Synthetic Biological Systems Research Articles

Kinetic Model for the Heterogeneous Biocatalytic Reactions Using Tethered Cofactors.

Understanding the mechanism of interfacial enzyme kinetics is critical to the development of synthetic biological systems for the production of value-added chemicals. Here, the interfacial kinetics of the catalysis of β-nicotinamide adenine dinucleotide (NAD+)-dependent enzymes acting on NAD+ tethered to the surface of silica nanoparticles (SiNPs) has been investigated using two complementary and supporting kinetic approaches: enzyme excess and reactant (NAD+) excess. Kinetic models developed for these two approaches characterize several critical reaction steps including reversible enzyme adsorption, complexation, decomplexation, and catalysis of the surface-bound enzyme/NAD+ complex. The analysis reveals a concentrating effect resulting in a very high local concentration of enzyme and cofactor on the particle surface, in which the enzyme is saturated by surface-bound NAD, facilitating a rate enhancement of enzyme/NAD+ complexation and catalysis. This resulted in high enzyme efficiency within the tethered NAD+ system compared to that of the free enzyme/NAD+ system, which increases with decreasing enzyme concentration. The role of enzyme adsorption onto solid substrates with a tethered catalyst (such as NAD+) has potential for creating highly efficient flow biocatalytic systems.

Designer Micro-/Nanocrumpled MXene Multilayer Coatings Accelerate Osteogenesis and Regulate Macrophage Polarization.

Effective tissue regeneration and immune responses are essential for the success of biomaterial implantation. Although the interaction between synthetic materials and biological systems is well-recognized, the role of surface topographical cues in regulating the local osteoimmune microenvironment─specifically, their impact on host tissue and immune cells, and their dynamic interactions─remains underexplored. This study addresses this gap by investigating the impact of surface topography on osteogenesis and immunomodulation. We fabricated MXene/hydroxyapatite (HAP)-coated surfaces with controlled 2.5D nano-, submicro-, and microscale topographical patterns using our custom bottom-up patterning method. These engineered surfaces were employed to assess the behavior of osteoblast precursor cells and macrophage polarization. Our results demonstrate that MXene/HAP-coated surfaces with microscale crumpled topography significantly influence osteogenic activity and macrophage polarization: these surfaces notably enhanced osteoblast precursor cell spreading, proliferation, and differentiation and facilitated a shift in macrophages toward an anti-inflammatory, prohealing M2 phenotype. The observed cell responses indicate that the physical cues from the crumpled topographies, combined with the chemical cues from the MXene/HAP coatings, synergistically create a favorable osteoimmune microenvironment. This study presents the first evidence of employing MXene/HAP-multilayer coated surfaces with finely crumpled topography to concurrently facilitate osteogenesis and immunomodulation for improved implant-to-tissue integration. The tunable topographic patterns of these coatings coupled with a facile and scalable fabrication process make them widely applicable for various biomedical purposes. Our results highlight the potential of these multilayer coatings with controlled topography to improve the in vivo performance and fate of implants by modulating the host response at the material interface.

Measuring differential fitness costs and interactions between genetic cassettes using fluorescent spectrophotometry.

In this article, we present a method for designing, executing, and analyzing data from a microbial competition experiment. We use fluorescent reporters to label different competing strains and resolve individual growth curves using a fluorescent spectrophotometer. Our comprehensive data analysis pipeline integrates multiple experiments to simultaneously infer sources of variation, extract selection coefficients, and estimate the genetic contributions to fitness for various synthetic genetic cassettes (SGCs). To demonstrate the method, we employ a synthetic biological system based on Escherichia coli. Strains carry 1 of 10 different plasmids and one of three genomically integrated fluorescent markers. All strains are co-cultured to obtain real-time measurements of optical density (total population density) and fluorescence (sub-population densities). We identify challenges in calibrating between fluorescence and density and of fluorescent proteins maturing at different rates. To resolve these issues, we compare two methods of fluorescence calibration and correct for maturation by measuring in vivo maturation times. We provide evidence of genetic interactions occurring between our SGCs and further show how to use our statistical model to test some hypotheses about microbial growth and the costs of protein expression.IMPORTANCEFluorescently labeled co-cultures are becoming increasingly popular. The approach proposed here offers a high standard for experimental design and data analysis to measure selection coefficients and growth rates in competition. Measuring competitive differences is useful in many laboratory studies, allowing for fitness cost-correction of growth rates and ecological interactions and testing hypotheses in synthetic biology. Using time-resolved growth curves, rather than endpoint measurements, for competition assays allows us to construct a detailed scientific model that can be used to ask questions about fine-grained phenomena, such as bacterial growth dynamics, as well as higher-level phenomena, such as the interactions between synthetic cassette expression.

Development of new binary expression systems for plant synthetic biology.

