Chemometric modelling based on 2D-fluorescence spectra without a calibration measurement

MOTIVATION: 2D fluorescence spectra provide information from intracellular compounds. Fluorophores like trytophan, tyrosine and phenylalanin as well as NADH and flavins make the corresponding measurement systems very important for bioprocess supervision and control. The evaluation is usually based on chemometric modelling using for their calibration procedure off-line measurements of the desired process variables. Due to the data driven approach lots of off-line measurements are required. Here a methodology is presented, which enables to perform a calibration procedure of chemometric models without any further measurement. RESULTS: The necessary information for the calibration procedure is provided by means of the a priori knowledge about the process, i.e. a mathematical model, whose model parameters are estimated during the calibration procedure, as well as the fact that the substrate should be consumed at the end of the process run. The new methodology for chemometric calibration is applied for a batch cultivation of aerobically grown S. cerevisiae on the glucose Schatzmann medium. As will be presented the chemometric models, which are determined by this method, can be used for prediction during new process runs. AVAILABILITY: The MATHLAB routine is free available on request from the authors.     

The use of the maharanobis and modified distances for the improvement of simulation of glutamic acid production

A modified simulation procedure based on a statistical approach was investigated. The procedure predicts the time course of fed-batch culture for glutamic acid production by a temperature-sensitive strain of Brevibacterium flavum. The statistical approach requires only a data base of state points obtained in experiments, and not perfect identification of fermentation models. The simulation procedure is based on regression analysis to estimate specific rate parameters of system equations using the data points selected with reference to the Euclid distance. It was modified in that the data selection procedure included the use of the Maharanobis distance as well as a modified distance defined in this study. Simulation results using the modified procedure allow reasonable prediction of the time course of fed-batch culture for glutamic acid compared to that involving the Euclid distance alone.     

Computer control of pH and DO in a laboratory fermenter using a neural network technique

In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good.     

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model

The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.     

A new approach for assigning bone material properties from CT images into finite element models

Generation of subject-specific finite element (FE) models from computed tomography (CT) datasets is of significance for application of the FE analysis to bone structures. A great challenge that remains is the automatic assignment of bone material properties from CT Hounsfield Units into finite element models. This paper proposes a new assignment approach, in which material properties are directly assigned to each integration point. Instead of modifying the dataset of FE models, the proposed approach divides the assignment procedure into two steps: generating the data file of the image intensity of a bone in a MATLAB program and reading the file into ABAQUS via user subroutines. Its accuracy has been validated by assigning the density of a bone phantom into a FE model. The proposed approach has been applied to the FE model of a sheep tibia and its applicability tested on a variety of element types. The proposed assignment approach is simple and illustrative. It can be easily modified to fit users’ situations.     

Inverse labeling-mass spectrometry for the rapid identification of differentially expressed protein markers/targets

Comparative proteomic studies can lead to the identification of protein markers for disease diagnostics and protein targets for potential disease interventions. An inverse labeling strategy based on the principle of protein stable isotope labeling and mass spectrometric detection has been successfully applied to three general protein labeling methods. In contrast to the conventional single experiment approach, two labeling experiments are performed in which the initial labeling is reversed in the second experiment. Signals from differentially expressed proteins will distinguish themselves by exhibiting a characteristic pattern of isotope intensity profile reversal that will lead to the rapid identification of these proteins. Application of the inverse labeling method is demonstrated using model systems for protein chemical labeling, protein proteolytic labeling, and protein metabolic labeling. The methodology has clear advantages which are illustrated in the various studies. The inverse labeling strategy permits quick focus on signals from differentially expressed proteins (markers/targets) and eliminates ambiguities caused by the dynamic range of detection. In addition, the inverse labeling approach enables the unambiguous detection of covalent changes of proteins responding to a perturbation.     

Quantifying nonlinear anisotropic elastic material properties of biological tissue by use of membrane inflation

Determination of material parameters for soft tissue frequently involves regression of material parameters for nonlinear, anisotropic constitutive models against experimental data from heterogeneous tests. Here, parameter estimation based on membrane inflation is considered. A four parameter nonlinear, anisotropic hyperelastic strain energy function was used to model the material, in which the parameters are cast in terms of key response features. The experiment was simulated using finite element (FE) analysis in order to predict the experimental measurements of pressure versus profile strain. Material parameter regression was automated using inverse FE analysis; parameter values were updated by use of both local and global techniques, and the ability of these techniques to efficiently converge to a best case was examined. This approach provides a framework in which additional experimental data, including surface strain measurements or local structural information, may be incorporated in order to quantify heterogeneous nonlinear material properties.     

Single-layer CVD-grown graphene decorated with metal nanoparticles as a promising biosensing platform

A new approach to the development of a single-layer graphene sensor decorated with metal nanoparticles is presented. Chemical vapor deposition is used to grow single layer graphene on copper. Decoration of the single-layer graphene is achieved by electroless deposition of Au nanoparticles using the copper substrate as a source of electrons. Transfer of the decorated single-layer graphene on glassy carbon electrodes offers a sensitive platform for biosensor development. As a proof of concept, 10 units of glucose oxidase were deposited on the surface in a Nafion matrix to stabilize the enzyme as well as to prevent interference from ascorbic acid and uric acid. Amperometric linear response calibration in the μmoll(-1) is obtained. The presented methodology enables highly sensitive platforms for biosensor development, providing a scalable roll-to-roll production with a much more reproducible scheme when compared to the graphene biosensors reported previously based on drop-cast of multi-layer graphene suspensions.     

