Design and Engineering of Synthetic Gene Networks

Synthetic gene networks have evolved from simple proof-of-concept circuits to

complex therapy-oriented networks over the past fifteen years. This advancement has

greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a

mechanism that cells use to achieve a discrete number of mutually exclusive states in

response to environmental inputs. However, complex contextual connections of gene

regulatory networks in natural settings often impede the experimental establishment of

the function and dynamics of each specific gene network.

In this work, diverse synthetic gene networks are rationally designed and

constructed using well-characterized biological components to approach the cell fate

determination and state transition dynamics in multistable systems. Results show that

unimodality and bimodality and trimodality can be achieved through manipulation of the

signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to

communicate with each other.

Moreover, a synthetic quadrastable circuit is also built and experimentally

demonstrated to have four stable steady states. Experiments, guided by mathematical

modeling predictions, reveal that sequential inductions generate distinct cell fates by

changing the landscape in sequence and hence navigating cells to different final states.

Circuit function depends on the specific protein expression levels in the circuit.

We then establish a protein expression predictor taking into account adjacent

transcriptional regions’ features through construction of ~120 synthetic gene circuits

(operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks.

These combined results illustrate applications of synthetic gene networks to

understand the cell fate determination and state transition dynamics in multistable

systems. A protein-expression predictor is also developed to evaluate and tune circuit

dynamics.