consists of two complementary programs, SAm and BA, using a simulated annealing approach and a backtracking algorithm, respectively. The software is freely available as C code or as an interactive JAVA applet at http://igs-server.cnrs-mrs.fr . Proteins 29:252–257, 1997. © 1997 Wiley-Liss, Inc.