A novel plant binary expression system was developed from the compactin biosynthetic pathway 27 of Penicillium citrinum ML-236B. The system achieved >fivefold activation of gene expression in 28 transgenic tobacco. A diverse and well-characterized genetic toolset is fundamental to achieve the overall goals of plant synthetic biology. To properly coordinate expression of a multigene pathway, this toolset should include binary systems that control gene expression at the level of transcription. In plants, few highly functional, orthogonal transcriptional regulators have been identified. Here, we describe the process of developing synthetic plant transcription factors using regulatory elements from the Penicillium citrinum ML-236B (compactin) pathway. This pathway contains several genes including mlcA and mlcC that are transcriptionally regulated in a dose-dependent manner by the activator mlcR. In Nicotiana benthamiana, we first expressed mlcR with several cognate synthetic promoters driving expression of GFP. Synthetic promoters contained operator sequences from the compactin gene cluster. Following identification of the most active synthetic promoter, the DNA-binding domain from mlcR was used to generate chimeric transcription factors containing variable activation domains, including QF from the Neurospora crassa Q-system. Activity was measured at both protein and RNA levels which correlated with an R2 value of 0.94. A synthetic transcription factor with a QF activation domain increased gene expression from its synthetic promoter up to sixfold in N. benthamiana. Two systems were characterized in transgenic tobacco plants. The QF-based plants maintained high expression in tobacco, increasing expression from the cognate synthetic promoter by fivefold. Transgenic plants and non-transgenic plants were morphologically indistinguishable. The framework of this study can easily be adopted for other putative transcription factors to continue improvement of the plant synthetic biology toolbox.

Organization of an Artificial Multicellular System with a Tunable DNA Patch on a Membrane Surface.

Coordinating multiple artificial cellular compartments into a well-organized artificial multicellular system (AMS) is of great interest in bottom-up synthetic biology. However, developing a facile strategy for fabricating an AMS with a controlled arrangement remains a challenge. Herein, utilizing in situ DNA hybridization chain reaction on the membrane surface, we developed a DNA patch-based strategy to direct the interconnection of vesicles. By tuning the DNA patch that generates heterotrophic adhesion for the attachment of vesicles, we could produce an AMS with higher-order structures straightforwardly and effectively. Furthermore, a hybrid AMS comprising live cells and vesicles was fabricated, and we found the hybrid AMS with higher-order structures arouses efficient molecular transportation from vesicles to living cells. In brief, our work provides a versatile strategy for modulating the self-assembly of AMSs, which could expand our capability to engineer synthetic biological systems and benefit synthetic cell research in programmable manipulation of intercellular communications.

The Construction of the Self-Induced Sal System and Its Application in Salicylic Acid Production

The design and construction of more complex and delicate genetic control circuits suffer from poor orthogonality in quorum sensing (QS) systems. The Sal system, which relies on salicylic acid as a signaling molecule, is an artificially engineered regulatory system with a structure that differs significantly from that of natural QS signaling molecules. Salicylic acid is an important drug precursor, mainly used in the production of drugs such as aspirin and anti-HIV drugs. However, there have been no reports on the construction of a self-induced Sal system in single cells. In this study, a high-copy plasmid backbone was used to construct the regulatory proteins and a self-induced promoter of salicylic acid in E. coli by adjusting the precise regulation of key gene expression; the sensitivity and induction range of this system were improved. Subsequently, the exogenous gene pchBA was introduced in E. coli to extend the shikimate pathway and synthesize salicylic acid, resulting in the construction of the first complete self-induced Sal system. Finally, the self-induced Sal System was combined with artificial trans-encoded sRNAs (atsRNAs) to repress the growth-essential gene ppc and accumulate the precursor substance PEP, thereby increasing the titer of salicylic acid by 151%. This construction of a self-induced artificial system introduces a new tool for selecting communication tools and induction systems in synthetic biology and metabolic engineering, but also demonstrates a self-inducible pathway design strategy for salicylic acid biosynthesis.

An orthogonalized PYR1-based CID module with reprogrammable ligand-binding specificity

Plants sense abscisic acid (ABA) using chemical-induced dimerization (CID) modules, including the receptor PYR1 and HAB1, a phosphatase inhibited by ligand-activated PYR1. This system is unique because of the relative ease with which ligand recognition can be reprogrammed. To expand the PYR1 system, we designed an orthogonal ‘*’ module, which harbors a dimer interface salt bridge; X-ray crystallographic, biochemical and in vivo analyses confirm its orthogonality. We used this module to create PYR1*MANDI/HAB1* and PYR1*AZIN/HAB1*, which possess nanomolar sensitivities to their activating ligands mandipropamid and azinphos-ethyl. Experiments in Arabidopsis thaliana and Saccharomyces cerevisiae demonstrate the sensitive detection of banned organophosphate contaminants using living biosensors and the construction of multi-input/output genetic circuits. Our new modules enable ligand-programmable multi-channel CID systems for plant and eukaryotic synthetic biology that can empower new plant-based and microbe-based sensing modalities.

