Balancing a genetic toggle switch by real-time feedback control and periodic forcing

Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control

The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to synchronise cells, these usually work only in the short-term. Here, we build a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a controller algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing an avenue for yeast cell cycle synchronisation, our work shows that control engineering can be used to automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles.

Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits

Qualitative modeling approaches are promising and still underexploited tools for the analysis and design of synthetic circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. We review qualitative modeling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, discrete representations of the network dynamics that are generated in both modeling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives.

Dynamical Modeling of Optogenetic Circuits in Yeast for Metabolic Engineering Applications

Dynamic control of engineered microbes using light via optogenetics has been demonstrated as an effective strategy for improving the yield of biofuels, chemicals, and other products. An advantage of using light to manipulate microbial metabolism is the relative simplicity of interfacing biological and computer systems, thereby enabling in silico control of the microbe. Using this strategy for control and optimization of product yield requires an understanding of how the microbe responds in real-time to the light inputs. Toward this end, we present mechanistic models of a set of yeast optogenetic circuits. We show how these models can predict short- and long-time response to varying light inputs and how they are amenable to use with model predictive control (the industry standard among advanced control algorithms). These models reveal dynamics characterized by time-scale separation of different circuit components that affect the steady and transient levels of the protein under control of the circuit. Ultimately, this work will help enable real-time control and optimization tools for improving yield and consistency in the production of biofuels and chemicals using microbial fermentations.

Potential Landscapes, Bifurcations, and Robustness of Tristable Networks

Bistable switches that produce all-or-none responses have been found to regulate a number of natural cellular decision making processes, and subsequently synthetic switches were designed to exploit their potential. However, an increasing number of studies, particularly in the context of cellular differentiation, highlight the existence of a mixed state that can be explained by tristable switches. The criterion for designing robust tristable switches still remains to be understood from the perspective of network topology. To address such a question, we calculated the robustness of several 2- and 3-component network motifs, connected via only two positive feedback loops, in generating tristable signal response curves. By calculating the effective potential landscape and following its modifications with the bifurcation parameter, we constructed one-parameter bifurcation diagrams of these models in a high-throughput manner for a large combinations of parameters. We report here that introduction of a self-activatory positive feedback loop, directly or indirectly, into a mutual inhibition loop leads to generating the most robust tristable response. The high-throughput approach of our method further allowed us to determine the robustness of four types of tristable responses that originate from the relative locations of four bifurcation points. Using the method, we also analyzed the role of additional mutual inhibition loops in stabilizing the mixed state.

CRISPR interference and its applications

Sequence-specific control of gene expression is a powerful tool for identifying and studying gene functions and cellular processes. CRISPR interference (CRISPRi) is an RNA-based method for highly specific silencing of the transcription in prokaryotic or eukaryotic cells. The typical CRISPRi system is a type II CRISPR (clustered regularly interspaced palindromic repeats) machinery of Streptococcus pyogenes. CRISPRi requires two main components: A catalytically inactivated Cas9, namely dCas9 and a guide RNA (sgRNA). These two components associate and form a DNA recognition complex. The dCas9/sgRNA complex then specifically binds to the target DNA complementary with the sgRNA and sterically prevents the association of the promoter or transcription factors with their trans-acting sequences or blocks the transcription elongation. This chapter discusses CRISPRi structure, mechanism and its 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.

The origin of wing polyphenism in ants: An eco-evo-devo perspective

The evolution of eusociality, where solitary individuals integrate into a single colony, is a major transition in individuality. In ants, the origin of eusociality coincided with the origin of a wing polyphenism approximately 160 million years ago, giving rise to colonies with winged queens and wingless workers. As a consequence, both eusociality and wing polyphenism are nearly universal features of all ants. Here, we synthesize fossil, ecological, developmental, and evolutionary data in an attempt to understand the factors that contributed to the origin of wing polyphenism in ants. We propose multiple models and hypotheses to explain how wing polyphenism is orchestrated at multiple levels, from environmental cues to gene networks. Furthermore, we argue that the origin of wing polyphenism enabled the subsequent evolution of morphological diversity across the ants. We finally conclude by outlining several outstanding questions for future work.

