(4ad) The Specificity and Kinetics of RNA-RNA Interactions

Summary: My research investigates how RNA molecules control their interaction partners, with implications for cellular engineering, viral diagnostics, bio-material design, and RNA-based therapeutics.

Motivation: RNA-RNA interactions are integral to diverse and essential biological processes in cells and in the lab. Mutations affecting these interactions have been linked to devastating human diseases, including neurodegenerative disorders and cancers. Despite their centrality to modern biology, the physical underpinnings of RNA-RNA interactions remain poorly understood. In principle, these interactions are straightforward: if two RNA molecules are complementary, they will hybridize. In practice however, quantitative models predicting interactions in natural settings remain elusive.

Gap: There are two main challenges to predicting RNA-RNA interactions: (a) intramolecular secondary structure creates equilibrium and kinetic barriers to intermolecular interactions; (b) RNA-RNA interactions are affected by the presence of other RNA in solution. This first challenge has been explored mostly in its simplest form (strand-displacement reactions); the second remains largely unstudied. Consequently, quantitative models of RNA-RNA interactions typically fail in natural settings.

Proposal: My research program will use transdisciplinary theory-based methods to study RNA-RNA interactions, bolstered by experimental collaborations. These methods include statistical mechanics, dynamical systems theory, information theory, machine learning, and RNA structure prediction. Ultimately, we will learn how biology leverages its inherent out-of-equilibrium aspects to realize specific RNA-RNA interactions, and to what extent we can implement similar processes in laboratory settings.

Impact: Understanding RNA-RNA interactions will have substantive implications for human health and technology development, including: (a) improvements in timescale, cost, and accuracy in viral diagnostics, e.g. with CRISPR-Cas13 [1]; (b) combating devastating human diseases that arise from dysregulated RNA-RNA interactions; (c) cellular therapeutics based on introducing RNA molecules to modify RNA-RNA interactions in vivo directly, enabling precise and specific changes to cellular behavior.

Research Interests:

My research involves analyzing large datasets obtained from complex biological systems and building minimal models to understand these systems from the ground up, with these approaches building off each other. My group will leverage this combination to bridge the gap between what biology can achieve and what we can design.

Focus 1: The specificity of RNA-RNA binding.

(a) Specificity in equilibrium. The challenge of RNA-RNA recognition specificity is ubiquitous: random 1kb RNAs have 14-nt stretches of perfect complementarity with half of all other 1kb RNAs. How does RNA selectively bind to its preferred interaction partners? We will use information theory and RNA structure prediction to test whether intramolecular secondary structure can be the primary means by which RNA controls its interactions in equilibrium.

(b) Specificity through kinetics. Leveraging a large experimental dataset of RNA-RNA interactions (~10⁴ sequence pairs) generated during my Ph.D. [2], we will explore the kinetic effects of secondary structure. We will build quantitative models for the kinetic barrier created by intramolecular structure through automatic differentiation and other data analysis methods, to understand how kinetics impact specificity.

Focus 2: The speed of RNA-RNA binding.

(a): The CRISPR-Cas13 search process. When there are many competing RNA molecules in solution (such as in the cell) how can an RNA quickly find its binding partner? I hypothesize that RNA-binding proteins enable qualitative speedups in this RNA search process. As a model protein, we will use the RNA-guided RNA-targeting CRISPR nuclease Cas13, whose trans cleavage activity enables kinetic readouts of RNA-RNA binding, and which has emerged as a powerful tool for viral diagnostics and transcriptome engineering. By measuring the scaling properties of Cas13 activation kinetics as a function of various conditions (e.g. length of target/non-target RNA) and comparing these to analytical theory, we will elucidate the mechanism behind Cas13’s search, and address the long-standing question of how sequence-specific RNA-binding enzymes search for their targets.

(b): Speed/specificity tradeoff. In previous work, I designed RNA secondary structure to improve Cas13 hybridization sensitivity at the cost of lowering hybridization rates to preferred targets [1]. Are natural biological systems limited by this same tradeoff? By tackling the forward and inverse problems simultaneously, my group will develop a model to describe the sensitivity/activity tradeoff and place practical bounds on our design capabilities.

Focus 3: RNA-based condensates.

(a): Homotypic RNA condensates. Homotypic RNA clusters form during Drosophila embryogenesis and in C. elegans and appear to play an important role in these organisms’ development. How do RNAs distinguish self from non-self to achieve homotypic clustering? Building on preliminary work in collaboration with Liz Gavis (Princeton) we will address how natural RNA molecules control their homotypic clustering properties.

(b): Designing synthetic RNA phases. What are the limits on the number of distinct phases RNA can form as a function of the number of RNA species and their lengths? What properties of RNA sequences enable maximizing this number? Through complete landscape enumeration (for short sequences) and heuristic optimization (longer sequences) we will uncover design rules for RNA condensates, with implications for synthetic design and real biological systems.

