Biology by design: reduction and synthesis of cellular components and behaviour

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Design Methodologies and the Limits of the Engineering-Dominated Conception of Synthetic Biology

Synthetic biology is described as a new field of biotechnology that models itself on engineering sciences. However, this view of synthetic biology as an engineering field has received criticism, and both biologists and philosophers have argued for a more nuanced and heterogeneous understanding of the field. This paper elaborates the heterogeneity of synthetic biology by clarifying the role of design and the variability of design methodologies in synthetic biology. I focus on two prominent design methodologies: rational design and directed evolution. Rational design resembles the design methodology of traditional engineering sciences. However, it is often replaced and complemented by the more biologically-inspired method of directed evolution, which models itself on natural evolution. These two approaches take philosophically different stances to the design of biological systems. Rational design aims to make biological systems more machine-like, whereas directed evolution utilizes variation and emergent features of living systems. I provide an analysis of the methodological basis of these design approaches, and highlight important methodological differences between them. By analyzing the respective benefits and limitations of these approaches, I argue against the engineering-dominated conception of synthetic biology and its "methodological monism", where the rational design approach is taken as the default design methodology. Alternative design methodologies, like directed evolution, should be considered as complementary, not competitive, to rational design.

Information transmission in microbial and fungal communication: from classical to quantum

Microbes have their own communication systems. Secretion and reception of chemical signaling molecules and ion-channels mediated electrical signaling mechanism are yet observed two special ways of information transmission in microbial community. In this article, we address the aspects of various crucial machineries which set the backbone of microbial cell-to-cell communication process such as quorum sensing mechanism (bacterial and fungal), quorum sensing regulated biofilm formation, gene expression, virulence, swarming, quorum quenching, role of noise in quorum sensing, mathematical models (therapy model, evolutionary model, molecular mechanism model and many more), synthetic bacterial communication, bacterial ion-channels, bacterial nanowires and electrical communication. In particular, we highlight bacterial collective behavior with classical and quantum mechanical approaches (including quantum information). Moreover, we shed a new light to introduce the concept of quantum synthetic biology and possible cellular quantum Turing test.

Biochemical mechanisms determine the functional compatibility of heterologous genes

Elucidating the factors governing the functional compatibility of horizontally transferred genes is important to understand bacterial evolution, including the emergence and spread of antibiotic resistance, and to successfully engineer biological systems. In silico efforts and work using single-gene libraries have suggested that sequence composition is a strong barrier for the successful integration of heterologous genes. Here we sample 200 diverse genes, representing >80% of sequenced antibiotic resistance genes, to interrogate the factors governing genetic compatibility in new hosts. In contrast to previous work, we find that GC content, codon usage, and mRNA-folding energy are of minor importance for the compatibility of mechanistically diverse gene products at moderate expression. Instead, we identify the phylogenetic origin, and the dependence of a resistance mechanism on host physiology, as major factors governing the functionality and fitness of antibiotic resistance genes. These findings emphasize the importance of biochemical mechanism for heterologous gene compatibility, and suggest physiological constraints as a pivotal feature orienting the evolution of antibiotic resistance.

Sequence composition is thought to be a major factor governing the functionality of horizontally transferred genes. In contrast, Porse et al. show that phylogenetic origin, and the type of resistance mechanism, are major factors affecting the functionality of horizontally transferred antibiotic resistance genes.

Societal impact of synthetic biology: responsible research and innovation (RRI)

Synthetic biology is an emerging field at the interface between biology and engineering, which has generated many expectations for beneficial biomedical and biotechnological applications. At the same time, however, it has also raised concerns about risks or the aim of producing new forms of living organisms. Researchers from different disciplines as well as policymakers and the general public have expressed the need for a form of technology assessment that not only deals with technical aspects, but also includes societal and ethical issues. A recent and very influential model of technology assessment that tries to implement these aims is known as RRI (Responsible Research and Innovation). In this paper, we introduce this model and its historical precursor strategies. Based on the societal and ethical issues which are presented in the current literature, we discuss challenges and opportunities of applying the RRI model for the assessment of synthetic biology.

