An Overview of the Use of Equine Collagen as Emerging Material for Biomedical Applications

Type I collagen has always aroused great interest in the field of life-science and bioengineering, thanks to its favorable structural properties and bioactivity. For this reason, in the last five decades it has been widely studied and employed as biomaterial for the manufacture of implantable medical devices. Commonly used sources of collagen are represented by bovine and swine but their applications are limited because of the zoonosis transmission risks, the immune response and the religious constrains. Thus, type-I collagen isolated from horse tendon has recently gained increasing interest as an attractive alternative, so that, although bovine and porcine derived collagens still remain the most common ones, more and more companies started to bring to market a various range of equine collagen-based products. In this context, this work aims to overview the properties of equine collagen making it particularly appealing in medicine, cosmetics and pharmaceuticals, as well as its main biomedical applications and the currently approved equine collagen-based medical devices, focusing on experimental studies and clinical trials of the last 15 years. To the best of our knowledge, this is the first review focusing on the use of equine collagen, as well as on equine collagen-based marketed products for healthcare.

Electron availability in CO2 , CO and H2 mixtures constrains flux distribution, energy management and product formation in Clostridium ljungdahlii

Acetogens such as Clostridium ljungdahlii can play a crucial role reducing the human CO2 footprint by converting industrial emissions containing CO2 , CO and H2 into valuable products such as organic acids or alcohols. The quantitative understanding of cellular metabolism is a prerequisite to exploit the bacterial endowments and to fine-tune the cells by applying metabolic engineering tools. Studying the three gas mixtures CO2  + H2 , CO and CO + CO2  + H2 (syngas) by continuously gassed batch cultivation experiments and applying flux balance analysis, we identified CO as the preferred carbon and electron source for growth and producing alcohols. However, the total yield of moles of carbon (mol-C) per electrons consumed was almost identical in all setups which underlines electron availability as the main factor influencing product formation. The Wood-Ljungdahl pathway (WLP) showed high flexibility by serving as the key NAD+ provider for CO2  + H2, whereas this function was strongly compensated by the transhydrogenase-like Nfn complex when CO was metabolized. Availability of reduced ferredoxin (Fdred ) can be considered as a key determinant of metabolic control. Oxidation of CO via carbon monoxide dehydrogenase (CODH) is the main route of Fdred formation when CO is used as substrate, whereas Fdred is mainly regenerated via the methyl branch of WLP and the Nfn complex utilizing CO2  + H2 . Consequently, doubled growth rates, highest ATP formation rates and highest amounts of reduced products (ethanol, 2,3-butanediol) were observed when CO was the sole carbon and electron source.

ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes

Differential expressed (DE) genes analysis is valuable for understanding comparative transcriptomics between cells, conditions or time evolution. However, the predominant way of identifying DE genes is to use arbitrary threshold fold or expression changes as cutoff. Here, we developed a more objective method, Scatter Overlay or ScatLay, to extract and graphically visualize DE genes across any two samples by utilizing their pair-wise scatter or transcriptome-wide noise, while factoring replicate variabilities. We tested ScatLay for 3 cell types: between time points for Escherichia coli aerobiosis and Saccharomyces cerevisiae hypoxia, and between untreated and Etomoxir treated Mus Musculus embryonic stem cell. As a result, we obtain 1194, 2061 and 2932 DE genes, respectively. Next, we compared these data with two widely used current approaches (DESeq2 and NOISeq) with typical twofold expression changes threshold, and show that ScatLay reveals significantly larger number of DE genes. Hence, our method provides a wider coverage of DE genes, and will likely pave way for finding more novel regulatory genes in future works.

High-Resolution 3D Bioprinting of Photo-Cross-linkable Recombinant Collagen to Serve Tissue Engineering Applications

Various biopolymers, including gelatin, have already been applied to serve a plethora of tissue engineering purposes. However, substantial concerns have arisen related to the safety and the reproducibility of these materials due to their animal origin and the risk associated with pathogen transmission as well as batch-to-batch variations. Therefore, researchers have been focusing their attention toward recombinant materials that can be produced in a laboratory with full reproducibility and can be designed according to specific needs (e.g., by introducing additional RGD sequences). In the present study, a recombinant protein based on collagen type I (RCPhC1) was functionalized with photo-cross-linkable methacrylamide (RCPhC1-MA), norbornene (RCPhC1-NB), or thiol (RCPhC1-SH) functionalities to enable high-resolution 3D printing via two-photon polymerization (2PP). The results indicated a clear difference in 2PP processing capabilities between the chain-growth-polymerized RCPhC1-MA and the step-growth-polymerized RCPhC1-NB/SH. More specifically, reduced swelling-related deformations resulting in a superior CAD-CAM mimicry were obtained for the RCPhC1-NB/SH hydrogels. In addition, RCPhC1-NB/SH allowed the processing of the material in the presence of adipose tissue-derived stem cells that survived the encapsulation process and also were able to proliferate when embedded in the printed structures. As a consequence, it is the first time that successful HD bioprinting with cell encapsulation is reported for recombinant hydrogel bioinks. Therefore, these results can be a stepping stone toward various tissue engineering applications.

