The Experts below are selected from a list of 54345 Experts worldwide ranked by ideXlab platform
John Goutsias - One of the best experts on this subject based on the ideXlab platform.
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Thermodynamically consistent Bayesian analysis of closed Biochemical Reaction systems.
BMC bioinformatics, 2010Co-Authors: Garrett Jenkinson, Xiaogang Zhong, John GoutsiasAbstract:Background Estimating the rate constants of a Biochemical Reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to Biochemical Reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a Biochemical Reaction system is highly desirable.
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A comparison of approximation techniques for variance-based sensitivity analysis of Biochemical Reaction systems
BMC bioinformatics, 2010Co-Authors: Hong-xuan Zhang, John GoutsiasAbstract:Background Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of Biochemical Reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the Reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large Biochemical Reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by Biochemical Reaction systems that consist of a large number of Reactions and molecular species.
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A comparison of approximation techniques for variance-based sensitivity analysis of Biochemical Reaction systems
BMC Bioinformatics, 2010Co-Authors: Hong-xuan Zhang, John GoutsiasAbstract:Background Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of Biochemical Reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the Reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large Biochemical Reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by Biochemical Reaction systems that consist of a large number of Reactions and molecular species. Results We present four techniques, derivative approximation (DA), polynomial approximation (PA), Gauss-Hermite integration (GHI), and orthonormal Hermite approximation (OHA), for analytically approximating the variance-based sensitivity indices associated with a Biochemical Reaction system. By using a well-known model of the mitogen-activated protein kinase signaling cascade as a case study, we numerically compare the approximation quality of these techniques against traditional Monte Carlo sampling. Our results indicate that, although DA is computationally the most attractive technique, special care should be exercised when using it for sensitivity analysis, since it may only be accurate at low levels of uncertainty. On the other hand, PA, GHI, and OHA are computationally more demanding than DA but can work well at high levels of uncertainty. GHI results in a slightly better accuracy than PA, but it is more difficult to implement. OHA produces the most accurate approximation results and can be implemented in a straightforward manner. It turns out that the computational cost of the four approximation techniques considered in this paper is orders of magnitude smaller than traditional Monte Carlo estimation. Software, coded in MATLAB^®, which implements all sensitivity analysis techniques discussed in this paper, is available free of charge. Conclusions Estimating variance-based sensitivity indices of a large Biochemical Reaction system is a computationally challenging task that can only be addressed via approximations. Among the methods presented in this paper, a technique based on orthonormal Hermite polynomials seems to be an acceptable candidate for the job, producing very good approximation results for a wide range of uncertainty levels in a fraction of the time required by traditional Monte Carlo sampling.
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GENSiPS - A screening method for dimensionality reduction in Biochemical Reaction system calibration
2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), 2010Co-Authors: Garrett Jenkinson, John GoutsiasAbstract:Estimating the rate constants of a Biochemical Reaction model of cellular function is an important, albeit computationally intensive, problem in systems biology. In this paper, a variance-based sensitivity analysis approach is proposed, which can be used, as a pre-screening step, to identify parameters in a Biochemical Reaction system that do not appreciably influence the cost of estimation and, therefore, whose values cannot be precisely determined by parameter estimation. By only estimating the remaining parameters, appreciable qualitative and quantitative improvements can be achieved. A subset of a well-known Biochemical Reaction model of the EGF/ERK signaling pathway is used to illustrate the benefits achieved by the proposed method.
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Probabilistic sensitivity analysis of Biochemical Reaction systems.
Journal of Chemical Physics, 2009Co-Authors: Hong-xuan Zhang, William P. Dempsey, John GoutsiasAbstract:Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of Biochemical Reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of Biochemical Reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for Biochemical Reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of Biochemical Reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical.
