1. A Blueprint for a Synthetic Genetic Feedback Controller to Reprogram Cell Fate 
Domitilla Del Vecchio, Hussein Abdallah, Yili Qian, James J. Collins 
Cell Systems 
Volume 4, Issue 1, Pages e11 (January 2017)
DOI: /j.cels
Copyright © 2017 The Authors Terms and Conditions

2. Cell Systems 2017 4, 109-120.e11DOI: (10.1016/j.cels.2016.12.001)
Copyright © 2017 The Authors Terms and Conditions

3. Figure 1 Reprogramming a Multistable Network
(A) Basic idea of reprogramming a system Σu to a target state S0. Colored regions represent different regions of attraction for the states shown, S0′ represents the unique stable steady state following perturbation, and green trace represents the system’s trajectory.
(B) Generic cooperative network. The arrowheads on edges represent positive activation and circles represent indeterminate regulation. Only three nodes shown, but an arbitrary number can be present.
Cell Systems 2017 4, e11DOI: ( /j.cels )
Copyright © 2017 The Authors Terms and Conditions

4. Figure 2 Reprogramming a Cooperative Network
(A) Two-node cooperative network example.
(B) Nullclines (dx1/dt = dx2/dt = 0) and vector field for the two-node network in (A). Increasing u1 or u2 changes the shapes of the dx1/dt = 0 or dx2/dt = 0 nullclines, respectively, such that the intersection in the red region disappears before the intersection in the blue region. Therefore, reprogramming to S0 is not possible. Parameters are given in “Type 1 Interactions & Reprogramming Properties” in the STAR Methods.
(C) A ball rolling in a valley’s landscape under the force of gravity with increasing perturbation.
(D) Type 1 (positive) and type 2 (undetermined or negative) regulatory interactions act as “perturbations” to an n-node cooperative network.
Cell Systems 2017 4, e11DOI: ( /j.cels )
Copyright © 2017 The Authors Terms and Conditions

5. Figure 3
Reprogramming Gene Regulatory Network via Feedback Overexpression
(A) Two-node cooperative network with feedback overexpression of TFs.
(B) High-gain feedback makes the network monostable at the target state x∗, located in the pink region of attraction of target state S0.
(C) Pictorial representation of the effect of high-gain negative feedback input on a valley landscape (compare to Figure 1E).
(D) Synthetic genetic controller circuit that implements feedback overexpression of TF xi. Species xie, mie, xis, mis represent endogenous TF and mRNA, synthetic TF, and mRNA, respectively. Ii,1 and Ii,2 are inducers, and si is siRNA targeting mie and mis.
(E) Time traces of total TF concentrations x1 and x2 (corresponding to the network of A), where each TF is controlled by a copy of the circuit in (D).
(F) Trajectories in (x1,x2) plane corresponding to time traces of (E) and nullclines of network in (A). Parameters equivalent to those of Figure 1 (listed in Table S1), for which it is not possible to transition to S0 with preset overexpression.
Cell Systems 2017 4, e11DOI: ( /j.cels )
Copyright © 2017 The Authors Terms and Conditions

6. Figure B1
Reprogramming a Network Motif of the Pluripotency Gene Regulatory Network
(A) Two-node network motif with Oct4 and Nanog. Sox2 is lumped with Oct4 because these two TFs often act as a heterodimer (Tapia et al., 2015).
(B) Representative steady-state landscape with three stable steady states: trophectoderm (TR), pluripotent (PL), and primitive endoderm (PE).
(C) Bifurcation diagrams show number, location, and stability of the steady states as u1 or u2 increase.
(D) Time traces (10 realizations) of Nanog and Oct4 concentrations while the controller circuit is active (left of arrow) and after shut down (right of arrow). Simulations using the chemical Langevin equation (see “Stochastic Model” in the STAR Methods). Parameters for which preset overexpression fails.
Cell Systems 2017 4, e11DOI: ( /j.cels )
Copyright © 2017 The Authors Terms and Conditions