Towards continuous industrial bioprocessing with solventogenic and acetogenic clostridia: challenges, progress and perspectives

The sustainable production of solvents from above ground carbon is highly desired. Several clostridia naturally produce solvents and use a variety of renewable and waste-derived substrates such as lignocellulosic biomass and gas mixtures containing H2/CO2 or CO. To enable economically viable production of solvents and biofuels such as ethanol and butanol, the high productivity of continuous bioprocesses is needed. While the first industrial-scale gas fermentation facility operates continuously, the acetone-butanol-ethanol (ABE) fermentation is traditionally operated in batch mode. This review highlights the benefits of continuous bioprocessing for solvent production and underlines the progress made towards its establishment. Based on metabolic capabilities of solvent producing clostridia, we discuss recent advances in systems-level understanding and genome engineering. On the process side, we focus on innovative fermentation methods and integrated product recovery to overcome the limitations of the classical one-stage chemostat and give an overview of the current industrial bioproduction of solvents.

Genetic Circuit Dynamics: Hazard and Glitch Analysis

Multiple input changes can cause unwanted switching variations, or glitches, in the output of genetic combinational circuits. These glitches can have drastic effects if the output of the circuit causes irreversible changes within or with other cells such as a cascade of responses, apoptosis, or the release of a pharmaceutical in an off-target tissue. Therefore, avoiding unwanted variation of a circuit's output can be crucial for the safe operation of a genetic circuit. This paper investigates what causes unwanted switching variations in combinational genetic circuits using hazard analysis and a new dynamic model generator. The analysis is done in previously built and modeled genetic circuits with known glitching behavior. The dynamic models generated not only predict the same steady states as previous models but can also predict the unwanted switching variations that have been observed experimentally. Multiple input changes may cause glitches due to propagation delays within the circuit. Modifying the circuit's layout to alter these delays may change the likelihood of certain glitches, but it cannot eliminate the possibility that the glitch may occur. In other words, function hazards cannot be eliminated. Instead, they must be avoided by restricting the allowed input changes to the system. Logic hazards, on the other hand, can be avoided using hazard-free logic synthesis. This paper demonstrates this by showing how a circuit designed using a popular genetic design automation tool can be redesigned to eliminate logic hazards.

Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology

Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals  <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.

In an inter-laboratory study, the authors compare the accuracy and performance of three optical density calibration protocols (colloidal silica, serial dilution of silica microspheres, and colony-forming unit (CFU) assay). They demonstrate that serial dilution of silica microspheres is the best of these tested protocols, allowing precise and robust calibration that is easily assessed for quality control and can also evaluate the effective linear range of an instrument.

Additive Biomanufacturing with Collagen Inks

Collagen is a natural polymer found abundantly in the extracellular matrix (ECM). It is easily extracted from a variety of sources and exhibits excellent biological properties such as biocompatibility and weak antigenicity. Additionally, different processes allow control of physical and chemical properties such as mechanical stiffness, viscosity and biodegradability. Moreover, various additive biomanufacturing technology has enabled layer-by-layer construction of complex structures to support biological function. Additive biomanufacturing has expanded the use of collagen biomaterial in various regenerative medicine and disease modelling application (e.g., skin, bone and cornea). Currently, regulatory hurdles in translating collagen biomaterials still remain. Additive biomanufacturing may help to overcome such hurdles commercializing collagen biomaterials and fulfill its potential for biomedicine.

Study of the effects of 3.1 THz radiation on the expression of recombinant red fluorescent protein (RFP) in E. coli

In recent years, many studies have been conducted to investigate the non-thermal effects of THz radiation on different organisms, but further studies are needed to fully elucidate the effects, especially on the molecular level. In this study, we explored the effects of at 3.1 THz radiation on protein expression in Escherichia coli (E. coli) using red fluorescent protein as a reporter molecule. After 8 hours of continuous THz irradiation of bacteria on LB (Luria-Bertani) solid plates at an average power of 33 mW/cm² and 10 Hz pulse repetition frequency, we found that the plasmid copy number, protein expression and fluorescence intensity of bacteria from the irradiated area were 3.8-, 2.7-, and 3.3 times higher than in bacteria from the un-irradiated area, respectively. These findings suggest that plasmid replication changed significantly in bacteria exposed to 3.1 THz radiation, resulting in increased protein expression as evidenced by increased fluorescence intensity of the RFP reporter.

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.

Synthetic biology in various cellular and molecular fields: applications, limitations, and perspective

Synthetic biology breakthroughs have facilitated genetic circuit engineering to program cells through novel biological functions, dynamic gene expressions, as well as logic controls. SynBio can also participate in the rapid development of new treatments required for the human lifestyle. Moreover, these technologies are applied in the development of innovative therapeutic, diagnostic, as well as discovery-related methods within a wide range of cellular and molecular applications. In the present review study, SynBio applications in various cellular and molecular fields such as novel strategies for cancer therapy, biosensing, metabolic engineering, protein engineering, and tissue engineering were highlighted and summarized. The major safety and regulatory concerns about synthetic biology will be the environmental release, legal concerns, and risks of the engineered organisms. The final sections focused on limitations to SynBio.

