Design Of Synthetic Genetic Circuits Research Articles

Data-driven statistical modeling of the emergent behavior of biohybrid microrobots.

Multi-agent biohybrid microrobotic systems, owing to their small size and distributed nature, offer powerful solutions to challenges in biomedicine, bioremediation, and biosensing. Synthetic biology enables programmed emergent behaviors in the biotic component of biohybrid machines, expounding vast potential benefits for building biohybrid swarms with sophisticated control schemes. The design of synthetic genetic circuits tailored toward specific performance characteristics is an iterative process that relies on experimental characterization of spatially homogeneous engineered cell suspensions. However, biohybrid systems often distribute heterogeneously in complex environments, which will alter circuit performance. Thus, there is a critically unmet need for simple predictive models that describe emergent behaviors of biohybrid systems to inform synthetic gene circuit design. Here, we report a data-driven statistical model for computationally efficient recapitulation of the motility dynamics of two types of Escherichia coli bacteria-based biohybrid swarms—NanoBEADS and BacteriaBots. The statistical model was coupled with a computational model of cooperative gene expression, known as quorum sensing (QS). We determined differences in timescales for programmed emergent behavior in BacteriaBots and NanoBEADS swarms, using bacteria as a comparative baseline. We show that agent localization and genetic circuit sensitivity strongly influence the timeframe and the robustness of the emergent behavior in both systems. Finally, we use our model to design a QS-based decentralized control scheme wherein agents make independent decisions based on their interaction with other agents and the local environment. We show that synergistic integration of synthetic biology and predictive modeling is requisite for the efficient development of biohybrid systems with robust emergent behaviors.

Mechanogenetics for the remote and non‐invasive control of cancer immunotherapy

While cell-based immunotherapy, especially chimeric antigen receptor (CAR)-expressing T cells, is becoming a paradigm-shifting therapeutic approach for cancer treatment, there is a lack of general methods to remotely and non-invasively regulate genetics in live mammalian cells and animals for cancer immunotherapy within confined local tissue space. To address this limitation, we have identified a mechanically sensitive Piezo1 ion channel (mechanosensor) activatable by ultrasound stimulation and integrated it with engineered genetic circuits (genetic transducer) in live HEK cells to convert the ultrasound-activated Piezo1 into transcriptional activities. We have further engineered Jurkat T cell line and primary T cells (periphery blood mononuclear cells, PBMCs) to remotely sense the ultrasound wave and transduce it into transcriptional activation for the CAR expression to recognize and eradicate target tumor cells. This approach is modular and can be extended for remote-controlled activation of different cell types with a high spatiotemporal precision for therapeutic applications. Support or Funding Information This work is supported by grants from NIH HL121365, GM125379 (S.C. and Y. Wang), CA204704 and CA209629 (Y. Wang), NSF CBET1360341, DMS1361421 (Y. Wang and S.L.), and the Beckman Laser Institute Foundation. The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Design of synthetic genetic circuits remotely activatable by ultrasound This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Realizing 'integral control' in living cells: how to overcome leaky integration due to dilution?

A major problem in the design of synthetic genetic circuits is robustness to perturbations and uncertainty. Because of this, there have been significant efforts in recent years in finding approaches to implement integral control in genetic circuits. Integral controllers have the unique ability to make the output of a process adapt perfectly to disturbances. However, implementing an integral controller is challenging in living cells. This is because a key aspect of any integral controller is a 'memory' element that stores the accumulation (integral) of the error between the output and its desired set-point. The ability to realize such a memory element in living cells is fundamentally challenged by the fact that all biomolecules dilute as cells grow, resulting in a 'leaky' memory that gradually fades away. As a consequence, the adaptation property is lost. Here, we propose a general principle for designing integral controllers such that the performance is practically unaffected by dilution. In particular, we mathematically prove that if the reactions implementing the integral controller are all much faster than dilution, then the adaptation error due to integration leakiness becomes negligible. We exemplify this design principle with two synthetic genetic circuits aimed at reaching adaptation of gene expression to fluctuations in cellular resources. Our results provide concrete guidance on the biomolecular processes that are most appropriate for implementing integral controllers in living cells.

Cell-free systems in the new age of synthetic biology

The advent of synthetic biology has ushered in new applications of cell-free transcription-translation systems. These cell-free systems are reconstituted using cellular proteins, and are amenable to modular control of their composition. Here, we discuss the historical advancement of cell-free systems, as well as their new applications in the rapid design of synthetic genetic circuits and components, directed evolution of biomolecules, diagnosis of infectious diseases, and synthesis of vaccines. Finally, we present our vision on the future direction of cell-free synthetic biology.

Understanding and engineering beneficial plant-microbe interactions: plant growth promotion in energy crops.

