Hi! I am a biophysicist, currently working as a Computational Biologist within the AI and Data Intelligence team at Pivotal Life Sciences, where I build predictive biological models to enable intelligent and data-driven investments for early-stage life science companies. I also serve on the Editorial Board of Frontiers in Molecular Biosciences as a Review Editor.

I have 10 years of diverse research experience, spanning theoretical chemical physics, computational chemistry, and experimental molecular biology. I have an acute interest in developing physics and machine learning-based computational models and validating them directly in experiments to solve fundamental biomedical challenges. I am passionate about exploring the frontiers of knowledge while creating new business opportunities and discovering novel therapies/medicines for a better future.

Before Pivotal, I worked as a Multiscale Modeling Researcher at Genentech, where I used tools from Machine Learning, Molecular Dynamics Simulations, and Non-equilibrium Statistical Mechanics to model the kinetics of immune signaling pathways. These pathways are essential to defend against invading pathogens, and their dysregulation can lead to several autoimmune and neurodegenerative diseases. Additionally, I developed computational approaches to predict hydrodynamic properties of portfolio-relevant drug candidates in physiologically relevant environments.

During my postdoc, I worked with Carlos J Bustamante to investigate fundamental questions in the field of protein folding using optical tweezers-based single molecule experiments (in collaboration with Robert Sosa, Jeonghoon Kim, Alex Tong, and Ben Kuznets-Speck). In addition, in collaboration with Alan Shaw and Susan Marqusee, I developed a simple data-driven model to interpret power spectra of DNA Nanotube tethers in optical tweezers.

I received my PhD from the University of Texas at Austin (Institute of Cell and Molecular Biology) in 2020, advised by Dmitrii E Makarov. In my research, I developed theoretical and computational tools to investigate structural transitions, specifically the origin of broad distributions of transition path times, as observed in single molecule experiments and Molecular Dynamics (MD) simulations of protein folding (in collaboration with Atanu Das, Eduardo Medina, Sasha Berezhkovskii, and the groups of J Enderlein and Ben Schuler).

I completed my Bachelor's and Master's degrees from the Indian Institute of Technology Delhi (Department of Biochemical Engineering and Biotechnology) in 2015, where I worked in the group of James Gomes to study the role of RNASE4 polymorphisms in Amyotrophic Lateral Sclerosis (ALS). As an undergrad, I participated in the International Genetically Engineered Machines (IGEM) competition and received the Summer Undergraduate Research Fellowship (SURF) award, both of which were influential in propelling me towards scientific research.

Main Papers

• Satija, R., Berezhkovskii, A. M., & Makarov, D. E. (2020). Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes. Proceedings of the National Academy of Sciences, 117(44), 27116-27123.

• Satija, R., & Makarov, D. E. (2019). Generalized Langevin equation as a model for barrier crossing dynamics in biomolecular folding. The Journal of Physical Chemistry B, 123(4), 802-810.

• Satija, R., Das, A., & Makarov, D. E. (2017). Transition path times reveal memory effects and anomalous diffusion in the dynamics of protein folding. The Journal of Chemical Physics, 147(15), 152707.

Additional Papers

• Hibbard, J. V., Vazquez, N., Satija, R., & Wallingford, J. B. (2021). Protein turnover dynamics suggest a diffusion-to-capture mechanism for peri-basal body recruitment and retention of intraflagellar transport proteins. Molecular Biology of the Cell, 32(12), 1171-1180.

• Satija, R., Das, A., Mühle, S., Enderlein, J., & Makarov, D. E. (2020). Kinetics of loop closure in disordered proteins: Theory vs simulations vs experiments. The Journal of Physical Chemistry B, 124(17), 3482-3493.

• Zijlstra, N., Nettels, D., Satija, R., Makarov, D. E., & Schuler, B. (2020). Transition path dynamics of a dielectric particle in a bistable optical trap. Physical Review Letters, 125(14), 146001.

• Padhi, A. K., Narain, P., Dave, U., Satija, R., Patir, A., & Gomes, J. (2019). Insights into the role of ribonuclease 4 polymorphisms in amyotrophic lateral sclerosis. Journal of Biomolecular Structure and Dynamics, 37(1), 116-130.

• Medina, E., Satija, R., & Makarov, D. E. (2018). Transition path times in non-Markovian activated rate processes. The Journal of Physical Chemistry B, 122(49), 11400-11413.

• Avdoshenko, S. M., Das, A., Satija, R., Papoian, G. A., & Makarov, D. E. (2017). Theoretical and computational validation of the Kuhn barrier friction mechanism in unfolded proteins. Scientific Reports, 7(1), 1-14.

• Sen, S., Apurva, D., Satija, R., Siegal, D., & Murray, R. M. (2017). Design of a Toolbox of RNA Thermometers. ACS Synthetic Biology, 6(8), 1461-1470.