John Yin

Professor of Chemical & Biological Engineering, Vilas Distinguished Achievement Professor, Theme Leader of Wisconsin Institute for Discovery

330 N. Orchard Street
Madison, WI 53715

Ph: (608) 316-4323
Fax: (608) 316-4604
john.yin@wisc.edu

Primary Affiliation:
Chemical and Biological Engineering

Additional Affiliations:
Biomedical Engineering,


Profile Summary

Viruses cause a diversity of human diseases including acquired immunodeficiency syndrome(AIDS), influenza, hepatitis, and cancer. The focus of our research is to develop new experimental and computational methods to better understand how viruses grow and how their infections spread. Our ultimate goal is to apply these methods to create more effective vaccines, design potent anti-viral therapies, and engineer useful viruses. We currently study human rhinovirus, which causes the common cold and promotes asthma in infants, and vesicular stomatitis virus (VSV), a virus that may be engineered to destroy cancers.

As the smallest organisms viruses range from 20 to 300 nanometers in diameter and carry genomes that encode from 5 to 200 genes. To grow, a virus must develop an intimate relationship with a living cell. After docking with receptors at the cell surface the virus enters the cell, releases its genome, and thereby sets in motion processes that ultimately divert resources of the cell toward the mass production of virus proteins and virus genomes. Self-assembly of these parts give rise to progeny viruses that, following release, may encounter and infect other susceptible cells. Despite the small size and relative simplicity of virus genomes, the network of molecular transformations that define virus growth within the cell remain complex. Thus it is a major challenge to predict how engineered differences, natural mutations, virus-targeted drugs or cell differences can influence how viruses grow.

Integration of diverse information. We address the complexity of virus growth by casting the molecular interactions in the mathematical and computable language of chemical reaction engineering, a process that enables us to weave together mechanisms and data drawn from biochemical, biophysical and genetic studies spanning the last 40 years. Through our model building we integrate themes of molecular synthesis and decay, template-directed information transfers, physical interactions and regulatory feedbacks, and macro-molecular assembly. Our models provide a functional link between static genomes of viruses and the dynamic processes of infection that they encode.

Nature versus nurture. Our genome-to-organism models of virus growth have opened the door to better understanding how interactions between virus genomes and their intracellular environments influence virus development. Specifically, we have shown that protein synthesis is the limiting resource for virus growth, quantified how interactions among genes contribute to virus fitness and robustness, and identified conditions under which wild-type genome designs perform optimally. On the applications side we have used these models to suggest novel anti-viral strategies that resist escape.

Challenging paradigms and taking risks. The modus operandi in biology employs average measures of molecular levels to elucidate mechanisms. However, in the study of viruses, where genetic variability can be rampant, such average measures can mask variations that are likely to be central to viral growth and persistence. We are beginning to address such issues by probing virus growth and host-cell responses at the single-cell level, using flow cytometry to sort and analyze single infected cells, computer modeling to test potential mechanisms, and novel experimental methods to visualize and quantify the dynamics of virus populations from single infected cells. In the process we are identifying and illuminating new themes and variations as the smallest genomes come to life.

Education

  • BA (Liberal Arts) Columbia College
  • BS (Chemical Engineering) Columbia University
  • PhD (Chemical Engineering) University of California, Berkeley
  • Post-doctoral Fellowship (Biochemical Kinetics) Max-Planck-Institut fuer Biophysikalische Chemie, Goettingen, German

Research Interests

 

  • systems biology - virus-host interactions
  • systems chemistry - molecular replicators

Awards, Honors and Societies

  • Robert Emmett Dolan Prize, supporting cello studies at Juilliard School of Music, New York (1978-1983)
  • Alexander von Humboldt Fellow, Germany (1988)
  • NSF Young Investigator Award (1994)
  • NSF Presidential Early Career Award for Scientists and Engineers (PECASE) (1996)
  • Systems Biology, Theme Leader, Wisconsin Institute for Discovery (since 2010)
  • NIH Study Section, Modeling and Analysis of Biological Systems (MABS), 2010-2014
  • Vilas Distinguished Achievement Professorship, University of Wisconsin-Madison, 2015-2020