Hong-xuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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A comparison of approximation techniques for variance-based sensitivity analysis of Biochemical Reaction systems
BMC Bioinformatics, 2010Co-Authors: Hong-xuan Zhang, John GoutsiasAbstract:Background Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of Biochemical Reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the Reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large Biochemical Reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by Biochemical Reaction systems that consist of a large number of Reactions and molecular species. Results We present four techniques, derivative approximation (DA), polynomial approximation (PA), Gauss-Hermite integration (GHI), and orthonormal Hermite approximation (OHA), for analytically approximating the variance-based sensitivity indices associated with a Biochemical Reaction system. By using a well-known model of the mitogen-activated protein kinase signaling cascade as a case study, we numerically compare the approximation quality of these techniques against traditional Monte Carlo sampling. Our results indicate that, although DA is computationally the most attractive technique, special care should be exercised when using it for sensitivity analysis, since it may only be accurate at low levels of uncertainty. On the other hand, PA, GHI, and OHA are computationally more demanding than DA but can work well at high levels of uncertainty. GHI results in a slightly better accuracy than PA, but it is more difficult to implement. OHA produces the most accurate approximation results and can be implemented in a straightforward manner. It turns out that the computational cost of the four approximation techniques considered in this paper is orders of magnitude smaller than traditional Monte Carlo estimation. Software, coded in MATLAB^®, which implements all sensitivity analysis techniques discussed in this paper, is available free of charge. Conclusions Estimating variance-based sensitivity indices of a large Biochemical Reaction system is a computationally challenging task that can only be addressed via approximations. Among the methods presented in this paper, a technique based on orthonormal Hermite polynomials seems to be an acceptable candidate for the job, producing very good approximation results for a wide range of uncertainty levels in a fraction of the time required by traditional Monte Carlo sampling.
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A comparison of approximation techniques for variance-based sensitivity analysis of Biochemical Reaction systems
BMC bioinformatics, 2010Co-Authors: Hong-xuan Zhang, John GoutsiasAbstract:Background Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of Biochemical Reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the Reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large Biochemical Reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by Biochemical Reaction systems that consist of a large number of Reactions and molecular species.
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Probabilistic sensitivity analysis of Biochemical Reaction systems.
Journal of Chemical Physics, 2009Co-Authors: Hong-xuan Zhang, William P. Dempsey, John GoutsiasAbstract:Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of Biochemical Reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of Biochemical Reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for Biochemical Reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of Biochemical Reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical.
Daniel Weiskopf - One of the best experts on this subject based on the ideXlab platform.
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iVUN: interactive Visualization of Uncertain Biochemical Reaction Networks
BMC Bioinformatics, 2013Co-Authors: Corinna Vehlow, Jan Hasenauer, Andrei Kramer, Sabine Hug, Nicole Radde, Andreas Raue, Jens Timmer, Fabian J Theis, Daniel WeiskopfAbstract:BackgroundMathematical models are nowadays widely used to describe Biochemical Reaction networks. One of the main reasons for this is that models facilitate the integration of a multitude of different data and data types using parameter estimation. Thereby, models allow for a holistic understanding of biological processes. However, due to measurement noise and the limited amount of data, uncertainties in the model parameters should be considered when conclusions are drawn from estimated model attributes, such as Reaction fluxes or transient dynamics of biological species.Methods and resultsWe developed the visual analytics system iVUN that supports uncertainty-aware analysis of static and dynamic attributes of Biochemical Reaction networks modeled by ordinary differential equations. The multivariate graph of the network is visualized as a node-link diagram, and statistics of the attributes are mapped to the color of nodes and links of the graph. In addition, the graph view is linked with several views, such as line plots, scatter plots, and correlation matrices, to support locating uncertainties and the analysis of their time dependencies. As demonstration, we use iVUN to quantitatively analyze the dynamics of a model for Epo-induced JAK2/STAT5 signaling.ConclusionOur case study showed that iVUN can be used to perform an in-depth study of Biochemical Reaction networks, including attribute uncertainties, correlations between these attributes and their uncertainties as well as the attribute dynamics. In particular, the linking of different visualization options turned out to be highly beneficial for the complex analysis tasks that come with the biological systems as presented here.
