Robert F. Murphy (computational biologist)

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Bob Murphy
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Website www.andrew.cmu.edu/user/murphy/

Robert F. Murphy is Ray and Stephanie Lane Professor of Computational Biology Emeritus and Director of the M.S. Program in Automated Science at Carnegie Mellon University. Prior to his retirement in May 2021, he was the Ray and Stephanie Lane Professor of Computational Biology as well as Professor of Biological Sciences, Biomedical Engineering, and Machine Learning. He was founding Director (with Jelena Kovacevic [1] ) of the Center for Bioimage Informatics at Carnegie Mellon and founded (with Ivet Bahar) the Joint CMU-Pitt Ph.D. Program in Computational Biology. He also founded the Computational Biology Department at Carnegie Mellon University (originally the Lane Center for Computational Biology) and served as its head from 2009 to 2020.

Biography

Prior to arriving at Carnegie Mellon, Murphy was a Damon Runyon Cancer Research Foundation postdoctoral fellow with Dr. Charles R. Cantor at Columbia University from 1979 through 1983. Murphy earned an A. B. in biochemistry from Columbia College in 1974 and a Ph.D. in biochemistry from the California Institute of Technology in 1980. He received a Presidential Young Investigator Award [2] from the National Science Foundation shortly after joining the faculty at Carnegie Mellon in 1983. In 2005, NIH selected him as the first full-term chair of its new Biodata Management and Analysis Study Section. [3] In 2006, he was named a Fellow of the American Institute for Medical and Biological Engineering. [4] In 2019, he was elected as a Fellow of the IEEE for his contributions to machine learning algorithms for biological images. [5] Murphy has received research grants from the National Institutes of Health, the National Science Foundation, the American Cancer Society, the American Heart Association, the Arthritis Foundation, and the Rockefeller Brothers Fund. He has co-edited two books and two special journal issues on “Cell and Molecular Imaging,” and published over 200 research papers. [6] He served as President of the International Society for the Advancement of Cytometry, [7] was named as the first External Senior Fellow of the School of Life Sciences in the Freiburg (Germany) Institute for Advanced Studies, and has been named as an Honorary Professor at the University of Freiburg. He was a member of the National Advisory General Medical Sciences Council and the National Institutes of Health Council of Councils. [8]

Murphy’s career has centered on combining fluorescence-based cell measurement methods with quantitative and computational methods. He and his collaborators did extensive work on the application of flow cytometry to analyze endocytic membrane traffic beginning in the early 1980s [9] [10] [11] and pioneered the application of machine learning methods to high-resolution fluorescence microscope images depicting subcellular location patterns in the mid-1990s. [12] [13] This work led to the development of the first systems for automatically recognizing all major organelle patterns in 2D and 3D images. [14] [15] [16] He founded the CellOrganizer project for learning generative models of cell organization directly from microscope images. He also leads the image analysis and modeling efforts for the National Center for Multiscale Modeling of Biological Systems. He has also done extensive work on using active machine learning to drive biomedical discovery, [17] [18] [19] and founded the world’s first M.S. program in Automated Science. His research publications have been cited over 13,000 times and his h-index is 57 [20]

Murphy’s leadership experience includes developing the first formal undergraduate program in computational biology in 1987 and founding the Merck Computational Biology and Chemistry program at Carnegie Mellon in 1999. These programs were important forerunners to the 2005 establishment of a Ph.D. program in computational biology in partnership with the University of Pittsburgh. Murphy cofounded Quantitative Medicine, LLC based upon work from his research group.[ citation needed ] The company was sold to Predictive Oncology, Inc. [21] in 2020.

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

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<span class="mw-page-title-main">Flow cytometry</span> Lab technique in biology and chemistry

Flow cytometry (FC) is a technique used to detect and measure physical and chemical characteristics of a population of cells or particles.