Four different mechanisms for switching cell polarity

The mechanisms and design principles of regulatory systems establishing stable polarized protein patterns within cells are well studied. However, cells can also dynamically control their cell polarity. Here, we ask how an upstream signaling system can switch the orientation of a polarized pattern. We use a mathematical model of a core polarity system based on three proteins as the basis to study different mechanisms of signal-induced polarity switching. The analysis of this model reveals four general classes of switching mechanisms with qualitatively distinct behaviors: the transient oscillator switch, the reset switch, the prime-release switch, and the push switch. Each of these regulatory mechanisms effectively implements the function of a spatial toggle switch, however with different characteristics in their nonlinear and stochastic dynamics. We identify these characteristics and also discuss experimental signatures of each type of switching mechanism.

An optimal control approach for enhancing natural killer cells' secretion of cytolytic molecules

Natural killer (NK) cells are immune effector cells that can detect and lyse cancer cells. However, NK cell exhaustion, a phenotype characterized by reduced secretion of cytolytic models upon serial stimulation, limits the NK cell's ability to lyse cells. In this work, we investigated in silico strategies that counteract the NK cell's reduced secretion of cytolytic molecules. To accomplish this goal, we constructed a mathematical model that describes the dynamics of the cytolytic molecules granzyme B (GZMB) and perforin-1 (PRF1) and calibrated the model predictions to published experimental data using a Bayesian parameter estimation approach. We applied an information-theoretic approach to perform a global sensitivity analysis, from which we found that the suppression of phosphatase activity maximizes the secretion of GZMB and PRF1. However, simply reducing the phosphatase activity is shown to deplete the cell's intracellular pools of GZMB and PRF1. Thus, we added a synthetic Notch (synNotch) signaling circuit to our baseline model as a method for controlling the secretion of GZMB and PRF1 by inhibiting phosphatase activity and increasing production of GZMB and PRF1. We found that the optimal synNotch system depends on the frequency of NK cell stimulation. For only a few rounds of stimulation, the model predicts that inhibition of phosphatase activity leads to more secreted GZMB and PRF1; however, for many rounds of stimulation, the model reveals that increasing production of the cytolytic molecules is the optimal strategy. In total, we developed a mathematical framework that provides actionable insight into engineering robust NK cells for clinical applications.

Autonomous and Assisted Control for Synthetic Microbiology

The control of microbes and microbial consortia to achieve specific functions requires synthetic circuits that can reliably cope with internal and external perturbations. Circuits that naturally evolved to regulate biological functions are frequently robust to alterations in their parameters. As the complexity of synthetic circuits increases, synthetic biologists need to implement such robust control "by design". This is especially true for intercellular signaling circuits for synthetic consortia, where robustness is highly desirable, but its mechanisms remain unclear. Cybergenetics, the interface between synthetic biology and control theory, offers two approaches to this challenge: external (computer-aided) and internal (autonomous) control. Here, we review natural and synthetic microbial systems with robustness, and outline experimental approaches to implement such robust control in microbial consortia through population-level cybergenetics. We propose that harnessing natural intercellular circuit topologies with robust evolved functions can help to achieve similar robust control in synthetic intercellular circuits. A "hybrid biology" approach, where robust synthetic microbes interact with natural consortia and-additionally-with external computers, could become a useful tool for health and environmental applications.