Toolkit Development for Cyanogenic and Gold Biorecovery Chassis Chromobacterium violaceum

Chromobacterium violaceum has been of interest recently due to its cyanogenic ability and its potential role in environmental sustainability via the biorecovery of gold from electronic waste. However, as with many nonmodel bacteria, there are limited genetic tools to implement the use of this Gram-negative chassis in synthetic biology. We propose a system that involves assaying spontaneous antibiotic resistances and using broad host range vectors to develop episomal vectors for nonmodel Gram-negative bacteria. These developed vectors can subsequently be used to characterize inducible promoters for gene expressions and implementing CRISPRi to inhibit endogenous gene expression for further studies. Here, we developed the first episomal genetic toolkit for C. violaceum consisting of two origins of replication, three antibiotic resistance genes, and four inducible promoter systems. We examined the occurrences of spontaneous resistances of the bacterium to the chosen selection markers to prevent incidences of false positives. We also tested broad host range vectors from four different incompatibility groups and characterized four inducible promoter systems, which potentially can be applied in other Gram-negative nonmodel bacteria. CRISPRi was also implemented to inhibit violacein pigment production in C. violaceum. This systematic toolkit will aid future genetic circuitry building in this chassis and other nonmodel bacteria for synthetic biology and biotechnological applications.

Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli

For any dynamical system, like living organisms, an attractor state is a set of variables or mechanisms that converge towards a stable system behavior despite a wide variety of initial conditions. Here, using multi-dimensional statistics, we investigate the global gene expression attractor mechanisms shaping anaerobic to aerobic state transition (AAT) of Escherichia coli in a bioreactor at early times. Out of 3,389 RNA-Seq expression changes over time, we identified 100 sharply changing genes that are key for guiding 1700 genes into the AAT attractor basin. Collectively, these genes were named as attractor genes constituting of 6 dynamic clusters. Apart from the expected anaerobic (glycolysis), aerobic (TCA cycle) and fermentation (succinate pathways) processes, sulphur metabolism, ribosome assembly and amino acid transport mechanisms together with 332 uncharacterised genes are also key for AAT. Overall, our work highlights the importance of multi-dimensional statistical analyses for revealing novel processes shaping AAT.

Extrinsic Noise and Heavy-Tailed Laws in Gene Expression

Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our ansatz allows us to incorporate the effects of extrinsic noise-encompassing factors external to the transcription of the individual gene-and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling

Designing and implementing synthetic biological pattern formation remains challenging due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks biochemically. Here, we introduce a cell-in-the-loop approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present an easy-to-use theoretical test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrate spontaneous checkerboard patterning in an optogenetic setup, where cell-to-cell signaling is emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produces spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.

Fly-QMA: Automated analysis of mosaic imaginal discs in Drosophila

Mosaic analysis provides a means to probe developmental processes in situ by generating loss-of-function mutants within otherwise wildtype tissues. Combining these techniques with quantitative microscopy enables researchers to rigorously compare RNA or protein expression across the resultant clones. However, visual inspection of mosaic tissues remains common in the literature because quantification demands considerable labor and computational expertise. Practitioners must segment cell membranes or cell nuclei from a tissue and annotate the clones before their data are suitable for analysis. Here, we introduce Fly-QMA, a computational framework that automates each of these tasks for confocal microscopy images of Drosophila imaginal discs. The framework includes an unsupervised annotation algorithm that incorporates spatial context to inform the genetic identity of each cell. We use a combination of real and synthetic validation data to survey the performance of the annotation algorithm across a broad range of conditions. By contributing our framework to the open-source software ecosystem, we aim to contribute to the current move toward automated quantitative analysis among developmental biologists.

Developing synthetic biology for industrial biotechnology applications

Since the beginning of the 21st Century, synthetic biology has established itself as an effective technological approach to design and engineer biological systems. Whilst research and investment continues to develop the understanding, control and engineering infrastructural platforms necessary to tackle ever more challenging systems - and to increase the precision, robustness, speed and affordability of existing solutions - hundreds of start-up companies, predominantly in the US and UK, are already translating learnings and potential applications into commercially viable tools, services and products. Start-ups and SMEs have been the predominant channel for synthetic biology commercialisation to date, facilitating rapid response to changing societal interests and market pull arising from increasing awareness of health and global sustainability issues. Private investment in start-ups across the US and UK is increasing rapidly and now totals over $12bn. Health-related biotechnology applications have dominated the commercialisation of products to date, but significant opportunities for the production of bio-derived materials and chemicals, including consumer products, are now being developed. Synthetic biology start-ups developing tools and services account for between 10% (in the UK) and ∼25% (in the US) of private investment activity. Around 20% of synthetic biology start-ups address industrial biotechnology targets, but currently, only attract ∼11% private investment. Adopting a more networked approach - linking specialists, infrastructure and ongoing research to de-risk the economic challenges of scale-up and supported by an effective long-term funding strategy - is set to transform the impact of synthetic biology and industrial biotechnology in the bioeconomy.

