Conformational calculations on the Ala14-Pro27 LCI segment of rabbit skeletal myosin

In order to define the conformational characteristics of a singular Ala¹⁴-Pro²⁷ segment in myosin LC1, conformational calculations were performed using the Simplex algorithm of Nelder and Mead (Computer J. 7 (1965) 308–313) in the ACME program proposed by Tournarie (J. Appl. Cryst. 6 (1973) 309–346). The (Ala-Pro) n = 1 unit was assigned a given conformation x; the conformation energy was then minimized for n = 1 to n = 7 by adjusting structural parameters (angle values). Similarly, 13 different possible conformations were optimized and compared, showing that a (β₂R)₇ conformation is favored by about 20 kcal per mol over the next most probable conformation (C₇R)₇. In the β₂R conformation, the (Ala-Pro)₇ segment is a wide helix, 15 Å in length and 8.65 Å in diameter, while the C₇R conformation results in a semi-extended structure of 25 Å long, with an approximate diameter of 6 Å. These characteristics are in agreement with available experimental data and putative functions of the LC1 N-terminus.     

Conformational calculations on the Ala14-Pro27 LC1 segment of rabbit skeletal myosin

In order to define the conformational characteristics of a singular Ala14-Pro27 segment in myosin LC1, conformational calculations were performed using the Simplex algorithm of Nelder and Mead (Computer J. 7 (1965) 308-313) in the ACME program proposed by Tournarie (J. Appl. Cryst. 6 (1973) 309-346). The (Ala-Pro) n = 1 unit was assigned a given conformation x; the conformation energy was then minimized for n = 1 to n = 7 by adjusting structural parameters (angle values). Similarly, 13 different possible conformations were optimized and compared, showing that a (beta 2R)7 conformation is favored by about 20 kcal per mol over the next most probable conformation (C7R)7. In the beta 2R conformation, the (Ala-Pro)7 segment is a wide helix, 15 A in length and 8.65 A in diameter, while the C7R conformation results in a semi-extended structure of 25 A long, with an approximate diameter of 6 A. These characteristics are in agreement with available experimental data and putative functions of the LC1 N-terminus.     

Searching for structural bias in particle swarm optimization and differential evolution algorithms

During the last two decades, a large number of metaheuristics have been proposed, leading to various studies that call for a deeper insight into the behaviour, efficiency and effectiveness of such methods. Among numerous concerns that are briefly reviewed in this paper, the presence of a structural bias (i.e. the tendency, not justified by the fitness landscape, to visit some regions of the search space more frequently than other regions) has recently been detected in simple versions of the genetic algorithm and particle swarm optimization. As of today, it remains unclear how frequently such a behaviour occurs in population-based swarm intelligence and evolutionary computation methods, and to what extent structural bias affects their performance. The present study focuses on the search for structural bias in various variants of particle swarm optimization and differential evolution algorithms, as well as in the traditional direct search methods proposed by Nelder–Mead and Rosenbrock half a century ago. We found that these historical direct search methods are structurally unbiased. However, most tested new metaheuristics are structurally biased, and at least some presence of structural bias can be observed in almost all their variants. The presence of structural bias seems to be stronger in particle swarm optimization algorithms than in differential evolution algorithms. The relationships between the strength of the structural bias and the dimensionality of the search space, the number of allowed function calls and the population size are complex and hard to generalize. For 14 algorithms tested on the CEC2011 real-world problems and the CEC2014 artificial benchmarks, no clear relationship between the strength of the structural bias and the performance of the algorithm was found. However, at least for artificial benchmarks, such old and structurally unbiased methods like Nelder–Mead algorithm performed relatively well. This is a warning that the presence of structural bias in novel metaheuristics may hamper their search abilities.     