Artificial intelligence and synthetic biology approaches for human gut microbiome

The gut microbiome comprises a variety of microorganisms whose genes encode proteins to carry out crucial metabolic functions that are responsible for the majority of health-related issues in human beings. The advent of the technological revolution in artificial intelligence (AI) assisted synthetic biology (SB) approaches will play a vital role in the modulating the therapeutic and nutritive potential of probiotics. This can turn human gut as a reservoir of beneficial bacterial colonies having an immense role in immunity, digestion, brain function, and other health benefits. Hence, in the present review, we have discussed the role of several gene editing tools and approaches in synthetic biology that have equipped us with novel tools like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR-Cas) systems to precisely engineer probiotics for diagnostic, therapeutic and nutritive value. A brief discussion over the AI techniques to understand the metagenomic data from the healthy and diseased gut microbiome is also presented. Further, the role of AI in potentially impacting the pace of developments in SB and its current challenges is also discussed. The review also describes the health benefits conferred by engineered microbes through the production of biochemicals, nutraceuticals, drugs or biotherapeutics molecules etc. Finally, the review concludes with the challenges and regulatory concerns in adopting synthetic biology engineered microbes for clinical applications. Thus, the review presents a synergistic approach of AI and SB toward human gut microbiome for better health which will provide interesting clues to researchers working in the area of rapidly evolving food and nutrition science.

dMSCC: a microfluidic platform for microbial single-cell cultivation of Corynebacterium glutamicum under dynamic environmental medium conditions

In nature and in technical systems, microbial cells are often exposed to rapidly fluctuating environmental conditions. These conditions can vary in quality, e.g., the existence of a starvation zone, and quantity, e.g., the average residence time in this zone. For strain development and process design, cellular response to such fluctuations needs to be systematically analysed. However, the existing methods for physically imitating rapidly changing environmental conditions are limited in spatio-temporal resolution. Hence, we present a novel microfluidic system for cultivation of single cells and small cell clusters under dynamic environmental conditions (dynamic microfluidic single-cell cultivation (dMSCC)). This system enables the control of nutrient availability and composition between two media with second to minute resolution. We validate our technology using the industrially relevant model organism Corynebacterium glutamicum. The organism was exposed to different oscillation frequencies between nutrient excess (feasts) and scarcity (famine). The resulting changes in cellular physiology, such as the colony growth rate and cell morphology, were analysed and revealed significant differences in the growth rate and cell length between the different conditions. dMSCC also allows the application of defined but randomly changing nutrient conditions, which is important for reproducing more complex conditions from natural habitats and large-scale bioreactors. The presented system lays the foundation for the cultivation of cells under complex changing environmental conditions.

The Promise of Optogenetics for Bioproduction: Dynamic Control Strategies and Scale-Up Instruments

Progress in metabolic engineering and synthetic and systems biology has made bioproduction an increasingly attractive and competitive strategy for synthesizing biomolecules, recombinant proteins and biofuels from renewable feedstocks. Yet, due to poor productivity, it remains difficult to make a bioproduction process economically viable at large scale. Achieving dynamic control of cellular processes could lead to even better yields by balancing the two characteristic phases of bioproduction, namely, growth versus production, which lie at the heart of a trade-off that substantially impacts productivity. The versatility and controllability offered by light will be a key element in attaining the level of control desired. The popularity of light-mediated control is increasing, with an expanding repertoire of optogenetic systems for novel applications, and many optogenetic devices have been designed to test optogenetic strains at various culture scales for bioproduction objectives. In this review, we aim to highlight the most important advances in this direction. We discuss how optogenetics is currently applied to control metabolism in the context of bioproduction, describe the optogenetic instruments and devices used at the laboratory scale for strain development, and explore how current industrial-scale bioproduction processes could be adapted for optogenetics or could benefit from existing photobioreactor designs. We then draw attention to the steps that must be undertaken to further optimize the control of biological systems in order to take full advantage of the potential offered by microbial factories.