Modeling ethanol production through gas fermentation: a biothermodynamics and mass transfer-based hybrid model for microbial growth in a large-scale bubble column bioreactor

BACKGROUND

Ethanol production through fermentation of gas mixtures containing CO, CO₂ and H₂ has just started operating at commercial scale. However, quantitative schemes for understanding and predicting productivities, yields, mass transfer rates, gas flow profiles and detailed energy requirements have been lacking in literature; such are invaluable tools for process improvements and better systems design. The present study describes the construction of a hybrid model for simulating ethanol production inside a 700 m³ bubble column bioreactor fed with gas of two possible compositions, i.e., pure CO and a 3:1 mixture of H₂ and CO₂.

RESULTS

Estimations made using the thermodynamics-based black-box model of microbial reactions on substrate threshold concentrations, biomass yields, as well as CO and H₂ maximum specific uptake rates agreed reasonably well with data and observations reported in literature. According to the bioreactor simulation, there is a strong dependency of process performance on mass transfer rates. When mass transfer coefficients were estimated using a model developed from oxygen transfer to water, ethanol productivity reached 5.1 g L^-1 h^-1; when the H₂/CO₂ mixture is fed to the bioreactor, productivity of CO fermentation was 19% lower. Gas utilization reached 23 and 17% for H₂/CO₂ and CO fermentations, respectively. If mass transfer coefficients were 100% higher than those estimated, ethanol productivity and gas utilization may reach 9.4 g L^-1 h^-1 and 38% when feeding the H₂/CO₂ mixture at the same process conditions. The largest energetic requirements for a complete manufacturing plant were identified for gas compression and ethanol distillation, being higher for CO fermentation due to the production of CO₂.

CONCLUSIONS

The thermodynamics-based black-box model of microbial reactions may be used to quantitatively assess and consolidate the diversity of reported data on CO, CO₂ and H₂ threshold concentrations, biomass yields, maximum substrate uptake rates, and half-saturation constants for CO and H₂ for syngas fermentations by acetogenic bacteria. The maximization of ethanol productivity in the bioreactor may come with a cost: low gas utilization. Exploiting the model flexibility, multi-objective optimizations of bioreactor performance might reveal how process conditions and configurations could be adjusted to guide further process development.

Searching for simple rules in Pseudomonas aeruginosa biofilm formation

OBJECTIVE

Living cells display complex and non-linear behaviors, especially when posed to environmental threats. Here, to understand the self-organizing cooperative behavior of a microorganism Pseudomonas aeruginosa, we developed a discrete spatiotemporal cellular automata model based on simple physical rules, similar to Conway's game of life.

RESULTS

The time evolution model simulations were experimentally verified for P. aeruginosa biofilm for both control and antibiotic azithromycin (AZM) treated condition. Our model suggests that AZM regulates the single cell motility, thereby resulting in delayed, but not abolished, biofilm formation. In addition, the model highlights the importance of reproduction by cell to cell interaction is key for biofilm formation. Overall, this work highlights another example where biological evolutionary complexity may be interpreted using rules taken from theoretical disciplines.

Pathways to cellular supremacy in biocomputing

Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found.

Synthetic biology uses cells as its computing substrate, often based on the genetic circuit concept. In this Perspective, the authors argue that existing synthetic biology approaches based on classical models of computation limit the potential of biocomputing, and propose that living organisms have under-exploited capabilities.

Sockeye salmon demonstrate robust yet distinct transcriptomic kidney responses to rhabdovirus (IHNV) exposure and infection

Aquatic rhabdoviruses are globally significant pathogens associated with disease in both wild and cultured fish. Infectious hematopoietic necrosis virus (IHNV) is a rhabdovirus that causes the internationally regulated disease infectious hematopoietic necrosis (IHN) in most species of salmon. Yet not all naïve salmon exposed to IHNV become diseased, and the mechanisms by which some individuals evade or rapidly clear infection following exposure are poorly understood. Here we used RNA-sequencing to evaluate transcriptomic changes in sockeye salmon, a keystone species in the North Pacific and natural host for IHNV, to evaluate the consequences of IHNV exposure and/or infection on host cell transcriptional pathways. Immersion challenge of sockeye salmon smolts with IHNV resulted in approximately 33% infection prevalence, where both prevalence and viral kidney load peaked at 7 days post challenge (dpc). De novo assembly of kidney transcriptomes at 7 dpc revealed that both infected and exposed but noninfected individuals experienced substantial transcriptomic modification; however, stark variation in gene expression patterns were observed between exposed but noninfected, infected, and unexposed populations. GO and KEGG pathway enrichment in concert with differential expression analysis identified that kidney responses in exposed but noninfected fish emphasised a global pattern of transcriptional down-regulation, particularly for pathways involved in DNA transcription, protein biosynthesis and macromolecule metabolism. In contrast, transcriptomes of infected fish demonstrated a global emphasis of transcriptional up-regulation highlighting pathways involved in antiviral response, inflammation, apoptosis, and RNA processing. Quantitative PCR was subsequently used to highlight differential and time-specific regulation of acute phase, antiviral, inflammatory, cell boundary, and metabolic responsive transcripts in both infected and exposed but noninfected groups. This data demonstrates that waterborne exposure with IHNV has a dramatic effect on the sockeye salmon kidney transcriptome that is discrete between resistant and acutely susceptible individuals. We identify that metabolic, acute phase and cell boundary pathways are transcriptionally affected by IHNV and kidney responses to local infection are highly divergent from those generated as part of a disseminated response. These data suggest that primary resistance of naïve fish to IHNV may involve global responses that encourage reduced cellular signaling rather than promoting classical innate antiviral responses.