Plant production systems globally must be optimized to produce stable high yields from limited land under changing and variable climates. Demands for food, animal feed, and feedstocks for bioenergy and biorefining applications, are increasing with population growth, urbanization and affluence. Low-input, sustainable, alternatives to petrochemical-derived fertilizers and pesticides are required to reduce input costs and maintain or increase yields, with potential biological solutions having an important role to play. In contrast to crops that have been bred for food, many bioenergy crops are largely undomesticated, and so there is an opportunity to harness beneficial plant–microbe relationships which may have been inadvertently lost through intensive crop breeding. Plant–microbe interactions span a wide range of relationships in which one or both of the organisms may have a beneficial, neutral or negative effect on the other partner. A relatively small number of beneficial plant–microbe interactions are well understood and already exploited; however, others remain understudied and represent an untapped reservoir for optimizing plant production. There may be near-term applications for bacterial strains as microbial biopesticides and biofertilizers to increase biomass yield from energy crops grown on land unsuitable for food production. Longer term aims involve the design of synthetic genetic circuits within and between the host and microbes to optimize plant production. A highly exciting prospect is that endosymbionts comprise a unique resource of reduced complexity microbial genomes with adaptive traits of great interest for a wide variety of applications.

Coarse-grained optimization-driven design and piecewise linear modeling of synthetic genetic circuits

The aim of synthetic biology is to confer novel functions to cells by rationally interconnecting basic genetic parts into circuits. A key barrier in the design of synthetic genetic circuits is that only a qualitative description of the performance and interactions of the basic genetic parts is available in databases such as the Registry of Standard Biological Parts. Modeling approaches capable of harnessing this qualitative knowledge are thus timely. Here, we introduce an optimization-based framework, which makes use of the available qualitative information about basic biological parts to automatically identify the circuit elements and structures enabling a desired response to the presence/absence of input signals. Promoters and ribosome binding sites are categorized as high, medium or low efficiency and protein expressions in the circuit are described using piecewise linear differential equations. The desired function of the circuit is also mathematically described as the maximization/minimization of a constrained objective function. We employed this framework for the design of a toggle switch, a genetic decoder and a genetic half adder unit. The identified designs are consistent with previously constructed circuit configurations and in some cases point to completely new architectures. The identified non-intuitive circuit structures highlight the importance of accounting for ribosome binding site efficiencies and relative protein abundance levels in circuit design. Our results reaffirm the usefulness of the qualitative information for the coarse-grained genetic circuit design and simulation in the absence of detailed quantitative information.

Growth Rate-Dependent Global Effects on Gene Expression in Bacteria

Bacterial gene expression depends not only on specific regulatory mechanisms, but also on bacterial growth, because important global parameters such as the abundance of RNA polymerases and ribosomes are all growth-rate dependent. Understanding of these global effects is necessary for a quantitative understanding of gene regulation and for the design of synthetic genetic circuits. We find that the observed growth-rate dependence of constitutive gene expression can be explained by a simple model using the measured growth-rate dependence of the relevant cellular parameters. More complex growth dependencies for genetic circuits involving activators, repressors, and feedback control were analyzed and verified experimentally with synthetic circuits. Additional results suggest a feedback mechanism mediated by general growth-dependent effects that does not require explicit gene regulation if the expressed protein affects cell growth. This mechanism can lead to growth bistability and promote the acquisition of important physiological functions such as antibiotic resistance and tolerance (persistence).

IBioSim: a tool for the analysis and design of genetic circuits

iBioSim is a tool that supports learning of genetic circuit models, efficient abstraction-based analysis of these models and the design of synthetic genetic circuits. iBioSim includes project management features and a graphical user interface that facilitate the development and maintenance of genetic circuit models as well as both experimental and simulation data records. iBioSim is available for download for Windows, Linux, and MacOS at http://www.async.ece.utah.edu/iBioSim/ myers@ece.utah.edu.

Quantitative Characteristics of Gene Regulation by Small RNA

An increasing number of small RNAs (sRNAs) have been shown to regulate critical pathways in prokaryotes and eukaryotes. In bacteria, regulation by trans-encoded sRNAs is predominantly found in the coordination of intricate stress responses. The mechanisms by which sRNAs modulate expression of its targets are diverse. In common to most is the possibility that interference with the translation of mRNA targets may also alter the abundance of functional sRNAs. Aiming to understand the unique role played by sRNAs in gene regulation, we studied examples from two distinct classes of bacterial sRNAs in Escherichia coli using a quantitative approach combining experiment and theory. Our results demonstrate that sRNA provides a novel mode of gene regulation, with characteristics distinct from those of protein-mediated gene regulation. These include a threshold-linear response with a tunable threshold, a robust noise resistance characteristic, and a built-in capability for hierarchical cross-talk. Knowledge of these special features of sRNA-mediated regulation may be crucial toward understanding the subtle functions that sRNAs can play in coordinating various stress-relief pathways. Our results may also help guide the design of synthetic genetic circuits that have properties difficult to attain with protein regulators alone.

Inherent multistability in arrays of autoinducer coupled genetic oscillators

Rhythm generation mechanisms are very important for genetic network functions as well as for the design of synthetic genetic circuits. A significant attention to date has been focused on the synchronization of communicating genetic units, which results in the production of an unified rhythm. In contrast to this we address the question: what mechanisms of intercell communication can be responsible for multirhythmicity in globally coupled genetic units? Here, we show that an autoinducer intercell communication system that provides coupling between synthetic genetic oscillators will inherently lead to multirhythmicity and the appearance of several coexisting dynamical regimes, if the time evolution of the genetic network can be split in two well-separated time scales. We investigate in detail a variety of dynamical regimes in a genetic population and show the possibility for multiple element distributions between clusters, as well as the possibility of generating complex oscillations with different return times in one limit cycle.