Malarial Antibody Detection with an Engineered Yeast Agglutination Assay.

Malaria, a disease caused by the parasite carried by mosquitoes, is commonly diagnosed by microscopy of peripheral blood smears and with rapid diagnostic tests. Both methods show limited detection of low parasitemia that may maintain transmission and hinder malaria elimination. We have developed a novel agglutination assay in which modified cells act as antigen-displaying bead-like particles to capture malaria antibodies.

E.coli Nissle increases transcription of flagella assembly and formate hydrogenlyase genes in response to colitis.

Nissle (EcN), a probiotic bacterium, has been employed in treating inflammatory bowel disease, but the nature of its therapeutic effect is not fully understood. Intestinal inflammation alters the environment, exposing the microbial population to new stresses and eliciting transcriptional responses. We administered EcN to germ-free mice and then compared its transcriptional response between DSS-treated and untreated conditions using RNA-seq analysis to identify 187 differentially expressed genes (119 upregulated, 68 downregulated) and verifying a subset with qRT-PCR.

Improving the design of an oxidative stress sensing biosensor in yeast.

Transcription factor (TF)-based biosensors have proven useful for increasing biomanufacturing yields, large-scale functional screening, and in environmental monitoring. Most yeast TF-based biosensors are built from natural promoters, resulting in large DNA parts retaining considerable homology to the host genome, which can complicate biological engineering efforts. There is a need to explore smaller, synthetic biosensors to expand the options for regulating gene expression in yeast.

Role of Dilution Rate and Nutrient Availability in the Formation of Microbial Biofilms.

We revisited the mathematical model of the chemostat and examined consequences of considerably decreasing the concentration of limiting nutrient in the inflow for the growth of both the planktonic and biofilm cells in the chemostat tank (fermenter). The model predicts a substantially lower steady-state biomass of planktonic cells in response to decreasing inflowing nutrient concentration. Contrarily, the steady-state concentration of nutrient inside the fermenter is expected to remain the same, as long as the inflowing concentration does not fall below its value.

Programmable T7-based synthetic transcription factors.

Despite recent progress on synthetic transcription factor generation in eukaryotes, there remains a need for high-activity bacterial versions of these systems. In synthetic biology applications, it is useful for transcription factors to have two key features: they should be orthogonal (influencing only their own targets, with minimal off-target effects), and programmable (able to be directed to a wide range of user-specified transcriptional start sites). The RNA polymerase of the bacteriophage T7 has a number of appealing properties for synthetic biological designs: it can produce high transcription rates; it is a compact, single-subunit polymerase that has been functionally expressed in a variety of organisms; and its viral origin reduces the connection between its activity and that of its host's transcriptional machinery.

Design and Experimental Validation of Small Activating RNAs Targeting an Exogenous Promoter in Human Cells.

It is increasingly practical to co-opt many native cellular components into use as elements of synthetic biological systems. We present the design and experimental investigation of the first exogenous genetic construct to be successfully targeted by RNA activation, a phenomenon whereby small double-stranded RNAs increase gene expression from sequence-similar promoters by a mechanism thought to be related to that of RNA interference. Our selection of activating RNA candidates was informed by a custom-written computer program designed to choose target sites in the promoter of interest according to a set of empirical optimality criteria drawn from prior research.

Design principles for the analysis and construction of robustly homeostatic biological networks.

Homeostatic biological systems resist external disturbances, allowing cells and organisms to maintain a constant internal state despite perturbations from their surroundings. Many biological regulatory networks are known to act homeostatically, with examples including thermal adaptation, osmoregulation, and chemotaxis. Understanding the network topologies (sets of regulatory interactions) and biological parameter regimes that can yield homeostasis in a biological system is of interest both for the study of natural biological system, and in the context of designing new biological control schemes for use in synthetic biology.

Automated inference procedure for the determination of cell growth parameters.

The growth rate and carrying capacity of a cell population are key to the characterization of the population's viability and to the quantification of its responses to perturbations such as drug treatments. Accurate estimation of these parameters necessitates careful analysis. Here, we present a rigorous mathematical approach for the robust analysis of cell count data, in which all the experimental stages of the cell counting process are investigated in detail with the machinery of Bayesian probability theory.

An Escherichia coli system for evolving improved light-controlled DNA-binding proteins.

Light-switchable proteins offer numerous opportunities as tools for manipulating biological systems with exceptional degrees of spatiotemporal control. Most designed light-switchable proteins currently in use have not been optimised using the randomisation and selection/screening approaches that are widely used in other areas of protein engineering. Here we report an approach for screening light-switchable DNA-binding proteins that relies on light-dependent repression of the transcription of a fluorescent reporter.

Design and characterization of a dual-mode promoter with activation and repression capability for tuning gene expression in yeast.

Modularity in controlling gene expression artificially is becoming an essential aspect of synthetic biology. Artificial transcriptional control of gene expression is one of the most well-developed methods for the design of novel synthetic regulatory networks. Such networks are intended to help understand natural cellular phenomena and to enable new biotechnological applications.

Tuning response curves for synthetic biology.

Synthetic biology may be viewed as an effort to establish, formalize, and develop an engineering discipline in the context of biological systems. The ability to tune the properties of individual components is central to the process of system design in all fields of engineering, and synthetic biology is no exception. A large and growing number of approaches have been developed for tuning the responses of cellular systems, and here we address specifically the issue of tuning the rate of response of a system: given a system where an input affects the rate of change of an output, how can the shape of the response curve be altered experimentally? This affects a system's dynamics as well as its steady-state properties, both of which are critical in the design of systems in synthetic biology, particularly those with multiple components.

