Optimisation of integrated biodiesel production. Part II: a study of the material balance

A study was made of the material balance for the fatty acid methyl ester (biodiesel) synthesis from sunflower oil using potassium hydroxide as the catalyst. A factorial design of experiments and a central composite design have been used to evaluate the influence of operating conditions on the process material balance. The responses chosen were the biodiesel yield and the yield losses due to triglyceride saponification and methyl ester dissolution in glycerol, while the variables studied were temperature, initial catalyst concentration and the methanol:vegetable oil molar ratio. The biodiesel yield increased and therefore the yield losses decreased by decreasing catalyst concentration and temperature. However, the methanol:sunflower oil molar ratio did not affect the material balance variables significantly. Second-order models were obtained to predict the biodiesel yield and both yield losses. Within the experimental range studied, these models largely matched the results from the experiments.     

Computational modeling of evolutionary learning processes in the brain

The evolutionary selection circuits model of learning has been specified algorithmically. The basic structural components of the selection circuits model are enzymatic neurons, that is, neurons whose firing behavior is controlled by membrane-bound macromolecules called excitases. Learning involves changes in the excitase contents of neurons through a process of variation and selection. In this paper we report on the behavior of a basic version of the learning algorithm which has been developed through extensive interactive experiments with the model. This algorithm is effective in that it enables single neurons or networks of neurons to learn simple pattern classification tasks in a number of time steps which appears experimentally to be a linear function of problem size, as measured by the number of patterns of presynaptic input. The experimental behavior of the algorithm establishes that evolutionary mechanisms of learning are competent to serve as major mechanisms of neuronal adaptation. As an example, we show how the evolutionary learning algorithm can contribute to adaptive motor control processes in which the learning system develops the ability to reach a target in the presence of randomly imposed disturbances.     

Mechanistic approaches to the study of evolution: the functional synthesis

An emerging synthesis of evolutionary biology and experimental molecular biology is providing much stronger and deeper inferences about the dynamics and mechanisms of evolution than were possible in the past. The new approach combines statistical analyses of gene sequences with manipulative molecular experiments to reveal how ancient mutations altered biochemical processes and produced novel phenotypes. This functional synthesis has set the stage for major advances in our understanding of fundamental questions in evolutionary biology. Here we describe this emerging approach, highlight important new insights that it has made possible, and suggest future directions for the field.     

Transgenerational plasticity and climate change experiments: Where do we go from here?

Phenotypic plasticity, both within and across generations, is an important mechanism that organisms use to cope with rapid climate change. While an increasing number of studies show that plasticity across generations (transgenerational plasticity or TGP) may occur, we have limited understanding of key aspects of TGP, such as the environmental conditions that may promote it, its relationship to within‐generation plasticity (WGP) and its role in evolutionary potential. In this review, we consider how the detection of TGP in climate change experiments is affected by the predictability of environmental variation, as well as the timing and magnitude of environmental change cues applied. We also discuss the need to design experiments that are able to distinguish TGP from selection and TGP from WGP in multigenerational experiments. We conclude by suggesting future research directions that build on the knowledge to date and admit the limitations that exist, which will depend on the way environmental change is simulated and the type of experimental design used. Such an approach will open up this burgeoning area of research to a wider variety of organisms and allow better predictive capacity of the role of TGP in the response of organisms to future climate change.     

Adaptive evolution in ecological communities

Understanding how natural selection drives evolution is a key challenge in evolutionary biology. Most studies of adaptation focus on how a single environmental factor, such as increased temperature, affects evolution within a single species. The biological relevance of these experiments is limited because nature is infinitely more complex. Most species are embedded within communities containing many species that interact with one another and the physical environment. To understand the evolutionary significance of such ecological complexity, experiments must test the evolutionary impact of interactions among multiple species during adaptation. Here we highlight an experiment that manipulates species composition and tracks evolutionary responses within each species, while testing for the mechanisms by which species interact and adapt to their environment. We also discuss limitations of previous studies of adaptive evolution and emphasize how an experimental evolution approach can circumvent such shortcomings. Understanding how community composition acts as a selective force will improve our ability to predict how species adapt to natural and human-induced environmental change.     