Parameter Estimation for the Compartmental Model

An estimation procedure is obtained for a stochastic compartmental model. Compartmental analysis assumes that a system may be divided into homogeneous components, or compartments. The main theory for the compartmental system was studied by Matis and Hartley (1971) with a discrete population in a steady state. All the transitions among the particles are considered to be stochastic in nature. An estimation procedure, Regular Best Asymptotic Normal (RBAN), discussed by Chiang (1956) is investigated for a stochastic m-compartmental system. The detailed proof of the procedure is provided here. Asymptotic properties for the estimator has been studied and computation has been carried out on our proposed nonlinear model. The downhill simplex search method, originally developed by Nelder and Mead (1965), and applied to minimize our quadratic form is inherently nonlinear in nature, thus avoiding the need to evaluate any derivative for point estimation of the parameters. The procedure applied to an experimental situation involving two compartments gives very encouraging results.     

Application of simplex‐based experimental optimization to challenging bioprocess development problems: Case studies in downstream processing

The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression‐based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression‐based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex‐based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression‐based methods to reach favorable operating conditions. The Simplex‐variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex‐variant is ideally suited to rapid optimization in early‐phase process development. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:404–419, 2016     

Optimization and robustness analysis of hybridoma cell fed-batch cultures using the overflow metabolism model

The maximization of biomass productivity in fed-batch cultures of hybridoma cells is analyzed based on the overflow metabolism model. Due to overflow metabolism, often attributed to limited oxygen capacity, lactate and ammonia are formed when the substrate concentrations (glucose and glutamine) are above a critical value, which results in a decrease in biomass productivity. Optimal feeding rate, on the one hand, for a single feed stream containing both glucose and glutamine and, on the other hand, for two separate feed streams of glucose and glutamine are determined using a Nelder–Mead simplex optimization algorithm. The optimal multi exponential feed rate trajectory improves the biomass productivity by 10 % as compared to the optimal single exponential feed rate. Moreover, this result is validated by the one obtained with the analytical approach in which glucose and glutamine are fed to the culture so as to control the hybridoma cells at the critical metabolic state, which allows maximizing the biomass productivity. The robustness analysis of optimal feeding profiles obtained with different optimization strategies is considered, first, with respect to parameter uncertainties and, finally, to model structure errors.     

What is the status of metabolic theory one century after Pütter invented the von Bertalanffy growth curve?

Metabolic theory aims to tackle ecological and evolutionary problems by explicitly including physical principles of energy and mass exchange, thereby increasing generality and deductive power. Individual growth models (IGMs) are the fundamental basis of metabolic theory because they represent the organisational level at which energy and mass exchange processes are most tightly integrated and from which scaling patterns emerge. Unfortunately, IGMs remain a topic of great confusion and controversy about the origins of the ideas, their domain and breadth of application, their logical consistency and whether they can sufficiently capture reality. It is now 100 years since the first theoretical model of individual growth was put forward by Pütter. His insights were deep, but his model ended up being attributed to von Bertalanffy and his ideas largely forgotten. Here I review Pütter's ideas and trace their influence on existing theoretical models for growth and other aspects of metabolism, including those of von Bertalanffy, the Dynamic Energy Budget (DEB) theory, the Gill-Oxygen Limitation Theory (GOLT) and the Ontogenetic Growth Model (OGM). I show that the von Bertalanffy and GOLT models are minor modifications of Pütter's original model. I then synthesise, compare and critique the ideas of the two most-developed theories, DEB theory and the OGM, in relation to Pütter's original ideas. I formulate the Pütter, DEB and OGM models in the same structure and with the same notation to illustrate the major similarities and differences among them. I trace the confusion and controversy regarding these theories to the notions of anabolism, catabolism, assimilation and maintenance, the connections to respiration rate, and the number of parameters and state variables their models require. The OGM model has significant inconsistencies that stem from the interpretation of growth as the difference between anabolism and maintenance, and these issues seriously challenge its ability to incorporate development, reproduction and assimilation. The DEB theory is a direct extension of Pütter's ideas but with growth being the difference between assimilation and maintenance rather than anabolism and catabolism. The DEB theory makes the dynamics of Pütter's ‘nutritive material’ explicit as well as extending the scheme to include reproduction and development. I discuss how these three major theories for individual growth have been used to explain ‘macrometabolic’ patterns including the scaling of respiration, the temperature–size rule (first modelled by Pütter), and the connection to life history. Future research on the connections between theory and data in these macrometabolic topics have the greatest potential to advance the status of metabolic theory and its value for pure and applied problems in ecology and evolution.     