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iVUN: interactive Visualization of Uncertain Biochemical Reaction Networks.
BMC bioinformatics, 2013Co-Authors: Corinna Vehlow, Jan Hasenauer, Andrei Kramer, Sabine Hug, Nicole Radde, Andreas Raue, Jens Timmer, Fabian J Theis, Daniel WeiskopfAbstract:Mathematical models are nowadays widely used to describe Biochemical Reaction networks. One of the main reasons for this is that models facilitate the integration of a multitude of different data and data types using parameter estimation. Thereby, models allow for a holistic understanding of biological processes. However, due to measurement noise and the limited amount of data, uncertainties in the model parameters should be considered when conclusions are drawn from estimated model attributes, such as Reaction fluxes or transient dynamics of biological species. We developed the visual analytics system iVUN that supports uncertainty-aware analysis of static and dynamic attributes of Biochemical Reaction networks modeled by ordinary differential equations. The multivariate graph of the network is visualized as a node-link diagram, and statistics of the attributes are mapped to the color of nodes and links of the graph. In addition, the graph view is linked with several views, such as line plots, scatter plots, and correlation matrices, to support locating uncertainties and the analysis of their time dependencies. As demonstration, we use iVUN to quantitatively analyze the dynamics of a model for Epo-induced JAK2/STAT5 signaling. Our case study showed that iVUN can be used to perform an in-depth study of Biochemical Reaction networks, including attribute uncertainties, correlations between these attributes and their uncertainties as well as the attribute dynamics. In particular, the linking of different visualization options turned out to be highly beneficial for the complex analysis tasks that come with the biological systems as presented here.
Mustafa Khammash - One of the best experts on this subject based on the ideXlab platform.
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Equilibrium distributions of simple Biochemical Reaction systems for time-scale separation in stochastic Reaction networks.
Journal of the Royal Society Interface, 2014Co-Authors: Bence Mélykúti, Joao P. Hespanha, Mustafa KhammashAbstract:Many Biochemical Reaction networks are inherently multiscale in time and in the counts of participating molecular species. A standard technique to treat different time scales in the stochastic kinetics framework is averaging or quasi-steady-state analysis: it is assumed that the fast dynamics reaches its equilibrium (stationary) distribution on a time scale where the slowly varying molecular counts are unlikely to have changed. We derive analytic equilibrium distributions for various simple Biochemical systems, such as enzymatic Reactions and gene regulation models. These can be directly inserted into simulations of the slow time-scale dynamics. They also provide insight into the stimulus–response of these systems. An important model for which we derive the analytic equilibrium distribution is the binding of dimer transcription factors (TFs) that first have to form from monomers. This gene regulation mechanism is compared to the cases of the binding of simple monomer TFs to one gene or to multiple copies of a gene, and to the cases of the cooperative binding of two or multiple TFs to a gene. The results apply equally to ligands binding to enzyme molecules.
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SPSens: a software package for stochastic parameter sensitivity analysis of Biochemical Reaction networks.
Bioinformatics, 2012Co-Authors: Patrick W. Sheppard, Muruhan Rathinam, Mustafa KhammashAbstract:Summary: SPSens is a software package for the efficient computation of stochastic parameter sensitivities of Biochemical Reaction networks. Parameter sensitivity analysis is a valuable tool that can be used to study robustness properties, for drug targeting, and many other purposes. However its application to stochastic models has been limited when Monte Carlo methods are required due to extremely high computational costs. SPSens provides efficient, state of the art sensitivity analysis algorithms in a single software package so that sensitivity analysis can be easily performed on stochastic models of Biochemical Reaction networks. SPSens implements the algorithms in C and estimates sensitivities with respect to both infinitesimal and finite perturbations to system parameters, in many cases reducing variance by orders of magnitude compared to basic methods. Included among the features of SPSens are serial and parallel command line versions, an interface with Matlab, and several example problems. Availability: SPSens is distributed freely under GPL version 3 and can be downloaded from http://sourceforge.net/projects/spsens/. The software can be run on Linux, Mac OS X and Windows platforms. Contact: mustafa.khammash@bsse.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.