Research Experience

Lewis-Sigler Scholar, Princeton University (2021—current). As an independent postdoctoral fellow, I have demonstrated an ability to design and implement my own research program—including carrying out theory-focused projects and recruiting experimental collaborators—while remaining open to possibilities and collaborations that arise organically. Specific projects include: (a) determining that repeat-expanded RNA-based condensates implicated in human neurological disorders are driven by entropic considerations rather than energetics, and predicting a sequence-dependent reentrant phase transition [3]; (b) in collaboration with Cameron Myhrvold (Princeton), modeling how intramolecular RNA structure affects Cas13 activity and using our findings to improve Cas13’s specificity by 1-2 orders of magnitude [1]; (c) in collaboration with Liz Gavis (Princeton), investigating how homotypic condensates form in Drosophila embryogenesis.

Intern, Google Research (2019). I studied how machine learning and neural networks can be applied to scientific research, analyzing large time-series datasets of patient continuous glucose monitoring (CGM) data to find what information is encoded in CGMs, and conducting large-scale dataset analysis on text documents to quantify the length of correlations in the English language.

Ph.D. Biophysics, Harvard University (2016 – 2021). With Michael Brenner, I explored protein self-assembly and RNA structure in and out of equilibrium. Specific projects include: (a) developing a statistical mechanics framework for self-assembly of heterogeneous, non-spherical proteins [4]; (b) using protein self-assembly to build a post-translational oscillator [6]; (c) developing a Feynman diagram-like graphical formalism to predict RNA pseudoknots using only two parameters [7] and a new RNA structure prediction algorithm using this formalism [5]; (d) leveraging the out-of-equilibrium nature of RNA hybridization to measure the structures of large RNA molecules [2].

Teaching Interests

Philosophy: My goal as a teacher is to empower students to address challenges outside of the classroom. To do this, I establish concrete quantitative and analytical skills, then build on that foundation to enable students to see shared patterns in problems that (at first) appear entirely dissimilar.

Teaching Interests: Given the transdisciplinarity of my research and education, I am well-equipped to teach a variety of courses, both quantitative and reading/writing focused. I would enjoy developing a course focused on probability and statistical mechanics in biology.

Experience. I have been a TA for four distinct courses, including three undergraduate and one graduate course. I received teaching awards from Harvard for the two courses in which I was eligible. I also developed my own teaching plan for several courses, including two on reading/writing scientific papers.

Selected Awards/Fellowships

Branco Weiss Fellow, 2024 – 2029

• $664,000 over 5 years
• Awarded to up to 10 postdocs worldwide across natural sciences, social sciences, engineering, and humanities.
• Supports transition from postdoc to faculty.

Lewis-Sigler Scholar, Princeton University, 2021 – current

• Independent postdoctoral fellowship.
• ~$525,000 over 5 years.

Quantitative Biology Ph.D. Fellowship, Harvard University, 2019 – 2021

National Defense Science and Engineering Graduate Fellowship, 2016 – 2019

Kusaka Memorial Prize in Physics; Applied & Computational Mathematics Independent project Prize; Quantitative & Computational Biology Award, Princeton University, 2016

Selected Publications:

1. Kimchi^*, B. B. Larsen^*, O. R. S. Dunkley, A. J. W. te Velthuis, C. A. Myhrvold. RNA structure modulates Cas13 activity and enables mismatch detection. bioRxiv 560533 (2023).
2. Chiang, O. Kimchi, H. K. Dhaliwal, D. A. Villarreal, F. F. Vasquez, M. P. Brenner, V. Manoharan, R. Garmann. Measuring intramolecular connectivity in long RNA molecules using two-dimensional DNA patch-probe arrays. bioRxiv 532302 (2023).
3. Kimchi^†, E. M. King, M. P. Brenner. Uncovering the mechanism for aggregation in repeat expanded RNA reveals a reentrant transition. Nature Communications 14 (2023).
4. I. Curatolo, O. Kimchi, C. P. Goodrich, R. K. Krueger, M. P. Brenner. A computational toolbox for the assembly yield of complex, heterogeneous structures. Nature Communications 14 (2023).
5. Kimchi^†, M. P. Brenner, L. J. Colwell. RNA structure prediction including pseudoknots through direct enumeration of states: A user’s guide to the LandscapeFold algorithm. RNA structure prediction, Methods in Mol. Biol. Springer (2022).
6. Kimchi^†, C. P. Goodrich, A. Courbet, A. I. Curatolo, N. B. Woodall, D. Baker, M. P. Brenner. Self-assembly based post-translational protein oscillators. Science Advances 6(51) (2020).
7. Kimchi^†, T. Cragnolini, M. P. Brenner, L. J. Colwell^†. A polymer physics framework for the entropy of arbitrary pseudoknots. Biophysical Journal 117(3):520-532 (2019).

^*Co-first authors ^†Corresponding author