Computational model for monitoring cholesterol metabolism

A non-deterministic finite automaton is designed to observe the cholesterol metabolism with the states of acceptance and rejection. The acceptance state of the automaton depicts the normal level of metabolism and production of good cholesterol as an end product. The rejection state of this machine shows the inhibition of enzymatic activity in cholesterol synthesis and removal of free fatty acids. The deficiency in human cholesterol metabolism pathway results in abnormal accumulation of cholesterol in plasma, arterial tissues leading to diseases such as hypercholesterolemia, atherosclerosis respectively and formation of gallstones. The designed machine can be used to monitor the cholesterol metabolism at molecular level through regulation of enzymes involved in the biosynthesis and metabolism of cholesterol for the treatment of diseases incident due to the respective metabolic disorder. In addition, an algorithm for this machine has been developed to compare the programmed string with the given string. This study demonstrates the construction of a machine that is used for the development of molecular targeted therapy for the disorders in cholesterol metabolism.

Insights into structural variations and genome rearrangements in prokaryotic genomes

Structural variations (SVs) are genomic rearrangements that affect fairly large fragments of DNA. Most of the SVs such as inversions, deletions and translocations have been largely studied in context of genetic diseases in eukaryotes. However, recent studies demonstrate that genome rearrangements can also have profound impact on prokaryotic genomes, leading to altered cell phenotype. In contrast to single-nucleotide variations, SVs provide a much deeper insight into organization of bacterial genomes at a much better resolution. SVs can confer change in gene copy number, creation of new genes, altered gene expression and many other functional consequences. High-throughput technologies have now made it possible to explore SVs at a much refined resolution in bacterial genomes. Through this review, we aim to highlight the importance of the less explored field of SVs in prokaryotic genomes and their impact. We also discuss its potential applicability in the emerging fields of synthetic biology and genome engineering where targeted SVs could serve to create sophisticated and accurate genome editing.

A designer cell-based histamine-specific human allergy profiler

Allergic disorders are markedly increasing in industrialized countries. The identification of compounds that trigger the immunoglobulin E-dependent allergic reaction remains the key to limit patients’ exposure to critical allergens and improve their quality of life. Here we use synthetic biology principles to design a mammalian cell-based allergy profiler that scores the allergen-triggered release of histamine from whole-blood-derived human basophils. A synthetic signalling cascade engineered within the allergy profiler rewires histamine input to the production of reporter protein, thereby integrating histamine levels in whole-blood samples with remarkable sensitivity and a wide dynamic range, allowing for rapid results or long-term storage of output, respectively. This approach provides non-intrusive allergy profiles for the personalized medicine era.

The advancement of sensitive, accurate and non-invasive methods to identify the allergen that drives allergic disease in an individual remains a challenge. Here, the authors develop a synthetic biology approach using human designer cells to profile allergic reactions against an array of allergens measuring histamine release from whole blood.

Systematic Approach to Computational Design of Gene Regulatory Networks with Information Processing Capabilities

We present several measures that can be used in de novo computational design of biological systems with information processing capabilities. Their main purpose is to objectively evaluate the behavior and identify the biological information processing structures with the best dynamical properties. They can be used to define constraints that allow one to simplify the design of more complex biological systems. These measures can be applied to existent computational design approaches in synthetic biology, i.e., rational and automatic design approaches. We demonstrate their use on a) the computational models of several basic information processing structures implemented with gene regulatory networks and b) on a modular design of a synchronous toggle switch.

A prospective overview of the essential requirements in molecular modeling for nanomedicine design

Nanotechnology has presented many new challenges and opportunities in the area of nanomedicine design. The issues related to nanoconjugation, nanosystem-mediated targeted drug delivery, transitional stability of nanovehicles, the integrity of drug transport, drug-delivery mechanisms and chemical structural design require a pre-estimated and determined course of assumptive actions with property and characteristic estimations for optimal nanomedicine design. Molecular modeling in nanomedicine encompasses these pre-estimations and predictions of pertinent design data via interactive computographic software. Recently, an increasing amount of research has been reported where specialized software is being developed and employed in an attempt to bridge the gap between drug discovery, materials science and biology. This review provides an assimilative and concise incursion into the current and future strategies of molecular-modeling applications in nanomedicine design and aims to describe the utilization of molecular models and theoretical-chemistry computographic techniques for expansive nanomedicine design and development.

