I enjoy thinking about how mathematical models and physical laws can describe (and predict!) cellular behavior. I'm fascinated by developmental plasticity and self-organization in biological systems.
I did my undergrad at UCSD studying molecular biology and math. While there, I was a tech in Dr. Wolfgang Busch's plant genetics group at the Salk Institute, where I studied root-environment interactions in Arabidopsis thaliana using quantitative and computational methods. Before that, I worked in Dr. Patrick Hsu's lab, where I helped develop CRISPR-Cas13d for programmable RNA editing. See my CV for more details, or read about some of my projects below:
I built a website showcasing my team's analysis of the dynamics of microtubule growth, based on Griffin Chure's template for reproducible research. Class project for BE/Bi 103a.
Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting Jingyi Wei, Peter Lotfy, Kian Faizi, Eleanor Wang, Hannah Slabodkin, Emily Kinnaman, Sita Chandrasekaran, Hugo Kitano, Matthew G. Durrant, Connor V. Duffy, Patrick D. Hsu, Silvana Konermann bioRxiv. (2021)
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Transcriptome engineering technologies that can effectively and precisely perturb mammalian RNAs are needed to accelerate biological discovery and RNA therapeutics. However, the broad utility of programmable CRISPR-Cas13 ribonucleases has been hampered by an incomplete understanding of the design rules governing guide RNA activity as well as cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we sought to characterize and develop Cas13d systems for efficient and specific RNA knockdown with low cellular toxicity in human cells. We first quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs in the largest-scale screen to date and systematically evaluated three linear, two ensemble, and two deep learning models to build a guide efficiency prediction algorithm validated across multiple human cell types in orthogonal secondary screens (http://RNAtargeting.org). Deep learning model interpretation revealed specific sequence motifs at spacer position 15-24 along with favored secondary features for highly efficient guides. We next identified 46 novel Cas13d orthologs through metagenomic mining for activity screening, discovering that the metagenome-derived DjCas13d ortholog achieves low cellular toxicity and high transcriptome-wide specificity when deployed against high abundance transcripts or in sensitive cell types, including hESCs. Finally, our Cas13d guide efficiency model successfully generalized to DjCas13d, highlighting the utility of a comprehensive approach combining machine learning with ortholog discovery to advance RNA targeting in human cells.
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[preprint]
I helped conduct a 127,000-guide CRISPR-Cas13d screen to identify guide RNA design rules and optimize transcriptome editing efficiency.
Branch-Pipe: Improving graph skeletonization around branch points in 3D point clouds Illia Ziamtsov, Kian Faizi, Saket Navlakha Remote Sensing (open access). (2021)
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Modern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. In this manuscript, we studied skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles, even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes.
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[paper] /
[thread]
I assisted in the development of a software package for plant phenotyping, with a focus on accurate skeleton graph extraction from noisy 3D point clouds.
I developed Ariadne, a time-series image segmentation tool for root phenotyping, and used it to measure Pareto-optimal trade-offs in root growth.
Predicting Functional Homologs from Single-Cell Co-expression Networks
[code]
I built a basic workflow for predicting functional homologs of genes driving plant root growth, via co-expression network analysis of single-cell RNA-seq data.
A Boolean Network Model of the Bacterial lac Operon
[code]
I programmed a toy model of the lac operon as an asynchronous Boolean network, and simulated gene knockout and overexpression experiments. Class project for MATH 111A.
I wrote a Python pipeline to mine new orthologs of CRISPR-Cas13d from publicly available metagenomic sequence data at terabyte scale.
Teaching
Helping people learn is one of my favorite things in the world. In the future, I'd like to compile a list of tools and resources that have helped me get better at doing it. For now, here's a list of classes I've helped teach, as well as any formal feedback I've received:
Note: Caltech doesn't provide a nice way to collate instructor reviews, so I've just copied over any comments I received in plaintext.