Optimally Designed Model Selection for Synthetic Biology

Modeling parts and circuits represents a significant roadblock to automating the Design-Build-Test-Learn cycle in synthetic biology. Once models are developed, discriminating among them requires informative data, computational resources, and skills that might not be readily available. The high cost entailed in model discrimination frequently leads to subjective choices on the selected structures and, in turn, to suboptimal models. Here, we outline frequentist and Bayesian approaches to model discrimination. We ranked three candidate models of a genetic toggle switch, which was adopted as a test case, according to the support from in vivo data. We show that, in each framework, efficient model discrimination can be achieved via optimally designed experiments. We offer a dynamical-systems interpretation of our optimization results and investigate their sensitivity to key parameters in the characterization of synthetic circuits. Our approach suggests that optimal experimental design is an effective strategy to discriminate between competing models of a gene regulatory network. Independent of the adopted framework, optimally designed perturbations exploit regions in the input space that maximally distinguish predictions from the competing models.

Analysis of the organization and expression patterns of the convergent Pseudomonas aeruginosa lasR/rsaL gene pair uncovers mutual influence

The two adjacent genes encoding the major Pseudomonas aeruginosa quorum-sensing regulator, LasR, and its opponent, RsaL, overlap in their coding 3' ends and produce mRNA transcripts with long untranslated 3' ends that overlap with the sense transcripts of the gene on the opposing DNA strand. In this study, we evaluated whether the overlapping genes are involved in mutual regulatory events and studied interference by natural antisense transcripts. We introduced various gene expression constructs into a P. aeruginosa PA14 lasR/rsaL double deletion mutant, and found that although complementary RNA is produced, this does not interfere with the sense gene expression levels of lasR and rsaL and does not have functional consequences on down-stream gene regulation. Nevertheless, expression of lasR, but not of rsaL, was shown to be enhanced if transcription was terminated at the end of the respective gene so that no overlapping transcription was allowed. Our data indicate that the natural organization with a partial overlap at the 3' ends of the lasR/rsaL genes gives rise to a system of checks and balances to prevent dominant and unilateral control by LasR over the RsaL transcriptional regulator of opposing function.

Nonlinear Observability Algorithms with Known and Unknown Inputs: Analysis and Implementation

The observability of a dynamical system is affected by the presence of external inputs, either known (such as control actions) or unknown (disturbances). Inputs of unknown magnitude are especially detrimental for observability, and they also complicate its analysis. Hence, the availability of computational tools capable of analysing the observability of nonlinear systems with unknown inputs has been limited until lately. Two symbolic algorithms based on differential geometry, ORC-DF and FISPO, have been recently proposed for this task, but their critical analysis and comparison is still lacking. Here we perform an analytical comparison of both algorithms and evaluate their performance on a set of problems, while discussing their strengths and limitations. Additionally, we use these analyses to provide insights about certain aspects of the relationship between inputs and observability. We found that, while ORC-DF and FISPO follow a similar approach, they differ in key aspects that can have a substantial influence on their applicability and computational cost. The FISPO algorithm is more generally applicable, since it can analyse any nonlinear ODE model. The ORC-DF algorithm analyses models that are affine in the inputs, and if those models have known inputs it is sometimes more efficient. Thus, the optimal choice of a method depends on the characteristics of the problem under consideration. To facilitate the use of both algorithms, we implemented the ORC-DF condition in a new version of STRIKE-GOLDD, a MATLAB toolbox for structural identifiability and observability analysis. Since this software tool already had an implementation of the FISPO algorithm, the new release allows modellers and model users the convenience of choosing between different algorithms in a single tool, without changing the coding of their model.

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

We study both in silico and in vivo the real-time feedback control of a molecular titration motif that has been earmarked as a fundamental component of antithetic and multicellular feedback control schemes in E. coli. We show that an external feedback control strategy can successfully regulate the average fluorescence output of a bacterial cell population to a desired constant level in real-time. We also provide in silico evidence that the same strategy can be used to track a time-varying reference signal where the set-point is switched to a different value halfway through the experiment. We use the experimental data to refine and parametrize an in silico model of the motif that can be used as an error computation module in future embedded or multicellular control experiments.

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The Finite State Projection based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological data sets, but to close the loop between quantitative modeling and experimental data collection.

Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch

The formation of spatiotemporal patterns of gene expression is frequently guided by gradients of diffusible signaling molecules. The toggle switch subnetwork, composed of two cross-repressing transcription factors, is a common component of gene regulatory networks in charge of patterning, converting the continuous information provided by the gradient into discrete abutting stripes of gene expression. We present a synthetic biology framework to understand and characterize the spatiotemporal patterning properties of the toggle switch. To this end, we built a synthetic toggle switch controllable by diffusible molecules in Escherichia coli. We analyzed the patterning capabilities of the circuit by combining quantitative measurements with a mathematical reconstruction of the underlying dynamical system. The toggle switch can produce robust patterns with sharp boundaries, governed by bistability and hysteresis. We further demonstrate how the hysteresis, position, timing, and precision of the boundary can be controlled, highlighting the dynamical flexibility of the circuit.

Balancing Cell Populations Endowed with a Synthetic Toggle Switch via Adaptive Pulsatile Feedback Control

The problem of controlling cells endowed with a genetic toggle switch has been recently highlighted as a benchmark problem in synthetic biology. It has been shown that a carefully selected periodic forcing can balance a population of such cells in an undifferentiated state. The effectiveness of these control strategies, however, can be hindered by the presence of stochastic perturbations and uncertainties typically observed in biological systems and is therefore not robust. Here, we propose the use of feedback control strategies to enhance robustness and performance of the balancing action by selecting in real-time both the amplitude and the duty-cycle of the pulsatile inputs affecting the toggle switch behavior. We show, via in silico experiments and realistic agent-based simulations, the effectiveness of the proposed strategies even in the presence of uncertainties, stochastic effects, cell growth, and inducer diffusion. In so doing, we confirm previous observations made in the literature about coherence of the population when pulsatile forcing inputs are used, but, contrary to what was proposed in the past, we leverage feedback control techniques to endow the balancing strategy with unprecedented robustness and stability properties. We compare via in silico experiments different external control solutions and show their advantages and limitations from an in vivo implementation viewpoint.

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.

Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling

Designing and implementing synthetic biological pattern formation remains challenging due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks biochemically. Here, we introduce a cell-in-the-loop approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present an easy-to-use theoretical test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrate spontaneous checkerboard patterning in an optogenetic setup, where cell-to-cell signaling is emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produces spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.

Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset

Engineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information.

Isolating live cells after high-throughput, long-term, time-lapse microscopy

Single-cell genetic screens can be incredibly powerful, but current high-throughput platforms do not track dynamic processes, and even for non-dynamic properties they struggle to separate mutants of interest from phenotypic outliers of the wild-type population. Here we introduce SIFT, single-cell isolation following time-lapse imaging, to address these limitations. After imaging and tracking individual bacteria for tens of consecutive generations under tightly controlled growth conditions, cells of interest are isolated and propagated for downstream analysis, free of contamination and without genetic or physiological perturbations. This platform can characterize tens of thousands of cell lineages per day, making it possible to accurately screen complex phenotypes without the need for barcoding or genetic modifications. We applied SIFT to identify a set of ultraprecise synthetic gene oscillators, with circuit variants spanning a 30-fold range of average periods. This revealed novel design principles in synthetic biology and demonstrated the power of SIFT to reliably screen diverse dynamic phenotypes.

Discrete and continuous models of probability flux of switching dynamics: Uncovering stochastic oscillations in a toggle-switch system

The probability flux and velocity in stochastic reaction networks can help in characterizing dynamic changes in probability landscapes of these networks. Here, we study the behavior of three different models of probability flux, namely, the discrete flux model, the Fokker-Planck model, and a new continuum model of the Liouville flux. We compare these fluxes that are formulated based on, respectively, the chemical master equation, the stochastic differential equation, and the ordinary differential equation. We examine similarities and differences among these models at the nonequilibrium steady state for the toggle switch network under different binding and unbinding conditions. Our results show that at a strong stochastic condition of weak promoter binding, continuum models of Fokker-Planck and Liouville fluxes deviate significantly from the discrete flux model. Furthermore, we report the discovery of stochastic oscillation in the toggle-switch system occurring at weak binding conditions, a phenomenon captured only by the discrete flux model.

