Transcription regulation networks control the expression of genes. The transcription networks of well-studied microorganisms appear to be made up of a small set of recurring regulation patterns, called network motifs. The same network motifs have recently been found in diverse organisms from bacteria to humans, suggesting that they serve as basic building blocks of transcription networks. Here I review network motifs and their functions, with an emphasis on experimental studies. Network motifs in other biological networks are also mentioned, including signalling and neuronal networks.

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Network motifs: theory and experimental approaches

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

The widespread use of controlled molecular-level motion in key natural processes suggests that great rewards could come from bridging the gap between the present generation of synthetic molecular systems, which by and large rely upon electronic and chemical effects to carry out their functions, and the machines of the macroscopic world, which utilize the synchronized movements of smaller parts to perform specific tasks. This is a scientific area of great contemporary interest and extraordinary recent growth, yet the notion of molecular-level machines dates back to a time when the ideas surrounding the statistical nature of matter and the laws of thermodynamics were first being formulated. Here we outline the exciting successes in taming molecular-level movement thus far, the underlying principles that all experimental designs must follow, and the early progress made towards utilizing synthetic molecular structures to perform tasks using mechanical motion. We also highlight some of the issues and challenges that still need to be overcome.

Synthetic molecular motors and mechanical machines.

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

Network reconstructions are a common denominator in systems biology. Bottom–up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

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A protocol for generating a high-quality genome-scale metabolic reconstruction.

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.

/pdf/a-synthetic-multicellular-system-for-programmed-pattern-2kdu5t2ayq.pdf

A synthetic multicellular system for programmed pattern formation

Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications. We outline the basic features of synthetic biology as a new engineering discipline, covering examples from the latest literature and reflecting on the features that make it unique among all other existing engineering fields. We discuss methods for designing and constructing engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. The discussion brings to light issues at the heart of designing complex living systems and provides a trajectory for future development.

/pdf/synthetic-biology-new-engineering-rules-for-an-emerging-zp8lknacr7.pdf

Synthetic biology: new engineering rules for an emerging discipline

One of the important challenges in the emerging field of synthetic biology is designing artificial networks that achieve coordinated behavior in cell communities. Here we present a synthetic multicellular bacterial system where receiver cells exhibit transient gene expression in response to a long-lasting signal from neighboring sender cells. The engineered sender cells synthesize an inducer, an acyl-homoserine lactone (AHL), which freely diffuses to spatially proximate receiver cells. The receiver cells contain a pulse-generator circuit that incorporates a feed-forward regulatory motif. The circuit responds to a long-lasting increase in the level of AHL by transiently activating, and then repressing, the expression of a GFP. Based on simulation models, we engineered variants of the pulse-generator circuit that exhibit different quantitative responses such as increased duration and intensity of the pulse. As shown by our models and experiments, the maximum amplitude and timing of the pulse depend not only on the final inducer concentration, but also on its rate of increase. The ability to differentiate between various rates of increase in inducer concentrations affords the system a unique spatiotemporal behavior for cells grown on solid media. Specifically, receiver cells can respond to communication from nearby sender cells while completely ignoring communication from senders cells further away, despite the fact that AHL concentrations eventually reach high levels everywhere. Because of the resemblance to naturally occurring feed-forward motifs, the pulse generator can serve as a model to improve our understanding of such systems.

/pdf/spatiotemporal-control-of-gene-expression-with-pulse-5c3bqji3fp.pdf

Spatiotemporal control of gene expression with pulse-generating networks

In this paper, we review an emerging engineering discipline to program cell behaviors by embedding synthetic gene networks that perform computation, communications, and signal processing. To accomplish this goal, we begin with a genetic component library and a biocircuit design methodology for assembling these components into compound circuits. The main challenge in biocircuit design lies in selecting well-matched genetic components that when coupled, reliably produce the desired behavior. We use simulation tools to guide circuit design, a process that consists of selecting the appropriate components and genetically modifying existing components until the desired behavior is achieved. In addition to such rational design, we also employ directed evolution to optimize genetic circuit behavior. Building on Nature's fundamental principle of evolution, this unique process directs cells to mutate their own DNA until they find gene network configurations that exhibit the desired system characteristics. The integration of all the above capabilities in future synthetic gene networks will enable cells to perform sophisticated digital and analog computation, both as individual entities and as part of larger cell communities. This engineering discipline and its associated tools will advance the capabilities of genetic engineering, and allow us to harness cells for a myriad of applications not previously achievable.

/pdf/genetic-circuit-building-blocks-for-cellular-computation-2eibzycmwy.pdf

Genetic circuit building blocks for cellular computation, communications, and signal processing

Engineered biosynthetic pathways have the potential to produce high-value molecules from inexpensive feedstocks, but a key limitation is engineering enzymes with high activity and specificity for new reactions. Here, we developed a method for combining structure-based computational protein design with library-based enzyme screening, in which inter-residue correlations favored by the design are encoded into a defined-sequence library. We validated this approach by engineering a glucose 6-oxidase enzyme for use in a proposed pathway to convert D-glucose into D-glucaric acid. The most active variant, identified after only one round of diversification and screening of only 10,000 wells, is approximately 400-fold more active on glucose than is the wild-type enzyme. We anticipate that this strategy will be broadly applicable to the discovery of new enzymes for engineered biological pathways.

Subhayu Basu

Papers

A synthetic multicellular system for programmed pattern formation

Synthetic biology: new engineering rules for an emerging discipline

Spatiotemporal control of gene expression with pulse-generating networks

Genetic circuit building blocks for cellular computation, communications, and signal processing

Engineering Enzyme Specificity Using Computational Design of a Defined-Sequence Library