An attempt to construct a (general) mathematical framework to model biological "context-dependence"

Context-dependent nature of biological phenomena is well documented in every branch of biology. While there have been few previous attempts to (implicitly) model various (particular) facets of biological context-dependence, a formal and general mathematical construct to model the wide spectrum of context-dependence, eludes the students of biology. Such an objective model, from both 'bottom-up' as well as 'top-down' perspective, is proposed here to serve as the template to describe the various kinds of context-dependence that we encounter in different branches of biology. Interactions between biological contexts was found to be transitive but non-commutative. It is found that a hierarchical nature of dependence among the biological contexts models the emergent biological properties efficiently. Reasons for these findings are provided in a general model to describe biological reality. Scheme to algorithmically implement the hierarchic structure of organization of biological contexts was proposed with a construct named 'Context tree'. A 'Context tree' based analysis of context interactions among biophysical factors influencing protein structure was performed.

Microbial synthetic biology for human therapeutics

The emerging field of synthetic biology holds tremendous potential for developing novel drugs to treat various human conditions. The current study discusses the scope of synthetic biology for human therapeutics via microbial approach. In this context, synthetic biology aims at designing, engineering and building new microbial synthetic cells that do not pre-exist in nature as well as re-engineer existing microbes for synthesis of therapeutic products. It is expected that the construction of novel microbial genetic circuitry for human therapeutics will greatly benefit from the data generated by 'omics' approaches and multidisciplinary nature of synthetic biology. Development of novel antimicrobial drugs and vaccines by engineering microbial systems are a promising area of research in the field of synthetic biology for human theragnostics. Expression of plant based medicinal compounds in the microbial system using synthetic biology tools is another avenue dealt in the present study. Additionally, the study suggest that the traditional medicinal knowledge can do value addition for developing novel drugs in the microbial systems using synthetic biology tools. The presented work envisions the success of synthetic biology for human therapeutics via microbial approach in a holistic manner. Keeping this in view, various legal and socio-ethical concerns emerging from the use of synthetic biology via microbial approach such as patenting, biosafety and biosecurity issues have been touched upon in the later sections.

Leading a successful iGEM team

The International Genetically Engineered Machines (iGEM) competition allows undergraduate teams to develop projects in synthetic biology within the context of a large, international Jamboree. Organizing and managing a successful iGEM team is an exercise in advanced agile project development. While many of the principles applicable to such teams are derived from management of agile software teams, iGEM presents several unique challenges.

Programming microbial population dynamics by engineered cell-cell communication

A major aim of synthetic biology is to program novel cellular behavior using engineered gene circuits. Early endeavors focused on building simple circuits that fulfill simple functions, such as logic gates, bistable toggle switches, and oscillators. These gene circuits have primarily focused on single-cell behaviors since they operate intracellularly. Thus, they are often susceptible to cell-cell variations due to stochastic gene expression. Cell-cell communication offers an efficient strategy to coordinate cellular behavior at the population level. To this end, we review recent advances in engineering cell-cell communication to achieve reliable population dynamics, spanning from communication within single species to multispecies, from one-way sender-receiver communication to two-way communication in synthetic microbial ecosystems. These engineered systems serve as well-defined model systems to better understand design principles of their naturally occurring counterparts and to facilitate novel biotechnology applications.

Engineering the logical properties of a genetic AND gate

Synthetic biology promises to enhance our ability to control biological systems by creating a systematic approach for the construction of genetic circuits that reliably program cellular function. As part of this approach, efficient methods are needed for the tuning of genetic circuits so as to allow for optimization of a design despite varying cellular contexts and incomplete understanding of in vivo biological interactions. Here we outline an optimization method that we have used to improve the logical responses of a genetic AND logic gate derived from components of the LuxI-LuxR bacterial quorum-sensing system. Basing our approach on the idea of evolutionary design, we improved the properties of our genetic AND logic gate by using directed evolution and a two-step screening process to alter the activities of the LuxR transcriptional activator. Using this method, we were able to rapidly enhance the AND gate's logical responses and have increased the specificities of these responses by ∼1.5-fold.

