The goal of this research is to develop design principles for enhancing functional robustness of engineered cells under mutation pressure by using genetic homology and regulatory circuit topology. For this goal, we will apply mathematical, computational, and experimental approaches by incorporating various aspects of genetic circuits such as RNA-based circuits, functional redundancy, feedback regulation, and genetic homology. This study will help researchers analyze and predict the degree of robustness of synthetic and natural genetic circuits, and identify and replace vulnerable genetic components and network structures.
We will perform two layers of optimization: local (DNA sequence) and global (genetic regulation).
Local optimization: We investigate how functional robustness is related to genetic components and media conditions. We will also investigate whether RNA-aptamer based fluorescent probes have a growth advantage compared to fluorescent protein based probes by reducing metabolic loads. Malachite-green aptamers that we have developed and tested in E. coli will be used along with other RNA aptamers such as Mango and Spinach.
Global optimization: We will improve functional robustness at the circuit level by optimizing regulation patterns and by identifying genetic components that are vulnerable under mutation. Functional robustness will be investigated at the circuit level by considering network topology. Our preliminary studies compared two different type of regulation – inhibition and activation – and found that activation is much less robust than inhibition. Based on this result, we will propose design principles for enhanced robustness, and numerical methods to identify genes that are vulnerable under mutations. The design principles to reduce/prevent the vulnerability will be proposed. Computational models to predict metabolic loads on cells will be developed.
Pending proposal to NSF currently.
RNAs serve many important roles in biology as transcripts and regulators. They typically show short lifetimes and low copy numbers compared to proteins. Thus, to characterize the roles of RNAs, requires fluorophores that emit sufficiently bight signals. As a postdoctoral fellow, you will be developing such probes based on RNA aptamers.
Recently, two new fluorescent tagging methods for RNAs that are analogous to fluorescent protein-based approaches have been developed. They are based on RNA aptamers that have binding sites for small fluorophore molecules: 3,5-difluoro-4-hydroxybenzylidene imidazolinone (DFHBI) and thiazole orange (TO-1). These aptamers, named Spinach aptamer (SpA) and Mango aptamer (MangoA) respectively, induce green and yellow-green fluorescence signals from DFHBI and TO-1, only when they bind to their fluorophores. To expand the number of dynamic processes that can be observed, in our previous study (thesis of Wilbert Copeland), we developed an RNA aptamer probe that is spectroscopically compatible with SpA and MangoA. Our new aptamer, tested in E. coli MG1655, induces malachite green (MG) to fluoresce deep red signals only upon binding. Thus, in principle, the new malachite green aptamer (MGA) probe can be used together with probes constructed with SpA or MangoA, making it possible to directly and simultaneously observe multiple RNA-RNA and RNA-protein regulation, as well as multiple transcription and translation processes.
These three aptamer probes are, however, less bright than green fluorescent proteins, and typically much less bright when they are used to tag RNAs. Furthermore, when considering the fact that the copy number of RNA is typically low, the brightness needs to be improved. To characterize RNA regulation and other properties, it is important to monitor RNA concentration levels in live cells and even to track single RNA molecules. We aim to enhance the signal intensities by constructing RNA-aptamer scaffolds having multiple binding sites for fluorophores (MG, DFHBI, TO-1 and their variants), and as its application we aim to characterize pure RNA regulatory networks and genetic regulation at multiple time scales.
- Novel way to control cell-to-cell variability via triplet controls of (1) transcription efficiency (2) translation efficiency (3) plasmid copy number. This project aims to design noise-induced phenotypes in E. coli (Journal of Chemical Physics 2013) (in progress).
- Interaction between stochasticity and nonlinearity (Hill coefficient): Genetic regulation measured at the population level (for example, by using a plate reader) can be significantly different from the one measured at the single cell level (single cell microscopy and flow cytometry). This is because stochasticity (cell-to-cell variability) affects the population average due to the skewed distribution (of transcription factors) and its nonlinear signal processing (genetic regulation). In this project, we focus on the nonlinear signal processing and its effect on cellular phenotypes (in progress).
- Applied the engineering concept of fan-out to gene regulatory networks for quantifying the degree of modularity (J. Biol. Eng, IEEE CDC 2012).
- Proposed an experimental method for measuring fan-out and retroactivity based on gene expression noise (Biophys. J, IEEE CDC 2012 ).
- Gene circuit analysis by mapping between gene regulatory networks and analog electrical circuits (J. Biol. Eng).
- Experimental verification of fan-out and retroactivity.
- “Stochastic Control Analysis” (SCA): Developed a sensitivity analysis method for adjusting phenotypes by noise control (PLoS Computational Biology, Mathematical Biosciences).
- SCA application to noise control in E. coli (in progress).
- Analyzed noise propagation and its effect on system sensitivities in reaction networks (Journal of Chemical Physics 2013).
- Application of SCA to real biological systems such as stochastic switching in the HIV-1 long terminal repeat (LTR) promoter activity (future work).
- Extended the metabolic control analysis (MCA) to the stochastic regime (PLoS Computational Biology, Mathematical Biosciences).
- Visualization of signal propagation in biological networks.
- Metabolic flux optimization under the constraint of the total enzyme mass
Non-equilibrium statistical physics (Jarzynski equality and fluctuation theorem; Ph.D. thesis)
- Extended the Jarzynski equality and the fluctuation theorems for feed-back control systems (Physical Review E, Physical Review Letters)
- One-dimension stochastic flow (two-species asymmetric exclusion process) and its universal scaling behaviors (critical phenomena) and phase transition (Phys. Rev. E)