What’s next for computational systems biology?

Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the next-generation of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis.

An Intelligent Synthetic Bacterium for Chronological Toxicant Detection, Biodegradation, and Its Subsequent Suicide.

Modules, toolboxes, and synthetic biology systems may be designed to address environmental bioremediation. However, weak and decentralized functional modules require complex control. To address this issue, an integrated system for toxicant detection and biodegradation, and subsequent suicide in chronological order without exogenous inducers is constructed. Salicylic acid, a typical pollutant in industrial wastewater, is selected as an example to demonstrate this design. Biosensors are optimized by regulating the expression of receptors and reporters to get 2-fold sensitivity and 6-fold maximum output. Several stationary phase promoters are compared, and promoter Pfic is chosen to express the degradation enzyme. Two concepts for suicide circuits are developed, with the toxin/antitoxin circuit showing potent lethality. The three modules are coupled in a stepwise manner. Detection and biodegradation, and suicide are sequentially completed with partial attenuation compared to pre-integration, except for biodegradation, being improved by the replacements of ribosome binding site. Finally, a long-term stability test reveals that the engineered strain maintained its function for ten generations. The study provides a novel concept for integrating and controlling functional modules that can accelerate the transition of synthetic biology from conceptual to practical applications.

Acoustic Levitation-Assisted Contactless Printing of Microdroplets for Biomedical Applications

Abstract The cell is a microcapsule system wherein biological materials are encapsulated by a thin membrane, which provides valuable information on the metabolism, morphology, development, and signal transduction pathways of the studied cell. The cell-inspired microdroplet has the characteristics of efficient nanoscale substance transportation, self-organization, and morphological adaptation. However, it is extremely difficult to manufacture such systems. Mostly vesicles such as liposomes, polymersomes, and microcapsules are first produced by a high-pressure homogenizer and microfluidizer as an emulsion and then encapsulated microcapsules by the drop or emulsion method. Currently, acoustic levitation opens entirely new possibilities for creating bioinspired microdroplets because of its ability to suspend tiny droplets in an antigravity and noncontact manner. Herein, we propose contactless printing of single-core or multi-core cell-inspired microdroplets via acoustic levitation. First, the oscillation mode and microscopic morphology of the droplets under different ultrasonic vibration frequencies are shown by simulation, and the curing characteristics of the shell structure under different ultraviolet illumination conditions are quantitatively measured. The feasibility of manufacturing multi-core microdroplets and manufacturing submillimeter-scale particles based on oil trapping is extensively studied. To explore the morphological adaptability of microdroplets, ferromagnetic Fe3O4 nanoparticles are used to give magnetic-responsive properties to cells, and the microscopic deformation and motion in microfluidic channels under the magnetic field are characterized. Finally, the proposed printing method proves the versatility of in-space contactless printing of complex 3D beam structures and provides a powerful platform for developing biomedical devices and microrobots and studying morphogenesis and synthetic biological systems.

Antimicrobial Peptides: The Production of Novel Peptide-Based Therapeutics in Plant Systems.

The increased prevalence of antibiotic resistance is alarming and has a significant impact on the economies of emerging and underdeveloped nations. The redundancy of antibiotic discovery platforms (ADPs) and injudicious use of conventional antibiotics has severely impacted millions, across the globe. Potent antimicrobials from biological sources have been extensively explored as a ray of hope to counter the growing menace of antibiotic resistance in the population. Antimicrobial peptides (AMPs) are gaining momentum as powerful antimicrobial therapies to combat drug-resistant bacterial strains. The tremendous therapeutic potential of natural and synthesized AMPs as novel and potent antimicrobials is highlighted by their unique mode of action, as exemplified by multiple research initiatives. Recent advances and developments in antimicrobial discovery and research have increased our understanding of the structure, characteristics, and function of AMPs; nevertheless, knowledge gaps still need to be addressed before these therapeutic options can be fully exploited. This thematic article provides a comprehensive insight into the potential of AMPs as potent arsenals to counter drug-resistant pathogens, a historical overview and recent advances, and their efficient production in plants, defining novel upcoming trends in drug discovery and research. The advances in synthetic biology and plant-based expression systems for AMP production have defined new paradigms in the efficient production of potent antimicrobials in plant systems, a prospective approach to countering drug-resistant pathogens.

