Quantitative cell biology with the Virtual Cell☆

Slepchenko, Boris M.; Schaff, James C.; Macara, Ian G.; Loew, Leslie M.

doi:10.1016/j.tcb.2003.09.002

Cited by 230 publications

(154 citation statements)

References 35 publications

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“…This imposes a demand for systems biology community to develop tools that allow automatic sensitivity analysis of systems biology models. Several software tools have been developed to perform sensitivity analysis for biological models, for example, BioSens [82], COPASI [83], SBML-SAT [12], Systems Biology Toolbox 2 [84], SensSB [85], TinkerCell [86] and Virtual Cell [87]. These software tools can perform local sensitivity analysis and some of them are able to execute global sensitivity analysis (e.g.…”

Section: Software Tools For Sensitivity Analysis Of Systems Biology Mmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Systems Biology

332

263

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With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

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Section: Software Tools For Sensitivity Analysis Of Systems Biology Mmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Systems Biology

332

263

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“…Its development has been motivated by the need to include a capability of modeling surface diffusion in the Virtual Cell computational framework (VCell) [14], a general-purpose tool designed for experimental cell biologists (as well as theorists) to test their hypothesis and models. It allows a user to couple cellular chemical kinetics, diffusionadvection transport and electrophysiological properties of membranes into a system of timedependent partial differential equations (PDEs) in two or three dimensions and solve the system numerically on arbitrary geometry [15,16].…”

Section: Introductionmentioning

confidence: 99%

Diffusion on a curved surface coupled to diffusion in the volume: Application to cell biology

Novak

Gao

Choi

et al. 2007

Journal of Computational Physics

Self Cite

106

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An algorithm is presented for solving a diffusion equation on a curved surface coupled to diffusion in the volume, a problem often arising in cell biology. It applies to pixilated surfaces obtained from experimental images and performs at low computational cost. In the method, the Laplace-Beltrami operator is approximated locally by the Laplacian on the tangential plane and then a finite volume discretization scheme based on a Voronoi decomposition is applied. Convergence studies show that mass conservation built in the discretization scheme and cancellation of sampling error ensure convergence of the solution in space with an order between 1 and 2. The method is applied to a cellbiological problem where a signaling molecule, G-protein Rac, cycles between the cytoplasm and cell membrane thus coupling its diffusion in the membrane to that in the cell interior. Simulations on realistic cell geometry are performed to validate, and determine the accuracy of, a recently proposed simplified quantitative analysis of fluorescence loss in photobleaching. The method is implemented within the Virtual Cell computational framework freely accessible at www.vcell.org.

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“…Software such as the Virtual Cell offers userfriendly graphical interfaces for solving PDEs [74]. Whereas the current implementation of the Virtual Cell program is useful, additional enhancements are needed to facilitate the development of progressively realistic models, and continued support to develop and enhance such programs is essential for progress in computational cell biology.…”

Section: Concluding Remarks: the Cell As An Ensemble Of Interacting Nmentioning

confidence: 99%

Computational approaches for modeling regulatory cellular networks

Eungdamrong

Iyengar

2004

Trends in Cell Biology

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Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.During the past two decades, substantial progress has been made in understanding the biochemical properties of cellular components, in addition to the organization of various molecular machines such as those involved in transcription and motility. From these studies of components and pathways, the design principles underlying cellular networks have emerged [1,2]. However, a substantial number of experiments is still needed to build up thè parts list' for a cell and to specify `parts function' in terms of cellular location, dynamics and regulatory organization. Because of the sheer number of components and interactions, the analysis of regulatory interactions is not easily achieved through intuition; even pathways and networks with few components are configured into systems that display complex behaviors. Hence, it is becoming increasingly clear that quantitative descriptions that lead to predictive models can be of great use in analyzing signaling pathways.Models of regulatory networks can be developed at various levels. Each level has its value. The simplest models of regulatory pathways and networks depict them as connections maps (http://stke.sciencemag.org), which are useful starting points for detailed analyses of signaling pathways. Although many signaling pathways have been identified by the study of binary reactions, connections are increasingly deduced from high-throughput experimental analyses of both protein-protein interactions [3][4][5][6] and protein location and expression patterns [7,8]. However, these connection maps are largely qualitative and, hence, only limited mathematical analysis can be undertaken. Such analyses often fall along the line of statistical correlations (`clustering'), which reveals co-regulation of each component [9], or an analysis of how the components are connected, which describes the statistical properties of the network as a whole [10][11][12]. An advantage of these models is that they can be developed for large numbers of components and interactions, and are useful in obtaining an To understand how extracellular signals evoke dynamic cellular responses, an analysis of the chemical reactions that constitute a biological system is needed. Typically, such models are built in three stages. First, a biochemical scheme that depicts the chemical reactions between the components in the...

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Quantitative cell biology with the Virtual Cell☆

Cited by 230 publications

References 35 publications

Sensitivity analysis approaches applied to systems biology models

Sensitivity analysis approaches applied to systems biology models

Diffusion on a curved surface coupled to diffusion in the volume: Application to cell biology

Computational approaches for modeling regulatory cellular networks

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