Synthetic control systems for high performance gene expression in mammalian cells

Tunable induction of gene expression is an essential tool in biology and biotechnology. In spite of that, current induction systems often exhibit unpredictable behavior and performance shortcomings, including high sensitivity to transactivator dosage and plasmid take-up variation, and excessive consumption of cellular resources. To mitigate these limitations, we introduce here a novel family of gene expression control systems of varying complexity with significantly enhanced performance. These include: (i) an incoherent feedforward circuit that exhibits output tunability and robustness to plasmid take-up variation; (ii) a negative feedback circuit that reduces burden and provides robustness to transactivator dosage variability; and (iii) a new hybrid circuit integrating negative feedback and incoherent feedforward that combines the benefits of both. As with endogenous circuits, the complexity of our genetic controllers is not gratuitous, but is the necessary outcome of more stringent performance requirements. We demonstrate the benefits of these controllers in two applications. In a culture of CHO cells for protein manufacturing, the circuits result in up to a 2.6-fold yield improvement over a standard system. In human-induced pluripotent stem cells they enable precisely regulated expression of an otherwise poorly tolerated gene of interest, resulting in a significant increase in the viability of the transfected cells.

Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models

In this paper, we address the system identification problem in the context of biological modelling. We present and demonstrate a methodology for (i) assessing the possibility of inferring the unknown quantities in a dynamic model and (ii) effectively estimating them from output data. We introduce the term Full Input-State-Parameter Observability (FISPO) analysis to refer to the simultaneous assessment of state, input and parameter observability (note that parameter observability is also known as identifiability). This type of analysis has often remained elusive in the presence of unmeasured inputs. The method proposed in this paper can be applied to a general class of nonlinear ordinary differential equations models. We apply this approach to three models from the recent literature. First, we determine whether it is theoretically possible to infer the states, parameters and inputs, taking only the model equations into account. When this analysis detects deficiencies, we reformulate the model to make it fully observable. Then we move to numerical scenarios and apply an optimization-based technique to estimate the states, parameters and inputs. The results demonstrate the feasibility of an integrated strategy for (i) analysing the theoretical possibility of determining the states, parameters and inputs to a system and (ii) solving the practical problem of actually estimating their values.

Biological signal generators: integrating synthetic biology tools and in silico control

Biological networks sense extracellular stimuli and generate appropriate outputs within the cell that determine cellular response. Biological signal generators are becoming an important tool for understanding how information is transmitted in these networks and controlling network behavior. Signal generators produce well-defined, dynamic, intracellular signals of important network components, such as kinase activity or the concentration of a specific transcription factor. Synthetic biology tools coupled with in silico control have enabled the construction of these sophisticated biological signal generators. Here we review recent advances in biological signal generator construction and their use in systems biology studies. Challenges for constructing signal generators for a wider range of biological networks and generalizing their use are discussed.

Cell-machine interfaces for characterizing gene regulatory network dynamics

Gene regulatory networks and the dynamic responses they produce offer a wealth of information about how biological systems process information about their environment. Recently, researchers interested in dissecting these networks have been outsourcing various parts of their experimental workflow to computers. Here we review how, using microfluidic or optogenetic tools coupled with fluorescence imaging, it is now possible to interface cells and computers. These platforms enable scientists to perform informative dynamic stimulations of genetic pathways and monitor their reaction. It is also possible to close the loop and regulate genes in real time, providing an unprecedented view of how signals propagate through the network. Finally, we outline new tools that can be used within the framework of cell-machine interfaces.