Rationally designed logic integration of regulatory signals in mammalian cells

Molecular-level information processing is essential for 'smart' in vivo nanosystems. Natural molecular computing, such as the regulation of messenger RNA (mRNA) synthesis by special proteins called transcription factors, has inspired engineered systems that can control the levels of mRNA with certain combinations of transcription factors. Here, we show an alternative approach to achieving general-purpose control of mRNA and protein levels by logic integration of transcription factor input signals in mammalian cells. The transcription factors regulate synthetic genes coding for small regulatory RNAs (called microRNAs), which, in turn, control the mRNA of interest (the output) via an RNA interference pathway. The simplicity of these modular interactions makes it possible, in theory, to implement any arbitrary logic relation between the transcription factors and the output. We construct, test and optimize increasingly complex circuits with up to three transcription factor inputs, establishing a platform for in vivo molecular computing.

Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology

Synthetic biology seeks to enable programmed control of cellular behavior though engineered biological systems. These systems typically consist of synthetic circuits that function inside, and interact with, complex host cells possessing pre-existing metabolic and regulatory networks. Nevertheless, while designing systems, a simple well-defined interface between the synthetic gene circuit and the host is frequently assumed. We describe the generation of robust but unexpected oscillations in the densities of bacterium Escherichia coli populations by simple synthetic suicide circuits containing quorum components and a lysis gene. Contrary to design expectations, oscillations required neither the quorum sensing genes (luxR and luxI) nor known regulatory elements in the P_luxI promoter. Instead, oscillations were likely due to density-dependent plasmid amplification that established a population-level negative feedback. A mathematical model based on this mechanism captures the key characteristics of oscillations, and model predictions regarding perturbations to plasmid amplification were experimentally validated. Our results underscore the importance of plasmid copy number and potential impact of “hidden interactions” on the behavior of engineered gene circuits - a major challenge for standardizing biological parts. As synthetic biology grows as a discipline, increasing value may be derived from tools that enable the assessment of parts in their final context.

An environment-sensitive synthetic microbial ecosystem

Microbial ecosystems have been widely used in industrial production, but the inter-relationships of organisms within them haven't been completely clarified due to complex composition and structure of natural microbial ecosystems. So it is challenging for ecologists to get deep insights on how ecosystems function and interplay with surrounding environments. But the recent progresses in synthetic biology show that construction of artificial ecosystems where relationships of species are comparatively clear could help us further uncover the meadow of those tiny societies. By using two quorum-sensing signal transduction circuits, this research designed, simulated and constructed a synthetic ecosystem where various population dynamics formed by changing environmental factors. Coherent experimental data and mathematical simulation in our study show that different antibiotics levels and initial cell densities can result in correlated population dynamics such as extinction, obligatory mutualism, facultative mutualism and commensalism. This synthetic ecosystem provides valuable information for addressing questions in ecology and may act as a chassis for construction of more complex microbial ecosystems.

How to turn a genetic circuit into a synthetic tunable oscillator, or a bistable switch

Systems and Synthetic Biology use computational models of biological pathways in order to study in silico the behaviour of biological pathways. Mathematical models allow to verify biological hypotheses and to predict new possible dynamical behaviours. Here we use the tools of non-linear analysis to understand how to change the dynamics of the genes composing a novel synthetic network recently constructed in the yeast Saccharomyces cerevisiae for In-vivo Reverse-engineering and Modelling Assessment (IRMA). Guided by previous theoretical results that make the dynamics of a biological network depend on its topological properties, through the use of simulation and continuation techniques, we found that the network can be easily turned into a robust and tunable synthetic oscillator or a bistable switch. Our results provide guidelines to properly re-engineering in vivo the network in order to tune its dynamics.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.

Gemini, a bifunctional enzymatic and fluorescent reporter of gene expression

Background

The development of collections of quantitatively characterized standard biological parts should facilitate the engineering of increasingly complex and novel biological systems. The existing enzymatic and fluorescent reporters that are used to characterize biological part functions exhibit strengths and limitations. Combining both enzymatic and fluorescence activities within a single reporter protein would provide a useful tool for biological part characterization.

Methodology/Principal Findings

Here, we describe the construction and quantitative characterization of Gemini, a fusion between the β-galactosidase (β-gal) α-fragment and the N-terminus of full-length green fluorescent protein (GFP). We show that Gemini exhibits functional β-gal activity, which we assay with plates and fluorometry, and functional GFP activity, which we assay with fluorometry and microscopy. We show that the protein fusion increases the sensitivity of β-gal activity and decreases the sensitivity of GFP.

