Metagenomics and 16S pyrosequencing have enabled the study of ecosystem structure and dynamics to great depth and accuracy. Co-occurrence and correlation patterns found in these data sets are increasingly used for the prediction of species interactions in environments ranging from the oceans to the human microbiome. In addition, parallelized co-culture assays and combinatorial labelling experiments allow high-throughput discovery of cooperative and competitive relationships between species. In this Review, we describe how these techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.

Microbial interactions: from networks to models

Differently from passive Brownian particles, active particles, also known as self-propelled Brownian particles or microswimmers and nanoswimmers, are capable of taking up energy from their environment and converting it into directed motion. Because of this constant flow of energy, their behavior can be explained and understood only within the framework of nonequilibrium physics. In the biological realm, many cells perform directed motion, for example, as a way to browse for nutrients or to avoid toxins. Inspired by these motile microorganisms, researchers have been developing artificial particles that feature similar swimming behaviors based on different mechanisms. These man-made micromachines and nanomachines hold a great potential as autonomous agents for health care, sustainability, and security applications. With a focus on the basic physical features of the interactions of self-propelled Brownian particles with a crowded and complex environment, this comprehensive review will provide a guided tour through its basic principles, the development of artificial self-propelling microparticles and nanoparticles, and their application to the study of nonequilibrium phenomena, as well as the open challenges that the field is currently facing.

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Active Particles in Complex and Crowded Environments

Microbial engineering often requires fine control over protein expression--for example, to connect genetic circuits or control flux through a metabolic pathway. To circumvent the need for trial and error optimization, we developed a predictive method for designing synthetic ribosome binding sites, enabling a rational control over the protein expression level. Experimental validation of >100 predictions in Escherichia coli showed that the method is accurate to within a factor of 2.3 over a range of 100,000-fold. The design method also correctly predicted that reusing identical ribosome binding site sequences in different genetic contexts can result in different protein expression levels. We demonstrate the method's utility by rationally optimizing protein expression to connect a genetic sensor to a synthetic circuit. The proposed forward engineering approach should accelerate the construction and systematic optimization of large genetic systems.

Automated design of synthetic ribosome binding sites to control protein expression

Synthetic biology is bringing together engineers and biologists to design and build novel biomolecular components, networks and pathways, and to use these constructs to rewire and reprogram organisms. These re-engineered organisms will change our lives over the coming years, leading to cheaper drugs, 'green' means to fuel our cars and targeted therapies for attacking 'superbugs' and diseases, such as cancer. The de novo engineering of genetic circuits, biological modules and synthetic pathways is beginning to address these crucial problems and is being used in related practical applications.

Synthetic biology: applications come of age.

Synthetic biology is a research field that combines the investigative nature of biology with the constructive nature of engineering. Efforts in synthetic biology have largely focused on the creation and perfection of genetic devices and small modules that are constructed from these devices. But to view cells as true 'programmable' entities, it is now essential to develop effective strategies for assembling devices and modules into intricate, customizable larger scale systems. The ability to create such systems will result in innovative approaches to a wide range of applications, such as bioremediation, sustainable energy production and biomedical therapies.

http://fbae.org/2009/FBAE/website/images/pdf/the-Second-Wave-of-Synthetic-Biology-from-modules-to-systems.pdf

The second wave of synthetic biology: from modules to systems

Pattern formation is a hallmark of coordinated cell behaviour in both single and multicellular organisms. It typically involves cell–cell communication and intracellular signal processing. Here we show a synthetic multicellular system in which genetically engineered ‘receiver’ cells are programmed to form ring-like patterns of differentiation based on chemical gradients of an acyl-homoserine lactone (AHL) signal that is synthesized by ‘sender’ cells. In receiver cells, ‘band-detect’ gene networks respond to user-defined ranges of AHL concentrations. By fusing different fluorescent proteins as outputs of network variants, an initially undifferentiated ‘lawn’ of receivers is engineered to form a bullseye pattern around a sender colony. Other patterns, such as ellipses and clovers, are achieved by placing senders in different configurations. Experimental and theoretical analyses reveal which kinetic parameters most significantly affect ring development over time. Construction and study of such synthetic multicellular systems can improve our quantitative understanding of naturally occurring developmental processes and may foster applications in tissue engineering, biomaterial fabrication and biosensing.

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A synthetic multicellular system for programmed pattern formation

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.

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A synthetic Escherichia coli predator-prey ecosystem.

Microbial fatty acid-derived fuels have emerged as promising alternatives to petroleum-based transportation fuels. Here we report a modular engineering approach that systematically removed metabolic pathway bottlenecks and led to significant titre improvements in a multi-gene fatty acid metabolic pathway. On the basis of central pathway architecture, E. coli fatty acid biosynthesis was re-cast into three modules: the upstream acetyl coenzyme A formation module; the intermediary acetyl-CoA activation module; and the downstream fatty acid synthase module. Combinatorial optimization of transcriptional levels of these three modules led to the identification of conditions that balance the supply of acetyl-CoA and consumption of malonyl-CoA/ACP. Refining protein translation efficiency by customizing ribosome binding sites for both the upstream acetyl coenzyme A formation and fatty acid synthase modules enabled further production improvement. Fed-batch cultivation of the engineered strain resulted in a final fatty acid production of 8.6 g l(-1). The modular engineering strategies demonstrate a generalized approach to engineering cell factories for valuable metabolites production.

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Modular optimization of multi-gene pathways for fatty acids production in E. coli

Towards synthetic microbial consortia for bioprocessing.

The transcription factor LuxR activates gene expression in response to binding the signaling molecule 3-oxo-hexanoyl-homoserine lactone (3OC6HSL), an acyl-HSL with a carbonyl substituent at the third carbon of the acyl chain. We previously described a LuxR variant, LuxR-G2E, that activates gene expression by binding a broader range of acyl-HSLs, including straight-chain acyl-HSLs to which LuxR does not respond. Here, we use a dual positive-negative selection system to identify a variant of LuxR-G2E that retains the response to straight-chain acyl-HSLs, but no longer responds to 3OC6HSL. A single mutation, R67M, reduces LuxR-G2E's response to acyl-HSLs having a carbonyl substituent at the third carbon of the acyl chain. This specificity-enhancing mutation would not have been identified by positive selection alone. The dual selection system provides a rapid and reliable method for identifying LuxR variants that have or lack the desired response to a given set of acyl-HSL signals. LuxR variants with altered signaling specificities might become useful components for constructing artificial cell-cell communication systems that program population level behaviors.

Cynthia H. Collins

Papers

A synthetic multicellular system for programmed pattern formation

A synthetic Escherichia coli predator-prey ecosystem.

Modular optimization of multi-gene pathways for fatty acids production in E. coli

Towards synthetic microbial consortia for bioprocessing.

Dual selection enhances the signaling specificity of a variant of the quorum-sensing transcriptional activator LuxR