Next generation synthetic memory via intercepting recombinase function

Here we present a technology to facilitate synthetic memory in a living system via repurposing Transcriptional Programming (i.e., our decision-making technology) parts, to regulate (intercept) recombinase function post-translation. We show that interception synthetic memory can facilitate programmable loss-of-function via site-specific deletion, programmable gain-of-function by way of site-specific inversion, and synthetic memory operations with nested Boolean logical operations. We can expand interception synthetic memory capacity more than 5-fold for a single recombinase, with reconfiguration specificity for multiple sites in parallel. Interception synthetic memory is ~10-times faster than previous generations of recombinase-based memory. We posit that the faster recombination speed of our next-generation memory technology is due to the post-translational regulation of recombinase function. This iteration of synthetic memory is complementary to decision-making via Transcriptional Programming – thus can be used to develop intelligent synthetic biological systems for myriad applications.

Carotenoid ornaments and the spandrels of physiology: a critique of theory to explain condition dependency.

Even as numerous studies have documented that the red and yellow coloration resulting from the deposition of carotenoids serves as an honest signal of condition, the evolution of condition dependency is contentious. The resource trade-off hypothesis proposes that condition-dependent honest signalling relies on a trade-off of resources between ornamental display and body maintenance. By this model, condition dependency can evolve through selection for a re-allocation of resources to promote ornament expression. By contrast, the index hypothesis proposes that selection focuses mate choice on carotenoid coloration that is inherently condition dependent because production of such coloration is inexorably tied to vital cellular processes. These hypotheses for the origins of condition dependency make strongly contrasting and testable predictions about ornamental traits. To assess these two models, we review the mechanisms of production of carotenoids, patterns of condition dependency involving different classes of carotenoids, and patterns of behavioural responses to carotenoid coloration. We review evidence that traits can be condition dependent without the influence of sexual selection and that novel traits can show condition-dependent expression as soon as they appear in a population, without the possibility of sexual selection. We conclude by highlighting new opportunities for studying condition-dependent signalling made possible by genetic manipulation and expression of ornamental traits in synthetic biological systems.

System-wide optimization of an orthogonal translation system with enhanced biological tolerance.

Over the past two decades, synthetic biological systems have revolutionized the study of cellular physiology. The ability to site-specifically incorporate biologically relevant non-standard amino acids using orthogonal translation systems (OTSs) has proven particularly useful, providing unparalleled access to cellular mechanisms modulated by post-translational modifications, such as protein phosphorylation. However, despite significant advances in OTS design and function, the systems-level biology of OTS development and utilization remains underexplored. In this study, we employ a phosphoserine OTS (pSerOTS) as a model to systematically investigate global interactions between OTS components and the cellular environment, aiming to improve OTS performance. Based on this analysis, we design OTS variants to enhance orthogonality by minimizing host process interactions and reducing stress response activation. Our findings advance understanding of system-wide OTS:host interactions, enabling informed design practices that circumvent deleterious interactions with host physiology while improving OTS performance and stability. Furthermore, our study emphasizes the importance of establishing a pipeline for systematically profiling OTS:host interactions to enhance orthogonality and mitigate mechanisms underlying OTS-mediated host toxicity.

A Cell-Free Gene Expression Platform for Discovering and Characterizing Stop Codon Suppressing tRNAs.

Non-canonical amino acids (ncAAs) can be incorporated into peptides and proteins to create new properties and functions. Site-specific ncAA incorporation is typically enabled by orthogonal translation systems comprising a stop codon suppressing tRNA (typically UAG), an aminoacyl-tRNA synthetase, and an ncAA of interest. Unfortunately, methods to discover and characterize suppressor tRNAs are limited because of laborious and time-consuming workflows in living cells. In this work, we develop anEscherichia coli crude extract-based cell-free gene expression system to rapidly express and characterize functional suppressor tRNAs. Our approach co-expresses orthogonal tRNAs using endogenous machinery alongside a stop-codon containing superfolder green fluorescent protein (sfGFP) reporter, which can be used as a simple read-out for suppression. As a model, we evaluate the UAG and UAA suppressing activity of several orthogonal tRNAs. Then, we demonstrate that co-transcription of two mutually orthogonal tRNAs can direct the incorporation of two unique ncAAs within a single modified sfGFP. Finally, we show that the cell-free workflow can be used to discover putative UAG-suppressor tRNAs found in metagenomic data, which are nonspecifically recognized by endogenous aminoacyl-tRNA synthetases. We anticipate that our cell-free system will accelerate the development of orthogonal translation systems for synthetic biology.

Cell-Free Biosensing Genetic Circuit Coupled with Ribozyme Cleavage Reaction for Rapid and Sensitive Detection of Small Molecules.

