In Vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli Using Microfluidics

Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.

Noise properties of adaptation-conferring biochemical control modules

A key goal of synthetic biology is to develop functional biochemical modules with network-independent properties. Antithetic integral feedback (AIF) is a recently developed control module in which two control species perfectly annihilate each other's biological activity. The AIF module confers robust perfect adaptation to the steady-state average level of a controlled intracellular component when subjected to sustained perturbations. Recent work has suggested that such robustness comes at the unavoidable price of increased stochastic fluctuations around average levels. We present theoretical results that support and quantify this trade-off for the commonly analyzed AIF variant in the idealized limit with perfect annihilation. However, we also show that this trade-off is a singular limit of the control module: Even minute deviations from perfect adaptation allow systems to achieve effective noise suppression as long as cells can pay the corresponding energetic cost. We further show that a variant of the AIF control module can achieve significant noise suppression even in the idealized limit with perfect adaptation. This atypical configuration may thus be preferable in synthetic biology applications.

Regulation strategies for two-output biomolecular networks

Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.

L-2L ladder digital-to-analogue converter for dynamics generation of chemical concentrations

Cellular response to dynamic chemical stimulation encodes rich information about the underlying reaction pathways and their kinetics. Microfluidic chemical stimulators play a key role in generating dynamic concentration waveforms by mixing several aqueous solutions. In this article, we propose a multi-layer microfluidic chemical stimulator capable of modulating chemical concentrations by a simple binary logic based on the electronic-hydraulic analogy of electronic R-2R ladder circuits. The proposed device, which we call L-2L ladder digital-to-analogue converter (DAC), allows us to systematically modulate 2 ⁿ levels of concentrations from single sources of solution and solvent by a single operation of 2n membrane valves, which contrasts with existing devices that require complex channel geometry with multiple input sources and valve operations. We fabricated the L-2L ladder DAC with n = 3 bit resolution and verified the concept by comparing the generated waveforms with computational simulations. The response time of the proposed DAC was within the order of seconds because of its simple operation logic of membrane valves. Furthermore, detailed analysis of the waveforms revealed that the transient concentration can be systematically predicted by a simple addition of the transient waveforms of 2n = 6 base patterns, enabling facile optimization of the channel geometry to fine-tune the output waveforms.

Cybergenetic control of microbial community composition

The use of bacterial communities in bioproduction instead of monocultures has potential advantages including increased productivity through division of labour, ability to utilise cheaper substrates, and robustness against perturbations. A key challenge in the application of engineered bacterial communities is the ability to reliably control the composition of the community in terms of its constituent species. This is crucial to prevent faster growing species from outcompeting others with a lower relative fitness, and to ensure that all species are present at an optimal ratio during different steps in a biotechnological process. In contrast to purely biological approaches such as synthetic quorum sensing circuits or paired auxotrophies, cybergenetic control techniques - those in which computers interface with living cells-are emerging as an alternative approach with many advantages. The community composition is measured through methods such as fluorescence intensity or flow cytometry, with measured data fed real-time into a computer. A control action is computed using a variety of possible control algorithms and then applied to the system, with actuation taking the form of chemical (e.g., inducers, nutrients) or physical (e.g., optogenetic, mechanical) inputs. Subsequent changes in composition are then measured and the cycle repeated, maintaining or driving the system to a desired state. This review discusses recent and future developments in methods for implementing cybergenetic control systems, contrasts their capabilities with those of traditional biological methods of population control, and discusses future directions and outstanding challenges for the field.

Hardware, Software, and Wetware Codesign Environment for Synthetic Biology

Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.

Ratiometric control of cell phenotypes in monostrain microbial consortia

We address the problem of regulating and keeping at a desired balance the relative numbers between cells exhibiting a different phenotype within a monostrain microbial consortium. We propose a strategy based on the use of external control inputs, assuming each cell in the community is endowed with a reversible, bistable memory mechanism. Specifically, we provide a general analytical framework to guide the design of external feedback control strategies aimed at balancing the ratio between cells whose memory is stabilized at either one of two equilibria associated with different cell phenotypes. We demonstrate the stability and robustness properties of the control laws proposed and validate them in silico, implementing the memory element via a genetic toggle-switch. The proposed control framework may be used to allow long-term coexistence of different populations, with both industrial and biotechnological applications. As a representative example, we consider the realistic agent-based implementation of our control strategy to enable cooperative bioproduction of a dimer in a monostrain microbial consortium.

Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modeling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch, which includes additive process noise and exhibits bistability. We compare the results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of an in vivo experiment using an agent-based simulator describing cell growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

Control engineering and synthetic biology: working in synergy for the analysis and control of microbial systems

The implementation of novel functionalities in living cells is a key aspect of synthetic biology. In the last decade, the field of synthetic biology has made progress working in synergy with control engineering, whose solid framework has provided concepts and tools to analyse biological systems and guide their design. In this review, we briefly highlight recent work focused on the application of control theoretical concepts and tools for the analysis and design of synthetic biology systems in microbial cells.

Cheetah: A Computational Toolkit for Cybergenetic Control

Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.

Ultrasensitive molecular controllers for quasi-integral feedback

Feedback control has enabled the success of automated technologies by mitigating the effects of variability, unknown disturbances, and noise. While it is known that biological feedback loops reduce the impact of noise and help shape kinetic responses, many questions remain about how to design molecular integral controllers. Here, we propose a modular strategy to build molecular quasi-integral feedback controllers, which involves following two design principles. The first principle is to utilize an ultrasensitive response, which determines the gain of the controller and influences the steady-state error. The second is to use a tunable threshold of the ultrasensitive response, which determines the equilibrium point of the system. We describe a reaction network, named brink controller, that satisfies these conditions by combining molecular sequestration and an activation/deactivation cycle. With computational models, we examine potential biological implementations of brink controllers, and we illustrate different example applications.

ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices

Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool that segments bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows us to segment individual cells in high cell density microfluidic devices, to quantify fluorescent protein expression over a time-lapse experiment, and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customized for other experimental settings and research aims.