Performance of an iterated T-HMM for homology detection

MOTIVATION: Much information about new protein sequences is derived from identifying homologous proteins. Such tasks are difficult when the evolutionary relationships are distant. Some modern methods achieve better results by building a model of a set of related sequences, and then identifying new proteins that fit the model. A further advance was the development of iterative methods that refine the model as more homologs are discovered. These methods are generally limited by ad hoc methods of sequence weighting, neglect of underlying evolutionary relationships and the representation of the set with a single one-size-fits-all model. These limitations are avoided through the use of a Tree hidden Markov model (T-HMM) approach. Our previous work described how a non-iterative version of the T-HMM method could identify distant homologs with superior performance compared with other non-iterated approaches, and described how this method was particularly appropriate for being implemented as an iterative algorithm. RESULTS: We describe an iterative version of the T-HMM algorithm, and evaluate its performance for the detection of distant homologs. Significant improvement over other commonly used methods is found. AVAILABILITY: The software (C++, Perl) is available from the corresponding author.     

Avian psychology and communication

The evolution of animal communication is a complex issue and one that attracts much research and debate. 'Receiver psychology' has been highlighted as a potential selective force, and we review how avian psychological processes and biases can influence the evolution and design of signals as well as the progress that has been made in testing these ideas in behavioural studies. Interestingly, although birds are a focal group for experimental psychologists and behavioural ecologists alike, the integration of theoretical ideas from psychology into studies of communication has been relatively slow. However, recent operant experiments are starting to address how birds perceive and respond to complex natural signals in an attempt to answer evolutionary problems in communication. This review outlines how a psychological approach to understanding communication is useful, and we hope that it stimulates further research addressing the role of psychological mechanisms in signal evolution.     

What have two decades of laboratory life-history evolution studies onDrosophila melanogaster taught us?

A series of laboratory selection experiments onDrosophila melanogaster over the past two decades has provided insights into the specifics of life-history tradeoffs in the species and greatly refined our understanding of how ecology and genetics interact in life-history evolution. Much of what has been learnt from these studies about the subtlety of the microevolutionary process also has significant implications for experimental design and inference in organismal biology beyond life-history evolution, as well as for studies of evolution in the wild. Here we review work on the ecology and evolution of life-histories in laboratory populations ofD. melanogaster, emphasizing how environmental effects on life-history-related traits can influence evolutionary change. We discuss life-history tradeoffs—many unexpected—revealed by selection experiments, and also highlight recent work that underscores the importance to life-history evolution of cross-generation and cross-life-stage effects and interactions, sexual antagonism and sexual dimorphism, population dynamics, and the possible role of biological clocks in timing life-history events. Finally, we discuss some of the limitations of typical selection experiments, and how these limitations might be transcended in the future by a combination of more elaborate and realistic selection experiments, developmental evolutionary biology, and the emerging discipline of phenomics.     

纤维素酶解-超声偶联法提取葛根中总异黄酮的工艺优化

采用酶解-超声偶联新工艺对葛根中总异黄酮提取进行优化,以提高其收率.在单因素试验的基础上,利用Box-Behnken响应面试验设计,以蒸馏水作为提取溶剂,对温度、酶浓度、时间进行三因素三水平的试验设计优化.结果表明最佳提取工艺条件为:温度34℃、酶浓度0.63 mg/mL、时间62 min.在此条件下,葛根总异黄酮提取率为7.10％,与预测值的相对误差为0.34％.酶解-超声偶联工艺是一种新型、快速、有效的总黄酮提取方法.     

Is maximization of molar yield in metabolic networks favoured by evolution?

Stoichiometric analysis of metabolic networks allows the calculation of possible metabolic flux distributions in the absence of kinetic data. In order to predict which of the possible fluxes are present under certain conditions, additional constraints and optimization principles can be applied. One approach of calculating unknown fluxes (frequently called flux balance analysis) is based on the optimality principle of maximizing the molar yield of biotransformations. Here, the relevance and applicability of that approach are examined, and it is compared with the principle of maximizing pathway flux. We discuss diverse experimental evidence showing that, often, those biochemical pathways are operative that allow fast but low-yield synthesis of important products, such as fermentation in Saccharomyces cerevisiae and several other yeast species. Together with arguments based on evolutionary game theory, this leads us to the conclusion that maximization of molar yield is by no means a universal principle.     

From mechanisms to function: an integrated framework of animal innovation

Animal innovations range from the discovery of novel food types to the invention of completely novel behaviours. Innovations can give access to new opportunities, and thus enable innovating agents to invade and create novel niches. This in turn can pave the way for morphological adaptation and adaptive radiation. The mechanisms that make innovations possible are probably as diverse as the innovations themselves. So too are their evolutionary consequences. Perhaps because of this diversity, we lack a unifying framework that links mechanism to function. We propose a framework for animal innovation that describes the interactions between mechanism, fitness benefit and evolutionary significance, and which suggests an expanded range of experimental approaches. In doing so, we split innovation into factors (components and phases) that can be manipulated systematically, and which can be investigated both experimentally and with correlational studies. We apply this framework to a selection of cases, showing how it helps us ask more precise questions and design more revealing experiments.     