Synthetic biological systems have been utilized to develop a wide range of genetic circuits and components that enhance the performance of biosensing systems. Among them, cell-free systems are emerging as important platforms for synthetic biology applications. Genetic circuits play an essential role in cell-free systems, mainly consisting of sensing modules, regulation modules, and signal output modules. Currently, fluorescent proteins and aptamers are commonly used as signal outputs. However, these signal output modes cannot simultaneously achieve faster signal output, more accurate and reliable performance, and signal amplification. Ribozyme is a highly structured and catalytic RNA molecule that can specifically recognize and cut specific substrate sequences. Here, by adopting ribozyme as the signal output, we developed a cell-free biosensing genetic circuit coupled with the ribozyme cleavage reaction, enabling rapid and sensitive detection of small molecules. More importantly, we have also successfully constructed a 3D-printed sensor array and thereby achieved high-throughput analysis of an inhibitory drug. Furthermore, our method will help expand the application range of ribozyme in the field of synthetic biology and also optimize the signal output system of cell-free biosensing, thus promoting the development of cell-free synthetic biology in biomedical research, clinical diagnosis, environmental monitoring, and food inspection.

Carbene transfer in a cell

Synthetic chemists have long used carbenes to create cyclopropyl groups and to strategically insert carbon into certain bonds. But these highly reactive intermediates haven’t been available in the realm of synthetic biology—until now. Scientists have created Streptomyces albus cells that can biosynthesize azaserine, a natural product and carbene precursor, and they got it to react with styrene also made by the same cellular system. An engineered P450 enzyme catalyzed the cyclopropanation reaction (shown). “The whole process end to end is happening in the cell. You basically are adding glucose on one side and getting a cyclopropanated bioactive compound on the other side,” says Aindrila Mukhopadhyay, an expert in microbial processes at Lawrence Berkeley National Laboratory, and a leader on the project. The synthetic biology system could help researchers create cyclopropanated versions of natural products, says John F. Hartwig, a chemist at the University of California, Berkeley, who also co-led the

