A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience.
A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience.
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DOI:
10.1007/s12021-021-09546-3
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发表时间:
2022-01
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影响因子:
3
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中科院分区:
文献类型:
--
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Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model. The online version contains supplementary material available at 10.1007/s12021-021-09546-3.
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影响因子:
3.5
作者:
Lindroos R;Dorst MC;Du K;Filipović M;Keller D;Ketzef M;Kozlov AK;Kumar A;Lindahl M;Nair AG;Pérez-Fernández J;Grillner S;Silberberg G;Hellgren Kotaleski J
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Hellgren Kotaleski J
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Chylek LA;Harris LA;Faeder JR;Hlavacek WS
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Hlavacek WS
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Cannon, Robert C.;Gewaltig, Marc-Oliver;De Schutter, Erik
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De Schutter, Erik
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Djurfeldt, Mikael;Hjorth, Johannes;Ekeberg, Orjan
通讯作者:
Ekeberg, Orjan
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5.8
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Bois, Frederic Y.
通讯作者:
Bois, Frederic Y.