Programmed hierarchical patterning of bacterial populations

Structure of random Turing-like patterns in discrete-time systems is determined by the initial conditions

Patterns, spatiotemporal ordered structures, are prevalent in diverse systems, arising from the emergence of complexity. Turing proposed a mechanism that involves a short-range activator and a long-range inhibitor to explain the formation of patterns, and patterns that satisfy this mechanism are called Turing patterns. Patterns with similar structures but not caused by the Turing mechanism are referred to as Turing-like patterns. In the absence of external influences, the structure of Turing patterns is generally determined by control parameters. In this study, we revealed that the structure of Turing-like patterns in discrete-time systems is only determined by the ratio of states in the initial conditions. As the ratio changes, the structure of patterns transitions from spots to labyrinth and eventually to inverse spots. We proposed the structure parameter for the quantitative description of the structure of the patterns. And the structure parameter is directly proportional to the ratio in the initial conditions. The mechanism underlying this structure control is attributed to the traversability of multiperiodic states in discrete-time systems, where each local point will go through all states in the periodic orbit. Our findings shed light on the pattern formation for Turing-like patterns in discrete-time systems.

A three-node Turing gene circuit forms periodic spatial patterns in bacteria

Turing patterns are self-organizing systems that can form spots, stripes, or labyrinths. Proposed examples in tissue organization include zebrafish pigmentation, digit spacing, and many others. The theory of Turing patterns in biology has been debated because of their stringent fine-tuning requirements, where patterns only occur within a small subset of parameters. This has complicated the engineering of synthetic Turing gene circuits from first principles, although natural genetic Turing networks have been identified. Here, we engineered a synthetic genetic reaction-diffusion system where three nodes interact according to a non-classical Turing network with improved parametric robustness. The system reproducibly generated stationary, periodic, concentric stripe patterns in growing E. coli colonies. A partial differential equation model reproduced the patterns, with a Turing parameter regime obtained by fitting to experimental data. Our synthetic Turing system can contribute to nanotechnologies, such as patterned biomaterial deposition, and provide insights into developmental patterning programs. A record of this paper's transparent peer review process is included in the supplemental information.

Assembly of functional microbial ecosystems: from molecular circuits to communities

Microbes compete and cooperate with each other via a variety of chemicals and circuits. Recently, to decipher, simulate, or reconstruct microbial communities, many researches have been engaged in engineering microbiomes with bottom-up synthetic biology approaches for diverse applications. However, they have been separately focused on individual perspectives including genetic circuits, communications tools, microbiome engineering, or promising applications. The strategies for coordinating microbial ecosystems based on different regulation circuits have not been systematically summarized, which calls for a more comprehensive framework for the assembly of microbial communities. In this review, we summarize diverse cross-talk and orthogonal regulation modules for de novo bottom-up assembling functional microbial ecosystems, thus promoting further consortia-based applications. First, we review the cross-talk communication-based regulations among various microbial communities from intra-species and inter-species aspects. Then, orthogonal regulations are summarized at metabolites, transcription, translation, and post-translation levels, respectively. Furthermore, to give more details for better design and optimize various microbial ecosystems, we propose a more comprehensive design-build-test-learn procedure including function specification, chassis selection, interaction design, system build, performance test, modeling analysis, and global optimization. Finally, current challenges and opportunities are discussed for the further development and application of microbial ecosystems.

Construction of the genetic switches in response to mannitol based on artificial MtlR box

Synthetic biology has rapidly advanced from the setup of native genetic devices to the design of artificial elements able to provide organisms with highly controllable functions. In particular, genetic switches are crucial for deploying new layers of regulation into the engineered organisms. While the assembly and mutagenesis of native elements have been extensively studied, limited progress has been made in rational design of genetic switches due to a lack of understanding of the molecular mechanism by which a specific transcription factor interacts with its target gene. Here, a reliable workflow is presented for designing two categories of genetic elements, one is the switch element-MtlR box and the other is the transcriptional regulatory element- catabolite control protein A (CcpA) box. The MtlR box was designed for ON/OFF-state selection and is controlled by mannitol. The rational design of MtlR box-based molecular structures can flexibly tuned the selection of both ON and OFF states with different output switchability in response to varied kind effectors. Different types of CcpA boxes made the switches with more markedly inducer sensitivities. Ultimately, the OFF-state value was reduced by 90.69%, and the maximum change range in the presence of two boxes was 15.31-fold. This study presents a specific design of the switch, in a plug-and-play manner, which has great potential for controlling the flow of the metabolic pathway in synthetic biology.