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Since its dawn, systems-and-control theory has been strongly and deeply interlaced with the life sciences, as is emphasized by the work of some of its greatest pioneers. Norbert Wiener presented Cybernetics in his book as Control and Communication in the Animal and the Machine, thus stressing the remarkable similarities between engineering mechanisms and regulation in living beings. Ludwig von Bertalanffy, the founder of General Systems Theory, advocated the use of mathematical approaches and system-level thinking in the study of biology, and was a biologist himself. The approaches developed within the realm of systems-and-control theory can be successfully employed to better understand the functioning of systems in the life sciences at all scales, from biochemical reaction networks to biological systems, from biomedical systems to ecological networks and epidemic phenomena. Novel rigorous methods can guide the control, optimization, and bottom-up design of systems in synthetic biology, as well as the development of approaches for biomedicine and of strategies for the control of epidemics. Systems in the life sciences are characterized by a high level of complexity, steep nonlinearities, and largely uncertain dynamics: their features pose many challenges that can inspire the development of new theory in robust and nonlinear control. This special issue has collected recent advances and new exciting ideas at the frontiers between systems-and-control theory and the life sciences, presented by renowned research teams worldwide. The scope of the special issue includes several complementary aspects, ranging from systems biology and synthetic biology (modeling, analysis, control, and design of biochemical and biological systems at all scales) to the analysis and management of ecological networks, from the study of biomedical systems (including disease management and the design and analysis of novel treatments) to the analysis and the control of epidemiological systems (embracing the study of their dynamics and approaches to suppress or mitigate the spread of contagion). Attention is devoted to modeling, identification, parameter estimation, as well as to analysis, control, and optimization. The special issue embraces both work with novel theoretical approaches and work that advances theoretical contributions with meaningful applications. The COVID-19 pandemic has highlighted the importance of mathematical and systems-and-control approaches to understand, manage, and control health emergencies, to predict the trend of epidemics and to plan effective strategies that curb the contagion. Indeed, several contributions of the special issue revolve around models for epidemics and infectious diseases and tackle their analysis and their control to suppress the contagion and eradicate infections, resorting to nonpharmaceutical interventions as well as vaccination. Eduardo D. Sontag proposes “An explicit formula for minimizing the infected peak in an SIR epidemic model when using a fixed number of complete lockdowns”; his work deals with the careful timing of nonpharmaceutical interventions, such as physical distancing, with the aim of minimizing the peak in the number of infected individuals, and provides an insightful, explicit, and easily computable rule for the timing of a fixed number of complete lockdowns of prespecified lengths. In their work “Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID-19,” Rijad Alisic, Philip E. Paré, and Henrik Sandberg analyze the detectability of sudden parameter changes for nonlinear dynamical systems in the presence of measurement noise, so as to assess the impact of individual nonpharmaceutical interventions or viral mutations on the epidemic spread, and look for the most informative data sample allowing to discriminate at best between different output trajectories, revealing, for example, that monitoring the number of infected (instead of recovered/deceased) is preferable. “Semi-Markov models of epidemics over networks with time delays” are proposed by Mohammad Ghousein, Emmanuel Moulay, and Patrick Coirault, who modify Markov models for epidemics by including minimum viral incubation period and recovery period as time delays affecting the states, leading to a more general class of systems that loses the Markovian property; sufficient conditions for the global exponential stability of the resulting time-delay probabilistic dynamical models are offered via Lyapunov theory. “A time-varying network model for sexually transmitted infections accounting for behavior and control actions” is proposed by Kathinka Frieswijk, Lorenzo Zino, and Ming Cao, whose stochastic network model for the spread of sexually transmitted infections includes asymptomatic infections, as well as behavioral choices related to the use of protective measures and their time evolution depending on the perceived risk and on persuasion due to exchange of opinions; different control actions (awareness campaigns, routine screening, and partner notification) are investigated along with their effectiveness. Francesco Parino, Lorenzo Zino, Giuseppe C. Calafiore, and Alessandro Rizzo, in their article “A model predictive control approach to optimally devise a two-dose vaccination rollout: A case study on COVID-19 in Italy,” face the challenge of optimally planning massive two-dose vaccination rollouts by leveraging a nonlinear model predictive control strategy that accounts for both healthcare and socioeconomic costs, which they apply to the 2021 COVID-19 vaccination campaign in Italy by considering a tailored epidemic model with parameters inferred from real data. Moving from the between-host scale of epidemiological models that describe contagion spreading in a population to the in-host treatment of diseases, the paper “Scheduling collateral sensitivity-based cycling therapies toward eradication of drug-resistant infections,” by Josephine N. A. Tetteh, Sorin Olaru, Hans Crauel, and Esteban A. Hernandez-Vargas, explores the control-theoretic aspects and implications of facing drug-resistant pathogens through the sequential use of drugs where resistance to the former drug induces sensitivity to the next (collateral sensitivity); a switched system formulation allows to optimally tailor the order and time of cycling between drugs to the pathogen population present in the host. Systems-and-control theory offers powerful tools to gain insight into the behavior of biological systems, including their response to perturbations, their stability properties, and their ability to give rise to sustained and synchronized oscillations. In his paper “Sign-sensitivity of metabolic networks: Which structures determine the sign of the responses,” Nicola Vassena follows a structural approach to investigate the sign of the responses of metabolic networks to external perturbations, affecting both reaction rates and metabolite concentrations at the equilibrium, exclusively based on the network stoichiometry, without any information about the parameter values; subnetworks that have a crucial role in determining the sign sensitivity are identified and shown to be associated with kernel vectors of the stoichiometric matrix and thus independent of the chosen kinetics. Nicolas Augier, Madalena Chaves, and Jean-Luc Gouzé deal with “Weak synchronization and convergence in coupled genetic regulatory networks: Applications to damped oscillators and multistable circuits”: they consider a general model of genetic networks, compare the equilibria and stability properties of different interconnections based on either homogeneous or heterogeneous coupling, and find conditions for weak synchronization, applied to the synchronization of damped oscillators and to the control of multistable systems. Control-theoretic approaches allow us to govern, enhance, and improve biological phenomena at various scales, ranging from gene regulation and metabolism to the control of cell populations and their growth, and enables the design of biological mechanisms with the desired behavior. In a biological context, control strategies can aim at optimality, but most often need to face huge uncertainty and environmental variability, which requires fundamental robustness properties. Virginia Fusco, Davide Salzano, Davide Fiore, and Mario di Bernardo present a feedback strategy for the “Embedded control of cell growth using tuneable genetic systems”: they control the density of a microbial population, where cells self-regulate their growth rate, by tuning the production of a growth inhibitor protein based on the information on the population density obtained from a quorum sensing mechanism, they show that the set-point can be flexibly changed online, and they test the robustness of the designed controller with respect to disturbances and parameter