Biology subjects

Holtzapple, E.

Publications and source records attributed to Holtzapple, E..

3 recordsLinked to original sources

bioRxiv2024

The BioRECIPE Knowledge Representation Format

AO_SCPLOWBSTRACTC_SCPLOWThe BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to facilitate seamless human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems biology community. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/579694v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@76a130org.highwire.dtl.DTLVardef@505cb4org.highwire.dtl.DTLVardef@1f64e68org.highwire.dtl.DTLVardef@195bd03_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2021

Classifying Literature Extracted Events for Automated Model Extension

1In this study, we investigate the integration of three previously developed tools: FLUTE, VIOLIN, and CLARINET. We show how using these tools together adds additional capabilities in extending models from relevant research literature. We illustrate how we plan to address current modeling pitfalls with these tools (such as machine reading errors and literature volume), and how we plan to use these tools as the foundation for an automated model extension framework. Documentation and links to our tools can be found at: violin-tool.readthedocs.io clarinet-docs.readthedocs.io flute.readthedocs.io

systems biology↗

bioRxiv2021

Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data

Signaling network models are usually assembled from information in literature and expert knowledge or inferred from data. The goal of modeling is to gain mechanistic understanding of key signaling pathways and provide predictions on how perturbations affect large-scale processes such as disease progression. For glioblastoma multiforme (GBM), this task is critical, given the lack of effective treatments and pace of disease progression. Both manual and automated assembly of signaling networks from data or literature have drawbacks. Existing GBM networks, as well as networks assembled using state-of-the-art machine reading, fall short when judged by the quality and quantity of information, as well as certain attributes of the overall network structure. The contributions of this work are two-fold. First, we propose an automated methodology for verification of signaling networks. Next, we discuss automation of network assembly and extension that relies on methods and resources used for network verification, thus, implicitly including verification in these processes. In addition to these methods, we also present, and verify a comprehensive GBM network assembled with a hybrid of manual and automated methods. Finally, we demonstrate that, while an automated network assembly is fast, such networks still lack precision and realistic network topology.

systems biology↗