Systems biology最新文献

Sensitivity analysis is one of the most effective approaches for studying mathematical models of biochemical systems. A stiff Rosenbrock integrator has been developed for sensitivity analysis using a direct sensitivity approach. Automated sparse Jacobian and Hessian calculations of the coupled system (the original model equations and the sensitivity equations) have been implemented in the freely available software package CellSim. The accuracy and efficiency of the integrator are tested extensively on the complex mitogen-activated protein kinase (MAPK) pathway model of Bhalla and Iyengar. Both time-dependent concentration and parameter-based sensitivity coefficients are measured using several integration schemes. The method is shown to perform sensitivity analysis in a manner that is cost effective with moderate accuracy. The error control strategy between the decoupled direct method and the Rosenbrock with direct method is discussed and their computational accuracies are compared. The method is used to analyse the positive feedback loop within the MAPK signal transduction pathway.

The analysis of complex biochemical networks is conducted in two popular conceptual frameworks for modelling. The deterministic approach requires the solution of ordinary differential equations (ODEs, reaction rate equations) with concentrations as continuous state variables. The stochastic approach involves the simulation of differential-difference equations (chemical master equations, CMEs) with probabilities as variables. This is to generate counts of molecules for chemical species as realisations of random variables drawn from the probability distribution described by the CMEs. Although there are numerous tools available, many of them free, the modelling and simulation environment MATLAB is widely used in the physical and engineering sciences. We describe a collection of MATLAB functions to construct and solve ODEs for deterministic simulation and to implement realisations of CMEs for stochastic simulation using advanced MATLAB coding (Release 14). The program was successfully applied to pathway models from the literature for both cases. The results were compared to implementations using alternative tools for dynamic modelling and simulation of biochemical networks. The aim is to provide a concise set of MATLAB functions that encourage the experimentation with systems biology models. All the script files are available from www.sbi.uni-rostock.de/ publications_matlab-paper.html.

We consider the problems of multi-class cancer classification from gene expression data. After discussing the multinomial probit regression model with Bayesian gene selection, we propose two Bayesian gene selection schemes: one employs different strongest genes for different probit regressions; the other employs the same strongest genes for all regressions. Some fast implementation issues for Bayesian gene selection are discussed, including preselection of the strongest genes and recursive computation of the estimation errors using QR decomposition. The proposed gene selection techniques are applied to analyse real breast cancer data, small round blue-cell tumours, the national cancer institute's anti-cancer drug-screen data and acute leukaemia data. Compared with existing multi-class cancer classifications, our proposed methods can find which genes are the most important genes affecting which kind of cancer. Also, the strongest genes selected using our methods are consistent with the biological significance. The recognition accuracies are very high using our proposed methods.

Exploring how biological systems have been 'designed' by evolution to achieve robust behaviours is now a subject of increasing research effort. Yet, it still remains unclear how environmental factors may contribute to this process. This issue is addressed by employing a detailed computer model for the intracellular growth of phage T7. More than 150 000 in silico T7 mutants were generated and the rates and efficiencies of their growth in two host environments, namely, a realistic environment that offered finite host resources for the synthesis of phage functions and a hypothetical environment where the phage was supplied infinite host resources, were evaluated. Results revealed two key properties of phage T7. First, T7 growth was overall robust with respect to perturbations in its parameters, but fragile with respect to changes in the ordering of its genetic elements. Secondly, the wild-type T7 had close to optimal fitness in the finite environment. Furthermore, a strong correlation was found between fitness and growth efficiency in the finite environment. The results underscore the potential importance of the environment in shaping robust design of a biological system. In particular, the strong correlation between fitness and growth efficiency suggests that T7 may have evolved to maximise its growth rate by minimising waste of finite resources.

Systems with counter-clockwise input-output (I-O) dynamics were recently introduced in order to study the convergence of positive feedback loops (possibly to many different equilibrium states). The author shows how this notion can be used to perform bifurcation analysis and globally predict multistability of a closed-loop feedback interconnection just by using the knowledge of steady-state I-O responses of the systems. To illustrate the theory, this method is then applied to a recently published model of mitogen activated protein kinase (MAPK) cascade. Furthermore, some examples (mainly motivated by molecular biology) of systems that enjoy the property are presented and discussed.

Cellular processes are governed by complex networks of interacting genes and proteins. Theoretical molecular biologists attempt to describe these processes via mathematical models by writing biochemical reaction equations. Modellers are building increasingly larger and complex mathematical models to describe these cellular processes, making model evaluation a time consuming and difficult task. The authors describe an automatable process for model evaluation and a software system that implements this process. The software is adaptable to many types of models and is freely available along with all needed data files. The cell cycle control system for budding yeast is known in fine detail and constrained by more than 100 phenotypic observations in mutant strains. As an example, the authors apply their process to a model of cell cycle control in budding yeast containing dozens of regulatory equations and explaining nearly all of the known mutant phenotypes.

