Microscopic models of spin crossover

The basic model for thermal spin crossover (SCO) is discussed in its microscopic and thermodynamic formulation. Compared to the basic model, its more elaborated forms formulated in course of almost 50 years are briefly reviewed with emphasis on their additional features. A separate section is devoted to the newer developments in the field of modelling of the SCO nanoparticles. The presentation of models is led in a comparative way to provide an accessible outline of the foundations of modern theoretical research on SCO and a simple applicability in quantitative interpretation of experiments.     

Agent-Based Network Modeling Study of Immune Responses in Progression of Ulcerative Colitis

Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology.Ulcerative colitis causes a huge public health care burden particularly in developed countries.Many studies suggest that ulcerative colitis results from an abnormal immune response against components of commensal microbiota in genetically susceptible individuals.However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive.In this paper, a network model is developed based on our previous study and computer simulations are performed using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression.Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression.The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflammatory/regulatory cells such as alternatively activated macrophages and type Ⅱ natural killer T cells, proinflammatory cells including classically activated macrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels (lower than those in Crohn's disease, another inflammatory bowel disease).Long-term exposure to these proinflammatory components, causes mucosal tissue damage persistently, leading to ulcerative colitis.Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.     

Molecular modelling studies on the ORL1-receptor and ORL1-agonists

The ORLI (opioid receptor like 1)- receptor is a member of the family of rhodopsin-like G protein-coupled receptors (GPCR) and represents an interesting new therapeutical target since it is involved in a variety of biomedical important processes, such as anxiety, nociception, feeding, and memory. In order to shed light on the molecular basis of the interactions of the GPCR with its ligands, the receptor protein and a dataset of specific agonists were examined using molecular modelling methods. For that purpose, the conformational space of a very potent non-peptide ORL1-receptor agonist (Ro 64-6198) with a small number of rotatable bonds was analysed in order to derive a pharmacophoric arrangement. The conformational analyses yielded a conformation that served as template for the superposition of a set of related analogues. Structural superposition was achieved by employing the program FlexS. Using the experimental binding data and the superposition of the ligands, a 3D-QSAR analysis applying the GRID/GOLPE method was carried out. After the ligand-based modelling approach, a 3D model of the ORL1-receptor has been constructed using homology modelling methods based on the crystal structure of bovine rhodopsin. A representative structure of the model taken from molecular dynamics simulations was used for a manual docking procedure. Asp-130 and Thr-305 within the ORL1-receptor model served as important hydrophilic interaction partners. Furthermore, a hydrophobic cavity was identified stabilizing the agonists within their binding site. The manual docking results were supported using FlexX, which identified the same protein-ligand interaction points.     

Modelling Development in Radical (Co)Polymerization of Multivinyl Monomers

Radical polymerization (RP) of multivinyl monomers (MVMs) provides a facile solution for manipulating polymer topology and has received increasing attention due to their industrial and academic significance. Continuous efforts have been made to understand their mechanism, which is the key to regulating materials structure. Modelling techniques have become a powerful tool that can provide detailed information on polymerization kinetics which is inaccessible by experiments. Many publications have reported the combination of experiments and modelling for free radical polymerization (FRP) and reversible-deactivation radical polymerizations (RDRP) of MVMs. Herein, a minireview is presented for the most important modelling techniques and their applications in FRP/RDRP of MVMs. This review hopes to illustrate that the combination of modelling and wet experiments can be a great asset to polymer researchers and inspire new thinking for the future MVMs experiment optimization and product design.     

Computer-simulation data inclusive regression modelling in measurement

Measurement involves the determination of quantitative estimates of physical quantities from experiment, along with estimates of their associated uncertainties. Herewith an experimental system model is the key to extracting information from experimental data. With this concern novel regression modelling techniques have been fashioned by data combination from designed computer-simulation and physical experiments. These techniques have allowed attaining the advanced level of model completeness and precision via approximation of the true unknown model by mathematical product of appropriate theoretical and empirical functions. Pneumatic gauge candidate models’ building, solving and validation revealed that computer-simulation data inclusive response surface models permit to attain minimum discrepancy from empirical evidence.     