Low-Burden Biological Feedback Controllers for Near-Perfect Adaptation

The robustness and reliability of synthetic biological systems can be substantially improved by the introduction of feedback control architectures that parallel those employed in traditional engineering disciplines. One common control goal is adaptation (or disturbance rejection), which refers to a system's ability to maintain a constant output despite variation in some of its constituent processes (as frequently occurs in noisy cellular environments) or external perturbations. In this paper, we propose and analyze a control architecture that employs integrase and excisionase proteins to invert regions of DNA and an mRNA-mRNA annihilation reaction. Combined, these components approximate the functionality of a switching controller (as employed in classical control engineering) with three distinct operational modes. We demonstrate that this system is capable of near-perfect adaptation to variation in rates of both transcription and translation and can also operate without excessive consumption of cellular resources. The system's steady-state behavior is analyzed, and limits on its operating range are derived. Deterministic simulations of its dynamics are presented and are then extended to the stochastic case, which treats biochemical reactions as discrete events.

A Computational Workflow for the Automated Generation of Models of Genetic Designs

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

TASBE Flow Analytics: A Package for Calibrated Flow Cytometry Analysis

Flow cytometry is a powerful method for high-throughput precision measurement of cell fluorescence and size. Effective use of this tool for quantification of synthetic biology devices and circuits, however, generally requires careful application of complex multistage workflows for calibration, filtering, and analysis with appropriate statistics. The TASBE Flow Analytics package provides a free, open, and accessible implementation of such workflows in a form designed for high-throughput analysis of large synthetic biology data sets. Given a set of experimental samples and controls, this package can process them to output calibrated data, quantitative analyses and comparisons, automatically generated figures, and detailed debugging and diagnostic reports in both human-readable and machine-readable forms. TASBE Flow Analytics can be used through a simple user-friendly interactive Excel interface, as a library supporting Matlab, Octave, or Python interactive sessions, or as a component integrated into automated workflows.

An Automated Biomodel Selection System (BMSS) for Gene Circuit Designs

Constructing a complex functional gene circuit composed of different modular biological parts to achieve the desired performance remains challenging without a proper understanding of how the individual module behaves. To address this, mathematical models serve as an important tool toward better interpretation by quantifying the performance of the overall gene circuit, providing insights, and guiding the experimental designs. As different gene circuits might require exclusively different mathematical representations in the form of ordinary differential equations to capture their transient dynamic behaviors, a recurring challenge in model development is the selection of the appropriate model. Here, we developed an automated biomodel selection system (BMSS) which includes a library of pre-established models with intuitive or unintuitive features derived from a vast array of expression profiles. Selection of models is built upon the Akaike information criteria (AIC). We tested the automated platform using characterization data of routinely used inducible systems, constitutive expression systems, and several different logic gate systems (NOT, AND, and OR gates). The BMSS achieved a good agreement for all the different characterization data sets and managed to select the most appropriate model accordingly. To enable exchange and reproducibility of gene circuit design models, the BMSS platform also generates Synthetic Biology Open Language (SBOL)-compliant gene circuit diagrams and Systems Biology Markup Language (SBML) output files. All aspects of the algorithm were programmed in a modular manner to ease the efforts on model extensions or customizations by users. Taken together, the BMSS which is implemented in Python supports users in deriving the best mathematical model candidate in a fast, efficient, and automated way using part/circuit characterization data.

Sources of Collagen for Biomaterials in Skin Wound Healing

Collagen is the most frequently used protein in the fields of biomaterials and regenerative medicine. Within the skin, collagen type I and III are the most abundant, while collagen type VII is associated with pathologies of the dermal-epidermal junction. The focus of this review is mainly collagens I and III, with a brief overview of collagen VII. Currently, the majority of collagen is extracted from animal sources; however, animal-derived collagen has a number of shortcomings, including immunogenicity, batch-to-batch variation, and pathogenic contamination. Recombinant collagen is a potential solution to the aforementioned issues, although production of correctly post-translationally modified recombinant human collagen has not yet been performed at industrial scale. This review provides an overview of current collagen sources, associated shortcomings, and potential resolutions. Recombinant expression systems are discussed, as well as the issues associated with each method of expression.

