Reaction time to visual stimuli in child development

The simple and differential reaction time and time of cognitive processes were studied in 3-7-year-old children using age-adapted computer technique. The reaction time significantly decreased with age in parallel with improvement of cognitive processes. An experimental method is proposed, which makes it possible to determine what kind of cognitive process is responsible for age-related decrease in the reaction time.     

The Time Course of Corticospinal Excitability during a Simple Reaction Time Task

The production of movement in a simple reaction time task can be separated into two time periods: the foreperiod, which is thought to include preparatory processes, and the reaction time interval, which includes initiation processes. To better understand these processes, transcranial magnetic stimulation has been used to probe corticospinal excitability at various time points during response preparation and initiation. Previous research has shown that excitability decreases prior to the “go” stimulus and increases following the “go”; however these two time frames have been examined independently. The purpose of this study was to measure changes in CE during both the foreperiod and reaction time interval in a single experiment, relative to a resting baseline level. Participants performed a button press movement in a simple reaction time task and excitability was measured during rest, the foreperiod, and the reaction time interval. Results indicated that during the foreperiod, excitability levels quickly increased from baseline with the presentation of the warning signal, followed by a period of stable excitability leading up to the “go” signal, and finally a rapid increase in excitability during the reaction time interval. This excitability time course is consistent with neural activation models that describe movement preparation and response initiation.     

Studies on the Kinetic Properties of Photosystem Ⅱ Primary Reaction Using Fluorescence Spectroscopy with 470 fs Time Resolution

The primary reaction kinetics of the isolated photosystem Ⅱ particles and photosystem Ⅱ core complexes from spinach ( Spinacia deracea Mill. ) was investigated using the time-resolved fluorescence spectroscopy with 470 fs time resolution. 2 to 4 lifetime components were detected by the multi-exponential curve fining method. These components were analyzed and discussed in terms of different kinetic processes. It is suggested that 3 ps component is attributed to the charge separation and 0.8 ps, 12 ps, 25 ps and 100 ps components are related to the energy transfer processes. A possible kinetic scheme in photosystem Ⅱ reaction center was proposed based upon the reported previously result.     

外切核酸酶Ⅲ介导的DNA分子克隆

DNA克隆技术,作为最基本的现代分子生物学实验技术之一, 已经成为生物医学研究领域的重要研究手段。传统的分子克隆方法需要经过限制性内切酶酶切和DNA连接酶连接的步骤,是否存在合适的酶切位点和DNA连接酶的效率成为影响克隆的重要限制因素。本文描述了一种由外切核酸酶Ⅲ介导的,以3′-5′外切核酸酶活性和细菌细胞内DNA修复机制为理论基础的DNA分子克隆方法,称为不依赖连接酶的分子克隆（ligation-independent cloning, LIC）|证明了该方法的高效性和可靠性,并进一步对酶的用量、反应温度、反应时间、片段载体比例和量等多个参数进行了优化,建立了一种快速、简便和高效的DNA克隆方法。     

Sensitivity and control analysis of periodically forced reaction networks using the Green's function method

A general sensitivity and control analysis of periodically forced reaction networks with respect to small perturbations in arbitrary network parameters is presented. A well-known property of sensitivity coefficients for periodic processes in dynamical systems is that the coefficients generally become unbounded as time tends to infinity. To circumvent this conceptual obstacle, a relative time or phase variable is introduced so that the periodic sensitivity coefficients can be calculated. By employing the Green's function method, the sensitivity coefficients can be defined using integral control operators that relate small perturbations in the network's parameters and forcing frequency to variations in the metabolite concentrations and reaction fluxes. The properties of such operators do not depend on a particular parameter perturbation and are described by the summation and connectivity relationships within a control-matrix operator equation. The aim of this paper is to derive such a general control-matrix operator equation for periodically forced reaction networks, including metabolic pathways. To illustrate the general method, the two limiting cases of high and low forcing frequency are considered. We also discuss a practically important case where enzyme activities and forcing frequency are modulated simultaneously. We demonstrate the developed framework by calculating the sensitivity and control coefficients for a simple two reaction pathway where enzyme activities enter reaction rates linearly and specifically.     

Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester

Background  

Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes.     

Effect of unstirred layers on binding and reaction kinetics at a membrane surface

The effects of solution unstirred layers on the time course of chemical reactions and transport processes at a membrane surface are determined. A set of equations which describes non-steady-state diffusion through an unstirred layer coupled with chemical reaction at a membrane surface or transport through a membrane is developed. A numerical solution to the equations is obtained by uncoupling diffusive and chemical processes in an iterative manner. The diffusive process is solved by the Crank-Nicolson method; the chemical process is solved by integrating the differential equations describing the kinetics. Diffusive processes in one dimension, in three dimensions, and in the presence of an arbitrary potential near the membrane surface are solved. General characteristics of the calculated reaction time course are discussed using surface binding and membrane transport examples. Small, neglected, unstirred layers are shown to sometimes yield erroneous values of rate parameters for a surface reaction and to simulate competitive reaction kinetics. Experimental approaches for measuring unstirred layer thickness are reviewed.     