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.     

Hybrid Global Optimization Algorithms for Protein Structure Prediction: Alternating Hybrids

Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the αBB, and a stochastically based method, conformational space annealing (CSA). The αBB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the αBB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods.     

A Novel Algorithm for Independent Component Analysis with Reference and Methods for Its Applications

This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods.     

Quadratically convergent algorithm for computing real root of non-linear transcendental equations

Objectives

The present paper describes a new algorithm to find a root of non-linear transcendental equations. It is found that Regula-Falsi method always gives guaranteed result but slow convergence. However, Newton–Raphson method does not give guaranteed result but faster than Regula-Falsi method. Therefore, the present paper used these two ideas and developed a new algorithm which has better convergence than Regula-Falsi and guaranteed result. One of the major issue in Newton–Raphson method is, it fails when first derivative is zero or approximately zero.

Results

The proposed method implemented the failure condition of Newton–Raphson method with better convergence. Error calculation has been discussed for certain real life examples using Bisection, Regula-Falsi, Newton–Raphson method and new proposed method. The computed results show that the new proposed quadratically convergent method provides better convergence than other methods.

     

Parameter Estimations of Dynamic Energy Budget (DEB) Model over the Life History of a Key Antarctic Species: The Antarctic Sea Star Odontaster validus Koehler, 1906

Marine organisms in Antarctica are adapted to an extreme ecosystem including extremely stable temperatures and strong seasonality due to changes in day length. It is now largely accepted that Southern Ocean organisms are particularly vulnerable to global warming with some regions already being challenged by a rapid increase of temperature. Climate change affects both the physical and biotic components of marine ecosystems and will have an impact on the distribution and population dynamics of Antarctic marine organisms. To predict and assess the effect of climate change on marine ecosystems a more comprehensive knowledge of the life history and physiology of key species is urgently needed. In this study we estimate the Dynamic Energy Budget (DEB) model parameters for key benthic Antarctic species the sea star Odontaster validus using available information from literature and experiments. The DEB theory is unique in capturing the metabolic processes of an organism through its entire life cycle as a function of temperature and food availability. The DEB model allows for the inclusion of the different life history stages, and thus, becomes a tool that can be used to model lifetime feeding, growth, reproduction, and their responses to changes in biotic and abiotic conditions. The DEB model presented here includes the estimation of reproduction handling rules for the development of simultaneous oocyte cohorts within the gonad. Additionally it links the DEB model reserves to the pyloric caeca an organ whose function has long been ascribed to energy storage. Model parameters described a slowed down metabolism of long living animals that mature slowly. O. validus has a large reserve that—matching low maintenance costs- allow withstanding long periods of starvation. Gonad development is continuous and individual cohorts developed within the gonads grow in biomass following a power function of the age of the cohort. The DEB model developed here for O. validus allowed us to increase our knowledge on the ecophysiology of this species, providing new insights on the role of food availability and temperature on its life cycle and reproduction strategy.     

A dynamic energy budget model to describe the reproduction and growth of invasive starfish <Emphasis Type="Italic">Asterias amurensis</Emphasis> in southeast Australia