Advancement of Metabolic Engineering Assisted by Synthetic Biology

Synthetic biology has undergone dramatic advancements for over a decade, during which it has expanded our understanding on the systems of life and opened new avenues for microbial engineering. Many biotechnological and computational methods have been developed for the construction of synthetic systems. Achievements in synthetic biology have been widely adopted in metabolic engineering, a field aimed at engineering micro-organisms to produce substances of interest. However, the engineering of metabolic systems requires dynamic redistribution of cellular resources, the creation of novel metabolic pathways, and optimal regulation of the pathways to achieve higher production titers. Thus, the design principles and tools developed in synthetic biology have been employed to create novel and flexible metabolic pathways and to optimize metabolic fluxes to increase the cells’ capability to act as production factories. In this review, we introduce synthetic biology tools and their applications to microbial cell factory constructions.

Programming the Dynamic Control of Bacterial Gene Expression with a Chimeric Ligand- and Light-Based Promoter System

To program cells in a dynamic manner, synthetic biologists require precise control over the threshold levels and timing of gene expression. However, in practice, modulating gene expression is widely carried out using prototypical ligand-inducible promoters, which have limited tunability and spatiotemporal resolution. Here, we built two dual-input hybrid promoters, each retaining the function of the ligand-inducible promoter while being enhanced with a blue-light-switchable tuning knob. Using the new promoters, we show that both ligand and light inputs can be synchronously modulated to achieve desired amplitude or independently regulated to generate desired frequency at a specific amplitude. We exploit the versatile programmability and orthogonality of the two promoters to build the first reprogrammable logic gene circuit capable of reconfiguring into logic OR and N-IMPLY logic on the fly in both space and time without the need to modify the circuit. Overall, we demonstrate concentration- and time-based combinatorial regulation in live bacterial cells with potential applications in biotechnology and synthetic biology.

Regulation of Gene Expression and Signaling Pathway Activity in Mammalian Cells by Automated Microfluidics Feedback Control

Gene networks and signaling pathways display complex topologies and, as a result, complex nonlinear behaviors. Accumulating evidence shows that both static (concentration) and dynamical (rate-of-change) features of transcription factors, ligands and environmental stimuli control downstream processes and ultimately cellular functions. Currently, however, methods to generate stimuli with the desired features to probe cell response are still lacking. Here, combining tools from Control Engineering and Synthetic Biology (cybergenetics), we propose a simple and cost-effective microfluidics-based platform to precisely regulate gene expression and signaling pathway activity in mammalian cells by means of real-time feedback control. We show that this platform allows (i) to automatically regulate gene expression from inducible promoters in different cell types, including mouse embryonic stem cells; (ii) to precisely regulate the activity of the mTOR signaling pathway in single cells; (iii) to build a biohybrid oscillator in single embryonic stem cells by interfacing biological parts with virtual in silico counterparts. Ultimately, this platform can be used to probe gene networks and signaling pathways to understand how they process static and dynamic features of specific stimuli, as well as for the rapid prototyping of synthetic circuits for biotechnology and biomedical purposes.

Real-Time Genetic Compensation Defines the Dynamic Demands of Feedback Control

Biological signaling networks use feedback control to dynamically adjust their operation in real time. Traditional static genetic methods such as gene knockouts or rescue experiments can often identify the existence of feedback interactions but are unable to determine what feedback dynamics are required. Here, we implement a new strategy, closed-loop optogenetic compensation (CLOC), to address this problem. Using a custom-built hardware and software infrastructure, CLOC monitors, in real time, the output of a pathway deleted for a feedback regulator. A minimal model uses these measurements to calculate and deliver-on the fly-an optogenetically enabled transcriptional input designed to compensate for the effects of the feedback deletion. Application of CLOC to the yeast pheromone response pathway revealed surprisingly distinct dynamic requirements for three well-studied feedback regulators. CLOC, a marriage of control theory and traditional genetics, presents a broadly applicable methodology for defining the dynamic function of biological feedback regulators.

Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Here we find, using a fast-acting, optogenetic transcription factor in Saccharomyces cerevisiae, that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or the construction of synthetic gene networks.

Computing with biological switches and clocks

The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.