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Computational Science in Bio-metrology


Computational Science in Biological Metrology (CS in Bio Metrology) project is about Computational Science and Biology Delivering Measurements with Statistical Significance and Repeatability. The goal of the project is to ensure quality in biomedical research and clinical applications by evolving biology into a quantitative and information science. These activities will support applications such as: biomanufacturing, drug discovery, basic bio-medical research, medical intervention, diagnostics, and regenerative medicine.



With the increasing reliance of system biology researchers on microscopy imaging and the growing volumes of images generated during each experiment, there is a need to intertwine computational science and biological metrology. The motivation lies not only in saving human labor and time needed to achieve a biological discovery but also in understanding the accuracy and uncertainty introduced by computational steps of a discovery process. The overall objective is to ensure quality of outcomes obtained from collaborative research using microscopy imaging, and scalability of data-driven biological discovery with the increasing data volumes. This objective has to be achieved by bringing together multi-disciplinary expertise working on:

  1. Automated & Scalable & Web Accessible Cell Microscopy Image Analyses and Syntheses using Software & Systems, and
  2. Quantitative Cell Biology using Microscopy Imaging and Image Analyses.

The following are key activities being addressed in this work(link to activity pages):

  • Image sampling accuracy
  • Image quality and image segmentation/classification accuracy
  • Image segmentation based measurements
  • Selection of segmentation/classification based on image conformance to algorithmic assumptions
  • Image similarity metric selection based on application specific requirements
  • Web accessible image similarity measurements

Major Accomplishments:

  1. Dynamic Measurements in Live Cell Microscopy
  2. Automated microscopy acquisition control
  3. Sub-cellular features to measure cell microenvironments
  4. Collaborative infrastructure supporting the multidisciplinary team
High Level Overview

End Date:


Lead Organizational Unit:



Carole Parent, NIH, NCI
Christina Stuelten, NIH, NCI
Michael Weiger, NIH, NCI
Wolfgang Losert, University of Maryland at College Park

Marcin Kociolek, Medical Electronics Division, Technical University of Lodz Poland


Anne Plant, Cell Systems Science Group
Kiran Bhadriraju, Cell Systems Science Group
TN. Bhat, Cell Systems Science Group
John Elliott, Cell Systems Science Group
Michael Halter, Cell Systems Science Group

Mary Brady, Information Systems Group
Peter Bajcsy, Information Systems Group
Julien Amelot, Information Systems Group
Antonio Cardone, Information Systems Group
Joe Chalfoun, Information Systems Group
Frederic DeVaulx, Information Systems Group
Alden Dima, Information Systems Group
Walid Keyrouz, Information Systems Group
Ya-Shian Li-Baboud, Information Systems Group
Benjamin Long, Information Systems Group
Adele Peskin, Information Systems Group
Paul Khouri Saba, Information Systems Group
Bertrand Stivalet, Information Systems Group
Antoine Vandecreme, Information Systems Group

Afzal Godil, Visualization and Usability Group