Conclusions/Significance

Gemini is therefore a bifunctional reporter with a wider dynamic range than the β-gal α-fragment or GFP alone. Gemini enables the characterization of gene expression, screening assays via enzymatic activity, and quantitative single-cell microscopy or FACS via fluorescence activity. The analytical flexibility afforded by Gemini will likely increase the efficiency of research, particularly for screening and characterization of libraries of standard biological parts.

Emergent bistability by a growth-modulating positive feedback circuit

Synthetic gene circuits are often engineered by considering the host cell as an invariable 'chassis'. Circuit activation, however, may modulate host physiology, which in turn can substantially impact circuit behavior. We illustrate this point by a simple circuit consisting of mutant T7 RNA polymerase (T7 RNAP*) that activates its own expression in the bacterium Escherichia coli. Although activation by the T7 RNAP* is noncooperative, the circuit caused bistable gene expression. This counterintuitive observation can be explained by growth retardation caused by circuit activation, which resulted in nonlinear dilution of T7 RNAP* in individual bacteria. Predictions made by models accounting for such effects were verified by further experimental measurements. Our results reveal a new mechanism of generating bistability and underscore the need to account for host physiology modulation when engineering gene circuits.

Engineering multicellular systems by cell-cell communication

Synthetic biology encompasses the design of new biological parts and systems as well as the modulation of existing biological networks to generate novel functions. In recent years, increasing emphasis has been placed on the engineering of population-level behaviors using cell-cell communication. From the engineering perspective, cell-cell communication serves as a versatile regulatory module that enables coordination among cells in and between populations and facilitates the generation of reliable dynamics. In addition to exploring biological 'design principles' via the construction of increasingly complex dynamics, communication-based synthetic systems can be used as well-defined model systems to study ecological and social interactions such as competition, cooperation, and predation. Here we discuss the dynamic properties of cell-cell communication modules, how they can be engineered for synthetic circuit design, and applications of these systems.

Directed evolution: new parts and optimized function

Constructing novel biological systems that function in a robust and predictable manner requires better methods for discovering new functional molecules and for optimizing their assembly in novel biological contexts. By enabling functional diversification and optimization in the absence of detailed mechanistic understanding, directed evolution is a powerful complement to 'rational' engineering approaches. Aided by clever selection schemes, directed evolution has generated new parts for genetic circuits, cell-cell communication systems, and non-natural metabolic pathways in bacteria.

Rate and extent of protein localization is controlled by peptide-binding domain association kinetics and morphology

Protein localization is an important regulatory mechanism in many cell signaling pathways such as cytoskeletal organization and genetic regulation. The specific mechanism of protein localization determines the kinetics and morphological constraints of protein translocation, and thus affects the rate and extent of localization. To investigate the affect of localization kinetics and morphology on protein localization, we designed a protein localization system based on Ca(2+)-calmodulin and Src homology 3 domain binding peptides that can translocate between specific localizations in response to a Ca(2+) signal. We used a stochastic biomolecular simulator to predict that such a protein localization system will exhibit slower and less complete translocations when the association kinetics of a binding domain and peptide are reduced. As well, we predicted that increasing the diffusion resistance by manipulating the morphology of the system would similarly impair translocation speed and completeness. We then constructed a network of synthetic fusion proteins and showed that these predictions could be qualitatively confirmed in vitro. This work provides a basis for explaining the different characteristics (rate and extent) of protein transport and localization in cells as a consequence of the kinetics and morphology of the transport mechanism.

Optimal tuning of bacterial sensing potential

Through production and sensing of small signal molecules, quorum sensing (QS) enables bacteria to detect changes in their density and regulate their functions accordingly. QS systems are tremendously diverse in terms of their specific sensory components, the biochemical and transport properties of signaling molecules, their target functions and the context in which QS-mediated functions are activated. Cutting across this diversity, however, the central architecture of QS systems is universal; it comprises signal synthesis, secretion, degradation and detection. We are thus able to derive a general metric for QS ‘sensing potential' based on this ‘core' module. The sensing potential quantifies the ability of a single bacterium to sense the dimensions of its microenvironment. This simple metric captures the dominant activation properties of diverse QS systems, giving a concise description of the sensing characteristics. As such, it provides a convenient quantitative framework to study the phenotypic effects of QS characteristics. As an example, we show how QS characteristics uniquely determine the scenarios in which regulation of a typical QS-controlled function, such as exoenzyme secretion, becomes advantageous.