Probabilistic model‐based discriminant analysis and clustering methods in chemometrics

In chemometrics, the supervised and unsupervised classification of high‐dimensional data has become a recurrent problem. Model‐based techniques for discriminant analysis and clustering are popular tools, which are renowned for their probabilistic foundations and their flexibility. However, classical model‐based techniques show a disappointing behaviour in high‐dimensional spaces, which up to now have been limited in their use within chemometrics. The recent developments in model‐based classification overcame these drawbacks and enabled the efficient classification of high‐dimensional data, even in the ‘small n / large p’ condition. This work presents a comprehensive review of these recent approaches, including regularization‐based techniques, parsimonious modelling, subspace classification methods and classification methods based on variable selection. The use of these model‐based methods is also illustrated on real‐world classification problems in chemometrics using R packages. Copyright © 2013 John Wiley & Sons, Ltd.     

An agent-based system to discover protein-protein interactions, identify protein complexes and proteins with multiple peptide mass fingerprints

Proteins "work together" by actually binding to form multicomponent complexes that carry out specific functions. Proteomic analyses based on the mass spectrum are now key methods to determine the components in protein complexes. The protein-protein interaction or functional association may be known to exist among the extracted protein spots while analyzing the proteins on the 2D gel. In this study, we develop an agent-based system, namely AgentMultiProtIdent, which integrated two protein identification tools and a variety of databases storing relations among proteins and used to discover protein-protein interactions and protein functional associations, and identify protein complexes and proteins with multiple peptide mass fingerprints as input. The system takes Multiple Peptide Mass Fingerprints (PMFs) as a whole in the protein complex or protein identification. With the relations among proteins, it may greatly improve the accuracy of identification of protein complexes. Also, possible relationship of the multiple peptide mass fingerprints, such as ontology relation, can be discovered by our system, especially in the identification of protein complexes. The agent-based system is now available on the Web at http://dbms104.csie.ncu.edu.tw/ approximately protein/NEW2/.     

Molecular simulation of biosurfactants with relevance to food systems

The application of molecular modelling, and in particular all-atom and coarse grained molecular dynamics simulation, to the study of low molecular weight surfactants with relevance to food systems is reviewed. Two key aspects of surfactant behaviour — their ability to form micelles and their tendency to adsorb to fluid–fluid interfaces (air–water and oil–water) are covered. Since the modelling literature on synthetic amphiphilic surfactants is vast, and much of it not directly relevant to foods, the review concentrates on biosurfactants. Two particular topics are covered in detail: the behaviour of bile salts because of the importance of these in understanding food digestion; the behaviour of novel glycolipid and lipopeptide surfactants derived from microorganisms (bacteria and yeast) due to their increasing importance as functional ingredients in consumer products including foods.     

Integration of scale-down experimentation and general rate modelling to predict manufacturing scale chromatographic separations

Chromatography is an essential downstream processing step in the production of biopharmaceuticals. Here we present an approach to chromatography scale-up using scale-down experimentation integrated with general rate modelling. This type of modelling takes account all contributions to the mass transfer kinetics providing process understanding. The model is calibrated using a 2.5 cm height, 1 ml column and used to predict chromatograms for 20 cm height columns from 40 ml to 160 L volume. Simulations were found to be in good agreement with experimental results. The envisaged approach could potentially reduce the number of experiments, shorten development time and reduce costs.     

Studies towards the synthesis of the bicyclic 3,8-secotaxane diterpenoid system using a ring closing metathesis strategy

Molecular modelling studies on the interations between taxanes and tubulins, developed by us, revealed that modified Taxuspines U and X could adopt a conformation similar to that of the bioactive conformation of paclitaxel and could be well accommodated within the proposed model. Accordingly, simplified Taxuspine U and X analogues have been rationally designed and their bicyclic 3,8-secotaxane diterpenoids intermediates have been synthesized through an approach that involves ring closing metathesis (RCM) as the key step for the macrocycle formation. Extensive studies on RCM have been performed using chemically diverse substrates, outlining the influence in the macrocyclization of the presence and position of functionalities, the molecular constraints and the importance of the site of ring closure.     

