Investigating the dynamic behavior of biochemical networks using model families

MOTIVATION: Supporting the evolutionary modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. In the course of the modeling process, the modeler is typically concerned not only with a single model but also with sequences, alternatives and structural variants of models. Powerful automatic methods are then required to assist the modeler in the organization and the evaluation of alternative models. Moreover, the structure and peculiarities of the data require dedicated tool support. SUMMARY: To support all stages of an evolutionary modeling process, a new general formalism for the combinatorial specification of large model families is introduced. It allows for automatic navigation in the space of models and excludes biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. With MMT2, a versatile tool has been developed as a computational engine intended to be built into a tool chain. Using automatic code generation, automatic differentiation for sensitivity analysis and grid computing technology, a high performance computing environment is achieved. MMT2 supplies XML model specification and several software interfaces. The performance of MMT2 is illustrated by several examples from ongoing research projects. AVAILABILITY: http://www.simtec.mb.uni-siegen.de/ CONTACT: wiechert@simtec.mb.uni-siegen.de.     

Gene expression analysis using single molecule detection

Recent developments of single molecule detection techniques and in particular the introduction of fluorescence correlation spectroscopy (FCS) led to a number of important applications in biological research. We present a unique approach for the gene expression analysis using dual-color cross-correlation. The expression assay is based on gene-specific hybridization of two dye-labeled DNA probes to a selected target gene. The counting of the dual-labeled molecules within the solution allows the quantification of the expressed gene copies in absolute numbers. As detection and analysis by FCS can be performed at the level of single molecules, there is no need for any type of amplification. We describe the gene expression assay and present data demonstrating the capacity of this novel technology. In order to prove the gene specificity, we performed experiments with gene-depleted total cDNA. The biological application was demonstrated by quantifying selected high, medium and low abundant genes in cDNA prepared from HL-60 cells.     

Combining flux and energy balance analysis to model large-scale biochemical networks

Stoichiometric Network Theory is a constraints-based, optimization approach for quantitative analysis of the phenotypes of large-scale biochemical networks that avoids the use of detailed kinetics. This approach uses the reaction stoichiometric matrix in conjunction with constraints provided by flux balance and energy balance to guarantee mass conserved and thermodynamically allowable predictions. However, the flux and energy balance constraints have not been effectively applied simultaneously on the genome scale because optimization under the combined constraints is non-linear. In this paper, a sequential quadratic programming algorithm that solves the non-linear optimization problem is introduced. A simple example and the system of fermentation in Saccharomyces cerevisiae are used to illustrate the new method. The algorithm allows the use of non-linear objective functions. As a result, we suggest a novel optimization with respect to the heat dissipation rate of a system. We also emphasize the importance of incorporating interactions between a model network and its surroundings.     

Gene networks inference using dynamic Bayesian networks

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.     

Conservation analysis of large biochemical networks

MOTIVATION: Large biochemical networks pose a unique challenge from the point of view of evaluating conservation laws. The computational problem in most cases exceeds the capability of available software tools, often resulting in inaccurate computation of the number and form of conserved cycles. Such errors have profound effects on subsequent calculations, particularly in the evaluation of the Jacobian which is a critical quantity in many other calculations. The goal of this paper is to outline a new algorithm that is computationally efficient and robust at extracting the correct conservation laws for very large biochemical networks. RESULTS: We show that our algorithm can perform the conservation analysis of large biochemical networks, and can evaluate the correct conserved cycles when compared with other similar software tools. Biochemical simulators such as Jarnac and COPASI are successful at extracting only a subset of the conservation laws that our algorithm can. This is illustrated with examples for some large networks which show the advantages of our method.     

