10 research outputs found

    Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

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    <p>Abstract</p> <p>Background</p> <p>In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior.</p> <p>Results</p> <p>We introduce <it>PathwayOracle</it>, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates <it>PathwayOracle </it>from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, <it>PathwayOracle </it>includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model.</p> <p>Conclusion</p> <p><it>PathwayOracle </it>provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models. The application has been developed in Python and is designed to be easily extensible by groups interested in adding new or extending existing features. <it>PathwayOracle </it>is freely available for download and use.</p

    Deriving executable models of biochemical network dynamics from qualitative data

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    Progress in advancing our understanding of biological systems is limited by their sheer complexity, the cost of laboratory materials and equipment, and limitations of current laboratory technology. Computational and mathematical modeling provide ways to address these obstacles through hypothesis generation and testing without experimentation---allowing researchers to analyze system structure and dynamics in silico and, then, design lab experiments that yield desired information about phenomena of interest. These models, however, are only as accurate and complete as the data used to build them. Currently, most models are constructed from quantitative experimental data. However, since accurate quantitative measurements are hard to obtain and difficult to adapt from literature and online databases, new sources of data for building models need to be explored. In my work, I have designed methods for building and executing computational models of cellular network dynamics based on qualitative experimental data, which are more abundant, easier to obtain, and reliably reproducible. Such executable models allow for in silico perturbation, simulation, and exploration of biological systems. In this thesis, I present two general strategies for building and executing tokenized models of biochemical networks using only qualitative data. Both methods have been successfully used to model and predict the dynamics of signaling networks in normal and cancer cell lines, rivaling the accuracy of existing methods trained on quantitative data. I have implemented these methods in the software tools PathwayOracle and Monarch, making the new techniques I present here accessible to experimental biologists and other domain experts in cellular biology

    Odefy -- From discrete to continuous models

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    <p>Abstract</p> <p>Background</p> <p>Phenomenological information about regulatory interactions is frequently available and can be readily converted to Boolean models. Fully quantitative models, on the other hand, provide detailed insights into the precise dynamics of the underlying system. In order to connect discrete and continuous modeling approaches, methods for the conversion of Boolean systems into systems of ordinary differential equations have been developed recently. As biological interaction networks have steadily grown in size and complexity, a fully automated framework for the conversion process is desirable.</p> <p>Results</p> <p>We present <it>Odefy</it>, a MATLAB- and Octave-compatible toolbox for the automated transformation of Boolean models into systems of ordinary differential equations. Models can be created from sets of Boolean equations or graph representations of Boolean networks. Alternatively, the user can import Boolean models from the CellNetAnalyzer toolbox, GINSim and the PBN toolbox. The Boolean models are transformed to systems of ordinary differential equations by multivariate polynomial interpolation and optional application of sigmoidal Hill functions. Our toolbox contains basic simulation and visualization functionalities for both, the Boolean as well as the continuous models. For further analyses, models can be exported to SQUAD, GNA, MATLAB script files, the SB toolbox, SBML and R script files. Odefy contains a user-friendly graphical user interface for convenient access to the simulation and exporting functionalities. We illustrate the validity of our transformation approach as well as the usage and benefit of the Odefy toolbox for two biological systems: a mutual inhibitory switch known from stem cell differentiation and a regulatory network giving rise to a specific spatial expression pattern at the mid-hindbrain boundary.</p> <p>Conclusions</p> <p>Odefy provides an easy-to-use toolbox for the automatic conversion of Boolean models to systems of ordinary differential equations. It can be efficiently connected to a variety of input and output formats for further analysis and investigations. The toolbox is open-source and can be downloaded at <url>http://cmb.helmholtz-muenchen.de/odefy</url>.</p

    On the Modeling of Signaling Networks with Petri Nets

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    The whole-cell behavior arises from the interplay among signaling, metabolic, and regulatory processes. Proper modeling of the overall function requires accurate interpretations of each component. The highly concurrent nature of the inner-cell interactions motivates the use of Petri nets as a framework for the whole-cell modeling. Petri nets have been successfully used in modeling of metabolic pathways, as it allows for a straightforward mapping from its stoichiometric matrix to the Petri net structure. The Boolean interpretation and modeling of transcription regulation networks also lends itself easily to Petri net modeling. However, Petri net modeling of signal transduction networks has been largely lacking, with the exception of simple ad hoc applications to specific signaling pathways. In this thesis, I investigate the applicability of Petri nets to modeling of signaling networks, by systematically analyzing initial token assignments, firing strategies, and robustness to errors and abstractions in the estimates of molecule concentrations and reaction rates

    Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle-1

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    showing the selected path highlighted.<p><b>Copyright information:</b></p><p>Taken from "Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle"</p><p>http://www.biomedcentral.com/1752-0509/2/76</p><p>BMC Systems Biology 2008;2():76-76.</p><p>Published online 19 Aug 2008</p><p>PMCID:PMC2527501.</p><p></p

    Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle-2

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    Ghlighted in blue. (b) The color distribution for the selected marking in the group is applied to the network view in the main window. Note that signaling nodes for which values were not given are not assigned a color on the valid red to green spectrum.<p><b>Copyright information:</b></p><p>Taken from "Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle"</p><p>http://www.biomedcentral.com/1752-0509/2/76</p><p>BMC Systems Biology 2008;2():76-76.</p><p>Published online 19 Aug 2008</p><p>PMCID:PMC2527501.</p><p></p

    Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle-3

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    to place and zero tokens are assigned to place . The two places are connected by a transition, . The arcs in and out of indicate the direction in which tokens move. When fires, it moves some number of tokens from and puts them in . In (b), transition has fired and moved two tokens from to .<p><b>Copyright information:</b></p><p>Taken from "Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle"</p><p>http://www.biomedcentral.com/1752-0509/2/76</p><p>BMC Systems Biology 2008;2():76-76.</p><p>Published online 19 Aug 2008</p><p>PMCID:PMC2527501.</p><p></p

    Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle-5

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    Signaling Petri net. The table in (c) provides the markings for the Petri net over the course of a simulation run whose duration is two time blocks. The proteins are given the initial marking shown in the column. Each subsequent column corresponds to a single time step during which one transition fired, producing a new marking of the network. The bold number in each column indicates which protein's marking was affected by the transition that fired in that time step. The red columns – always the last time step in the block – highlight the markings whose values would be averaged and used as part of the final result. These red columns are the sources of the markings that reports.<p><b>Copyright information:</b></p><p>Taken from "Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle"</p><p>http://www.biomedcentral.com/1752-0509/2/76</p><p>BMC Systems Biology 2008;2():76-76.</p><p>Published online 19 Aug 2008</p><p>PMCID:PMC2527501.</p><p></p

    Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle-0

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    Wo High nodes, EGF and LKB-auto. EGF will be initialized with a token-count of 10, LKB-auto with a token-count of 3. The token-count of AMPK will be zero for the duration of the simulation. (b) The setup window for the differential simulator. Two different scenarios are being compared through simulation: different token assignments are being tried with EGF and LKB-auto, with and without AMPK being fixed low. (c) The plot window for the marking series generated by a simulation. Observe that the signaling nodes whose activity-levels are plotted correspond to those selected in the checklist directly to the left of the plot.<p><b>Copyright information:</b></p><p>Taken from "Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle"</p><p>http://www.biomedcentral.com/1752-0509/2/76</p><p>BMC Systems Biology 2008;2():76-76.</p><p>Published online 19 Aug 2008</p><p>PMCID:PMC2527501.</p><p></p
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