<span class="mw-page-title-main">Fluorescence microscope</span> Optical microscope that uses fluorescence and phosphorescence

A fluorescence microscope is an optical microscope that uses fluorescence instead of, or in addition to, scattering, reflection, and attenuation or absorption, to study the properties of organic or inorganic substances. "Fluorescence microscope" refers to any microscope that uses fluorescence to generate an image, whether it is a simple set up like an epifluorescence microscope or a more complicated design such as a confocal microscope, which uses optical sectioning to get better resolution of the fluorescence image.

CellProfiler is free, open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. Advanced algorithms for image analysis are available as individual modules that can be placed in sequential order together to form a pipeline; the pipeline is then used to identify and measure biological objects and features in images, particularly those obtained through fluorescence microscopy.

<span class="mw-page-title-main">Cytomics</span> Single-cell biology and biochemistry

Cytomics is the study of cell biology (cytology) and biochemistry in cellular systems at the single cell level. It combines all the bioinformatic knowledge to attempt to understand the molecular architecture and functionality of the cell system (Cytome). Much of this is achieved by using molecular and microscopic techniques that allow the various components of a cell to be visualised as they interact in vivo.

Bioimage informatics is a subfield of bioinformatics and computational biology. It focuses on the use of computational techniques to analyze bioimages, especially cellular and molecular images, at large scale and high throughput. The goal is to obtain useful knowledge out of complicated and heterogeneous image and related metadata.

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<span class="mw-page-title-main">Time-lapse microscopy</span> Type of microscopy

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<span class="mw-page-title-main">Cytometry</span> Measurement of number and characteristics of cells

Cytometry is the measurement of number and characteristics of cells. Variables that can be measured by cytometric methods include cell size, cell count, cell morphology, cell cycle phase, DNA content, and the existence or absence of specific proteins on the cell surface or in the cytoplasm. Cytometry is used to characterize and count blood cells in common blood tests such as the complete blood count. In a similar fashion, cytometry is also used in cell biology research and in medical diagnostics to characterize cells in a wide range of applications associated with diseases such as cancer and AIDS.

<span class="mw-page-title-main">Live-cell imaging</span> Study of living cells using time-lapse microscopy

Live-cell imaging is the study of living cells using time-lapse microscopy. It is used by scientists to obtain a better understanding of biological function through the study of cellular dynamics. Live-cell imaging was pioneered in the first decade of the 21st century. One of the first time-lapse microcinematographic films of cells ever made was made by Julius Ries, showing the fertilization and development of the sea urchin egg. Since then, several microscopy methods have been developed to study living cells in greater detail with less effort. A newer type of imaging using quantum dots have been used, as they are shown to be more stable. The development of holotomographic microscopy has disregarded phototoxicity and other staining-derived disadvantages by implementing digital staining based on cells’ refractive index.

Neuronal tracing, or neuron reconstruction is a technique used in neuroscience to determine the pathway of the neurites or neuronal processes, the axons and dendrites, of a neuron. From a sample preparation point of view, it may refer to some of the following as well as other genetic neuron labeling techniques,

<span class="mw-page-title-main">Mass cytometry</span> Laboratory technique

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Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results.

<span class="mw-page-title-main">Ziv Bar-Joseph</span> Israeli computational biologist