Publications

  • Haseltine, E., Rawlings, J., and Yin, J. (2005). Dynamics of viral infections: incorporating both the intracellular and extracellular levels. Computers and Chemical Engineering 29, 675-686.
  • Endler, E., Nealey, P., and Yin, J. (2005). Fidelity of micropatterned cell cultures. Journal of Biomedical Materials Research A 74, 92-103
  • Lam, V., Duca, K. A., and Yin, J. (2005). Arrested spread of vesicular stomatitis virus infections in vitro depends on interferon-mediated antiviral activity. Biotechnol Bioeng 90, 793-804.
  • Lim, K. I., and Yin, J. (2005). Localization of receptors in lipid rafts can inhibit signal transduction. Biotechnol Bioeng 90, 694-702.
  • Kim, H., and Yin, J. (2005). In silico mutagenesis of RNA splicing in HIV-1. Biotechnol Bioeng, 91(7), 877-93.
  • Kim, H., and Yin, J. (2005). Robust growth of human immunodeficiency virus type 1 (HIV-1). Biophysical Journal, 89(4), 2210-21.
  • Kim, H., and Yin, J. (2005). Effects of RNA Splicing and Posttranscriptional Regulation on HIV-1 Growth: a Quantitative and Integrated Perspective. IEE Proceedings-Systems Biology, 152(3), 138-152.
  • Napier, J., and Yin, J. (2006). Formation of Peptides in the Dry State. Peptides, 27(4):607-10.
  • Lim, K. and Yin, J. (2006). Dynamic tradeoffs in the raft-mediated entry of human immunodeficiency virus type 1 into cells, Biotechnol Bioeng 93(2):246-57.
  • Lam, V., Boehme, K.W., Compton, T. and Yin, J. (2006) Spatial Patterns of Protein Expression in Focal Infections of Human Cytomegalovirus. Biotechnol Bioeng,93(6):1029-39.
  • You, L. and Yin, J. (2006) Evolutionary Design on a Budget: Robustness and Optimality of Bacteriophage T7. IEE Proc. Systems Biology, 153(2), 46-52.
  • Lim, K., Lang, T., Lam, V. and Yin, J. (2006). Model-based Design of Growth-attenuated Viruses, PLoS Computational Biology, 2(9): e116.
  • Zhu, Y. and Yin, J. (2007). A quantitative comet assay: imaging and analysis of virus plaques formed with a liquid overlay, Journal of Virological Methods, 139, 100-102.
  • Middleton, J.K, Agostod, M.A., Severson, T.F., Yin, J., Nibert, M.L. (2007), Thermostabilizing mutations in reovirus outer-capsid protein mu1 selected by heat inactivation of infectious subvirion particles, Virology, 361(2):412-25.
  • Agosto M.A., Middleton J.K., Freimont E.C., Yin J., Nibert M.L. (2007), Thermolabilizing Pseudoreversions in Reovirus Outer-Capsid Protein mu1 Rescue the Entry Defect Conferred by a Thermostabilizing Mutation, J Virol., 81(14):7400-9.
  • Suthers, P., Gourse, R., Yin, J. (2007), Rapid Responses of Ribosomal RNA Synthesis to Nutrient Shifts, Biotech. Bioeng, 97(5):1230-45.
  • Yin, J. (2007), Chemical Engineering and Virology: Challenges and Opportunities at the Interface, AIChE Journal, Sept 2007.
  • Abedon S.T., Yin J. (2008). Impact of Spatial Structure on Phage Population Growth, in Bacteriophage Ecology: Population Growth, Evolution, and Impact of Bacterial Viruses, Cambridge University Press.
  • Haseltine, E.L., Yin, J., Rawlings, J.B. (2008), Implications of Decoupling the Intracellular and Extracellular Levels in Multi-level Models of Virus Growth, Biotech. Bioeng, 101(4):811-20.
  • Haseltine, E.L., Lam, V., Yin, J., Rawlings, J.B. (2008), Image-Guided Modeling of Virus Growth and Spread, Bulletin of Mathematical Biology, 70(6):1730-48.
  • Zhu, Y., Yongky, A., Yin, J. (2009), Growth of an RNA Virus in Single Cells Reveals a Broad Fitness Distribution, Virology, 385(1):39-46.
  • Lim, K. and J. Yin (2009). Computational Fitness Landscape for All Gene-Order Permutations of an RNA Virus, PLoS Computational Biology, Feb
  • 5(2), Epub.
  • Zhu, Y., J. Warrick, K. Haubert, D.J. Beebe and J. Yin (2009), Infection on a Chip: a microscale platform for simple and sensitive cell-based virus assays, Biomedical Microdevices, 11(3):565-70.
  • Stauffer Thompson, K.A.S., G.A. Rempala and J. Yin (2009). Multiple-Hit Inhibition of Infection by Defective Interfering Particles, J. Gen. Virology, 90(4): 888 - 899.
  • Hensel, S., J.B. Rawlings, and J. Yin (2009). Stochastic Kinetic Modeling of Vesicular Stomatitis Virus Intracellular Growth, Bulletin of Mathematical Biology, 71(7):1671-92.
  • Anekal, S.G., Y. Zhu, M.D. Graham, and J. Yin (2009). Dynamics of virus spread in the presence of fluid flow, Integrative Biology, 1, 664.
  • Thompson, K.A. and J. Yin (2010). Population dynamics of an RNA virus and its defective interfering particles in passage cultures, Virology Journal, Sep 29, 7:257.
  • Lindsay, S.M., A. Timm and J. Yin (2012). A quantitative comet infection assay for influenza virus, J. Virological Meth., 179(2):351-8.
  • Timm, A. and J. Yin (2012). Kinetics of virus production from single cells, Virology, 424(1):11-17.
  • Voigt E, Inankur B, Baltes A, Yin J (2013)  A quantitative infection assay for human type I, II, and III interferon antiviral activities.  Virology J. 10:224.
  • Timm, C., F. Akpinar and J. Yin (2014) Quantitative characterization of defective virus emergence by deep sequencing. J. Virol. Mar;88(5):2623-32. doi: 10.1128/JVI.02675-13.
  • Swick A, Baltes A, Yin J (2014) Visualizing infection spread: dual-color fluorescent reporting of virus-host interactions. Biotechnol Bioeng, 111(6):1200-1209.
  • Akpinar F, Yin J (2015).  Characterization of vesicular stomatitis virus populations by tunable resistive pulse sensing. J Virol Methods, 218:71-76.
  • Pesko K, Voigt EA, Swick A, Morley VJ, Timm C, Yin J, Turner PE (2015). Genome rearrangement affects RNA virus adaptability on prostate cancer cells. Front Genet, 6:121.
  • Timm C, Gupta A, Yin J (2015).  Robust kinetics of an RNA virus: Transcription rates are set by genome levels. Biotechnol Bioeng, 112(8):1655-1662.
  • Voigt E and Yin J (2015)  Kinetic Differences and Synergistic Antiviral Effects Between Type I and Type III Interferon Signaling Indicate Pathway Independence.  J Interferon Cytokine Res. 2015 May 4. [Epub ahead of print]