Microwave esterification of sunflower proteins in solvent-free conditions

The esterification of storage proteins from sunflower seeds renders these molecules more hydrophobic and increases internal plasticization, facilitating their use in the fabrication of materials by thermo-mechanical processes. The reaction was carried out in solvent-free conditions to simplify the process and to reduce costs. Optimization of esterification by the classical method, heating in a thermostat-regulated oil bath, resulted in 84% esterification for a reaction time of 4 h (amount of catalyst = 3.9 meq 5 N HCl/g protein, T = 90 degrees C). Microwave heating was then used to reduce the reaction time and the hydrolysis of protein chains. A similar level of esterification (89%) was obtained in 18 min (amount of catalyst = 3.4 meq 5 N HCl/g protein, microwave power (P) = 560 W). High yields were obtained with this method, demonstrating that this process limited hydrolysis.     

Inferring proteolytic processes from mass spectrometry time series data using degradation graphs

Background

Proteases play an essential part in a variety of biological processes. Besides their importance under healthy conditions they are also known to have a crucial role in complex diseases like cancer. In recent years, it has been shown that not only the fragments produced by proteases but also their dynamics, especially ex vivo, can serve as biomarkers. But so far, only a few approaches were taken to explicitly model the dynamics of proteolysis in the context of mass spectrometry.

Results

We introduce a new concept to model proteolytic processes, the degradation graph. The degradation graph is an extension of the cleavage graph, a data structure to reconstruct and visualize the proteolytic process. In contrast to previous approaches we extended the model to incorporate endoproteolytic processes and present a method to construct a degradation graph from mass spectrometry time series data. Based on a degradation graph and the intensities extracted from the mass spectra it is possible to estimate reaction rates of the underlying processes. We further suggest a score to rate different degradation graphs in their ability to explain the observed data. This score is used in an iterative heuristic to improve the structure of the initially constructed degradation graph.

Conclusion

We show that the proposed method is able to recover all degraded and generated peptides, the underlying reactions, and the reaction rates of proteolytic processes based on mass spectrometry time series data. We use simulated and real data to demonstrate that a given process can be reconstructed even in the presence of extensive noise, isobaric signals and false identifications. While the model is currently only validated on peptide data it is also applicable to proteins, as long as the necessary time series data can be produced.     

Are Accuracy and Reaction Time Affected via Different Processes?

A recent study by van Ede et al. (2012) shows that the accuracy and reaction time in humans of tactile perceptual decisions are affected by an attentional cue via distinct cognitive and neural processes. These results are controversial as they undermine the notion that accuracy and reaction time are influenced by the same latent process that underlie the decision process. Typically, accumulation-to-bound models (like the drift diffusion model) can explain variability in both accuracy and reaction time by a change of a single parameter. To elaborate the findings of van Ede et al., we fitted the drift diffusion model to their behavioral data. Results show that both changes in accuracy and reaction time can be partly explained by an increase in the accumulation of sensory evidence (drift rate). In addition, a change in non-decision time is necessary to account for reaction time changes as well. These results provide a subtle explanation of how the underlying dynamics of the decision process might give rise to differences in both the speed and accuracy of perceptual tactile decisions. Furthermore, our analyses highlight the importance of applying a model-based approach, as the observed changes in the model parameters might be ecologically more valid, since they have an intuitive relationship with the neuronal processes underlying perceptual decision making.     

Novel and simple high-performance liquid chromatographic method for determination of 3-hydroxy-3-methylglutaryl-coenzyme A reductase activity

We present here a high-performance liquid chromatographic method for the evaluation of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase activity. The automated method was applied to fungal and mouse liver extracts and validated by the addition of mevastatin to the reaction mixture and by several intra- and inter-day assays. This method offers important advantages over those previously reported because no radiolabeled substrates or expensive techniques such as mass spectrometry are required, and the time of analysis is relatively short. Moreover, the method can be successfully applied to different biological samples; hence, it should be very useful in evaluating potential inhibitors of the HMG-CoA enzyme and investigating cholesterol metabolism, cell growth and differentiation processes.     

A graphical user interface for a method to infer kinetics and network architecture (MIKANA)

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).     

