Research

Gene circuit stability under mutation pressurestability
NSF funded project (MCB, NSF abstract link)
The proposed research aimed for enhancing functional stability of synthetically designed genetic systems.

The goal of this research was to develop design principles for enhancing functional stability of engineered cells under mutation pressure by using genetic homology and regulatory circuit topology. For this goal, we applied 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 performed optimization at two levels: local (DNA sequence) and global (genetic regulation).

Local optimization: We investigated how functional robustness is related to genetic components and media conditions. We also investigated 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 were used along with other RNA aptamers such as Mango and Spinach.

Global optimization: We improved functional robustness at the circuit level by optimizing regulation patterns and by identifying genetic components that are vulnerable under mutation. Functional robustness was investigated at the circuit level by considering network topology. We showed that genetic activation is much less robust than inhibition. Based on this result, we proposed design principles for enhancing functional stability, and numerical methods for identifying genes that are vulnerable under mutations. One paper is in preparation.


RNA aptamer scaffold probes for RNA regulation characterizationscaffold

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 bright signals. In this project, we developed malachite green aptamer scaffolds. These new scaffold probes 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.


Nonlinearity in gene regulatory networks (interplay between stochasticity and nonlinearity)

This project aimed to provide computational and theoretical and experimental methods for controlling gene expression noise and creating noise-induced cellular phenotypes.

We provided new experimental methods for controlling gene expression noise by changing (1) transcription efficiency (2) translation efficiency (3) plasmid copy number.

We provided intuitive and yet fully quantitative theoretical and computational methods for understanding the interaction between stochasticity and nonlinearity (Hill coefficient). We showed that 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).


 Modularity in gene regulatory networks
  • Applied the engineering concept of fan-out to gene regulatory networks for quantifying the degree of modularity (J. Biol. EngIEEE CDC 2012).
  • Proposed an experimental method for measuring fan-out and retroactivity based on gene expression noise (Biophys. JIEEE CDC 2012 ).
  • Gene circuit analysis by mapping between gene regulatory networks and analog electrical circuits (J. Biol. Eng).

Stochasticity in gene regulatory networks
  • Stochastic Control Analysis” (SCA): Developed a sensitivity analysis method for adjusting phenotypes by noise control (PLoS Computational BiologyMathematical 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).

Metabolic Pathways

  • Extended the metabolic control analysis (MCA) to the stochastic regime (PLoS Computational BiologyMathematical 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 EPhysical Review Letters)
  • One-dimension stochastic flow (two-species asymmetric exclusion process) and its universal scaling behaviors (critical phenomena) and phase transition (Phys. Rev. E)
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