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An imaging cycler microscope (ICM) is a fully automated (epi)fluorescence microscope which overcomes the spectral resolution limit resulting in parameter- and dimension-unlimited fluorescence imaging. The principle and robotic device was described by Walter Schubert in 1997 and has been further developed with his co-workers within the human toponome project. The ICM runs robotically controlled repetitive incubation-imaging-bleaching cycles with dye-conjugated probe libraries recognizing target structures in situ (biomolecules in fixed cells or tissue sections). This results in the transmission of a randomly large number of distinct biological informations by re-using the same fluorescence channel after bleaching for the transmission of another biological information using the same dye which is conjugated to another specific probe, a.s.o. Thereby noise-reduced quasi-multichannel fluorescence images with reproducible physical, geometrical, and biophysical stabilities are generated. The resulting power of combinatorial molecular discrimination (PCMD) per data point is given by 65,536k, where 65,536 is the number of grey value levels (output of a 16-bit CCD camera), and k is the number of co-mapped biomolecules and/or subdomains per biomolecule(s). High PCMD has been shown for k = 100, and in principle can be expanded for much higher numbers of k. In contrast to traditional multichannel–few-parameter fluorescence microscopy (panel a in the figure) high PCMDs in an ICM lead to high functional and spatial resolution (panel b in the figure). Systematic ICM analysis of biological systems reveals the supramolecular segregation law that describes the principle of order of large, hierarchically organized biomolecular networks in situ (toponome). The ICM is the core technology for the systematic mapping of the complete protein network code in tissues (human toponome project). The original ICM method includes any modification of the bleaching step. Corresponding modifications have been reported for antibody retrieval and chemical dye-quenching debated recently. The Toponome Imaging Systems (TIS) and multi-epitope-ligand cartographs (MELC) represent different stages of the ICM technological development. Imaging cycler microscopy received the American ISAC best paper award in 2008 for the three symbol code of organized proteomes.

<span class="mw-page-title-main">Carnegie Mellon University Computational Biology Department</span>

The Computational Biology Department (CBD) is a division within the School of Computer Science at Carnegie Mellon University in Pittsburgh, Pennsylvania, United States. It is located in the Gates-Hillman Center. Established in 2007 by Robert F. Murphy as the Lane Center for Computational Biology with funding from Raymond J. Lane and Stephanie Lane, CBD became a department within the School of Computer Science in 2016.

Cell-based models are mathematical models that represent biological cells as discrete entities. Within the field of computational biology they are often simply called agent-based models of which they are a specific application and they are used for simulating the biomechanics of multicellular structures such as tissues. to study the influence of these behaviors on how tissues are organised in time and space. Their main advantage is the easy integration of cell level processes such as cell division, intracellular processes and single-cell variability within a cell population.

<span class="mw-page-title-main">Ujjwal Maulik</span> Indian computer scientist (born 1965)

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Tissue image cytometry or tissue cytometry is a method of digital histopathology and combines classical digital pathology and computational pathology into one integrated approach with solutions for all kinds of diseases, tissue and cell types as well as molecular markers and corresponding staining methods to visualize these markers. Tissue cytometry uses virtual slides as they can be generated by multiple, commercially available slide scanners, as well as dedicated image analysis software – preferentially including machine and deep learning algorithms. Tissue cytometry enables cellular analysis within thick tissues, retaining morphological and contextual information, including spatial information on defined cellular subpopulations. In this process, a tissue sample, either formalin-fixed paraffin-embedded (FFPE) or frozen tissue section, also referred to as “cryocut”, is labelled with either immunohistochemistry(IHC) or immunofluorescent markers, scanned with high-throughput slide scanners and the data gathered from virtual slides is processed and analyzed using software that is able to identify individual cells in tissue context automatically and distinguish between nucleus and cytoplasm for each cell. Additional algorithms can identify cellular membranes, subcellular structures and/or multicellular tissue structures.

