The application of feedback control theory in synthetic biology

Doctor of Philosophy, Graduate Program in Mechanical Engineering
University of California, Riverside, May 2017
Dr. Elisa Franco, Chairperson

Synthetic  biology  promise  to  provide  solution  to  many  challenges  in  energy, agriculture,  and  health  by  reprogramming  cells  to  execute  new  tasks  in  the  host organism. In order to do that, it requires (1) the understanding of the design principles that  underlie  complex  dynamics  in  biology,  (2)  the  development  of  computational tools that support the identification those principles and (3) the use of those principles and  computational  tools  to  guide  the  experimental  implementation  of  novel biomolecular programs. The main motivation of this thesis is to describe my current progress and future plans to expand (1)-(3).

We incorporate a new design principle, known as ultrasensitivity response, to design robust  biomolecular  dynamical  system.  We  show  that  molecular  titration  in  the context  of  feedback  circuits  enhance  the  emergence  of  oscillations  and  bistable behavior  in  the  parameter  space.  We  also  propose  and  analyze  a  new  molecular network,  termed  Brink  motif,  which  exhibits  an  ultrasensitive  input-output response similar  to  a  zero-order  ultrasensitive  switch.  We  discuss  the  Brink  motif  in  the context  of  robust  feedback  circuits  as  a  suitable  mechanism  to  build  (1)  reliable circuits,  oscillatory  and  bistable  dynamical behaviors,  under  parameters uncertainty, downstream load effects and shared resources and (2) robust closed loop controllers that  overcome  the  limitation  of  unidirectional  action  controllers.  Ultrasensitivity  is achieved by combining   molecular titration and an activation/deactivation cycle and requires  fast  titration  and  switching  rates.  Additionally,  the  response  of  the  Brink motif has a precisely tunable threshold, which can be determined by an external input to the motif. We assess the robustness of feedback circuits with numerical simulations
and mathematical analysis.