Unravelling the regulatory structure of biochemical networks using stimulus response experiments and large-scale model selection

To unravel the complex in vivo regulatory interdependences of biochemical networks, experiments with the living organism are absolutely necessary. Stimulus response experiments (SREs) have become increasingly popular in recent years. The response of metabolite concentrations from all major parts of the central metabolism is monitored over time by modem analytical methods, producing several thousand data points. SREs are applied to determine enzyme kinetic parameters and to find unknown enzyme regulatory mechanisms. Owing to the complex regulatory structure of metabolic networks and the amount of measured data, the evaluation of an SRE has to be extensively supported by modelling. If the enzyme regulatory mechanisms are part of the investigation, a large number of models with different enzyme kinetics have to be tested for their ability to reproduce the observed behaviour. In this contribution, a systematic model-building process for data-driven exploratory modelling is introduced with the aim of discovering essential features of the biological system. The process is based on data pre-processing, correlation-based hypothesis generation, automatic model family generation, large-scale model selection and statistical analysis of the best-fitting models followed by an extraction of common features. It is illustrated by the example of the aromatic amino acid synthesis pathway in Escherichia coli.     

Gene expression data classification using consensus independent component analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.     

Gene expression analysis

The response of cells to extracellular signals usually requires altered expression of many genes, possibly including several distinct metabolic pathways. In some cases, only a subset of genes involved in such responses are known, which requires techniques to analyze changes in the expression of multiple genes, both known and unknown. Three techniques, two-dimensional gel electrophoresis, differential display, and gene discovery arrays, provide opportunities for measuring changes in gene expression levels, as well as for identifying novel gene products.     

Timescale analysis of rule-based biochemical reaction networks

The flow of information within a cell is governed by a series of protein–protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed on reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor–ligand binding model and a rule‐based model of interleukin‐12 (IL‐12) signaling in naïve CD4+ T cells. The IL‐12 signaling pathway includes multiple protein–protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based on the available data. The analysis correctly predicted that reactions associated with Janus Kinase 2 and Tyrosine Kinase 2 binding to their corresponding receptor exist at a pseudo‐equilibrium. By contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL‐12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank‐ and flux‐based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule‐based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Gene expression model (in)validation by Fourier analysis

Background  

The determination of the right model structure describing a gene regulation network and the identification of its parameters are major goals in systems biology. The task is often hampered by the lack of relevant experimental data with sufficiently low noise level, but the subset of genes whose concentration levels exhibit an oscillatory behavior in time can readily be analyzed on the basis of their Fourier spectrum, known to turn complex signals into few relatively noise-free parameters. Such genes therefore offer opportunities of understanding gene regulation quantitatively.     

Graphical approach to model reduction for nonlinear biochemical networks

Model reduction is a central challenge to the development and analysis of multiscale physiology models. Advances in model reduction are needed not only for computational feasibility but also for obtaining conceptual insights from complex systems. Here, we introduce an intuitive graphical approach to model reduction based on phase plane analysis. Timescale separation is identified by the degree of hysteresis observed in phase-loops, which guides a "concentration-clamp" procedure for estimating explicit algebraic relationships between species equilibrating on fast timescales. The primary advantages of this approach over Jacobian-based timescale decomposition are that: 1) it incorporates nonlinear system dynamics, and 2) it can be easily visualized, even directly from experimental data. We tested this graphical model reduction approach using a 25-variable model of cardiac β(1)-adrenergic signaling, obtaining 6- and 4-variable reduced models that retain good predictive capabilities even in response to new perturbations. These 6 signaling species appear to be optimal "kinetic biomarkers" of the overall β(1)-adrenergic pathway. The 6-variable reduced model is well suited for integration into multiscale models of heart function, and more generally, this graphical model reduction approach is readily applicable to a variety of other complex biological systems.     

Gene expression in the vascular wall of the aortic arch in spontaneously hypertensive hyperlipidemic model rats using DNA microarray analysis

Aims

In recent years, there has been an increase in patients with arteriosclerosis and the risk of lifestyle-related diseases. However, the pathogenesis and medication of atherosclerosis have not been elucidated. We developed a rat model of lifestyle-related diseases by feeding a high-fat diet and 30% sucrose solution (HFDS) to spontaneously hypertensive hyperlipidemic rats (SHHR) and reported that this model is a useful model of early atherosclerosis. In order to elucidate the pathogenesis of early atherosclerosis, we searched for atherosclerosis-related genes by microarray analysis using the aortic arch rat model of lifestyle-related diseases.

