Bio

Kyle obtained his BS in Biochemistry from the University of Maryland College Park in 2010, conducting undergraduate research with Dr. Dorothy Beckett, PhD. He obtained his PhD in Biochemistry with a certificate in Structural Biology and Biophysics. His dissertation is titled "Kinetics of Coupled Binding and Conformational Change in Proteins and RNA" and was completed in the laboratory of Dr. Terrence G. Oas, PhD. Kyle performed postdoctoral training with Dr. Wendell A. Lim, PhD at UCSF studying how CAR T cell phenotype is encoded by modular signaling motifs within chimeric antigen receptors.

Kyle's lab is interested in harnessing the principles of modularity to engineer receptors and gene circuits to control cell functions.

The lab will use synthetic biology, medium- and high-throughput screens, and machine learning to: (1) Engineer immune cells to achieve robust and durable responses against various cancer targets, (2) Coordinate behavior of multiple engineered cell types in cancer, autoimmune disease, and payload delivery, (3) Control survival, proliferation, and differentiation of hematopoietic stem cells (HSCs) and immune cells, and (4) Explore principles of modularity related to engineering receptors and gene circuits in mammalian cells.

Academic Appointments

Contact

  • Academic
    kyledan@stanford.edu
    University - Faculty Department: Genetics Position: Assistant Professor

Additional Info

Links

2024-25 Courses

Stanford Advisees

All Publications

  • Harnessing the power of artificial intelligence to advance cell therapy. Immunological reviews Capponi, S., Daniels, K. G. 2023

    Abstract

    Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.

    View details for DOI 10.1111/imr.13236

    View details for PubMedID 37415280

  • Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning. Science (New York, N.Y.) Daniels, K. G., Wang, S., Simic, M. S., Bhargava, H. K., Capponi, S., Tonai, Y., Yu, W., Bianco, S., Lim, W. A. 2022; 378 (6625): 1194-1200

    Abstract

    Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations of 13 signaling motifs. These CARs promoted diverse human T cell fates, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, non-native combinations of motifs that bind tumor necrosis factor receptor-associated factors (TRAFs) and phospholipase C gamma 1 (PLCĪ³1) enhanced cytotoxicity and stemness associated with effective tumor killing. Thus, libraries built from minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes.

    View details for DOI 10.1126/science.abq0225

    View details for PubMedID 36480602

    View details for PubMedCentralID PMC10026561

https://orcid.org/0000-0001-9992-1210