The Experimental Study of Bacterial Evolution and Its Implications for the Modern Synthesis of Evolutionary Biology

Since the 1940s, microbiologists, biochemists and population geneticists have experimented with the genetic mechanisms of microorganisms in order to investigate evolutionary processes. These evolutionary studies of bacteria and other microorganisms gained some recognition from the standard-bearers of the modern synthesis of evolutionary biology, especially Theodosius Dobzhansky and Ledyard Stebbins. A further period of post-synthesis bacterial evolutionary research occurred between the 1950s and 1980s. These experimental analyses focused on the evolution of population and genetic structure, the adaptive gain of new functions, and the evolutionary consequences of competition dynamics. This large body of research aimed to make evolutionary theory testable and predictive, by giving it mechanistic underpinnings. Although evolutionary microbiologists promoted bacterial experiments as methodologically advantageous and a source of general insight into evolution, they also acknowledged the biological differences of bacteria. My historical overview concludes with reflections on what bacterial evolutionary research achieved in this period, and its implications for the still-developing modern synthesis.     

Repetitive lagging strand DNA synthesis by the bacteriophage T4 replisome

Our studies on the T4 replisome build on the seminal work from the Alberts laboratory. They discovered essentially all the proteins that constitute the T4 replisome, isolated them, and measured their enzymatic activities. Ultimately, in brilliant experiments they reconstituted in vitro a functioning replisome and in the absence of structural information created a mosaic as to how such a machine might be assembled. Their consideration of the problem of continuous leading strand synthesis opposing discontinuous lagging strand synthesis led to their imaginative proposal of the trombone model, an illustration that graces all textbooks of biochemistry. Our subsequent work deepens their findings through experiments that focus on defining the kinetics, structural elements, and protein-protein contacts essential for replisome assembly and function. In this highlight we address when Okazaki primer synthesis is initiated and how the primer is captured by a recycling lagging strand polymerase--problems that the Alberts laboratory likewise found mysterious and significant for all replisomes.     

Standing wave design and experimental validation of a tandem simulated moving bed process for insulin purification

A tandem simulated moving bed (SMB) process for insulin purification has been proposed and validated experimentally. The mixture to be separated consists of insulin, high molecular weight proteins, and zinc chloride. A systematic approach based on the standing wave design, rate model simulations, and experiments was used to develop this multicomponent separation process. The standing wave design was applied to specify the SMB operating conditions of a lab-scale unit with 10 columns. The design was validated with rate model simulations prior to experiments. The experimental results show 99.9% purity and 99% yield, which closely agree with the model predictions and the standing wave design targets. The agreement proves that the standing wave design can ensure high purity and high yield for the tandem SMB process. Compared to a conventional batch SEC process, the tandem SMB has 10% higher yield, 400% higher throughput, and 72% lower eluant consumption. In contrast, a design that ignores the effects of mass transfer and nonideal flow cannot meet the purity requirement and gives less than 96% yield.     

Metabolic flux estimation--a self-adaptive evolutionary algorithm with singular value decomposition

Metabolic flux analysis is important for metabolic system regulation and intracellular pathway identification. A popular approach for intracellular flux estimation involves using ¹³C tracer experiments to label states that can be measured by nuclear magnetic resonance spectrometry or gas chromatography mass spectrometry. However, the bilinear balance equations derived from ¹³C tracer experiments and the noisy measurements require a nonlinear optimization approach to obtain the optimal solution. In this paper, the flux quantification problem is formulated as an error-minimization problem with equality and inequality constraints through the ¹³C balance and stoichiometric equations. The stoichiometric constraints are transformed to a null space by singular value decomposition. Self-adaptive evolutionary algorithms are then introduced for flux quantification. The performance of the evolutionary algorithm is compared with ordinary least squares estimation by the simulation of the central pentose phosphate pathway. The proposed algorithm is also applied to the central metabolism of Corynebacterium glutamicum under lysine-producing conditions. A comparison between the results from the proposed algorithm and data from the literature is given. The complexity of a metabolic system with bidirectional reactions is also investigated by analyzing the fluctuations in the flux estimates when available measurements are varied.     

