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Doctoral Thesis Defense: Kasra Zandi

February 26, 2018

Speaker: Kasra Zandi

Supervisors: Drs. G. Butler, N. Kharma 

Examining Committee: Drs. P. Joyce, A. Krzyzak, F. Major, S.P. Mudur, L. Kardem (Chair)

Title: Computational Design and Experimental Validation of Functional Ribonucleic Acid Nanostructures

Date: Monday, February 16, 2018

Time: 13:00

Place: EV 3.309

ABSTRACT

In living cells, two major classes of ribonucleic acid (RNA) molecules can be found. The first class called the messenger RNA (mRNA), contains the genetic information required for synthesis of proteins. The second class called the non-coding RNA (ncRNA), does not code for proteins and is involved with various key cellular processes such as gene expression regulation, differentiation and development. NcRNAs fold into an ensemble of thermodynamically stable secondary structures, which will eventually lead the molecule to fold into specific 3D structures. It is widely accepted that RNA structure and molecular composition are highly correlated to its function.

The RNA design problem is to find artificial RNA sequences that are predicted to fold into target structure(s) while satisfying specific qualitative characteristics and design constraints. RNA design can be modelled as a combinatorial optimization problem and is shown to be NP-hard. Notably, most existing algorithms to solve the RNA design problem ignore an important structural motif named “pseudoknot” and therefore limit their application. On the other hand, the few existing pseudoknot designer methods, do not provide biological evidence to support the applicability of their proposed approach in designing functional RNAs. The objectives of this thesis are set to address these two shortcomings.

We present four contributions. First, we propose a novel combinatorial optimization algorithm to efficiently solve the RNA secondary structure design problem where pseudoknots are included. Second, we use our algorithmic development to implement a constrained RNA design pipeline called Enzymer. Enzymer uses evolutionary signals found in RNA homology libraries to generate initial design templates for further constrained optimization. Third we use Enzymer to reengineer three different species of pseudoknotted RNA enzymes called ribozymes. We designed a total of 18 ribozyme sequences and showed that 17 of them were active in-vitro. Finally, we propose a novel architecture for a gene regulatory network where a hammerhead ribozyme modulates the expression of a reporter gene when external stimuli is added. Our in-vivo results show the expected functional behavior in 7 out of 12 cases. These theses open door to efficient design and validation of artificial RNA sequences with complex structural features and new functionalities that have applications in synthetic biology, therapeutics and nanotechnology.




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