State of the Art in the Development of Adaptive Soft Sensors based on Just-in-Time Models

Data-driven soft sensors have gained popularity due to availability of the recorded historical plant data. The success stories of the implementations of soft sensors, however, involved some practical difficulties. Even if a good soft sensor is successfully developed, its predictive performance will gradually deteriorate after a certain time due to changes in the state of plants and process characteristics, such as catalyst deactivation and sensor and process drifts due to equipment ageing, fouling, clogging and wear, changes of raw materials and so on. To get soft sensor automatically updated, different kinds of methods have been introduced, such as Kalman filter, moving window average, recursive and ensemble methods. However, these methods have some drawbacks which motivate the development and implementation of just-in-time (JIT) model based adaptive soft sensor. This paper aims to report the current status of adaptive soft sensors based on just-in-time modelling approach. Critical review and discussion on the original and modified algorithms of the JIT modelling approach are presented. Proposed topics for future research and development are also outlined to provide a road map on the developing improved and more practical adaptive soft sensors based on JIT models.     

黄素酶的模拟

本综述介绍模拟黄素酶的进展, 涉及两条途径: 半合成黄素酶和黄素酶模型化合物。     

Climate change, global warming and coral reefs: modelling the effects of temperature

Climate change and global warming have severe consequences for the survival of scleractinian (reef-building) corals and their associated ecosystems. This review summarizes recent literature on the influence of temperature on coral growth, coral bleaching, and modelling the effects of high temperature on corals. Satellite-based sea surface temperature (SST) and coral bleaching information available on the internet is an important tool in monitoring and modelling coral responses to temperature. Within the narrow temperature range for coral growth, corals can respond to rate of temperature change as well as to temperature per se. We need to continue to develop models of how non-steady-state processes such as global warming and climate change will affect coral reefs.     

Data modelling with neural networks: Advantages and limitations

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.     

Transient response,model structure and systematic errors in hybrid respirometers: structural identifiability analysis based on OUR and DO measurements

Experimental respirometry shows oxygen uptake rate (OUR) transients which are not predicted by standard ASM models. These transients were analysed from a detailed modelling of hybrid respirometers. Structural identifiability analysis of several models based on local isomorphism transformation approach is carried out. It can be concluded that the knowledge of the OUR does not help positively on an accurate determination of biokinetic parameters. The measure of dissolved oxygen (DO) is just enough to calculate the group of identifiable parameters from the models in a calibration process. The analysis suggests to use only the DO data to avoid the systematic errors associated to OUR calculations. Furthermore, the existence of OUR transients after a precise modelling of the hybrid respirometer is deduced. The duration of these transients are the result of the coupling between the kinetics of the biological process and the residence time in the respiration chamber of the instrument. An expression for the duration of these transients is proposed.     

Mathematical modelling of actinide extraction equilibria and extraction processes

Progress is reviewed in the authors' works on actinide extraction equilibria (including activity coefficient evaluation from extraction data) and their mathematical modelling (including Tc influence on Pu/U separation).     

Modelling and simulation of hydro-gel growth mechanism by analysis of experimental data

We have experimentally studied the growth process of a water-absorbing polymer commonly known as hydro-gel. We took a number of spherical beads, immersed them in pure water, measured their diameters at regular intervals of time and plotted graphs showing the change in volume with the time of immersion, to analyze their swelling properties. We have formulated a dynamical model on the basis of curve-fitting to our experimental data. Our mathematical modelling is devoted solely towards the explanation of the swelling of spherical hydro-gels and we have interpreted our observations on these basis. And finally evaluating the constants of the process, we have shown that the results obtained from our modelling and numerical simulation exactly match the experimental data. The justification of this new approach lies in its successful application for the complete explanation of our observations through numerical and analytical ways.     

Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

Both the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.     

Testing homology with Contact Accepted mutatiOn (CAO): a contact-based Markov model of protein evolution

Point Accepted Mutation (PAM) is the Markov model of amino acid replacements in proteins introduced by Dayhoff and her co-workers (Dayhoff et al., 1978). The PAM matrices and other matrices based on the PAM model have been widely accepted as the standard scoring system of protein sequence similarity in protein sequence alignment tools. Here, we present Contact Accepted mutatiOn (CAO), a Markov model of protein residue contact mutations. The CAO model simulates the interchanging of structurally defined side-chain contacts, and introduces additional structural information into protein sequence alignments. Therefore, similarities between structurally conserved sequences can be detected even without apparent sequence similarity. CAO has been benchmarked on the HOMSTRAD database and a subset of the CATH database, by comparing sequence alignments with reference alignments derived from structural superposition. CAO yields scores that reflect coherently the structural quality of sequence alignments, which has implications particularly for homology modelling and threading techniques.