James A. Glazier

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James A. Glazier
BornJune 1962 (age 62)
NationalityUSA
Alma materHarvard University (BA) University of Chicago (MA, PhD)
Scientific career
Institutions Indiana University
Thesis Dynamics of Cellular Patterns
Academic advisors Albert Libchaber

James Alexander Glazier (born June 27, 1962) [1] is a biophysicist and bioengineer, author, and educator best known for his contributions to the field of multiscale modeling, pattern formation, and morphogenesis in biological systems. Glazier has published numerous articles in leading scientific journals, and his work has been widely recognized within the scientific community. [2] He has also been influential in promoting the use of computational modeling and simulation in the study of complex biological phenomena. [3]

Contents

Early life and education

James A. Glazier was born in Cambridge,Massachusetts, on June 27, 1962 to Ira A. Glazier (an economist and demographer) and Elaine A. Glazier (a psychologist and later an entrepreneur.[ citation needed ] He showed an early interest in science and mathematics, which led him to pursue an undergraduate degree in physics and mathematics from Harvard College. He later earned his Ph.D. in experimental condensed matter physics from the University of Chicago in 1989, where he worked under the supervision of Prof. Albert J. Libchaber, focusing on chaotic flows in fluids and the coarsening dynamics of liquid foams. [4] While at the University of Chicago, he began a collaboration with Dr. Gary Grest and Dr. David Srolovitz of Exxon Research applying the Potts Model to model the dynamics of foam coarsening. [5] His work on foams [6] led to extensive collaborations with the noted solid-state theorist Prof. Denis Weaire of Trinity College, Dublin. [7] [8]

Career

Following the completion of his Ph.D., Glazier held postdoctoral positions at AT&T Bell Laboratories (1989-1991) where he retrained in experimental developmental neuroscience under Dr. David W. Tank, and then held an NSF/JSPS fellowship (1991-1993) in the Research Institute of Electrical Communication, Tohoku University, Sendai, Japan, where he studied hydra regeneration and, in collaboration with Dr. Francois Graner, developed the Cellular Potts Model (CPM, also known as the Glazier-Graner-Hogeweg model, GGH) formalism for simulating the dynamics of cells in biological tissues. [9] In 1993, he accepted a faculty position in Physics at the University of Notre Dame. He moved to the Department of Physics at Indiana University in 2002, where he established the Biocomplexity Institute to advance interdisciplinary study of biological systems. He has held visiting faculty positions at the University of Western Australia, the University of Grenoble, Tohoku University, the University of California Los Angeles, and the University of California Santa Barbara,

Glazier's research interests lie at the intersection of physics, biology, and computer science, with a focus on understanding the fundamental principles governing the organization and dynamics of living systems. His most notable contributions have been in the area of multiscale modeling of tissues (Virtual Tissues) where he has developed models that have provided insights into a range of biological phenomena, such as morphogenesis, tissue development, vascular development, developmental diseases, including cancer [10] and polycystic kidney disease and toxicology. He has also conducted research on the physics of liquid foams, [11] high-Reynolds number turbulence, on biological ontologies and in microfluidics and biosensors.

Glazier is one of the key developers of CompuCell3D, [12] an open-source software platform for modeling cell behavior in a 3D environment based on the CPM/GGH methodology. CompuCell3D is designed to simulate cell-based biological processes, such as tissue development, morphogenesis, and cellular differentiation. As a professor and researcher, Dr. Glazier has played a significant role in the development and application of CompuCell3D for various biological systems. More recently, with Dr. Enrdre Somogyi [13] and Dr. TJ Sego, he has contributed to the development of the open-source Tissue Forge virtual-tissue simulation environment based on center-model methodologies. His work has contributed to the advancement of computational modeling and simulation techniques in the fields of biophysics, bioengineering, toxicology, and complex systems.

In addition to his research, Glazier has been an active participant in the scientific community. He has served on the editorial boards of Nonlinearity and Bulletin of Mathematical Biology, as well as on numerous grant review panels and advisory committees. He has also been involved in the organization of conferences and workshops aimed at fostering interdisciplinary collaboration among researchers studying complex biological systems and has organized more than 18 summer schools teaching multiscale modeling techniques to a diverse range of students from around the world. He has served as Chair of the Division of Biological Physics of the American Physical Society. In 2020, he co-founded the IMAG/MSM [14] Working Group on Multiscale Modeling and Viral Pandemics, [15] which provides a forum for the application of modeling methodologies to the understanding of infection and immune response. In 2023, with Prof. Tomas Helikar of the University of Nebraska, Lincoln, he co-founded the Global Alliance for Immune Prediction and Intervention, which aims to develop medical Digital Twins to optimize patient-specific medical care. [16] [17]

Awards and honors

Throughout his career, James A. Glazier has received numerous accolades for his research achievements, being named a fellow of the Institute of Physics (London), the American Physical Society, [18] and the American Association for the Advancement of Science. [19] His work has been cited extensively in the scientific literature and has inspired many researchers in the fields of biophysics and computational biology. He holds 13 patents in biosensors, microfluidics, drug discovery, and computational modeling.