Determination of the geometry of the RVE for cancellous bone by using the effective complex shear modulus

This contribution deals with the application of the inverse homogenization method to the determination of geometrical properties of cancellous bone. The approach represents a combination of an extended version of the Marquardt–Levenberg method with the multiscale finite element method. The former belongs to the group of gradient-based optimization strategies, while the latter is a numerical homogenization method, suitable for the modeling of materials with a highly heterogeneous microstructure. The extension of the Marquardt–Levenberg method is concerned with the selection strategy for distinguishing the global minimum from the plethora of local minima. Within the numerical examples, the bone is modeled as a biphasic viscoelastic medium and three different representative volume elements are taken into consideration. Different models enable the simulation of the bone either as a purely isotropic or as a transversally anisotropic medium. Main geometrical properties of trabeculae are determined from data on effective shear modulus but alternative schemes are also possible.     

Tendency modeling: a new approach to obtain simplified kinetic models of metabolism applied to Saccharomyces cerevisiae

A novel approach to construct kinetic models of metabolic pathways, to be used in metabolic engineering, is presented: the tendency modeling approach. This approach greatly facilitates the construction of these models and can easily be applied to complex metabolic networks. The resulting models contain a minimal number of parameters; identification of their values is straightforward. Use of in vitro obtained information in the identification of the kinetic equations is minimized. The tendency modeling approach has been used to derive a dynamic model of primary metabolism for aerobic growth of Saccharomyces cerevisiae on glucose, in which compartmentation is included. Simulation results obtained with the derived model are satisfying for most of the carbon metabolites that have been measured. Compared to a more detailed model, the simulations of our model are less accurate, but taking into account the much smaller number of kinetic parameters (35 instead of 84), the tendency the modeling approach is considered promising.     

APPLICATION OF THIN‐PLATE SPLINE TRANSFORMATIONS TO FINITE ELEMENT MODELS,OR, HOW TO TURN A BOG TURTLE INTO A SPOTTED TURTLE TO ANALYZE BOTH

Finite element (FE) models are popular tools that allow biologists to analyze the biomechanical behavior of complex anatomical structures. However, the expense and time required to create models from specimens has prevented comparative studies from involving large numbers of species. A new method is presented for transforming existing FE models using geometric morphometric methods. Homologous landmark coordinates are digitized on the FE model and on a target specimen into which the FE model is being transformed. These coordinates are used to create a thin‐plate spline function and coefficients, which are then applied to every node in the FE model. This function smoothly interpolates the location of points between landmarks, transforming the geometry of the original model to match the target. This new FE model is then used as input in FE analyses. This procedure is demonstrated with turtle shells: a Glyptemys muhlenbergii model is transformed into Clemmys guttata and Actinemys marmorata models. Models are loaded and the resulting stresses are compared. The validity of the models is tested by crushing actual turtle shells in a materials testing machine and comparing those results to predictions from FE models. General guidelines, cautions, and possibilities for this procedure are also presented.     

Dynamic model development for a continuous culture of Saccharomyces cerevisiae

The problem of dynamically modeling a chemostat is addressed. Using the results of continuous culture experiments for the growth of a strain of Saccharomyces cerevisiae on a glucose-limited medium, a general approach to developing dynamic models is discussed. The approach to develop and verify the model involves three different types of experiments: steady-state, dynamic step response, and feedback identification.     

An empirical Bayes approach to inferring large-scale gene association networks

MOTIVATION: Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem. METHODS: We introduce a novel framework for small-sample inference of graphical models from gene expression data. Specifically, we focus on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (1) improved (regularized) small-sample point estimates of partial correlation, (2) an exact test of edge inclusion with adaptive estimation of the degree of freedom and (3) a heuristic network search based on false discovery rate multiple testing. Steps (2) and (3) correspond to an empirical Bayes estimate of the network topology. RESULTS: Using computer simulations, we investigate the sensitivity (power) and specificity (true negative rate) of the proposed framework to estimate GGMs from microarray data. This shows that it is possible to recover the true network topology with high accuracy even for small-sample datasets. Subsequently, we analyze gene expression data from a breast cancer tumor study and illustrate our approach by inferring a corresponding large-scale gene association network for 3883 genes.     

eThread: A Highly Optimized Machine Learning-Based Approach to Meta-Threading and the Modeling of Protein Tertiary Structures

Template-based modeling that employs various meta-threading techniques is currently the most accurate, and consequently the most commonly used, approach for protein structure prediction. Despite the evident progress in this field, accurate structure models cannot be constructed for a significant fraction of gene products, thus the development of new algorithms is required. Here, we describe the development, optimization and large-scale benchmarking of eThread, a highly accurate meta-threading procedure for the identification of structural templates and the construction of corresponding target-to-template alignments. eThread integrates ten state-of-the-art threading/fold recognition algorithms in a local environment and extensively uses various machine learning techniques to carry out fully automated template-based protein structure modeling. Tertiary structure prediction employs two protocols based on widely used modeling algorithms: Modeller and TASSER-Lite. As a part of eThread, we also developed eContact, which is a Bayesian classifier for the prediction of inter-residue contacts and eRank, which effectively ranks generated multiple protein models and provides reliable confidence estimates as structure quality assessment. Excluding closely related templates from the modeling process, eThread generates models, which are correct at the fold level, for >80% of the targets; 40–50% of the constructed models are of a very high quality, which would be considered accurate at the family level. Furthermore, in large-scale benchmarking, we compare the performance of eThread to several alternative methods commonly used in protein structure prediction. Finally, we estimate the upper bound for this type of approach and discuss the directions towards further improvements.     

Comparison of probability and likelihood models for peptide identification from tandem mass spectrometry data

We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.