Nathaniel Linden

Ph.D. Student

I am currently a Ph.D. student in the Department of Mechanical and Aerospace Engineering and the Interfaces Graduate Training Programming at University of California San Diego, where I work in the labs of Boris Kramer and Padmini Rangamani. My research focuses on developing data-driven modeling and uncertainty quantification methods for systems biology. Specifically, I am interested in improving how we use experimental data to calibrate and inform mathematical models of intracellular signaling systems. To learn more about my current research, please visit the Projects page.

In 2020, I received my BS of Bioengineering with a minor in Applied Mathematics from the University of Washington in Seattle. At UW, I worked with Bing Brunton to develop a novel method to visualize spatiotemporal structures in widefield optical recordings of neural calcium activity.


Jun 22 2023 I am excited to learn more about optimal transport and normalizing flows at the Uncertainty Quantification Summer School in August, 2023!
Jun 22 2023 Looking forward to presenting my work on model averaging to account for model uncetainty when making predictions from blood glucose and insulin measurements in Diabetes at the Society for Mathematical Biology Annual Meeting in July 2023.
Jun 19 2023 Our paper on uncertainty quantification for progression damage simulation of composite materials was accepted in Composite Structures (link). This work part of an international collaboration, including Jonnahes Reiner at Deakin University, Navid Zobeiry at the University of Washington, Reza Vaziri at the University of British Columbia, and my advisor Boris Kramer at UCSD.
Jun 16 2023 I presented my current work on applications of Bayesian parameter estimation to study intracellular AMP-activated protein kinase signaling at the National Institutes of Health, National Institute of Bioimaging and Bioengineering (NIH NIBIB) Training Grantees Meeting in Bethesda, MD.
Jun 10 2023 I completed my first year of volunteer tutoring at the Preuss School UCSD. Throughout the school year, I had to the opportunity to support students in a 7th grade science classroom! I am looking forward to continuing this work next school year.
Mar 28 2023 I gave a talk on my research on multimodel modeling for clinical applications at the SOCAMS 2023 at UC Irvine in April.
Jan 15 2023 I presented a poster on my work on Bayesian inference for systems biology at the Biophysical Society 2023 Annual Meeting.
Oct 25 2022 My paper titled “Bayesian Parameter Estimation for Dynamical Models in Systems Biology” was published is PLoS Computational Biology and is available here.
Sep 30 2022 I co-organized a minisymposium on Parameter Inference and Uncertainty Quantification for Systems Biology and Medicine at the SIAM Conference on Mathematics of Data Science in San Diego, CA. Additionally, I presented my work on Bayesian parameter estimation and uncertainty quantification for systems biology.
May 21 2022 I presented my work on Bayesian parameter estimation in systems biology at the Southern California Applied Mathematics Symposium 2022 at Harvey Mudd College in Claremont, CA in May, 2022.

Selected Publications

  1. Compos Struct
    Bayesian parameter estimation for the inclusion of uncertainty in progressive damage simulation of composites
    Reiner, Johannes, Linden, Nathaniel, Vaziri, Reza, Zobeiry, Navid, and Kramer, Boris
    Composite Structures 2023
  2. SIAM News
    Identifiability and Sensitivity Analysis for Bayesian Parameter Estimation in Systems Biology
    Linden, Nathaniel, Rangamani, Padmini, and Kramer, Boris
    SIAM News 2023
  3. PLoS Comput. Biol
    Bayesian parameter estimation for dynamical models in systems biology
    Linden, Nathaniel, Kramer, Boris, and Rangamani, Padmini
    PLOS Computational Biology 2022