Engineering of regulated stochastic cell fate determination

Abstract

Both microbes and multicellular organisms actively regulate their cell fate determination to cope with changing environments or to ensure proper development. Here, we use synthetic biology approaches to engineer bistable gene networks to demonstrate that stochastic and permanent cell fate determination can be achieved through initializing gene regulatory networks (GRNs) at the boundary between dynamic attractors. We realize this experimentally by linking a synthetic GRN to a natural output of galactose metabolism regulation in yeast. Combining mathematical modeling and flow cytometry, we show that our engineered systems are bistable and that inherent gene expression stochasticity does not induce spontaneous state transitioning at steady state. Mathematical analysis predicts that stochastic cell fate determination in this case can only be realized when gene expression fluctuation occurs on or near attractor basin boundaries (the points of instability). Guided by numerical simulations, experiments are designed and performed with quantitatively diverse gene networks to test model predictions, which are verified by both flow cytometry and singlecell microscopy. By interfacing rationally designed synthetic GRNs with background gene regulation mechanisms, this work investigates intricate properties of networks that illuminate possible regulatory mechanisms for cell differentiation and development that can be initiated from points of instability.

Control of synthetic gene networks and its applications

“…In their study of synthetic ecosystems, Song et al modeled the spatio-temporal dynamics of two synthetic Escherichia coli populations using PDEs [59,60]. On an intracellular level, GA (or the related Monte Carlo simulation) is often used to simulate stochastic fluctuations in transcriptional regulator numbers [24,61]. These types of simulations can be used to determine likelihood of state transitions under varying amounts of noise [52] or to thoroughly analyze the GRNs potential landscape under a single noise condition [56].…”

“…(C) Schematic diagram for two representative logic gates, the XOR and AND gates reviewed by Singh et al [75]. demonstrated in multiple organisms [18,24,76], and examples of similar topologies are rife in nature [68]. A system can also express multistable behavior through autoactivation [74].…”

“…For example, by constructing a symmetrical circuit expressing two different fluorescent proteins, Elowitz et al demonstrated the existence of intrinsic and extrinsic stochasticity within a cell [22]. While Wu et al illustrated impacts of such stochasticity on cell fate determination using a synthetic toggle switch in yeast [24].…”

Synthetic biology platform technologies for antimicrobial applications

“…These works advanced our ability to engineer complex behavior such as memory encryption and oscillatory gene expression, and catalyzed advancements in the rapid design and implementation of synthetic gene networks [11][12][13][14][15][16][17][18][19][20]. The field has since moved towards repurposing natural biological processes for tunable and targetable synthetic gene regulation [21][22][23][24][25]. The innate biochemistry of microorganisms has been harnessed in the biosynthesis of organic compounds, such as the antimalarial drug artemisinin [26] and various opioids [27].…”

Programming the group behaviors of bacterial communities with synthetic cellular communication

“…Since the launch of the field in 2000 [1,2], a wide range of synthetic gene devices have been created, including switches [3][4][5][6][7][8][9], oscillators [10][11][12][13], memory elements [7,14,15], and communication modules [13,[16][17][18], as well as other electronics-inspired genetic devices, such as digital logic gates [19][20][21][22], pulse generators [23], and filters [24,25]. With designed cellular behaviors and functionalities, engineered circuits have been exploited to understand biological questions and to address various real-world problems [26].…”