Main methods

Four-month-old male Sprague–Dawley rats and SHHR were each divided into two normal diet (ND) groups and two HFDS groups. After a four-month treatment, the expression of mRNA in the aortic arch was detected using the oligo DNA microarray one-color method and quantified using real-time PCR.

Key findings

In this study, we detected 39 genes in microarray analysis. Esm1, Retnlb Mkks, and Grem2 showed particularly marked changes in gene expression in the SHHR-HFDS group. Compared with the SD-ND group, the SHHR-HFDS group had an increase in Mkks gene expression of about 26-fold and an approximately 22-fold increase in the expression of Grem2. Similarly, the expression of Esm1 increased by about 12-fold and that of Retnlg by about 10-fold as shown by quantitative real-time PCR.

Significance

This study suggested that these four genes might be important in early atherosclerosis development.     

Gene expression data analysis

Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices--tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification. Then we discuss how the gene expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.     

Gene expression data analysis

Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices, tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification as well as some possible future directions.     

Gene expression analysis in a canine model of X-linked Alport syndrome

Chronic kidney disease (CKD) often culminates in renal failure as a consequence of progressive interstitial fibrosis and is an important cause of illness and death in dogs. Identification of disease biomarkers and gene expression changes will yield valuable information regarding the specific biological pathways involved in disease progression. Toward these goals, gene expression changes in the renal cortex of dogs with X-linked Alport syndrome (XLAS) were examined using microarray technology. Extensive changes in inflammatory, metabolic, immune, and extracellular matrix biology were revealed in affected dogs. Statistical analysis showed 133 genes that were robustly induced or repressed in affected animals relative to age-matched littermates. Altered expression of numerous major histocompatibility complex (MHC) molecules suggests that the immune system plays a significant role in XLAS. Increased expression of COL4A1 and TIMP-1 at the end stage of disease supports the suggestion that expression increases in association with progression of fibrosis and confirms an observation of increased COL4A1 protein expression. Clusterin may function as one of the primary defenses of the renal cortex against progressive injury in dogs with XLAS, as demonstrated here by increased CLU gene expression. Cellular mechanisms that function during excess oxidative stress might also act to deter renal damage, as evidenced by alterations in gene expression of SOD1, ACO1, FDXR, and GPX1. This investigation provides a better understanding of interstitial fibrosis pathogenesis, and potential biomarkers for early detection, factors that are essential to discovering more effective treatments thereby reducing clinical illness and death due to CKD.     

Intercellular adhesion and cancer invasion: a discrete simulation using the extended Potts model

We develop a discrete model of malignant invasion using a thermodynamic argument. An extension of the Potts model is used to simulate a population of malignant cells experiencing interactions due to both homotypic and heterotypic adhesion while also secreting proteolytic enzymes and experiencing a haptotactic gradient. In this way we investigate the influence of changes in cell-cell adhesion on the invasion process. We demonstrate that the morphology of the invading front is influenced by changes in the adhesiveness parameters, and detail how the invasiveness of the tumour is related to adhesion. We show that cell-cell adhesion has less of an influence on invasion compared with cell-medium adhesion, and that increases in both proteolytic enzyme secretion rate and the coefficient of haptotaxis act in synergy to promote invasion. We extend the simulation by including proliferation, and, following experimental evidence, develop an algorithm for cell division in which the mitotic rate is explicitly related to changes in the relative magnitudes of homotypic and heterotypic adhesiveness. We show that although an increased proliferation rate usually results in an increased depth of invasion into the extracellular matrix, it does not invariably do so, and may, indeed, cause invasiveness to be reduced.