As a scientist at Takeda, I build models to support translation from preclinical studies into the clinic. I’ve supported both neuroscience and rare disease programs in a variety of modalities, including gene therapy, enzyme replacement, oligonucleotides, and small molecules. I work on a range of models depending on the program’s need and available data, spanning from empirical exposure-response models of preclinical animal data to QSP models integrated within nonlinear-mixed effects frameworks for clinical application.

During my postdoc, I worked on new classes of antiviral therapies. I built multiscale models to predict and understand efficacy of a ‘therapeutic interfering particle’ against SARS-CoV-2 based on clinical and laboratory data (Chaturvedi, Vasen, Pablo, et al. Cell. 2021). I also used a combination of computational and experimental methods to study ‘feedback disruptors’ of human cytomegalovirus, which create an antiviral effect by interfering with viral negative feedbacks (Chaturvedi, Pablo, et al. Cell. 2022).

During my PhD, I studied how biological signaling networks are spatially and temporally organized at single-molecule resolution. A major part of my thesis focused on how yeast harness and filter molecular “noise” during polarity establishment, in which a patch of activated Cdc42 is created. Based on the biochemistry of Cdc42 and its regulators, I built partial differential equation and agent-based models, and identified fundamental principles underlying polarization (Pablo M et al. PLoS Comp Biol 2018; Ramirez SA, Pablo M, et al. PLoS Comp Biol 2021). I also built mechanistic agent-based models that explained experimental data (Henderston NT, Pablo M, et al. PLoS Biol 2019; Clark-Cotton MR, Henderson NT, Pablo M, et al. Mol Biol Cell 2021).

In addition to my work on yeast, I also built tools to analyze single-molecule tracking data. My colleagues developed an experimental approach to track protein conformational changes in living cells at single molecule resolution. I developed a computational pipeline to analyze millions of these trajectories across time and space, producing maps of protein activity within molecular clusters and an understanding of their dynamic regulation (Liu*, Stone*, Pablo*, et al. Cell. 2021, *Equal Contribution).