variations due, for example, to cell-to-cell variability. “Self-regulation in a stochastic model of chemical self-replication” is investigated by Alessandro Borri, Massimiliano d'Angelo, and Pasquale Palumbo, who analyze burst noise propagation in the context of gene expression and investigate the impact of feedback on noise attenuation, both within a computational numerical framework based on a stochastic simulation algorithm and within an approximated analytical framework that replaces nonlinear terms with linear ones to enable closed-loop solutions for first- and second-order moments. Debojyoti Biswas, Sayak Bhattacharya, and Pablo A. Iglesias investigate “Enhanced chemotaxis through spatially regulated absolute concentration robustness” and introduce a control mechanism to enhance the efficiency of chemotaxis (the directional motility of cells in response to spatial chemical gradients) in amoeboid cells, whose movement is enabled by protrusions driven by the stochastic threshold crossing of an underlying excitable system, by suppressing undesirable protrusions in the wrong directions; the control action couples an absolute concentration robustness mechanism to the cellular signaling machinery. The work “Optimal control of probabilistic Boolean control networks: A scalable infinite horizon approach” by Sonam Kharade, Sarang Sutavani, Sushama Wagh, Amol Yerudkar, Carmen Del Vecchio, and Navdeep Singh deals with the optimal control of large-scale gene regulatory networks, modeled as probabilistic Boolean control networks based on Markov decision processes, with the aim of developing therapeutic intervention strategies that alter gene regulation dynamics so as to avoid diseased states. Nicolas Augier and Agustín G. Yabo deal with the “Time-optimal control of piecewise affine bistable gene-regulatory networks,” where the goal is to minimize the time that a piecewise affine bistable genetic toggle switch needs to transition between its two stable steady states, and show that time-optimal transitions pass through an undifferentiated state, which has fundamental importance in cell biology in relation to fate differentiation of cells and has applications in synthetic biology, biotechnology, and gene therapy. The paper “Optimal periodic resource allocation in reactive dynamical systems: application to Microalgal production” by Olivier Bernard, Liu-Di Lu, and Julien Salomon focuses on a periodic resource allocation problem applied to a dynamical system, which is inspired by the optimization of a mixing strategy to enhance the growth rate in a microalgal raceway system; the control is represented by a permutation applied on the system to reallocate the resources to the different activities. Estimating the state of a biological system based on experimentally measured quantities is fundamental. Alex dos Reis de Souza, Denis Efimov, Andrey Polyakov, Jean-Luc Gouzé, and Eugenio Cinquemani discuss “State observation in microbial consortia: A case study on a synthetic producer-cleaner consortium”; they frame an observability analysis and propose state estimators for each component of a system describing a two-strain microbial consortium that involves both producers and cleaners; they deal with observer design and they assess the advantage of measuring a fluorescent reporter in addition to the total biomass, even though measuring total biomass alone is enough to make the system detectable. System identification and the generation of reduced order models are of crucial importance in systems and synthetic biology. Francesco Montefusco, Anna Procopio, Declan G. Bates, Francesco Amato, and Carlo Cosentino tackle the “Scalable reverse-engineering of gene regulatory networks from time-course measurements”: they propose a computationally efficient method for topological inference of biological interaction networks from experimental data, which combines system identification based on instrumental variables with a regularization strategy to deal with over-parameterized systems, and can simultaneously exploit multiple time series from multiple experiments and be applied to large-scale networks with thousands of nodes. Gemma Massonis, Julio R. Banga, and Alejandro F. Villaverde propose “AutoRepar: a method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights”; their approach automatically re-parameterizes nonlinear ODE models to guarantee their structural identifiability and observability, so that the values of the model parameters and state variables can be inferred from data without ambiguities and the models can provide reliable predictions and insight. “Robustness guarantees for structured model reduction of dynamical systems with applications to biomolecular models” are offered by Ayush Pandey and Richard M. Murray, who focus on the robustness properties of the error in model reduction in the presence of uncertainties; they compute robustness guarantee metrics for a class of reduced models of engineered biological systems in uncertain environments, and propose an automated model reduction method to find the best possible reduced model, which is applied to gene expression systems with limited resources and to circuits for bacterial population control. Developing parameter identification, state estimation, and control approaches for biomedical systems is key to provide robust and theoretically grounded support to healthcare workers in the clinical practice. “Parameter identification and state estimation for a diabetic glucose-insulin model via an adaptive observer” are considered by Roberto Franco, Héctor Ríos, Alejandra Ferreira de Loza, Louis Cassany, David Gucik-Derigny, Jérôme Cieslak, and David Henry, who design an LMI-based adaptive observer for patients with type 1 diabetes mellitus, using Bergman's minimal model, to simultaneously estimate states and parameters corresponding to the insulin-dependent glucose disappearance rate in the presence of parameter uncertainties and food intake regarded as an external disturbance; the estimates are based on intravenous glucose measurements and the approach is validated in the UVA/Padova metabolic simulator. Luca Ranghetti, Daniel E. Rivera, Penghong Guo, Antonio Visioli, Jennifer Savage Williams, and Danielle Symons Downs discuss “A control-based observer approach for estimating energy intake during pregnancy” aimed at regulation of gestational weight so as to prevent risks for both the mother and her unborn child; an energy balance model predicts gestational weight based on physical activity and unmeasured disturbances associated with energy intake, and two different control-based observer formulations, relying on internal model control and model predictive control, are proposed and tested on real data. “Nonlinear dynamic modelling and model predictive control of thrombin generation to treat trauma-induced coagulopathy” are investigated by Damon E. Ghetmiri and Amor A. Menezes: they propose a nonlinear dynamic coagulation model including the complex biochemical reactions of clotting and demonstrate the effectiveness of a model predictive control scheme that administers blood proteins as system inputs to robustly automate clinical interventions for trauma-induced coagulopathy, even in the presence of experimentally observed uncertainties involving nonlinearities and delays. Two contributions deal with decision-making in populations and with the control of ecological systems. Leonardo Stella and Dario Bauso investigate “The impact of irrational behaviors in the optional prisoner's dilemma with game-environment feedback”, where players can choose to cooperate, defect or abstain, their behavior can be irrational according to prospect theory (capturing, e.g., reference dependence or loss aversion), and environmental effects exert a feedback over the population dynamics; the system is analyzed in the context of evolutionary games, with applications to opinion dynamics in society (elections) and collective decision-making in honeybee swarms. “Cyclic control equilibria for switched systems with applications to ecological systems” are discussed by Alejandro L. Anderson, Pablo Abuin, Antonio Ferramosca, Esteban A. Hernandez-Vargas, and Alejandro H. Gonzalez, who investigate permanence regions for switched systems under arbitrary waiting-time constraints and their (un)suitability as control targets, and propose a case study concerning population control and the stabilization of communities in an ecological system. We are deeply grateful to all the authors who contributed to this special issue by entrusting their valuable work and offering a rich and varied range of perspectives on “Control-Theoretic Approaches to Systems in the Life Sciences.” We also have heartful thanks for all the anonymous reviewers for their thorough assessment of submissions and for the insightful suggestions and comments they offered.