Turing patterns in simplicial complexes

The spontaneous emergence of patterns in nature, such as stripes and spots, can be mathematically explained by reaction-diffusion systems. These patterns are often referred as Turing patterns to honor the seminal work of Alan Turing in the early 1950s. With the coming of age of network science, and with its related departure from diffusive nearest-neighbor interactions to long-range links between nodes, additional layers of complexity behind pattern formation have been discovered, including irregular spatiotemporal patterns. Here we investigate the formation of Turing patterns in simplicial complexes, where links no longer connect just pairs of nodes but can connect three or more nodes. Such higher-order interactions are emerging as a new frontier in network science, in particular describing group interaction in various sociological and biological systems, so understanding pattern formation under these conditions is of the utmost importance. We show that a canonical reaction-diffusion system defined over a simplicial complex yields Turing patterns that fundamentally differ from patterns observed in traditional networks. For example, we observe a stable distribution of Turing patterns where the fraction of nodes with reactant concentrations above the equilibrium point is exponentially related to the average degree of 2-simplexes, and we uncover parameter regions where Turing patterns will emerge only under higher-order interactions, but not under pairwise interactions.

Engineering Escherichia coli asymmetry distribution-based synthetic consortium for shikimate production

Microbial consortia constitute a promising tool for achieving high-value chemical bio-production. However, customizing the consortium ratio remains challenging. Herein, an asymmetry distribution-based synthetic consortium (ADSC) was developed to switch cell phenotypes using shikimate synthesis for proof of concept. First, the cell pole-organizing protein PopZ was screened as a mediator of asymmetric protein distribution in Escherichia coli. The ADSC was then constructed to incorporate PopZ-mediated asymmetry distribution and a TetR-based transcription repression switch to achieve the dynamical control of microbial population ratio. Finally, the ADSC was used to decouple cell growth from shikimate synthesis by effectively coordinating the ratio of growing cells and production cells at the consortium level, thereby increasing shikimate titer to 30.1 g/L in the 7.5-L bioreactor with a minimal medium. This titer was further improved to 82.5 g/L when using rich medium fermentation. Our results illustrate a novel approach to control consortium structure through ADSC-mediated regulation, highlighting its potential as an efficient strategy for controlling metabolic state in microbes. This article is protected by copyright. All rights reserved.

Advances and challenges in programming pattern formation using living cells

Spatial patterning of cell populations is a ubiquitous phenomenon in nature. Patterns occur at various length and time scales and exhibit immense diversity. In addition to offering a deeper understanding of the emergence of patterns in nature, the ability to program synthetic patterns using living cells has the potential for broad applications. To date, however, progress in engineering pattern formation has been hampered by technical challenges. In this Review, we discuss recent advances in programming pattern formation in terms of biological insights, experimental and computational tool development, and potential applications.

Synthetic spatial patterning in bacteria: advances based on novel diffusible signals

Engineering multicellular patterning may help in the understanding of some fundamental laws of pattern formation and thus may contribute to the field of developmental biology. Furthermore, advanced spatial control over gene expression may revolutionize fields such as medicine, through organoid or tissue engineering. To date, foundational advances in spatial synthetic biology have often been made in prokaryotes, using artificial gene circuits. In this review, engineered patterns are classified into four levels of increasing complexity, ranging from spatial systems with no diffusible signals to systems with complex multi-diffusor interactions. This classification highlights how the field was held back by a lack of diffusible components. Consequently, we provide a summary of both previously characterized and some new potential candidate small-molecule signals that can regulate gene expression in Escherichia coli. These diffusive signals will help synthetic biologists to successfully engineer increasingly intricate, robust and tuneable spatial structures.

