Kevin A Janes

Summary

Affiliation: University of Virginia
Country: USA

Publications

  1. pmc Profiling Subcellular Protein Phosphatase Responses to Coxsackievirus B3 Infection of Cardiomyocytes.
    Millie Shah
    Mol Cell Proteomics 16:S244-S262. 2017
  2. pmc Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908 USA Electronic address
    Curr Opin Biotechnol 39:120-5. 2016
  3. pmc Cell-to-Cell Transcript Variability: Seeing Signal in the Noise
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA Electronic address
    Cell 163:1566-8. 2015
  4. pmc An analysis of critical factors for quantitative immunoblotting
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA E mail
    Sci Signal 8:rs2. 2015
  5. pmc RUNX1 and its understudied role in breast cancer
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
    Cell Cycle 10:3461-5. 2011
  6. pmc Identifying single-cell molecular programs by stochastic profiling
    Kevin A Janes
    Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
    Nat Methods 7:311-7. 2010
  7. pmc A time- and matrix-dependent TGFBR3-JUND-KRT5 regulatory circuit in single breast epithelial cells and basal-like premalignancies
    Chun Chao Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville Virginia 22908, USA
    Nat Cell Biol 16:345-56. 2014
  8. pmc Normal morphogenesis of epithelial tissues and progression of epithelial tumors
    Chun Chao Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
    Wiley Interdiscip Rev Syst Biol Med 4:51-78. 2012
  9. pmc Simultaneous profiling of 194 distinct receptor transcripts in human cells
    Byong H Kang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
    Sci Signal 6:rs13. 2013
  10. pmc Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells
    Lixin Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
    Nat Protoc 8:282-301. 2013

Collaborators

Detail Information

Publications20

  1. pmc Profiling Subcellular Protein Phosphatase Responses to Coxsackievirus B3 Infection of Cardiomyocytes.
    Millie Shah
    Mol Cell Proteomics 16:S244-S262. 2017
    ..Our assay provides a high-throughput way to capture perturbations in important negative regulators of intracellular signal-transduction networks...
  2. pmc Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908 USA Electronic address
    Curr Opin Biotechnol 39:120-5. 2016
    ..Single-cell states are more dependent on time, microenvironment, and low-abundance transcripts, emphasizing in situ methods that stress depth of profiling and quantitative accuracy. ..
  3. pmc Cell-to-Cell Transcript Variability: Seeing Signal in the Noise
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA Electronic address
    Cell 163:1566-8. 2015
    ..The noise from transcriptional bursts is buffered by a hallmark of eukaryotes-the nucleus. ..
  4. pmc An analysis of critical factors for quantitative immunoblotting
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA E mail
    Sci Signal 8:rs2. 2015
    ..The results showed that ignoring such diagnostics can lead to pseudoquantitative immunoblot data that markedly overestimate or underestimate true differences in protein abundance. ..
  5. pmc RUNX1 and its understudied role in breast cancer
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
    Cell Cycle 10:3461-5. 2011
    ..FOXOs could, therefore, represent a synthetic-lethal target for RUNX1-deficient tumors if the hypothesized link to breast cancer is correct...
  6. pmc Identifying single-cell molecular programs by stochastic profiling
    Kevin A Janes
    Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
    Nat Methods 7:311-7. 2010
    ..Thus, stochastic profiling can reveal single-cell heterogeneities without the need to measure expression in individual cells...
  7. pmc A time- and matrix-dependent TGFBR3-JUND-KRT5 regulatory circuit in single breast epithelial cells and basal-like premalignancies
    Chun Chao Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville Virginia 22908, USA
    Nat Cell Biol 16:345-56. 2014
    ..Intratumour heterogeneity need not stem from partial differentiation and could instead reflect dynamic toggling of cells between expression states that are not cell autonomous. ..
  8. pmc Normal morphogenesis of epithelial tissues and progression of epithelial tumors
    Chun Chao Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
    Wiley Interdiscip Rev Syst Biol Med 4:51-78. 2012
    ..Predictive understanding of morphogenesis at the systems level would prove especially valuable for diseases such as cancer, where epithelial tissue architecture is profoundly disrupted...
  9. pmc Simultaneous profiling of 194 distinct receptor transcripts in human cells
    Byong H Kang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
    Sci Signal 6:rs13. 2013
    ..Our array provides a rapid, inexpensive, and convenient means for assigning a receptor signature to any human cell or tissue type. ..
  10. pmc Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells
    Lixin Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
    Nat Protoc 8:282-301. 2013
    ..The protocol is readily optimized for specific biological applications and takes about 1 week to complete...
  11. pmc Modeling the latent dimensions of multivariate signaling datasets
    Karin J Jensen
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
    Phys Biol 9:045004. 2012
    ..Both approaches have been used widely in studies of cell signaling, and they should be standard analytical tools once highly multivariate datasets become straightforward to accumulate...
  12. pmc Network Architecture Predisposes an Enzyme to Either Pharmacologic or Genetic Targeting
    Karin J Jensen
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA Sanofi Oncology, Cambridge, MA 02139, USA
    Cell Syst 2:112-121. 2016
    ..Our findings provide a rationale for selecting pharmacologic versus genetic perturbations in vivo and point out the dangers of using knockdown approaches in search of drug targets...
  13. pmc TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors
    Zeinab Chitforoushzadeh
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA
    Sci Signal 9:ra59. 2016
    ..Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli. ..
  14. pmc Computational Models of Reactive Oxygen Species as Metabolic Byproducts and Signal-Transduction Modulators
    Elizabeth J Pereira
    Department of Biomedical Engineering, University of Virginia, Charlottesville VA, USA
    Front Pharmacol 7:457. 2016
    ..There remains a need for systems-level analyses that jointly incorporate ROS production, handling, and modulation of multiple signal-transduction cascades...
  15. pmc Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles
    Sameer S Bajikar
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908
    Proc Natl Acad Sci U S A 111:E626-35. 2014
    ..Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions. ..
  16. pmc Models of signalling networks - what cell biologists can gain from them and give to them
    Kevin A Janes
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
    J Cell Sci 126:1913-21. 2013
    ..Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally...
  17. pmc A high-throughput assay for phosphoprotein-specific phosphatase activity in cellular extracts
    Anjun K Bose
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
    Mol Cell Proteomics 12:797-806. 2013
    ..Our assay platform should be beneficial for phosphoproteomic surveys and computational-systems models of signaling, where phosphatases are known to be important but their activities are rarely measured...
  18. pmc Intersection of FOXO- and RUNX1-mediated gene expression programs in single breast epithelial cells during morphogenesis and tumor progression
    Lixin Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
    Proc Natl Acad Sci U S A 108:E803-12. 2011
    ..The coordinate function of these two tumor suppressors may provide a failsafe mechanism that inhibits cancer progression...
  19. ncbi request reprint A biological approach to computational models of proteomic networks
    Kevin A Janes
    Biological Engineering Division and Cell Decision Processes Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
    Curr Opin Chem Biol 10:73-80. 2006
    ..No single model can achieve all these goals, however, which is why it is critical to prioritize biological questions before specifying a particular modeling approach...
  20. pmc When microarrays Met epidermal-cell migration
    Kevin A Janes
    Department of Cell Biology, Harvard Medical School, Boston, MA, USA
    Mol Syst Biol 4:200. 2008