The introduction of alien species is a global phenomenon that alters ecosystems structure and functioning. Invasive species are responsible for substantial economic and ecological losses. Invasive species impact resource availability, outcompeting and even causing extinction of native species. The management of invasive species requires knowledge on the ecology, physiology and population dynamics of these species. In a world where environmental conditions are changing fast due to global climate change and other anthropogenic stressors, a more comprehensive knowledge of the life history and physiology of these species is urgently needed. The DEB theory is unique in capturing the metabolic processes of an organism through its entire life cycle, and thus, is a useful tool to model lifetime feeding, growth, reproduction, and responses to changes in biotic and abiotic conditions. In this work, we estimated the parameters of a DEB model for Asterias amurensis. This starfish was introduced in Tasmania and is considered the most serious marine pest in Australia where it has caused local extinctions of several species. Asterias amurensis is a major predator and is a keystone species exerting top-down control of its prey populations by achieving large densities. We determined the influence of biotic and abiotic factors on the performance of A. amurensis. The DEB model presented here includes energy handling rules to describe gonad and pyloric caeca cycles. Model parameters were used to explore population dynamics of populations of A. amurensis in Australia. The DEB model allowed us to characterise the ecophysiology of A. amurensis, providing new insights on the role of food availability and temperature on its life cycle and reproduction strategy. Moreover it is a powerful tool for risk management of already established invasive populations and of regions with a high invasion risk.     

Telling a Tale: Margaret Mead's Photographic Portraits of Fa'amotu,a Samoan Tāupou

In 1925 Margaret Mead spent 10 days in High Chief Ufuti's household in the village of Vaitogi, Tutuila, before sailing to Ta'u where she conducted most of her research on Samoan adolescence. In Vaitogi, Mead took many photographs of Fa'amotu, Ufuti's t # upou daughter, several of which appeared in Coming of Age in Samoa . This article examines a number of intertwined issues of visual representation which may have prompted Mead to use a disproportionate number of pictures of Fa'amotu in early editions of her book. Mead's approach to photographic representation and issues such as collaboration, performance, typicality, marketability, anonymity, and acknowledgement are examined through her photographs and writings.     

Influence of needling conditions on the corneal insertion force

Abstract

Needle insertion plays an important part in the process of corneal graft surgery. In this paper, a three-dimensional symmetry model of the human cornea is constructed using the finite element method. Simplification of specific optic physiology is defined for the model: The cornea constrained by the sclera is presented as two layers consisting of epithelium and stroma. A failure criterion based on the distortion energy theory has been proposed to predict the insertion process of the needle. The simulation results show a good agreement with the experimental data reported in the literature. The influence of needling conditions (e.g. insertion velocity, rotation parameters and vibration parameters) on the insertion force are then discussed. In addition, a multi-objective optimization based on particle swarm optimization (PSO) is applied to reduce the insertion force. The numerical results provide guidelines for selecting the motion parameters of the needle and a potential basis for further developments in robot-assisted surgery.     

Directional fidelity as a foraging constraint in the western harvester ant,Pogonomyrmex occidentalis

Summary Understanding the foraging behavior of an animal is critically dependent upon knowledge of the constraints on that animal. In this study, I tested whether fidelity to foraging direction acts as a behavioral constraint to foraging western harvester ants, Pogonomyrmex occidentalis. Individual P. occidentalis foragers showed strong fidelity to foraging route and direction. Directional fidelity in this population was not related to trunk trail use, food specialization, colony activity levels, or mortality risks. Directional fidelity constrained individual foraging decisions; when colonies were offered seeds of different quality in 2 directions, individuals did not switch directions to obtain the energetically more rewarding seeds. Colony-level recruitment was increased for energetically more profitable seeds, indicating that colonial responses may compensate for the constraints of directional fidelity on individual foragers.     

Fermentation of molasses using a thermotolerant yeast,Kluyveromyces marxianus IMB3: simplex optimisation of media supplements

 The use of molasses as a substrate for ethanol production by the thermotolerant yeast Kluyveromyces marxianus var. marxianus was investigated at 45°C. A maximum ethanol concentration of 7.4% (v/v) was produced from unsupplemented molasses at a concentration of 23% (v/v). The effect on ethanol production of increasing the sucrose concentration in 23% (v/v) molasses was determined. Increased sucrose concentration had a similar detrimental effect on the final ethanol produced as the increase in molasses concentration. This indicated that the effect may be due to increased osmotic activity as opposed to other components in the molasses. The optimum concentration of the supplements nitrogen, magnesium, potassium and fatty acid for maximum ethanol production rate was determined using the Nelder and Mead (Computer J 7:308–313, 1965) simplex optimisation method. The optimum concentrations of the supplements were 0.576 g l^-1 magnesium sulphate, 0.288 g l^-1 potassium dihydrogen phosphate and 0.36% (v/v) linseed oil. Added nitrogen in the form of ammonium sulphate did not affect the ethanol production rate. Received: 29 January 1996/Received revision: 23 April 1996/Accepted: 29 April 1996     