ABioTrans: A Biostatistical Tool for Transcriptomics Analysis

Here we report a bio-statistical/informatics tool, ABioTrans, developed in R for gene expression analysis. The tool allows the user to directly read RNA-Seq data files deposited in the Gene Expression Omnibus or GEO database. Operated using any web browser application, ABioTrans provides easy options for multiple statistical distribution fitting, Pearson and Spearman rank correlations, PCA, k-means and hierarchical clustering, differential expression (DE) analysis, Shannon entropy and noise (square of coefficient of variation) analyses, as well as Gene ontology classifications.

PartCrafter: find, generate and analyze BioParts

The field of Synthetic Biology is both practically and philosophically reliant on the idea of BioParts-concrete DNA sequences meant to represent discrete functionalities. While there are a number of software tools which allow users to design complex DNA sequences by stitching together BioParts or genetic features into genetic devices, there is a lack of tools assisting Synthetic Biologists in finding BioParts and in generating new ones. In practice, researchers often find BioParts in an ad hoc way. We present PartCrafter, a tool which extracts and aggregates genomic feature data in order to facilitate the search for new BioParts with specific functionalities. PartCrafter can also turn a genomic feature into a BioPart by packaging it according to any manufacturing standard, codon optimizing it for a new host, and removing forbidden sites. PartCrafter is available at partcrafter.com.

Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares

Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides an estimate related to the absolute quantity of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissue-infiltrating immune cell landscape. The source code for FARDEEP is implemented in R and available for download at https://github.com/YuningHao/FARDEEP.git.

Rapidly emerging evidence suggests that the tumor immune microenvironment not only predisposes cancer patients to diverse treatment outcomes but also represents a promising source of biomarkers for better patient stratification. Different from the immunohistochemistry-based scoring practice, which focuses on a few selected marker proteins, immune deconvolution pipelines inform a previously untapped method to comprehensively reveal the tumor-infiltrating immune landscape. Recognizing the numerous strengths of existing immune deconvolution algorithms, here we show data outliers, which are inevitable in whole tissue sequencing data sets, substantially skew estimation results. Moreover, an estimate related to the absolute amount of each immune subset offers valuable insight into the nature of the host response in addition to percentage information alone. Thus, we engineered a new immune deconvolution pipeline, coined as Fast and Robust Deconvolution of Expression Profiles (FARDEEP), to automatically detect and remove outliers prior feeding data into the deconvolution algorithm and to provide estimates related to the absolute quantity of each immune subset. Utilizing both synthetic and real data sets, we found that FARDEEP returns superior coefficients and offers a robust tool to reveal the immune landscape of human cancers.

Value Chains for Industrial Biotechnology in the Bioeconomy-Innovation System Analysis

Industrial Biotechnology (IB) is considered as a key technology with a strong potential to generate new growth, spur innovation, increase productivity, and tackle environmental and climate challenges. Industrial Biotechnology is applied in many segments of the bioeconomy ranging from chemicals, biofuels, bioenergy, bio-based plastics, and other biomaterials. However, the segments differ profoundly regarding volume, price, type, and amount of needed feedstock, market condition, societal contributions as well as maturity, etc. This article aims to analyse a set of five different value chains in the technological innovation system (TIS) framework in order to derive adequate policy conclusions. Hereby, we focus on quite distinctive value chains to take into account the high heterogeneity of biotechnological applications. The analysis points out that policy maker have to take into account the fundamental differences in the innovation systems and to implement differentiated innovation policy to address system weaknesses. In particular, market formation is often the key bottleneck innovation systems, but different policy instruments for various application segments needed.

Modeling Genetic Circuit Behavior in Transiently Transfected Mammalian Cells

Binning cells by plasmid copy number is a common practice for analyzing transient transfection data. In many kinetic models of transfected cells, protein production rates are assumed to be proportional to plasmid copy number. The validity of this assumption in transiently transfected mammalian cells is not clear; models based on this assumption appear unable to reproduce experimental flow cytometry data robustly. We hypothesize that protein saturation at high plasmid copy number is a reason previous models break down and validate our hypothesis by comparing experimental data and a stochastic chemical kinetics model. The model demonstrates that there are multiple distinct physical mechanisms that can cause saturation. On the basis of these observations, we develop a novel minimal bin-dependent ODE model that assumes different parameters for protein production in cells with low versus high numbers of plasmids. Compared to a traditional Hill-function-based model, the bin-dependent model requires only one additional parameter, but fits flow cytometry input-output data for individual modules up to twice as accurately. By composing together models of individually fit modules, we use the bin-dependent model to predict the behavior of six cascades and three feed-forward circuits. The bin-dependent models are shown to provide more accurate predictions on average than corresponding (composed) Hill-function-based models and predictions of comparable accuracy to EQuIP, while still providing a minimal ODE-based model that should be easy to integrate as a subcomponent within larger differential equation circuit models. Our analysis also demonstrates that accounting for batch effects is important in developing accurate composed models.