Optimisation of integrated biodiesel production. Part I. A study of the biodiesel purity and yield

This study consists of the development and optimisation of the potassium hydroxide-catalysed synthesis of fatty acid methyl esters (biodiesel) from sunflower oil. A factorial design of experiments and a central composite design have been used. The variables chosen were temperature, initial catalyst concentration by weight of sunflower oil and the methanol:vegetable oil molar ratio, while the responses were biodiesel purity and yield. The initial catalyst concentration is the most important factor, having a positive influence on biodiesel purity, but a negative one on biodiesel yield. Temperature has a significant positive effect on biodiesel purity and a significant negative influence on biodiesel yield. The methanol:vegetable oil molar ratio is only significant for the biodiesel purity, having a positive influence. Second-order models were obtained to predict biodiesel purity and yield as a function of these variables. The best conditions are 25 degrees C, a 1.3%wt for the catalyst concentration and a 6:1 methanol:sunflower oil molar ratio.     

Taking the Peppered Moth with a Grain of Salt

H. B. D. Kettlewell's (1955, 1956) classic field experiments on industrial melanism in polluted and unpolluted settings using the peppered moth, Biston betularia, are routinely cited as establishing that the melanic (dark) form of the moth rose in frequency downwind of industrial centers because of the cryptic advantage dark coloration provides against visual predators in soot-darkened environments. This paper critiques three common myths surrounding these investigations: (1) that Kettlewell used a model that identified crypsis as the only selective force responsible for the spread of the melanic gene, (2) that Kettlewell's field experiments alone established that selection for crypsis was the most important factor in the spread of melanic forms, and (3) that Kettlewell's investigations in an unpolluted wood near Dorset constituted a control for his earlier Birmingham studies (contra Hagen 1993, 1996). This analysis further identifies two features that distinguish manipulative experiments in evolutionary biology from experiments in other contexts. First, experiments in evolutionary biology rest on a wealth of information provided by strictly observational ecological studies; in the absence of such information experiments in evolutionary biology make no sense. Second, there is a trade-off between how much control investigators have over the conditions being studied and how informative the results of the experiment will be with regard to natural populations.     

Modeling of the enzymatic kinetic synthesis of cephalexin--influence of substrate concentration and temperature

During enzymatic kinetic synthesis of cephalexin, an activated phenylglycine derivative (phenylglycine amide or phenylglycine methyl ester) is coupled to the nucleus 7-aminodeacetoxycephalosporanic acid (7-ADCA). Simultaneously, hydrolysis of phenylglycine amide and hydrolysis of cephalexin take place. This results in a temporary high-product concentration that is subsequently consumed by the enzyme. To optimize productivity, it is necessary to develop models that predict the course of the reaction. Such models are known from literature but these are only applicable for a limited range of experimental conditions. In this article a model is presented that is valid for a wide range of substrate concentrations (0-490 mM for phenylglycine amide and 0-300 mM for 7-ADCA) and temperatures (273-298 K). The model was built in a systematic way with parameters that were, for an important part, calculated from independent experiments. With the constants used in the model not only the synthesis reaction but also phenylglycine amide hydrolysis and cephalexin hydrolysis could be described accurately. In contrast to the models described in literature, only a limited number (five) of constants was required to describe the reaction at a certain temperature. For the temperature dependency of the constants, the Arrhenius equation was applied, with the constants at 293 K as references. Again, independent experiments were used, which resulted in a model with high statistic reliability for the entire temperature range. Low temperatures were found beneficial for the process because more cephalexin and less phenylglycine is formed. The model was used to optimize the reaction conditions using criteria such as the yield on 7-ADCA or on activated phenylglycine. Depending on the weight of the criteria, either a high initial phenylglycine amide concentration (yield on 7-ADCA) or a high initial 7-ADCA concentration (yield on phenylglycine amide) is beneficial.     

Optimization of process parameters for ethanol production from sugar cane molasses by Zymomonas mobilis using response surface methodology and genetic algorithm

Ethanol is a potential energy source and its production from renewable biomass has gained lot of popularity. There has been worldwide research to produce ethanol from regional inexpensive substrates. The present study deals with the optimization of process parameters (viz. temperature, pH, initial total reducing sugar (TRS) concentration in sugar cane molasses and fermentation time) for ethanol production from sugar cane molasses by Zymomonas mobilis using Box–Behnken experimental design and genetic algorithm (GA). An empirical model was developed through response surface methodology to analyze the effects of the process parameters on ethanol production. The data obtained after performing the experiments based on statistical design was utilized for regression analysis and analysis of variance studies. The regression equation obtained after regression analysis was used as a fitness function for the genetic algorithm. The GA optimization technique predicted a maximum ethanol yield of 59.59 g/L at temperature 31 °C, pH 5.13, initial TRS concentration 216 g/L and fermentation time 44 h. The maximum experimental ethanol yield obtained after applying GA was 58.4 g/L, which was in close agreement with the predicted value.