Physical constraints on biological integral control design for homeostasis and sensory adaptation.

Synthetic biology includes an effort to use design-based approaches to create novel controllers, biological systems aimed at regulating the output of other biological processes. The design of such controllers can be guided by results from control theory, including the strategy of integral feedback control, which is central to regulation, sensory adaptation, and long-term robustness. Realization of integral control in a synthetic network is an attractive prospect, but the nature of biochemical networks can make the implementation of even basic control structures challenging.

An active intracellular device to prevent lethal disease outcomes in virus-infected bacterial cells.

Synthetic biology includes an effort to logically control cellular behavior. One long-term goal is to implement medical interventions inside living cells, creating intracellular "disease fighters"; one may imagine a system that detects viral infection and responds to halt the spread of the virus. Here, we explore a system designed to display some of the qualitative features that such disease prevention systems should have, while not claiming that the system itself has any medical application.

Minimal genetic device with multiple tunable functions.

The ability to design artificial genetic devices with predictable functions is critical to the development of synthetic biology. Given the highly variable requirements of biological designs, the ability to tune the behavior of a genetic device is also of key importance; such tuning will allow devices to be matched with other components into larger systems, and to be shifted into the correct parameter regimes to elicit desired behaviors. Here, we have developed a minimal synthetic genetic system that acts as a multifunction, tunable biodevice in the bacterium Escherichia coli.

Considerations for using integral feedback control to construct a perfectly adapting synthetic gene network.

It has long been known to control theorists and engineers that integral feedback control leads to, and is necessary for, "perfect" adaptation to step input perturbations in most systems. Consequently, implementation of this robust control strategy in a synthetic gene network is an attractive prospect. However, the nature of genetic regulatory networks (density-dependent kinetics and molecular signals that easily reach saturation) implies that the design and construction of such a device is not straightforward.

Increasing the efficiency of bacterial transcription simulations: when to exclude the genome without loss of accuracy.

Background: Simulating the major molecular events inside an Escherichia coli cell can lead to a very large number of reactions that compose its overall behaviour. Not only should the model be accurate, but it is imperative for the experimenter to create an efficient model to obtain the results in a timely fashion. Here, we show that for many parameter regimes, the effect of the host cell genome on the transcription of a gene from a plasmid-borne promoter is negligible, allowing one to simulate the system more efficiently by removing the computational load associated with representing the presence of the rest of the genome.

Plasmid-borne prokaryotic gene expression: sources of variability and quantitative system characterization.

One aim of synthetic biology is to exert systematic control over cellular behavior, either for medical purposes or to "program" microorganisms. An engineering approach to the design of biological controllers demands a quantitative understanding of the dynamics of both the system to be controlled and the controllers themselves. Here we focus on a widely used method of exerting control in bacterial cells: plasmid vectors bearing gene-promoter pairs.

Effects of protein maturation on the noise in gene expression.

Fluorescent proteins are frequently used as reporters for gene expression in living cells, either by being expressed in tandem with a protein of interest or through the creation of fusion proteins. The data yielded by the fluorescence output are of considerable interest in efforts to formulate quantitative models of cellular behavior underway in fields such as systems biology and synthetic biology. An often neglected aspect of these proteins, however, is their maturation: Before a fluorescent protein can generate a fluorescent signal, it must mature through a series of steps (folding, cyclization, and oxidation) that may take from many minutes to over a day.

Dark proteins: effect of inclusion body formation on quantification of protein expression.

Plasmid-borne gene expression systems have found wide application in the emerging fields of systems biology and synthetic biology, where plasmids are used to implement simple network architectures, either to test systems biology hypotheses about issues such as gene expression noise or as a means of exerting artificial control over a cell's dynamics. In both these cases, fluorescent proteins are commonly applied as a means of monitoring the expression of genes in the living cell, and efforts have been made to quantify protein expression levels through fluorescence intensity calibration and by monitoring the partitioning of proteins among the two daughter cells after division; such quantification is important in formulating the predictive models desired in systems and synthetic biology research. A potential pitfall of using plasmid-based gene expression systems is that the high protein levels associated with expression from plasmids can lead to the formation of inclusion bodies, insoluble aggregates of misfolded, nonfunctional proteins that will not generate fluorescence output; proteins caught in these inclusion bodies are thus "dark" to fluorescence-based detection methods.

Simplification of stochastic chemical reaction models with fast and slow dynamics.

Biological systems often involve chemical reactions occurring in low-molecule-number regimes, where fluctuations are not negligible and thus stochastic models are required to capture the system behaviour. The resulting models are generally quite large and complex, involving many reactions and species. For clarity and computational tractability, it is important to be able to simplify these systems to equivalent ones involving fewer elements.

Extracting biochemical parameters for cellular modeling: A mean-field approach.

Recent developments in molecular biology have made it feasible to carry out experimental verification of mathematical models for biochemical processes, offering the eventual prospect of creating a detailed, validated picture of gene expression. A persistent difficulty with this long-term goal is the incompleteness of the kinetic information available in the literature: Many rate constants cannot or have not yet been measured. Here, we present a method of filling in missing parameters using an approach conceptually analogous to mean-field approaches in statistical mechanics: When studying a particular gene, we extract key parameters by considering the averaged effect of all other genes in the system, analogously to considering the averaged magnetic field in a physical spin model.

Systematic reduction of a stochastic signalling cascade model.

Biochemical systems involve chemical reactions occurring in low-number regimes, wherein fluctuations are not negligible and thus stochastic models are required to capture the system behaviour. The resulting models are often quite large and complex, involving many reactions and species. For clarity and computational tractability, it is important to be able to simplify these systems to equivalent ones involving fewer elements.