Inducible plasmid copy number control for synthetic biology in commonly used E. coli strains
The ability to externally control gene expression has been paradigm shifting for all areas of biological research, especially for synthetic biology. Such control typically occurs at the transcriptional and translational level, while technologies enabling control at the DNA copy level are limited by either (i) relying on a handful of plasmids with fixed and arbitrary copy numbers; or (ii) require multiple plasmids for replication control; or (iii) are restricted to specialized strains. To overcome these limitations, we present TULIP (TUnable Ligand Inducible Plasmid): a self-contained plasmid with inducible copy number control, designed for portability across various Escherichia coli strains commonly used for cloning, protein expression, and metabolic engineering. Using TULIP, we demonstrate through multiple application examples that flexible plasmid copy number control accelerates the design and optimization of gene circuits, enables efficient probing of metabolic burden, and facilitates the prototyping and recycling of modules in different genetic contexts.
Cellular processes share building blocks and machinery that have limited availability
Based on models explicitly accounting for coupling, we can predict emergent behaviors
Our approach is validated experimentally in the lab using our customized Chi.Bio platform
A blueprint for a synthetic genetic feedback optimizer
Revise and resubmit in Nature Communications
Living organisms react to changes in their environment by fine-tuning and optimizing a vast array of biochemical processes. Synthetic biology enables us to (re)program cellular processes by designing and combining standardized biological parts in a modular fashion. We recently designed a feedback optimizer to enable adaptive fine-tuning and optimization of cellular performance, for instance, by dynamically re-allocating limited resources to maximize growth rate in shifting environments. To ensure optimality, this module combines readily available genetic parts to adjust the production and decay rate of a set of controlled species. The coarse-grained mechanistic model underpinning our approach yields a general blueprint that can be implemented flexibly. Closed-loop performance reliably approaches the optimum over a wide range of conditions, in the presence of noise, uncertainty, and abrupt changes in parameter values. Our dynamical model-based approach also reveals how to select crucial biophysical parameters (e.g., time-scales) to ensure robust closed-loop performance.
We design genetic modules that can be implemented flexibly relying on common parts
In addition to the analytical design of the genetic circuits, we also test them using living cells
Evolutionary selection between constitutive and feedback regulation
Gene products that are beneficial in one environment may become burdensome in another, prompting the emergence of diverse regulatory mechanisms. To illuminate the selection mechanisms between alternate modes of regulation, prior works focused on the fitness cost stemming from the presence/absence of the product, neglecting the expenditure that regulation itself represents. Here, we demonstrate that by ensuring that regulators are only expressed when needed, autoregulation offers an evolutionary advantage in an environment combining mutation, genetic drift, and time-varying selection, as long as feedback is not plagued by overwhelming delays or additional fitness cost. Self-activation/self-repression generally emerge as dominant strategies when the product is rarely/often needed, the opposite only occurs in a narrow range of population size and timescale. To further substantiate our findings, we verify that evolution favored autoregulation over constitutive control when selecting regulators by analyzing the transcription network of multiple model organisms.
We rely on a variety of quantitative tools to identify optimal regulatory strategies
We experimentally test model-based hypotheses by utilizing synthetic biology tools
How metabolic burden affects the behavior of genetic switches
Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we perform model-based analysis and experimental validation of how essential characteristics of toggle switches (such as their stability and robustness to noise) change due to metabolic burden. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.
For realizing large-scale synthetic genetic systems, modules need to be designed to specs
For every design, we assess their performance considering the design specifications
We rank different designs and assess their behavior with respect to model predictions
The effect of feedback on evolution
Synthetic biology researchers focus on engineering biological circuits to sense input signals, generate multi-state outputs, and perform tasks with temporal dynamics. Design principles from conventional engineering disciplines have been commonly adopted to facilitate the design of biological circuits. Besides rational engineering, directed evolution (DE) has played an increasingly important role in synthetic biology for protein engineering. Using DE, gene circuits can be optimized by evolving promoters or transcriptional factors (TF) to suppress basal leakage or increase TF binding specificity. Yet, whether and how gene circuits might affect molecular evolution in living cells has not been extensively studied. We are integrating synthetic gene circuits and DE in bacteria using CRISPR-related tools. In particular, we focus on possible ways to design genetic circuits to "program" evolution in the presence of regulated selection pressure, and examine how mutations are affected.
We perform experiments with living cells to uncover mechanistic principles of evolution
We leverage multi-scale modeling and analysis to uncover the role of feedback on evolution