Modeling biochemical transformation processes and information processing with Narrator

Mandel, Johannes; Fuß, Hendrik; Palfreyman, Niall; Dubitzky, Werner

doi:10.1186/1471-2105-8-103

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…Recent interest in modeling biochemical networks in systems biology and in neuroscience have provided several tools that can be used to simulate time-series behavior of the networks (see e.g., Lemerle et al, 2005 ; Pettinen et al, 2005 ; Alves et al, 2006 ; Gilbert et al, 2006 ; Strömbäck et al, 2006 ; Wierling et al, 2007 ; Bergmann and Sauro, 2008 ; Blackwell, 2013 ; Schöneberg et al, 2014 ; Bartocci and Lió, 2016 ; Olivier et al, 2016 ). In this study, we used the following simulation tools: GENESIS/Kinetikit (Wilson et al, 1989 ; Bower and Beeman, 1998 ; Bhalla and Iyengar, 1999 ; Bhalla, 2001 , 2002 ), Gepasi (Mendes, 1993 , 1997 ; Mendes and Kell, 1998 ), Jarnac/JDesigner (Sauro, 2000 , 2001 ), XPPAUT (Ermentrout, 2002 ), SimTool (Aho, 2003 ), Dizzy (Ramsey et al, 2005 ), Copasi (Hoops et al, 2006 ), NEURON (Carnevale and Hines, 2006 ), Systems Biology Toolbox (Schmidt and Jirstrand, 2006 ), and Narrator (Mandel et al, 2007 ) (see Table 1 ). Here, we do not provide any detailed overview of the simulation tools, because the topic has been already extensively discussed previously.…”

Section: Methodsmentioning

confidence: 99%

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

Manninen

Acimovic

Havela

et al. 2018

Front. Neuroinform.

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The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.

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Section: Methodsmentioning

confidence: 99%

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

Manninen

Acimovic

Havela

et al. 2018

Front. Neuroinform.

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“…With an increase in the number of biochemical interactions, there is a great need for computational tools with standard diagram notations for drawing a variety of biochemical reactions such as transcription, translation, transport, binding, modification and metabolic reactions( 1–7 ). Such notations require defining explicit models of molecular networks for computer simulation or organizing available information on molecular interactions that encompasses the possible processes or pathways and combinatorially complex processes.…”

Section: Introductionmentioning

confidence: 99%

Extended CADLIVE: a novel graphical notation for design of biochemical network maps and computational pathway analysis

Kurata

Inoue

Maeda

et al. 2007

Nucleic Acids Research

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Biochemical network maps are helpful for understanding the mechanism of how a collection of biochemical reactions generate particular functions within a cell. We developed a new and computationally feasible notation that enables drawing a wide resolution map from the domain-level reactions to phenomenological events and implemented it as the extended GUI network constructor of CADLIVE (Computer-Aided Design of LIVing systEms). The new notation presents ‘Domain expansion’ for proteins and RNAs, ‘Virtual reaction and nodes’ that are responsible for illustrating domain-based interaction and ‘InnerLink’ that links real complex nodes to virtual nodes to illustrate the exact components of the real complex. A modular box is also presented that packs related reactions as a module or a subnetwork, which gives CADLIVE a capability to draw biochemical maps in a hierarchical modular architecture. Furthermore, we developed a pathway search module for virtual knockout mutants as a built-in application of CADLIVE. This module analyzes gene function in the same way as molecular genetics, which simulates a change in mutant phenotypes or confirms the validity of the network map. The extended CADLIVE with the newly proposed notation is demonstrated to be feasible for computational simulation and analysis.

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“…Visualization tools to explore the dynamics of temporal network processes can be classified into three groups based on the components animated: (1) Species nodes animation, where the simulated species concentration is mapped onto the size/color of nodes (e.g. COPASI [Bergmann et al., 2017], Narrator [Xia et al., 2011], CytoModeler [Mandel et al., 2007]); (2) species nodes and edge animation, where the simulated species concentration is mapped onto the size/color of nodes, whereas reaction rate values are encoded into the edge thickness (e.g. DBSolve [Gizzatkulov et al., 2010]); and (3) rules nodes and edges animation (e.g.…”

Section: Discussionmentioning

confidence: 99%

Interactive Multiresolution Visualization of Cellular Network Processes

Ortega

López

2020

iScience

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SummaryVisualization plays a central role in the analysis of biochemical network models to identify patterns that arise from reaction dynamics and perform model exploratory analysis. To facilitate these analyses, we developed PyViPR, a visualization tool that generates static and dynamic representations of biochemical network processes within a Python-based environment. PyViPR embeds network visualizations within Jupyter notebooks, thus enabling integration with modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show that community-detection algorithms identify groups of molecular species that capture key biological functions and ease exploration of the apoptosis network. We then show how different kinetic parameter sets that fit the experimental data equally well exhibit significantly different signal-execution dynamics as the system progresses toward mitochondrial outer-membrane permeabilization. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.

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Modeling biochemical transformation processes and information processing with Narrator

Cited by 9 publications

References 27 publications

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

Extended CADLIVE: a novel graphical notation for design of biochemical network maps and computational pathway analysis

Interactive Multiresolution Visualization of Cellular Network Processes

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