Formation of three-dimensional tubular endothelial cell networks under defined serum-free cell culture conditions in human collagen hydrogels

Implementation of tubular endothelial cell networks is a prerequisite for 3D tissue engineering of constructs with clinically relevant size as nourishment of cells is challenged by the diffusion limit. In vitro generation of 3D networks is often achieved under conditions using serum containing cell culture medium and/or animal derived matrices. Here, 3D endothelial cell networks were generated by using human umbilical vein endothelial cells (HUVECs) in combination with human adipose tissue derived stromal cells (hASCs) employing human collagen I as hydrogel and decellularized porcine small intestinal submucosa as starter matrix. Matrigel/rat tail collagen I hydrogel was used as control. Resulting constructs were cultivated either in serum-free medium or in endothelial growth medium-2 serving as control. Endothelial cell networks were quantified, tested for lumen formation, and interaction of HUVECs and hASCs. Tube diameter was slightly larger in constructs containing human collagen I compared to Matrigel/rat tail collagen I constructs under serum-free conditions. All other network parameters were mostly similar. Thereby, the feasibility of generating 3D endothelial cell networks under serum-free culture conditions in human collagen I as hydrogel was demonstrated. In summary, the presented achievements pave the way for the generation of clinical applicable constructs.

Processing of collagen based biomaterials and the resulting materials properties

Collagen, the most abundant extracellular matrix protein in animal kingdom belongs to a family of fibrous proteins, which transfer load in tissues and which provide a highly biocompatible environment for cells. This high biocompatibility makes collagen a perfect biomaterial for implantable medical products and scaffolds for in vitro testing systems. To manufacture collagen based solutions, porous sponges, membranes and threads for surgical and dental purposes or cell culture matrices, collagen rich tissues as skin and tendon of mammals are intensively processed by physical and chemical means. Other tissues such as pericardium and intestine are more gently decellularized while maintaining their complex collagenous architectures. Tissue processing technologies are organized as a series of steps, which are combined in different ways to manufacture structurally versatile materials with varying properties in strength, stability against temperature and enzymatic degradation and cellular response. Complex structures are achieved by combined technologies. Different drying techniques are performed with sterilisation steps and the preparation of porous structures simultaneously. Chemical crosslinking is combined with casting steps as spinning, moulding or additive manufacturing techniques. Important progress is expected by using collagen based bio-inks, which can be formed into 3D structures and combined with live cells. This review will give an overview of the technological principles of processing collagen rich tissues down to collagen hydrolysates and the methods to rebuild differently shaped products. The effects of the processing steps on the final materials properties are discussed especially with regard to the thermal and the physical properties and the susceptibility to enzymatic degradation. These properties are key features for biological and clinical application, handling and metabolization.

Mechanistic Models of Inducible Synthetic Circuits for Joint Description of DNA Copy Number, Regulatory Protein Level, and Cell Load

Accurate predictive mathematical models are urgently needed in synthetic biology to support the bottom-up design of complex biological systems, minimizing trial-and-error approaches. The majority of models used so far adopt empirical Hill functions to describe activation and repression in exogenously-controlled inducible promoter systems. However, such equations may be poorly predictive in practical situations that are typical in bottom-up design, including changes in promoter copy number, regulatory protein level, and cell load. In this work, we derived novel mechanistic steady-state models of the lux inducible system, used as case study, relying on different assumptions on regulatory protein (LuxR) and cognate promoter (Plux) concentrations, inducer-protein complex formation, and resource usage limitation. We demonstrated that a change in the considered model assumptions can significantly affect circuit output, and preliminary experimental data are in accordance with the simulated activation curves. We finally showed that the models are identifiable a priori (in the analytically tractable cases) and a posteriori, and we determined the specific experiments needed to parametrize them. Although a larger-scale experimental validation is required, in the future the reported models may support synthetic circuits output prediction in practical situations with unprecedented details.

Synthesis of salt-stable fluorescent nanoparticles (quantum dots) by polyextremophile halophilic bacteria

Here we report the biological synthesis of CdS fluorescent nanoparticles (Quantum Dots, QDs) by polyextremophile halophilic bacteria isolated from Atacama Salt Flat (Chile), Uyuni Salt Flat (Bolivia) and the Dead Sea (Israel). In particular, a Halobacillus sp. DS2, a strain presenting high resistance to NaCl (3-22%), acidic pH (1-4) and cadmium (CdCl2 MIC: 1,375 mM) was used for QDs biosynthesis studies. Halobacillus sp. synthesize CdS QDs in presence of high NaCl concentrations in a process related with their capacity to generate S2- in these conditions. Biosynthesized QDs were purified, characterized and their stability at different NaCl concentrations determined. Hexagonal nanoparticles with highly defined structures (hexagonal phase), monodisperse size distribution (2-5 nm) and composed by CdS, NaCl and cysteine were determined by TEM, EDX, HRXPS and FTIR. In addition, QDs biosynthesized by Halobacillus sp. DS2 displayed increased tolerance to NaCl when compared to QDs produced chemically or biosynthesized by non-halophilic bacteria. This is the first report of biological synthesis of salt-stable QDs and confirms the potential of using extremophile microorganisms to produce novel nanoparticles. Obtained results constitute a new alternative to improve QDs properties, and as consequence, to increase their industrial and biomedical applications.