Stepwise Catalytic Mechanism via Short-Lived Intermediate Inferred from Combined QM/MM MERP and PES Calculations on Retaining Glycosyltransferase ppGalNAcT2

The glycosylation of cell surface proteins plays a crucial role in a multitude of biological processes, such as cell adhesion and recognition. To understand the process of protein glycosylation, the reaction mechanisms of the participating enzymes need to be known. However, the reaction mechanism of retaining glycosyltransferases has not yet been sufficiently explained. Here we investigated the catalytic mechanism of human isoform 2 of the retaining glycosyltransferase polypeptide UDP-GalNAc transferase by coupling two different QM/MM-based approaches, namely a potential energy surface scan in two distance difference dimensions and a minimum energy reaction path optimisation using the Nudged Elastic Band method. Potential energy scan studies often suffer from inadequate sampling of reactive processes due to a predefined scan coordinate system. At the same time, path optimisation methods enable the sampling of a virtually unlimited number of dimensions, but their results cannot be unambiguously interpreted without knowledge of the potential energy surface. By combining these methods, we have been able to eliminate the most significant sources of potential errors inherent to each of these approaches. The structural model is based on the crystal structure of human isoform 2. In the QM/MM method, the QM region consists of 275 atoms, the remaining 5776 atoms were in the MM region. We found that ppGalNAcT2 catalyzes a same-face nucleophilic substitution with internal return (S_Ni). The optimized transition state for the reaction is 13.8 kcal/mol higher in energy than the reactant while the energy of the product complex is 6.7 kcal/mol lower. During the process of nucleophilic attack, a proton is synchronously transferred to the leaving phosphate. The presence of a short-lived metastable oxocarbenium intermediate is likely, as indicated by the reaction energy profiles obtained using high-level density functionals.     

Multi-scale stochastic simulation of diffusion-coupled agents and its application to cell culture simulation

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems.     

Parallel steady state studies on a milliliter scale accelerate fed‐batch bioprocess design for recombinant protein production with Escherichia coli

In general, fed‐batch processes are applied for recombinant protein production with Escherichia coli (E. coli). However, state of the art methods for identifying suitable reaction conditions suffer from severe drawbacks, i.e. direct transfer of process information from parallel batch studies is often defective and sequential fed‐batch studies are time‐consuming and cost‐intensive. In this study, continuously operated stirred‐tank reactors on a milliliter scale were applied to identify suitable reaction conditions for fed‐batch processes. Isopropyl β‐d ‐1‐thiogalactopyranoside (IPTG) induction strategies were varied in parallel‐operated stirred‐tank bioreactors to study the effects on the continuous production of the recombinant protein photoactivatable mCherry (PAmCherry) with E. coli. Best‐performing induction strategies were transferred from the continuous processes on a milliliter scale to liter scale fed‐batch processes. Inducing recombinant protein expression by dynamically increasing the IPTG concentration to 100 µM led to an increase in the product concentration of 21% (8.4 g L^?1) compared to an implemented high‐performance production process with the most frequently applied induction strategy by a single addition of 1000 µM IPGT. Thus, identifying feasible reaction conditions for fed‐batch processes in parallel continuous studies on a milliliter scale was shown to be a powerful, novel method to accelerate bioprocess design in a cost‐reducing manner. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1426–1435, 2016     

A continuous wave laser T-jump apparatus and its application to chemical reactions in hemoglobin single crystals

A laser temperature jump apparatus is constructed where the T-jump is achieved by means of the direct absorption of continuous laser radiation of low intensity by a solid sample. The final temperature in the irradiated volume element is reached when the absorbed radiation power equals the dissipation of heat by heat conduction. The time range from the beginning of irradiation to the stationary state depends on the geometry of the irradiated volume element and is less than 10 ms. The heating laser beam is simultaneously used to detect the relaxation to the new chemical equilibrium in the sample. Relaxation processes with relaxation rates between 10(2) s-1 and less than 10(-3) s-1 on samples with volumes less than 10(-3) mm3 may be investigated using this T-jump method. One application of this method is the determination of reaction rates of ligand reactions in hemoglobin single crystals. Rate constants obtained for the reaction of thiocyanate with crystallized horse methemoglobin are presented.     

Parameter estimation in stochastic biochemical reactions

Gene regulatory, signal transduction and metabolic networks are major areas of interest in the newly emerging field of systems biology. In living cells, stochastic dynamics play an important role; however, the kinetic parameters of biochemical reactions necessary for modelling these processes are often not accessible directly through experiments. The problem of estimating stochastic reaction constants from molecule count data measured, with error, at discrete time points is considered. For modelling the system, a hidden Markov process is used, where the hidden states are the true molecule counts, and the transitions between those states correspond to reaction events following collisions of molecules. Two different algorithms are proposed for estimating the unknown model parameters. The first is an approximate maximum likelihood method that gives good estimates of the reaction parameters in systems with few possible reactions in each sampling interval. The second algorithm, treating the data as exact measurements, approximates the number of reactions in each sampling interval by solving a simple linear equation. Maximising the likelihood based on these approximations can provide good results, even in complex reaction systems.     

Neural correlates of decision processes: neural and mental chronometry

Recent studies aim to explain the duration and variability of behavioral reaction time in terms of neural processes. The time taken to make choices is occupied by at least two processes. Neurons in sensorimotor structures accumulate evidence that leads to alternative categorizations, while other neurons within these structures prepare and initiate overt responses. These distinct stages of stimulus encoding and response preparation support variable but flexible behavior.