Selected publications

Related Research Articles

<span class="mw-page-title-main">Biophysics</span> Study of biological systems using methods from the physical sciences

Biophysics is an interdisciplinary science that applies approaches and methods traditionally used in physics to study biological phenomena. Biophysics covers all scales of biological organization, from molecular to organismic and populations. Biophysical research shares significant overlap with biochemistry, molecular biology, physical chemistry, physiology, nanotechnology, bioengineering, computational biology, biomechanics, developmental biology and systems biology.

<span class="mw-page-title-main">Computational biology</span> Branch of biology

Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. It differs from biological computing, a subfield of computer science and engineering which uses bioengineering to build computers.

<span class="mw-page-title-main">Mathematical and theoretical biology</span> Branch of biology

Mathematical and theoretical biology, or biomathematics, is a branch of biology which employs theoretical analysis, mathematical models and abstractions of living organisms to investigate the principles that govern the structure, development and behavior of the systems, as opposed to experimental biology which deals with the conduction of experiments to test scientific theories. The field is sometimes called mathematical biology or biomathematics to stress the mathematical side, or theoretical biology to stress the biological side. Theoretical biology focuses more on the development of theoretical principles for biology while mathematical biology focuses on the use of mathematical tools to study biological systems, even though the two terms are sometimes interchanged.

<span class="mw-page-title-main">Multiscale modeling</span> Mathematical field

Multiscale modeling or multiscale mathematics is the field of solving problems that have important features at multiple scales of time and/or space. Important problems include multiscale modeling of fluids, solids, polymers, proteins, nucleic acids as well as various physical and chemical phenomena.

<span class="mw-page-title-main">CompuCell3D</span>

CompuCell3D (CC3D) is a three-dimensional C++ and Python software problem solving environment for simulations of biocomplexity problems, integrating multiple mathematical [morphogenesis] models. These include the cellular Potts model (CPM) or Glazier-Graner-Hogeweg model (GGH) which can model cell clustering, growth, division, death, adhesion, and volume and surface area constraints; as well as partial differential equation solvers for modeling reaction–diffusion of external chemical fields and cell type automata for differentiation. By integrating these models CompuCell3D enables modeling of cellular reactions to external chemical fields such as secretion or resorption, and responses such as chemotaxis and haptotaxis.

Systems immunology is a research field under systems biology that uses mathematical approaches and computational methods to examine the interactions within cellular and molecular networks of the immune system. The immune system has been thoroughly analyzed as regards to its components and function by using a "reductionist" approach, but its overall function can't be easily predicted by studying the characteristics of its isolated components because they strongly rely on the interactions among these numerous constituents. It focuses on in silico experiments rather than in vivo.

In computational biology, a Cellular Potts model is a computational model of cells and tissues. It is used to simulate individual and collective cell behavior, tissue morphogenesis and cancer development. CPM describes cells as deformable objects with a certain volume, that can adhere to each other and to the medium in which they live. The formalism can be extended to include cell behaviours such as cell migration, growth and division, and cell signalling. The first CPM was proposed for the simulation of cell sorting by François Graner and James A. Glazier as a modification of a large-Q Potts model. CPM was then popularized by Paulien Hogeweg for studying morphogenesis. Although the model was developed to describe biological cells, it can also be used to model individual parts of a biological cell, or even regions of fluid.

<span class="mw-page-title-main">Arieh Warshel</span> Israeli chemist, biochemist and biophysicist (born 1940)

Arieh Warshel is an Israeli-American biochemist and biophysicist. He is a pioneer in computational studies on functional properties of biological molecules, Distinguished Professor of Chemistry and Biochemistry, and holds the Dana and David Dornsife Chair in Chemistry at the University of Southern California. He received the 2013 Nobel Prize in Chemistry, together with Michael Levitt and Martin Karplus for "the development of multiscale models for complex chemical systems".

<span class="mw-page-title-main">Cellular model</span>

A cellular model is a mathematical model of aspects of a biological cell, for the purposes of in silico research.

<span class="mw-page-title-main">Martin Gruebele</span>

Martin Gruebele is a German-born American physical chemist and biophysicist who is currently emeritus James R. Eiszner Chair in Chemistry, Professor of Physics, Professor of Biophysics and Computational Biology at the University of Illinois Urbana-Champaign.

<span class="mw-page-title-main">Turing pattern</span> Concept from evolutionary biology

The Turing pattern is a concept introduced by English mathematician Alan Turing in a 1952 paper titled "The Chemical Basis of Morphogenesis" which describes how patterns in nature, such as stripes and spots, can arise naturally and autonomously from a homogeneous, uniform state. The pattern arises due to Turing instability which in turn arises due to the interplay between differential diffusion of chemical species and chemical reaction. The instability mechanism is unforeseen because a pure diffusion process would be anticipated to have a stabilizing influence on the system.

