A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Santos, João; Pajo, Kadri; Trpevski, Daniel; Stepaniuk, Andrey; Eriksson, Olivia; Nair, Archana Padmanabhan; Keller, Daniel; Kotaleski, Jeanette Hellgren; Kramer, Andrei

doi:10.1007/s12021-021-09546-3

Cited by 4 publications

(2 citation statements)

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“…Checking for conservation laws and thermodynamic constraints can be done in a semi-automatic fashion through stoichiometric analysis, as is done for conserved substances by the Copasi simulator ( Hoops et al, 2006 ). Software is also available to introduce such relationships semiautomatically into the SBtab format ( Santos et al, 2021 ).…”

Section: Framework For Fair Modeling Workflowsmentioning

confidence: 99%

“…FindSim ( Viswan et al, 2018 ) is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so that model output can be compared with experiments in an optimization workflow. Santos et al, 2021 describe an automatic workflow for parameter estimation using an SBtab file as input, rendering itself to automatic parameter estimation through many of the optimization tools that exist within Matlab, and with a final step of global sensitivity analysis. Ajustador ( Jedrzejewski-Szmek et al, 2018 ) is an optimization algorithm for models specified in declarative format for either MOOSE or NeuroRD that allows specification of the weights of various data features.…”

Section: Framework For Fair Modeling Workflowsmentioning

confidence: 99%

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Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Eriksson

Bhalla

Blackwell

et al. 2022

eLife

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Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

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Section: Framework For Fair Modeling Workflowsmentioning

confidence: 99%