Degradation of Antibiotic Resistance Genes by VADER with CRISPR-Cas Immunity.

The evolution and dissemination of antibiotic resistance genes (ARGs) are prompting severe health and environmental issues. While environmental processes, e.g., biological wastewater treatment, are key barriers to prevent the spread of ARGs, they are often sources of ARGs at the same time, requiring upgraded biotechnology. Here, we present VADER, a synthetic biology system for the degradation of ARGs based on CRISPR-Cas immunity, an archaeal and bacterial immune system for eliminating invading foreign DNAs, to be implemented for wastewater treatment processes. Navigated by programmable guide RNAs, VADER targets and degrades ARGs depending on their DNA sequences, and by employing an artificial conjugation machinery, IncP, it can be delivered via conjugation. The system was evaluated by degrading plasmid-borne ARGs in Escherichia coli and further demonstrated via the elimination of ARGs on the environmentally relevant RP4 plasmid in Pseudomonas aeruginosa. Next, a prototype conjugation reactor at a 10-mL scale was devised, and 100% of the target ARG was eliminated in the transconjugants receiving VADER, giving a proof of principle for the implementation of VADER in bioprocesses. By generating a nexus of synthetic biology and environmental biotechnology, we believe that our work is not only an enterprise for tackling ARG problems but also a potential solution for managing undesired genetic materials in general in the future. IMPORTANCE Antibiotic resistance has been causing severe health problems and has led to millions of deaths in recent years. Environmental processes, especially those of the wastewater treatment sector, are an important barrier to the spread of antibiotic resistance from the pharmaceutical industry, hospitals, or civil sewage. However, they have been identified as a nonnegligible source of antibiotic resistance at the same time, as antibiotic resistance with its main cause, antibiotic resistance genes (ARGs), may accumulate in biological treatment units. Here, we transplanted the CRISPR-Cas system, an immune system via programmable DNA cleavage, to tackle the antibiotic resistance problem raised in wastewater treatment processes, and we propose a new sector specialized in ARG removal with a conjugation reactor to implement the CRISPR-Cas system. Our study provides a new angle for resolving public health issues via the implementation of synthetic biology in environmental contexts at the process level.

Microbial lipopeptides: Properties, mechanics and engineering for novel lipopeptides

Microorganisms produce active surface agents called lipopeptides (LPs) which are amphiphilic in nature. They are cyclic or linear compounds and are predominantly isolated from Bacillus and Pseudomonas species. LPs show antimicrobial activity towards various plant pathogens and act by inhibiting the growth of these organisms. Several mechanisms are exhibited by LPs, such as cell membrane disruption, biofilm production, induced systematic resistance, improving plant growth, inhibition of spores, etc., making them suitable as biocontrol agents and highly advantageous for industrial utilization. The biosynthesis of lipopeptides involves large multimodular enzymes referred to as non-ribosomal peptide synthases. These enzymes unveil a broad range of engineering approaches through which lipopeptides can be overproduced and new LPs can be generated asserting high efficacy. Such approaches involve several synthetic biology systems and metabolic engineering techniques such as promotor engineering, enhanced precursor availability, condensation domain engineering, and adenylation domain engineering. Finally, this review provides an update of the applications of lipopeptides in various fields.