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Design automation for biological models: A pipeline that incorporates spatial and molecular complexity
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Carnegie Mellon University, Pittsburgh, PA, United States.
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2015 (English)In: Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, ACM Press, 2015, Vol. 20-22-May-2015, 321-323 p.Conference paper (Refereed)Text
Abstract [en]

Understanding the dynamics of biochemical networks is a major goal of systems biology. Due to the heterogeneity of cells and the low copy numbers of key molecules, spatially resolved approaches are required to fully understand and model these systems. Until recently, most spatial modeling was performed using geometries obtained either through manual segmentation or manual fabrication both of which are time-consuming and tedious. Similarly, the system of reactions associated with the model had to be manually defined, a process that is both tedious and error-prone for large networks. As a result, spatially resolved simulations have typically only been performed in a limited number of geometries, which are often highly simplified, and with small reaction networks.

Place, publisher, year, edition, pages
ACM Press, 2015. Vol. 20-22-May-2015, 321-323 p.
Keyword [en]
Automation of research, Cellular modeling, High throughput, Protein subcellular localization, Rule-based modeling, Spatial modeling
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-187391DOI: 10.1145/2742060.2743763ScopusID: 2-s2.0-84955487718ISBN: 978-145033474-7OAI: oai:DiVA.org:kth-187391DiVA: diva2:931251
Conference
25th Great Lakes Symposium on VLSI, GLSVLSI 2015; Pittsburgh Marriott City CenterPittsburgh; United States
Note

QC 20160527

Available from: 2016-05-27 Created: 2016-05-23 Last updated: 2016-05-27Bibliographically approved

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Sullivan, Devin
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Proteomics and NanobiotechnologyScience for Life Laboratory, SciLifeLab
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