A RNA perspective of functional genomics
Only 2% of the human genome consists of protein-coding genes. The remaining 98% is non-coding and thought to encode the regulatory information for gene expression. Interpreting this non-coding region is thus key to understanding the functional genome and its implications for complex diseases. To tackle this, we take advantage of biological data generated from breakthroughs in chemical biology and bioengineering such as short- and long-read sequencing, oligosynthesis, chemical probing, and click chemistry.
In particular, we focus on elements of the genome that are transcribed into functional RNAs. Advances in biochemical and high-throughput techniques provide strong evidence that 74.7% of the human genome undergoes transcription, thus highlighting the importance of RNA research in functional genomics. The technology-specific computational tools built in our lab offer the means towards integrative genomics and functional interpretation. Our goal is to achieve this at single-nucleotide resolution across transcription, processing, modification, translation, decay, and other stages of the RNA life cycle.
No free lunch for emerging high-throughput technologies
It’s an exciting time to work in modern biology and bioengineering. Innovations in high-throughput techniques such as single-cell sequencing and spatial transcriptomics provide the means to extract deeper molecular insights in organismal development, immunology, and cancer biology. Existing computational models are being challenged and improved, placing us closer to unraveling the complexity of biological systems.
The algorithmic task here is to address inherent computational and statistical challenges in handling data generated from each high-throughput technology. This could be anything from applying the right data normalization to correcting batch effects for data integration. The key is to incorporate biology-specific knowledge into the design of computational tools, statistical models, and neural architectures. In light of this, our lab is committed to the development of these tailored methods, which we then use to extract quantitative principles underlying the biological data.
Combinatorial optimization in translational bioengineering
RNA therapeutics, genome editing, and artificial organoids represent just a few examples in biological engineering that are changing the way we solve human biology. However, these endeavors are often combinatorial optimization problems with near-infinite potential but intractable with brute-force algorithms. In RNA engineering, for example, there are more than 1060
possible 100-nucleotide sequences with varying degrees of functionality. To put this into perspective, the estimated number of atoms on Earth is approximately 1050
atoms only. This simple mathematical exercise indicates the limit of solely relying on high-throughput screening for sequence design and optimization.
Our approach involves building powerful search algorithms and intelligent systems to navigate this vast combinatorial space. Inspired by works in translational medicine, we leverage meaningful insights and principles from molecular biology and functional genomics for the computational optimization of bioengineering. We are interested in accelerating a wide range of applications including next-generation molecular devices for diagnosis and automatic systems for synthetic biology.