Members of the ErbB receptor family are associated with several cancers and appear to be providing useful targets for pharmacological therapeutics for tumours of the lung and breast. Further improvements of these therapies may be guided by a quantitative, dynamic integrative systems understanding of the complexities of ErbB dimerisation, trafficking and activation, for it is these complexities that render difficult intuiting how perturbations such as drug intervention will affect ErbB signalling activities. Towards this goal, we have developed a computational model implementing commonly accepted principles governing ErbB receptor interaction, trafficking, phosphorylation and dephosphorylation. Using this model, we are able to investigate several hypotheses regarding the compartmental localisation of dephosphorylation. Model results applied to experimental data on ErbB 1, ErbB2 and ErbB3 phosphorylation in H292 human lung carcinoma cells support a hypothesis that key dephosphorylation activity for these receptors occurs largely in an intracellular, endosomal compartment rather than at the cell surface plasma membrane. Thus, the endocytic trafficking-related compartmentalisation of dephosphorylation may define a critical aspect of the ErbB signalling response to ligand.

The hierarchical clustering and statistical techniques usually used to analyse microarray data do not inherently represent the underlying biology. Herein, a hybrid approach involving characteristics of both supervised and unsupervised learning is presented. This approach is based on template matching in which the interaction of the variables of inherent malignancy and the ability to express the malignant phenotype are modelled. Immortalised normal urothelial cells and bladder cancer cells of different malignancy were grown in conventional two-dimensional tissue culture and in three dimensions on extracellular matrices (ECMs) that were either permissive or restrictive for expression of the malignant phenotype. The transcriptome represents the effects of two variables--inherent malignancy and the modulatory effect of ECM. By assigning values to each of the biological variables of inherent malignancy and the ability to express the malignant phenotype, a template was constructed, which encapsulated the interaction between them. Gene expression correlating both positively and negatively with the template was observed, but when iterative correlations were carried out, the different models for the template converged on the same actual template. A subset of 21 genes was identified, which correlated with two a priori models or an optimised model above the 95% confidence limits identified in a bootstrap resampling with 5000 permutations of the data set. The correlation coefficients of expression of several genes were > 0.8. Analysis of upstream transcriptional regulatory elements (TREs) confirmed that these genes were not a randomly selected set of genes. Several TREs were identified as significantly over-expressed in the sample of 20 genes for which TREs were identified, and the high correlations of several genes were consistent with transcriptional co-regulation. The authors suggest that the template method can be used to identify a unique set of genes for further investigation.

The cell membrane lies at the interface between an extracellular set of signals and the appropriate intracellular response. Specifically, lymphocyte activity is determined by the spatial and structural response to antigens, as mediated by cell surface receptors. In order to correlate experimentally observed cellular activities, such as secretion, anergy, death, survival and division to external stimuli, it is necessary to monitor cell surface dynamics. B-lymphocyte activation results from the stimulation by large immune complexes comprising antigens, B-cell receptors (BcRs) and co-receptors. Compartmentalisation of the interacting molecular components is required in order to assure the rapid initiation of specialised and sustained signalling cascades. In this study, a Monte Carlo simulation of the cell membrane dynamics was developed to clarify the receptor dynamics, aggregation mechanisms and their combined effect on cellular functions. This simulation is based on experimentally measured parameters and represents a feasible, advanced and reliable framework to investigate the cell surface. The current study focussed on B-cell surface dynamics. A model demonstrating the basic properties of BcR dynamics and how BcR kinetics is affected by lipid rafts is developed. The authors studied BcR interactions with multivalent ligands and the influence of lipid rafts on this interaction. Finally, the dynamics of the initial steps of BcR-mediated cell activation is estimated and the effect of the association of signalling molecules with lipid rafts is demonstrated. These results are used to suggest some novel hypotheses on BcR-mediated B-cell activation.

Systems biology is an approach to the analysis and prediction of the dynamic behaviour of biological networks through mathematical modelling based on experimental data. The current lack of reliable quantitative data, especially in the field of signal transduction, means that new methodologies in data acquisition and processing are needed. Here, we present methods to advance the established techniques of immunoprecipitation and immunoblotting to more accurate and quantitative procedures. We propose randomisation of sample loading to disrupt lane correlations and the use of normalisers and calibrators for data correction. To predict the impact of each method on improving the data quality we used simulations. These studies showed that randomisation reduces the standard deviation of a smoothed signal by 55% +/- 10%, independently from most experimental settings. Normalisation with appropriate endogenous or external proteins further reduces the deviation from the true values. As the improvement strongly depends on the quality of the normaliser measurement, a criteria-based normalisation procedure was developed. Our approach was experimentally verified by application of the proposed methods to time course data obtained by the immunoblotting technique. This analysis showed that the procedure is robust and can significantly improve the quality of experimental data.