<span class="mw-page-title-main">Microscale and macroscale models</span> Classes of computational models

Microscale models form a broad class of computational models that simulate fine-scale details, in contrast with macroscale models, which amalgamate details into select categories. Microscale and macroscale models can be used together to understand different aspects of the same problem.

Klaus Schulten was a German-American computational biophysicist and the Swanlund Professor of Physics at the University of Illinois at Urbana-Champaign. Schulten used supercomputing techniques to apply theoretical physics to the fields of biomedicine and bioengineering and dynamically model living systems. His mathematical, theoretical, and technological innovations led to key discoveries about the motion of biological cells, sensory processes in vision, animal navigation, light energy harvesting in photosynthesis, and learning in neural networks.

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.

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References

  1. ↑ ORCID   0000-0003-3634-190X
  2. ↑ "Google Scholar".
  3. ↑ "Workshops".
  4. ↑ Glazier, James A.; Gross, Steven P.; Stavans, Joel (1 July 1987). "Dynamics of two-dimensional soap froths". Physical Review A. 36 (1): 306–312. Bibcode:1987PhRvA..36..306G. doi:10.1103/PhysRevA.36.306. PMID   9898684.
  5. ↑ Glazier, James A.; Anderson, Michael P.; Grest, Gary S. (December 1990). "Coarsening in the two-dimensional soap froth and the large- Q Potts model: A detailed comparison". Philosophical Magazine B. 62 (6): 615–645. Bibcode:1990PMagB..62..615G. doi:10.1080/13642819008215259.
  6. ↑ "New York Times". The New York Times .
  7. ↑ Glazier, J A; Weaire, D (24 February 1992). "The kinetics of cellular patterns". Journal of Physics: Condensed Matter. 4 (8): 1867–1894. Bibcode:1992JPCM....4.1867G. doi:10.1088/0953-8984/4/8/004.
  8. ↑ Maddox, John (1989). [10.1038/338293a0 "Soap bubbles make serious physics"]. Nature. 338 (6213): 293. Bibcode:1989Natur.338..293M. doi:10.1038/338293a0.{{cite journal}}: Check |url= value (help)
  9. ↑ Graner, François; Glazier, James A. (28 September 1992). "Simulation of biological cell sorting using a two-dimensional extended Potts model". Physical Review Letters. 69 (13): 2013–2016. Bibcode:1992PhRvL..69.2013G. doi:10.1103/PhysRevLett.69.2013. PMID   10046374.
  10. ↑ Shirinifard, Abbas; Gens, J. Scott; Zaitlen, Benjamin L.; Popławski, Nikodem J.; Swat, Maciej; Glazier, James A. (16 October 2009). "3D Multi-Cell Simulation of Tumor Growth and Angiogenesis". PLOS ONE. 4 (10): e7190. Bibcode:2009PLoSO...4.7190S. doi: 10.1371/journal.pone.0007190 . PMC   2760204 . PMID   19834621.
  11. ↑ Maddox, John (March 1989). "Soap bubbles make serious physics". Nature. 338 (6213): 293. Bibcode:1989Natur.338..293M. doi: 10.1038/338293a0 .
  12. ↑ Izaguirre, J. A.; Chaturvedi, R.; Huang, C.; Cickovski, T.; Coffland, J.; Thomas, G.; Forgacs, G.; Alber, M.; Hentschel, G.; Newman, S. A.; Glazier, J. A. (1 May 2004). "C ompu C ell , a multi-model framework for simulation of morphogenesis". Bioinformatics. 20 (7): 1129–1137. doi:10.1093/bioinformatics/bth050. PMID   14764549.
  13. ↑ Somogyi, Endre T.; Coulter, Jeffery; Sun, Fanbo; Sauro, Herbert M.; Glazier, James A. (2021). "Real-Time Interactive Modeling and Simulation in Biological Physics and Active Matter with Mechanica". arXiv: 2105.02476 [q-bio.SC].
  14. ↑ "Home | Interagency Modeling and Analysis Group". www.imagwiki.nibib.nih.gov.
  15. ↑ "Modeling Virial Pandemics".
  16. ↑ Laubenbacher, Reinhard; Sluka, James P.; Glazier, James A. (12 March 2021). "Using digital twins in viral infection". Science. 371 (6534): 1105–1106. Bibcode:2021Sci...371.1105L. doi:10.1126/science.abf3370. PMC   8170388 . PMID   33707255.
  17. ↑ "Building the Star Trek Med-Bay".
  18. ↑ "APS Fellow Archive".
  19. ↑ "American Association of Science".
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