Control of synthetic microbial consortia in time, space, and composition

While synthetic microbial systems are becoming increasingly complicated, single-strain systems cannot match the complexity of their multicellular counterparts. Such complexity, however, is much more difficult to control. Recent advances have increased our ability to control temporal, spatial, and community compositional organization, including modular adhesive systems, strain growth relationships, and asymmetric cell division. While these systems generally work independently, combining them into unified systems has proven difficult. Once such unification is proven successful we will unlock a new frontier of synthetic biology and open the door to the creation of synthetic biological systems with true multicellularity.

Bespoke Turing Systems

Reaction-diffusion systems are an intensively studied form of partial differential equation, frequently used to produce spatially heterogeneous patterned states from homogeneous symmetry breaking via the Turing instability. Although there are many prototypical "Turing systems" available, determining their parameters, functional forms, and general appropriateness for a given application is often difficult. Here, we consider the reverse problem. Namely, suppose we know the parameter region associated with the reaction kinetics in which patterning is required-we present a constructive framework for identifying systems that will exhibit the Turing instability within this region, whilst in addition often allowing selection of desired patterning features, such as spots, or stripes. In particular, we show how to build a system of two populations governed by polynomial morphogen kinetics such that the: patterning parameter domain (in any spatial dimension), morphogen phases (in any spatial dimension), and even type of resulting pattern (in up to two spatial dimensions) can all be determined. Finally, by employing spatial and temporal heterogeneity, we demonstrate that mixed mode patterns (spots, stripes, and complex prepatterns) are also possible, allowing one to build arbitrarily complicated patterning landscapes. Such a framework can be employed pedagogically, or in a variety of contemporary applications in designing synthetic chemical and biological patterning systems. We also discuss the implications that this freedom of design has on using reaction-diffusion systems in biological modelling and suggest that stronger constraints are needed when linking theory and experiment, as many simple patterns can be easily generated given freedom to choose reaction kinetics.

Partners for life: building microbial consortia for the future

New technologies have allowed researchers to better design, build, and analyze complex consortia. These developments are fueling a wider implementation of consortium-based bioprocessing by leveraging synthetic biology, delivering on the field's multitudinous promises of higher efficiencies, superior resiliency, augmented capabilities, and modular bioprocessing. Here we chronicle current progress by presenting a range of screening, computational, and biomolecular tools enabling robust population control, efficient division of labor, and programmatic spatial organization; furthermore, we detail corresponding advancements in areas including machine learning, biocontainment, and standardization. Additionally, we show applications in myriad sectors, including medicine, energy and waste sustainability, chemical production, agriculture, and biosensors. Concluding remarks outline areas of growth that will promote the utilization of complex community structures across the biotechnology spectrum.

Interpretation of morphogen gradients by a synthetic bistable circuit

During development, cells gain positional information through the interpretation of dynamic morphogen gradients. A proposed mechanism for interpreting opposing morphogen gradients is mutual inhibition of downstream transcription factors, but isolating the role of this specific motif within a natural network remains a challenge. Here, we engineer a synthetic morphogen-induced mutual inhibition circuit in E. coli populations and show that mutual inhibition alone is sufficient to produce stable domains of gene expression in response to dynamic morphogen gradients, provided the spatial average of the morphogens falls within the region of bistability at the single cell level. When we add sender devices, the resulting patterning circuit produces theoretically predicted self-organised gene expression domains in response to a single gradient. We develop computational models of our synthetic circuits parameterised to timecourse fluorescence data, providing both a theoretical and experimental framework for engineering morphogen-induced spatial patterning in cell populations.

Morphogen gradients can be dynamic and transient yet give rise to stable cellular patterns. Here the authors show that a synthetic morphogen-induced mutual inhibition circuit produces stable boundaries when the spatial average of morphogens falls within the region of bistability.

