ArticleFrontiers in neuroinformatics2018
FindSim: A Framework for Integrating Neuronal Data and Signaling Models.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fitting and comparison of calcium-calmodulin kinetic schemes to a common data set using non-linear mixed effects modelling.PloS one · 2025Article
- Mathematical basis and toolchain for hierarchical optimization of biochemical networks.PLoS computational biology · 2024Article
- Article
- Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways.Neuroinformatics · 2022Review
- A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience.Neuroinformatics · 2022Article
- HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks.PLoS computational biology · 2021Article
- The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE).Journal of integrative bioinformatics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current experiments touch only small but overlapping parts of very complex subcellular signaling networks in neurons. Even with modern optical reporters and pharmacological manipulations, a given experiment can only monitor and control a very small subset of the diverse, multiscale processes of neuronal signaling. We have developed FindSim (Framework for Integrating Neuronal Data and SIgnaling Models) to anchor models to structured experimental datasets. FindSim is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so as to systematically refine and validate the model. We use a structured format for encoding the conditions of many standard physiological and pharmacological experiments, specifying which parts of the model are involved, and comparing experiment outcomes with model output. A database of such experiments is run against successive generations of composite cellular models to iteratively improve the model against each experiment, while retaining global model validity. We suggest that this toolchain provides a principled and scalable way to tackle model complexity and diversity of data sources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.