Decoding biological principles using gene circuits

A major flavor of synthetic biology is the creation of artificial gene circuits to perform user-defined tasks. One aspect of this area is to realize ever-increasingly more complicated circuit behavior. Such efforts have led to the identification and evaluation of design strategies that enable robust control of dynamics in single cells and in cell populations. On the other hand, there is increasing emphasis on using artificial systems programmed by simple circuits to explore fundamental biological questions of broad significance.

Diversity-based, model-guided construction of synthetic gene networks with predicted functions

Engineering artificial gene networks from modular components is a major goal of synthetic biology. However, the construction of gene networks with predictable functions remains hampered by a lack of suitable components and the fact that assembled networks often require extensive, iterative retrofitting to work as intended. Here we present an approach that couples libraries of diversified components (synthesized with randomized nonessential sequence) with in silico modeling to guide predictable gene network construction without the need for post hoc tweaking. We demonstrate our approach in Saccharomyces cerevisiae by synthesizing regulatory promoter libraries and using them to construct feed-forward loop networks with different predicted input-output characteristics. We then expand our method to produce a synthetic gene network acting as a predictable timer, modifiable by component choice. We use this network to control the timing of yeast sedimentation, illustrating how the plug-and-play nature of our design can be readily applied to biotechnology.

Towards programming languages for genetic engineering of living cells

Synthetic biology aims at producing novel biological systems to carry out some desired and well-defined functions. An ultimate dream is to design these systems at a high level of abstraction using engineering-based tools and programming languages, press a button, and have the design translated to DNA sequences that can be synthesized and put to work in living cells. We introduce such a programming language, which allows logical interactions between potentially undetermined proteins and genes to be expressed in a modular manner. Programs can be translated by a compiler into sequences of standard biological parts, a process that relies on logic programming and prototype databases that contain known biological parts and protein interactions. Programs can also be translated to reactions, allowing simulations to be carried out. While current limitations on available data prevent full use of the language in practical applications, the language can be used to develop formal models of synthetic systems, which are otherwise often presented by informal notations. The language can also serve as a concrete proposal on which future language designs can be discussed, and can help to guide the emerging standard of biological parts which so far has focused on biological, rather than logical, properties of parts.

Engineering microbial consortia: a new frontier in synthetic biology

Microbial consortia are ubiquitous in nature and are implicated in processes of great importance to humans, from environmental remediation and wastewater treatment to assistance in food digestion. Synthetic biologists are honing their ability to program the behavior of individual microbial populations, forcing the microbes to focus on specific applications, such as the production of drugs and fuels. Given that microbial consortia can perform even more complicated tasks and endure more changeable environments than monocultures can, they represent an important new frontier for synthetic biology. Here, we review recent efforts to engineer synthetic microbial consortia, and we suggest future applications.

A synthetic Escherichia coli predator-prey ecosystem

We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator-prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner.

Synthetic tissue biology: tissue engineering meets synthetic biology

We propose the term "synthetic tissue biology" to describe the use of engineered tissues to form biological systems with metazoan-like complexity. The increasing maturity of tissue engineering is beginning to render this goal attainable. As in other synthetic biology approaches, the perspective is bottom-up; here, the premise is that complex functional phenotypes (on par with those in whole metazoan organisms) can be effected by engineering biology at the tissue level. To be successful, current efforts to understand and engineer multicellular systems must continue, and new efforts to integrate different tissues into a coherent structure will need to emerge. The fruits of this research may include improved understanding of how tissue systems can be integrated, as well as useful biomedical technologies not traditionally considered in tissue engineering, such as autonomous devices, sensors, and manufacturing.

A synthetic biology challenge: making cells compute

Advances in biology and engineering have enabled the reprogramming of cells with well-defined functions, leading to the emergence of synthetic biology. Early successes in this nascent field suggest its potential to impact diverse areas. Here, we examine the feasibility of engineering circuits for cell-based computation. We illustrate the basic concepts by describing the mapping of several computational problems to engineered gene circuits. Revolving around these examples and past studies, we discuss technologies and computational methods available to design, test, and optimize gene circuits. We conclude with discussion of challenges involved in a typical design cycle, as well as those specific to cellular computation.