Turing Patterning in Stratified Domains

Reaction-diffusion processes across layered media arise in several scientific domains such as pattern-forming E. coli on agar substrates, epidermal-mesenchymal coupling in development, and symmetry-breaking in cell polarization. We develop a modeling framework for bilayer reaction-diffusion systems and relate it to a range of existing models. We derive conditions for diffusion-driven instability of a spatially homogeneous equilibrium analogous to the classical conditions for a Turing instability in the simplest nontrivial setting where one domain has a standard reaction-diffusion system, and the other permits only diffusion. Due to the transverse coupling between these two regions, standard techniques for computing eigenfunctions of the Laplacian cannot be applied, and so we propose an alternative method to compute the dispersion relation directly. We compare instability conditions with full numerical simulations to demonstrate impacts of the geometry and coupling parameters on patterning, and explore various experimentally relevant asymptotic regimes. In the regime where the first domain is suitably thin, we recover a simple modulation of the standard Turing conditions, and find that often the broad impact of the diffusion-only domain is to reduce the ability of the system to form patterns. We also demonstrate complex impacts of this coupling on pattern formation. For instance, we exhibit non-monotonicity of pattern-forming instabilities with respect to geometric and coupling parameters, and highlight an instability from a nontrivial interaction between kinetics in one domain and diffusion in the other. These results are valuable for informing design choices in applications such as synthetic engineering of Turing patterns, but also for understanding the role of stratified media in modulating pattern-forming processes in developmental biology and beyond.

Engineering and modeling of multicellular morphologies and patterns

Synthetic multicellular (MC) systems have the capacity to increase our understanding of biofilms and higher organisms, and to serve as engineering platforms for developing complex products in the areas of medicine, biosynthesis and smart materials. Here we provide an interdisciplinary perspective and review on emerging approaches to engineer and model MC systems. We lay out definitions for key terms in the field and identify toolboxes of standardized parts which can be combined into various MC algorithms to achieve specific outcomes. Many essential parts and algorithms have been demonstrated in some form. As key next milestones for the field, we foresee the improvement of these parts and their adaptation to more biological systems, the demonstration of more complex algorithms, the advancement of quantitative modeling approaches and compilers to support rational MC engineering, and implementation of MC engineering for practical applications.

Engineering pattern formation and morphogenesis

The development of natural tissues, organs and bodies depends on mechanisms of patterning and of morphogenesis, typically (but not invariably) in that order, and often several times at different final scales. Using synthetic biology to engineer patterning and morphogenesis will both enhance our basic understanding of how development works, and provide important technologies for advanced tissue engineering. Focusing on mammalian systems built to date, this review describes patterning systems, both contact-mediated and reaction-diffusion, and morphogenetic effectors. It also describes early attempts to connect the two to create self-organizing physical form. The review goes on to consider how these self-organized systems might be modified to increase the complexity and scale of the order they produce, and outlines some possible directions for future research and development.

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.

Genetic Circuit-Assisted Smart Microbial Engineering

Rapid advances in DNA synthesis, genetic manipulation, and biosensors have greatly improved the ability to engineer microorganisms with complex functions. By accurately integrating quality biosensors and complex genetic circuits, recently emerged smart microorganisms have enabled exciting opportunities for dissecting complex signaling networks and making responses without artificial intervention. However, because of the lack of design principles, developing such smart microorganisms remains challenging. In this review, we propose the concept of smart microbial engineering (SME) and describe the general features of basic SME, including the circuit architecture, components, and design process. We also summarize the latest SME achievements, remaining challenges, and potential solutions in this growing field.

Engineered cell-to-cell signalling within growing bacterial cellulose pellicles

Bacterial cellulose is a strong and flexible biomaterial produced at high yields by Acetobacter species and has applications in health care, biotechnology and electronics. Naturally, bacterial cellulose grows as a large unstructured polymer network around the bacteria that produce it, and tools to enable these bacteria to respond to different locations are required to grow more complex structured materials. Here, we introduce engineered cell-to-cell communication into a bacterial cellulose-producing strain of Komagataeibacter rhaeticus to enable different cells to detect their proximity within growing material and trigger differential gene expression in response. Using synthetic biology tools, we engineer Sender and Receiver strains of K. rhaeticus to produce and respond to the diffusible signalling molecule, acyl-homoserine lactone. We demonstrate that communication can occur both within and between growing pellicles and use this in a boundary detection experiment, where spliced and joined pellicles sense and reveal their original boundary. This work sets the basis for synthetic cell-to-cell communication within bacterial cellulose and is an important step forward for pattern formation within engineered living materials.

