Sharon Crook

About

Professor Sharon Crook holds an appointment with the School of Mathematical and Statistical Sciences at Arizona State University. She employs theory, computation, and data analysis to investigate the dynamics of neurons and networks of neurons, focusing on the mechanisms underlying changes in these cells and networks caused by trauma, learning, disorders, or disease. Her research aims to understand how neurons can change at both cellular and network levels due to various factors such as trauma, rehabilitation, learning, development, or aging. Professor Crook is a leader in an international effort to establish a common standard for describing computational models in neuroscience research. Through her work in neuroinformatics, she contributes to developing an ecosystem of tools that promote reproducibility, model sharing, and community-based collaborative model development in computational neuroscience. Additionally, she and her collaborators are creating cyber infrastructure and new formats to describe neuron anatomy at macro and micro levels, neural network connectivity, and membrane properties of neurons, facilitating the reproduction and sharing of complex neuroscience models after publication.

Research topics

• Computer Science
• Political Science
• Neuroscience
• Biology
• Sociology
• Paleontology
• Pedagogy
• Simulation
• Medicine
• Public relations

Selected publications

• International data governance for neuroscience

  Neuron · 2021 · 77 citations

  As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcend borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institu…

  PublisherOA PDFDOI

• Review: Mathematical Modeling of Prostate Cancer and Clinical Application

  Applied Sciences · 2020 · 49 citations

  We review and synthesize key findings and limitations of mathematical models for prostate cancer, both from theoretical work and data-validated approaches, especially concerning clinical applications. Our focus is on models of prostate cancer dynamics under treatment, particularly with a view toward optimizing hormone-based treatment schedules and estimating the onset of treatment resistance under various assumptions. Population models suggest that intermittent or adaptive therapy is more benefi…

  DOI

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

  eLife · 2022 · 38 citations

  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 di…

  PublisherDOI

• NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

  PLoS Computational Biology · 2023-03-03 · 20 citations

  articleOpen accessSenior authorCorresponding

  As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores ove…

  PublisherOA PDFDOI

• Editorial: Reproducibility and Rigour in Computational Neuroscience

  Frontiers in Neuroinformatics · 2020-05-27 · 11 citations

  editorialOpen access1st authorCorresponding

  International audience

  PublisherOA PDFDOI

Recent grants

• Behaviorally Relevant Neuronal Modification during Postembryonic Development

  NSF · $458k · 2006–2010

• Tools for Model Discovery, Validation and Selection in Neuroscience with NeuroML

  NIH · $1.6M · 2015–2020

• NIH Grant R01MH081905

  NIH · $891k · 2014

Frequent coauthors

• Padraig Gleeson

  University College London

  28 shared

• Andrew P. Davison

  Institut des Neurosciences Paris-Saclay

  21 shared

• R. Angus Silver

  University College London

  19 shared

• Robert C. Cannon

  WSP (New Zealand)

  16 shared

• Richard C. Gerkin

  Arizona State University

  15 shared

• Bóris Marin

  Universidade Federal do ABC

  14 shared

• Micaela Oertel

  Centre National de la Recherche Scientifique

  13 shared

• Salvador Durá-Bernal

  13 shared

Labs

• ICON LaboratoryPI

  The ICON Laboratory focuses on computational neuroscience research.

Education

• Ph.D., Applied Mathematics

  University of Maryland-College Park

  1996

Similar researchers at Arizona State University

• Hal Kiersteadh-34
• Zdzislaw Jackiewiczh-33
• Donatella Daniellih-22