Developing a graduate training program in Synthetic Biology: SynBioCDT

This article presents the experience of a team of students and academics in developing a post-graduate training program in the new field of Synthetic Biology. Our Centre for Doctoral Training in Synthetic Biology (SynBioCDT) is an initiative funded by the United Kingdom's Research Councils of Engineering and Physical Sciences (EPSRC), and Biotechnology and Biological Sciences (BBSRC). SynBioCDT is a collaboration between the Universities of Oxford, Bristol and Warwick, and has been successfully running since 2014, training 78 students in this field. In this work, we discuss the organization of the taught, research and career development training. We also address the challenges faced when offering an interdisciplinary program. The article concludes with future directions to continue the development of the SynBioCDT.

Utilising datasheets for the informed automated design and build of a synthetic metabolic pathway

BACKGROUND

The automation of modular cloning methodologies permits the assembly of many genetic designs. Utilising characterised biological parts aids in the design and redesign of genetic pathways. The characterisation information held on datasheets can be used to determine whether a biological part meets the design requirements. To manage the design of genetic pathways, researchers have turned to modelling-based computer aided design software tools.

RESULT

An automated workflow has been developed for the design and build of heterologous metabolic pathways. In addition, to demonstrate the powers of electronic datasheets we have developed software which can transfer part information from a datasheet to the Design of Experiment software JMP. To this end we were able to use Design of Experiment software to rationally design and test randomised samples from the design space of a lycopene pathway in E. coli. This pathway was optimised by individually modulating the promoter strength, RBS strength, and gene order targets.

CONCLUSION

The use of standardised and characterised biological parts will empower a design-oriented synthetic biology for the forward engineering of heterologous expression systems. A Design of Experiment approach streamlines the design-build-test cycle to achieve optimised solutions in biodesign. Developed automated workflows provide effective transfer of information between characterised information (in the form of datasheets) and DoE software.

Major histocompatibility complex class I in the horse (Equus caballus) placenta during pregnancy and parturition

BACKGROUND

Major histocompatibility protein class I (MHC-I) is believed to be expressed in the horse allantochorion only in limited areas at limited times. However, its expression has only been investigated in early pregnancy with non-quantitative techniques that cannot reliably detect small amounts of protein.

OBJECTIVE

To quantify the relative expression of MHC-I in the allantochorion and endometrium during days 90-240 of pregnancy (PREG), parturition with physiological delivery of fetal membranes (PHYS), and parturition with retention of these membranes (FMR). Also, to visualize protein expression and determine whether classical or non-classical MHC-I mRNA is expressed.

ANIMALS

Heavy draft horses.

SETTING

PREG horses (n = 12) were sampled postmortem at a slaughterhouse. PHYS (n = 6) and FMR (n = 5) horses were sampled at farms in the vicinity of Olsztyn, Poland.

METHODS

For relative quantification of MHC-I, western blotting with densitometry was used. To visualize MHC-I, immunohistochemistry was used. For mRNA identification, RT-PCR was performed.

RESULTS

Although the quantity of MHC-I was lower during PREG than parturition, it was present in the allantochorion and endometrium during PREG. During parturition, MHC-I expression was upregulated in the allantochorion (PHYS vs. PREG: 2.7-times higher, 95% confidence interval, 1.3- to 5.7-times higher; FMR vs. PREG: 3.2-times higher, 95% confidence interval, 1.5- to 6.7-times higher). At parturition, staining for MHC-I was detected in the microcotyledons. Classical and non-classical MHC-I were expressed in both tissues during PREG, PHYS, and FMR.

CONCLUSION

MHC-I protein is present in the horse allantochorion and endometrium for at least the first two-thirds of pregnancy and at parturition.

Programming the Dynamic Control of Bacterial Gene Expression with a Chimeric Ligand- and Light-Based Promoter System

To program cells in a dynamic manner, synthetic biologists require precise control over the threshold levels and timing of gene expression. However, in practice, modulating gene expression is widely carried out using prototypical ligand-inducible promoters, which have limited tunability and spatiotemporal resolution. Here, we built two dual-input hybrid promoters, each retaining the function of the ligand-inducible promoter while being enhanced with a blue-light-switchable tuning knob. Using the new promoters, we show that both ligand and light inputs can be synchronously modulated to achieve desired amplitude or independently regulated to generate desired frequency at a specific amplitude. We exploit the versatile programmability and orthogonality of the two promoters to build the first reprogrammable logic gene circuit capable of reconfiguring into logic OR and N-IMPLY logic on the fly in both space and time without the need to modify the circuit. Overall, we demonstrate concentration- and time-based combinatorial regulation in live bacterial cells with potential applications in biotechnology and synthetic biology.