Courses

Summer 2016

  • CBE 790 - Master\'s Research or Thesis

  • CBE 890 - Pre-Dissertator\'s Research
  • CBE 990 - Thesis-Research
  • CBE 470 - Process Dynamics and Control
  • CBE 599 - Special Problems
  • CBE 489 - Honors in Research
  • BME 399 - Independent Study
  • BME 389 - Honors in Research
  • BME 489 - Honors in Research
  • CBE 790 - Master\'s Research or Thesis
  • CBE 890 - Pre-Dissertator\'s Research
  • CBE 990 - Thesis-Research
  • CBE 599 - Special Problems
  • BME 890 - Pre-dissertation Research
  • Secondary Contact

    3633 Engineering Hall
    1415 Engineering Drive
    Madison, WI 53706

    Alt Ph: (608) 265-3779

    Profile Summary

    Viruses cause a diversity of human diseases including acquired immunodeficiency syndrome(AIDS), influenza, hepatitis, and cancer. The focus of our research is to develop new experimental and computational methods to better understand how viruses grow and how their infections spread. Our ultimate goal is to apply these methods to create more effective vaccines, design potent anti-viral therapies, and engineer useful viruses. We currently study human rhinovirus, which causes the common cold and promotes asthma in infants, and vesicular stomatitis virus (VSV), a virus that may be engineered to destroy cancers.

    As the smallest organisms viruses range from 20 to 300 nanometers in diameter and carry genomes that encode from 5 to 200 genes. To grow, a virus must develop an intimate relationship with a living cell. After docking with receptors at the cell surface the virus enters the cell, releases its genome, and thereby sets in motion processes that ultimately divert resources of the cell toward the mass production of virus proteins and virus genomes. Self-assembly of these parts give rise to progeny viruses that, following release, may encounter and infect other susceptible cells. Despite the small size and relative simplicity of virus genomes, the network of molecular transformations that define virus growth within the cell remain complex. Thus it is a major challenge to predict how engineered differences, natural mutations, virus-targeted drugs or cell differences can influence how viruses grow.

    Integration of diverse information. We address the complexity of virus growth by casting the molecular interactions in the mathematical and computable language of chemical reaction engineering, a process that enables us to weave together mechanisms and data drawn from biochemical, biophysical and genetic studies spanning the last 40 years. Through our model building we integrate themes of molecular synthesis and decay, template-directed information transfers, physical interactions and regulatory feedbacks, and macro-molecular assembly. Our models provide a functional link between static genomes of viruses and the dynamic processes of infection that they encode.

    Nature versus nurture. Our genome-to-organism models of virus growth have opened the door to better understanding how interactions between virus genomes and their intracellular environments influence virus development. Specifically, we have shown that protein synthesis is the limiting resource for virus growth, quantified how interactions among genes contribute to virus fitness and robustness, and identified conditions under which wild-type genome designs perform optimally. On the applications side we have used these models to suggest novel anti-viral strategies that resist escape.

    Challenging paradigms and taking risks. The modus operandi in biology employs average measures of molecular levels to elucidate mechanisms. However, in the study of viruses, where genetic variability can be rampant, such average measures can mask variations that are likely to be central to viral growth and persistence. We are beginning to address such issues by probing virus growth and host-cell responses at the single-cell level, using flow cytometry to sort and analyze single infected cells, computer modeling to test potential mechanisms, and novel experimental methods to visualize and quantify the dynamics of virus populations from single infected cells. In the process we are identifying and illuminating new themes and variations as the smallest genomes come to life.


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