Jan Hasenauer - One of the best experts on this subject based on the ideXlab platform.
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scalable parameter estimation for genome scale Biochemical Reaction networks
PLOS Computational Biology, 2017Co-Authors: Fabian Frohlich, Fabian J Theis, Barbara Kaltenbacher, Jan HasenauerAbstract:Mechanistic mathematical modeling of Biochemical Reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of Biochemical species and Reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale Biochemical Reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.
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iVUN: interactive Visualization of Uncertain Biochemical Reaction Networks
BMC Bioinformatics, 2013Co-Authors: Corinna Vehlow, Jan Hasenauer, Andrei Kramer, Sabine Hug, Nicole Radde, Andreas Raue, Jens Timmer, Fabian J Theis, Daniel WeiskopfAbstract:BackgroundMathematical models are nowadays widely used to describe Biochemical Reaction networks. One of the main reasons for this is that models facilitate the integration of a multitude of different data and data types using parameter estimation. Thereby, models allow for a holistic understanding of biological processes. However, due to measurement noise and the limited amount of data, uncertainties in the model parameters should be considered when conclusions are drawn from estimated model attributes, such as Reaction fluxes or transient dynamics of biological species.Methods and resultsWe developed the visual analytics system iVUN that supports uncertainty-aware analysis of static and dynamic attributes of Biochemical Reaction networks modeled by ordinary differential equations. The multivariate graph of the network is visualized as a node-link diagram, and statistics of the attributes are mapped to the color of nodes and links of the graph. In addition, the graph view is linked with several views, such as line plots, scatter plots, and correlation matrices, to support locating uncertainties and the analysis of their time dependencies. As demonstration, we use iVUN to quantitatively analyze the dynamics of a model for Epo-induced JAK2/STAT5 signaling.ConclusionOur case study showed that iVUN can be used to perform an in-depth study of Biochemical Reaction networks, including attribute uncertainties, correlations between these attributes and their uncertainties as well as the attribute dynamics. In particular, the linking of different visualization options turned out to be highly beneficial for the complex analysis tasks that come with the biological systems as presented here.
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iVUN: interactive Visualization of Uncertain Biochemical Reaction Networks.
BMC bioinformatics, 2013Co-Authors: Corinna Vehlow, Jan Hasenauer, Andrei Kramer, Sabine Hug, Nicole Radde, Andreas Raue, Jens Timmer, Fabian J Theis, Daniel WeiskopfAbstract:Mathematical models are nowadays widely used to describe Biochemical Reaction networks. One of the main reasons for this is that models facilitate the integration of a multitude of different data and data types using parameter estimation. Thereby, models allow for a holistic understanding of biological processes. However, due to measurement noise and the limited amount of data, uncertainties in the model parameters should be considered when conclusions are drawn from estimated model attributes, such as Reaction fluxes or transient dynamics of biological species. We developed the visual analytics system iVUN that supports uncertainty-aware analysis of static and dynamic attributes of Biochemical Reaction networks modeled by ordinary differential equations. The multivariate graph of the network is visualized as a node-link diagram, and statistics of the attributes are mapped to the color of nodes and links of the graph. In addition, the graph view is linked with several views, such as line plots, scatter plots, and correlation matrices, to support locating uncertainties and the analysis of their time dependencies. As demonstration, we use iVUN to quantitatively analyze the dynamics of a model for Epo-induced JAK2/STAT5 signaling. Our case study showed that iVUN can be used to perform an in-depth study of Biochemical Reaction networks, including attribute uncertainties, correlations between these attributes and their uncertainties as well as the attribute dynamics. In particular, the linking of different visualization options turned out to be highly beneficial for the complex analysis tasks that come with the biological systems as presented here.