Synthetic Biology for Multiscale Designed Biomimetic Assemblies: From Designed Self-Assembling Biopolymers to Bacterial Bioprinting

Nature is based on complex self-assembling systems that span from the nanoscale to the macroscale. We have already begun to design biomimetic systems with properties that have not evolved in nature, based on designed molecular interactions and regulation of biological systems. Synthetic biology is based on the principle of modularity, repurposing diverse building modules to design new types of molecular and cellular assemblies. While we are currently able to use techniques from synthetic biology to design self-assembling molecules and re-engineer functional cells, we still need to use guided assembly to construct biological assemblies at the macroscale. We review the recent strategies for designing biological systems ranging from molecular assemblies based on self-assembly of (poly)peptides to the guided assembly of patterned bacteria, spanning 7 orders of magnitude.

Recent Advances and Current Challenges in Synthetic Biology of the Plastid Genetic System and Metabolism

Building on recombinant DNA technology, leaps in synthesis, assembly, and analysis of DNA have revolutionized genetics and molecular biology over the past two decades (Kosuri and Church, 2014). These technological advances have accelerated the emergence of synthetic biology as a new discipline (Cameron et al., 2014). Synthetic biology is characterized by efforts targeted at the modification of existing and the design of novel biological systems based on principles adopted from information technology and engineering (Andrianantoandro et al., 2006; Khalil and Collins, 2010). As in more traditional engineering disciplines such as mechanical, electrical and civil engineering, synthetic biologists utilize abstraction, decoupling and standardization to make the design of biological systems more efficient and scalable. To facilitate the management of complexity, synthetic biology relies on an abstraction hierarchy composed of multiple levels (Endy, 2005): DNA as genetic material, "parts" as elements of DNA encoding basic biological functions (e.g. promoter, ribosome-binding site, terminator sequence), "devices" as any combination of parts implementing a human-defined function, and "systems" as any combination of devices fulfilling a predefined purpose. Parts are designated to perform predictable and modular functions in the context of higher-level devices or systems, which are successively refined through a cycle of designing, building, and testing.

Synthetic Biology Tools to Engineer Microbial Communities for Biotechnology

Microbial consortia have been used in biotechnology processes, including fermentation, waste treatment, and agriculture, for millennia. Today, synthetic biologists are increasingly engineering microbial consortia for diverse applications, including the bioproduction of medicines, biofuels, and biomaterials from inexpensive carbon sources. An improved understanding of natural microbial ecosystems, and the development of new tools to construct synthetic consortia and program their behaviors, will vastly expand the functions that can be performed by communities of interacting microorganisms. Here, we review recent advancements in synthetic biology tools and approaches to engineer synthetic microbial consortia, discuss ongoing and emerging efforts to apply consortia for various biotechnological applications, and suggest future applications.

Biological Engineered Living Materials: Growing Functional Materials with Genetically Programmable Properties

Natural biological materials exhibit remarkable properties: self-assembly from simple raw materials, precise control of morphology, diverse physical and chemical properties, self-repair, and the ability to sense-and-respond to environmental stimuli. Despite having found numerous uses in human industry and society, the utility of natural biological materials is limited. But, could it be possible to genetically program microbes to create entirely new and useful biological materials? At the intersection between microbiology, material science, and synthetic biology, the emerging field of biological engineered living materials (ELMs) aims to answer this question. Here we review recent efforts to program cells to produce living materials with novel functional properties, focusing on microbial systems that can be engineered to grow materials and on new genetic circuits for pattern formation that could be used to produce the more complex systems of the future.

Synthetic biology: engineering order in organisms across scales and species

[Formula: see text] Synthetic biology has enormous potential to solve problems in health, agriculture, and energy. Bill Jia and Arin Wongprommoon explore engineering approaches to controlling biological processes.