{"entity": "journal", "iuid": "00026f8492a34e84a3485da6f86c56b3", "timestamp": "2026-08-20T21:30:37.403Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Neuroinformatics.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Neuroinformatics"}}, "title": "Neuroinformatics", "issn": "1559-0089", "issn-l": null, "publications_count": 2, "publications": [{"entity": "publication", "iuid": "9d48d65709524fd7a4a4de2acd909918", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9d48d65709524fd7a4a4de2acd909918.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9d48d65709524fd7a4a4de2acd909918"}}, "title": "A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience.", "authors": [{"family": "Santos", "given": "Jo\u00e3o P G", "initials": "JPG", "orcid": "0000-0002-0395-039X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/57b4d19321b146e4be78794b7ea69ad6.json"}}, {"family": "Pajo", "given": "Kadri", "initials": "K", "orcid": "0000-0003-2110-7889", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e7288dc0795e49129c8d5b79b0ff1f90.json"}}, {"family": "Trpevski", "given": "Daniel", "initials": "D", "orcid": "0000-0001-9068-6744", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b007cf92b2cb4bc8a4c61436e4e3cefc.json"}}, {"family": "Stepaniuk", "given": "Andrey", "initials": "A", "orcid": "0000-0002-2543-4820", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7e95615d8a854b60a3f70821cfeddb8c.json"}}, {"family": "Eriksson", "given": "Olivia", "initials": "O", "orcid": "0000-0003-0740-4318", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/50ed8ead3a804dd28e4e7d5d3b3556d8.json"}}, {"family": "Nair", "given": "Anu G", "initials": "AG", "orcid": "0000-0002-1952-9583", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5a923f8455b54b32b90949faa67ea90c.json"}}, {"family": "Keller", "given": "Daniel", "initials": "D", "orcid": "0000-0003-3280-6255", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/815355e0fa8f4a02913e758c3d296bf5.json"}}, {"family": "Hellgren Kotaleski", "given": "Jeanette", "initials": "J", "orcid": "0000-0002-0550-0739", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a920517bd1c142878f03bee05e843b62.json"}}, {"family": "Kramer", "given": "Andrei", "initials": "A", "orcid": "0000-0002-3828-6978", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8181305816dc47afbd6a2bb17b9bab63.json"}}], "type": "journal article", "published": "2022-01-00", "journal": {"title": "Neuroinformatics", "issn": "1559-0089", "volume": "20", "issue": "1", "pages": "241-259", "issn-l": null}, "abstract": "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\u00ae 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.", "doi": "10.1007/s12021-021-09546-3", "pmid": "34709562", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9537196"}, {"db": "pii", "key": "10.1007/s12021-021-09546-3"}], "notes": [], "created": "2026-08-20T06:40:27.397Z", "modified": "2026-08-20T06:40:27.743Z"}, {"entity": "publication", "iuid": "b3c5a34d9afc4b5c8febfa44e3feae9d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b3c5a34d9afc4b5c8febfa44e3feae9d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b3c5a34d9afc4b5c8febfa44e3feae9d"}}, "title": "Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda.", "authors": [{"family": "Hjorth", "given": "J J Johannes", "initials": "JJJ", "orcid": "0000-0002-9302-0750", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ec9300bb976d4038a766d908fcd0ce86.json"}}, {"family": "Hellgren Kotaleski", "given": "Jeanette", "initials": "J", "orcid": "0000-0002-0550-0739", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a920517bd1c142878f03bee05e843b62.json"}}, {"family": "Kozlov", "given": "Alexander", "initials": "A", "orcid": "0000-0003-3994-0799", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bcd020bb912c4ff59b2cd86e28ac8d5c.json"}}], "type": "journal article", "published": "2021-10-00", "journal": {"title": "Neuroinformatics", "issn": "1559-0089", "volume": "19", "issue": "4", "pages": "685-701", "issn-l": null}, "abstract": "Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a 'bottom-up' manner. Simulated data from these models can be compared with experiments and 'top-down' modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further 'curate' data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.", "doi": "10.1007/s12021-021-09531-w", "pmid": "34282528", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8566446"}, {"db": "pii", "key": "10.1007/s12021-021-09531-w"}], "notes": [], "created": "2026-08-20T06:40:25.552Z", "modified": "2026-08-20T06:40:25.705Z"}], "created": "2026-08-20T06:40:25.674Z", "modified": "2026-08-20T06:40:25.674Z"}