Temperature tolerance and energetics: a dynamic energy budget-based comparison of North Atlantic marine species

Temperature tolerance and sensitivity were examined for some North Atlantic marine species and linked to their energetics in terms of species-specific parameters described by dynamic energy budget (DEB) theory. There was a general lack of basic information on temperature tolerance and sensitivity for many species. Available data indicated that the ranges in tolerable temperatures were positively related to optimal growth temperatures. However, no clear relationships with temperature sensitivity were established and no clear differences between pelagic and demersal species were observed. The analysis was complicated by the fact that for pelagic species, experimental data were completely absent and even for well-studied species, information was incomplete and sometimes contradictory. Nevertheless, differences in life-history strategies were clearly reflected in parameter differences between related species. Two approaches were used in the estimation of DEB parameters: one based on the assumption that reserve hardly contributes to physical volume; the other does not make this assumption, but relies on body-size scaling relationships, using parameter values of a generalized animal as pseudo-data. Temperature tolerance and sensitivity seemed to be linked with the energetics of a species. In terms of growth, relatively high temperature optima, sensitivity and/or tolerance were related to lower relative assimilation rates as well as lower maintenance costs. Making the step from limited observations to underlying mechanisms is complicated and extrapolations should be carefully interpreted. Special attention should be devoted to the estimation of parameters using body-size scaling relationships predicted by the DEB theory.     

Optimization of ion-exchange protein separations using a vector quantizing neural network

In this work, a previously proposed methodology for the optimization of analytical scale protein separations using ion-exchange chromatography is subjected to two challenging case studies. The optimization methodology uses a Doehlert shell design for design of experiments and a novel criteria function to rank chromatograms in order of desirability. This chromatographic optimization function (COF) accounts for the separation between neighboring peaks, the total number of peaks eluted, and total analysis time. The COF is penalized when undesirable peak geometries (i.e., skewed and/or shouldered peaks) are present as determined by a vector quantizing neural network. Results of the COF analysis are fit to a quadratic response model, which is optimized with respect to the optimization variables using an advanced Nelder and Mead simplex algorithm. The optimization methodology is tested on two case study sample mixtures, the first of which is composed of equal parts of lysozyme, conalbumin, bovine serum albumin, and transferrin, and the second of which contains equal parts of conalbumin, bovine serum albumin, tranferrin, beta-lactoglobulin, insulin, and alpha -chymotrypsinogen A. Mobile-phase pH and gradient length are optimized to achieve baseline resolution of all solutes for both case studies in acceptably short analysis times, thus demonstrating the usefulness of the empirical optimization methodology.     

Kinematic analysis of over-determinate biomechanical systems

In this paper, we introduce a new general method for kinematic analysis of rigid multi body systems subject to holonomic constraints. The method extends the standard analysis of kinematically determinate rigid multi body systems to the over-determinate case. This is accomplished by introducing a constrained optimisation problem with the objective function given as a function of the set of system equations that are allowed to be violated while the remaining equations define the feasible set.

We show that exact velocity and acceleration analysis can also be performed by solving linear sets of equations, originating from differentiation of the Karush–Kuhn–Tucker optimality conditions.

The method is applied to the analysis of an 18 degrees-of-freedom gait model where the kinematical drivers are prescribed with data from a motion capture experiment.

The results show that significant differences are obtained between applying standard kinematic analysis or minimising the least-square errors on the two fully equivalent 3D gait models with only the way the experimental data is processed being different.