References

  1. ↑ http://jelena.ece.cmu.edu/
  2. ↑ "NSF Award Search: Award#8351364 - Presidential Young Investigator Award". www.nsf.gov. Retrieved 2016-03-29.
  3. ↑ "Carnegie Mellon Press Release: March 3, 2005". www.cmu.edu. Retrieved 2016-03-04.
  4. ↑ "Fellow Directory - AIMBE". aimbe.org. Retrieved 2016-03-04.
  5. ↑ "About the IEEE Fellow Program". www.ieee.org. Retrieved 2019-12-09.
  6. ↑ "Robert F. Murphy - Google Scholar Citations". scholar.google.com. Retrieved 2016-03-04.
  7. ↑ International Society for the Advancement of Cytometry Past Presidents
  8. ↑ New NIH Council of Councils members named, December 10, 2011
  9. ↑ Murphy, R. F.; Jorgensen, E. D.; Cantor, C. R. (1982-02-25). "Kinetics of histone endocytosis in Chinese hamster ovary cells. A flow cytofluorometric analysis". Journal of Biological Chemistry. 257 (4): 1695–1701. doi: 10.1016/S0021-9258(19)68093-2 . ISSN   0021-9258. PMID   7056738.
  10. ↑ Murphy, R. F.; Powers, S.; Cantor, C. R. (1984-05-01). "Endosome pH measured in single cells by dual fluorescence flow cytometry: rapid acidification of insulin to pH 6". The Journal of Cell Biology. 98 (5): 1757–1762. doi:10.1083/jcb.98.5.1757. ISSN   0021-9525. PMC   2113196 . PMID   6144684.
  11. ↑ Cain, C. C.; Sipe, D. M.; Murphy, R. F. (1989-01-01). "Regulation of endocytic pH by the Na+,K+-ATPase in living cells". Proceedings of the National Academy of Sciences of the United States of America. 86 (2): 544–548. Bibcode:1989PNAS...86..544C. doi: 10.1073/pnas.86.2.544 . ISSN   0027-8424. PMC   286508 . PMID   2536168.
  12. ↑ Boland, M. V.; Markey, M. K.; Murphy, R. F. (1997-10-01). "Classification of protein localization patterns obtained via fluorescence light microscopy". Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136). Vol. 2. pp. 594–597 vol.2. doi:10.1109/IEMBS.1997.757680. ISBN   978-0-7803-4262-0. S2CID   13518296.
  13. ↑ Baker, Monya (2012-07-01). "The author file: Robert F. Murphy". Nature Methods. 9 (7): 629. doi: 10.1038/nmeth.2088 . ISSN   1548-7091.
  14. ↑ Boland, M. V.; Murphy, R. F. (2001-12-01). "A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells". Bioinformatics. 17 (12): 1213–1223. doi: 10.1093/bioinformatics/17.12.1213 . ISSN   1367-4803. PMID   11751230.
  15. ↑ Glory, Estelle; Murphy, Robert F. (2007-01-01). "Automated subcellular location determination and high-throughput microscopy". Developmental Cell. 12 (1): 7–16. doi: 10.1016/j.devcel.2006.12.007 . ISSN   1534-5807. PMID   17199037.
  16. ↑ Velliste, M.; Murphy, R. F. (2002-01-01). "Automated determination of protein subcellular locations from 3D fluorescence microscope images". Proceedings IEEE International Symposium on Biomedical Imaging. pp. 867–870. doi:10.1109/ISBI.2002.1029397. ISBN   978-0-7803-7584-0. S2CID   2730192.
  17. ↑ Naik, Armaghan W.; Kangas, Joshua D.; Langmead, Christopher J; Murphy, Robert F. (2013-12-17). "Efficient Modeling and Active Learning Discovery of Biological Responses". PLOS ONE. 8 (12): e83996. Bibcode:2013PLoSO...883996N. doi: 10.1371/journal.pone.0083996 . PMC   3866149 . PMID   24358322.
  18. ↑ Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F (2016-02-03). Ohler, Uwe (ed.). "Active machine learning-driven experimentation to determine compound effects on protein patterns". eLife. 5: e10047. doi: 10.7554/eLife.10047 . ISSN   2050-084X. PMC   4798950 . PMID   26840049.
  19. ↑ Sun, Huangqingbo (2021-10-15). "Evaluation of categorical matrix completion algorithms: toward improved active learning for drug discovery". Bioinformatics. 37 (20): 3538–3545. doi:10.1093/bioinformatics/btab322. PMC   8545350 . PMID   33983377.
  20. ↑ "Robert F. Murphy". scholar.google.com. Retrieved 2022-02-10.
  21. ↑ "Predictive Oncology Completes Acquisition of Quantitative Medicine". GlobeNewsWire.
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