The Potential of DAS in Teleseismic Studies: Insights From the Goldstone Experiment

Distributed acoustic sensing (DAS) is a recently developed technique that has demonstrated its utility in the oil and gas industry. Here we demonstrate the potential of DAS in teleseismic studies using the Goldstone OpticaL Fiber Seismic experiment in Goldstone, California. By analyzing teleseismic waveforms from the 10 January 2018 M7.5 Honduras earthquake recorded on ~5,000 DAS channels and the nearby broadband station GSC, we first compute receiver functions for DAS channels using the vertical-component GSC velocity as an approximation for the incident source wavelet. The Moho P-to-s conversions are clearly visible on DAS receiver functions. We then derive meter-scale arrival time measurements along the entire 20-km-long array. We are also able to measure path-averaged Rayleigh wave group velocity and local Rayleigh wave phase velocity. The latter, however, has large uncertainties. Our study suggests that DAS will likely play an important role in many fields of passive seismology in the near future

A synthetic biology approach to engineering living photovoltaics

The ability to electronically interface living cells with electron accepting scaffolds is crucial for the development of next-generation biophotovoltaic technologies. Although recent studies have focused on engineering synthetic interfaces that can maximize electronic communication between the cell and scaffold, the efficiency of such devices is limited by the low conductivity of the cell membrane. This review provides a materials science perspective on applying a complementary, synthetic biology approach to engineering membrane–electrode interfaces. It focuses on the technical challenges behind the introduction of foreign extracellular electron transfer pathways in bacterial host cells and past and future efforts to engineer photosynthetic organisms with artificial electron-export capabilities for biophotovoltaic applications. The article highlights advances in engineering protein-based, electron-exporting conduits in a model host organism, E. coli, before reviewing state-of-the-art biophotovoltaic technologies that use both unmodified and bioengineered photosynthetic bacteria with improved electron transport. A thermodynamic analysis is used to propose an energetically feasible pathway for extracellular electron transport in engineered cyanobacteria and identify metabolic bottlenecks amenable to protein engineering techniques. Based on this analysis, an engineered photosynthetic organism expressing a foreign, protein-based electron conduit yields a maximum theoretical solar conversion efficiency of 6–10% without accounting for additional bioengineering optimizations for light-harvesting

Complex and unexpected dynamics in simple genetic regulatory networks

Peer reviewedPublisher PD

Genetically encoded sender-receiver system in 3D mammalian cell culture

Engineering spatial patterning in mammalian cells, employing entirely genetically encoded components, requires solving several problems. These include how to code secreted activator or inhibitor molecules and how to send concentration-dependent signals to neighboring cells, to control gene expression. The Madin-Darby Canine Kidney (MDCK) cell line is a potential engineering scaffold as it forms hollow spheres (cysts) in 3D culture and tubulates in response to extracellular hepatocyte growth factor (HGF). We first aimed to graft a synthetic patterning system onto single developing MDCK cysts. We therefore developed a new localized transfection method to engineer distinct sender and receiver regions. A stable reporter line enabled reversible EGFP activation by HGF and modulation by a secreted repressor (a truncated HGF variant, NK4). By expanding the scale to wide fields of cysts, we generated morphogen diffusion gradients, controlling reporter gene expression. Together, these components provide a toolkit for engineering cell-cell communication networks in 3D cell culture.Facultad de Ciencias Exacta

Targeted Development of Registries of Biological Parts

BACKGROUND: The design and construction of novel biological systems by combining basic building blocks represents a dominant paradigm in synthetic biology. Creating and maintaining a database of these building blocks is a way to streamline the fabrication of complex constructs. The Registry of Standard Biological Parts (Registry) is the most advanced implementation of this idea. METHODS/PRINCIPAL FINDINGS: By analyzing inclusion relationships between the sequences of the Registry entries, we build a network that can be related to the Registry abstraction hierarchy. The distribution of entry reuse and complexity was extracted from this network. The collection of clones associated with the database entries was also analyzed. The plasmid inserts were amplified and sequenced. The sequences of 162 inserts could be confirmed experimentally but unexpected discrepancies have also been identified. CONCLUSIONS/SIGNIFICANCE: Organizational guidelines are proposed to help design and manage this new type of scientific resources. In particular, it appears necessary to compare the cost of ensuring the integrity of database entries and associated biological samples with their value to the users. The initial strategy that permits including any combination of parts irrespective of its potential value leads to an exponential and economically unsustainable growth that may be detrimental to the quality and long-term value of the resource to its users

Geophysical monitoring and reactive transport modeling of ureolytically-driven calcium carbonate precipitation

Ureolytically-driven calcium carbonate precipitation is the basis for a promising in-situ remediation method for sequestration of divalent radionuclide and trace metal ions. It has also been proposed for use in geotechnical engineering for soil strengthening applications. Monitoring the occurrence, spatial distribution, and temporal evolution of calcium carbonate precipitation in the subsurface is critical for evaluating the performance of this technology and for developing the predictive models needed for engineering application. In this study, we conducted laboratory column experiments using natural sediment and groundwater to evaluate the utility of geophysical (complex resistivity and seismic) sensing methods, dynamic synchrotron x-ray computed tomography (micro-CT), and reactive transport modeling for tracking ureolytically-driven calcium carbonate precipitation processes under site relevant conditions. Reactive transport modeling with TOUGHREACT successfully simulated the changes of the major chemical components during urea hydrolysis. Even at the relatively low level of urea hydrolysis observed in the experiments, the simulations predicted an enhanced calcium carbonate precipitation rate that was 3-4 times greater than the baseline level. Reactive transport modeling results, geophysical monitoring data and micro-CT imaging correlated well with reaction processes validated by geochemical data. In particular, increases in ionic strength of the pore fluid during urea hydrolysis predicted by geochemical modeling were successfully captured by electrical conductivity measurements and confirmed by geochemical data. The low level of urea hydrolysis and calcium carbonate precipitation suggested by the model and geochemical data was corroborated by minor changes in seismic P-wave velocity measurements and micro-CT imaging; the latter provided direct evidence of sparsely distributed calcium carbonate precipitation. Ion exchange processes promoted through NH4+ production during urea hydrolysis were incorporated in the model and captured critical changes in the major metal species. The electrical phase increases were potentially due to ion exchange processes that modified charge structure at mineral/water interfaces. Our study revealed the potential of geophysical monitoring for geochemical changes during urea hydrolysis and the advantages of combining multiple approaches to understand complex biogeochemical processes in the subsurface

Memory in Microbes: Quantifying History-Dependent Behavior in a Bacterium

Memory is usually associated with higher organisms rather than bacteria. However, evidence is mounting that many regulatory networks within bacteria are capable of complex dynamics and multi-stable behaviors that have been linked to memory in other systems. Moreover, it is recognized that bacteria that have experienced different environmental histories may respond differently to current conditions. These “memory” effects may be more than incidental to the regulatory mechanisms controlling acclimation or to the status of the metabolic stores. Rather, they may be regulated by the cell and confer fitness to the organism in the evolutionary game it participates in. Here, we propose that history-dependent behavior is a potentially important manifestation of memory, worth classifying and quantifying. To this end, we develop an information-theory based conceptual framework for measuring both the persistence of memory in microbes and the amount of information about the past encoded in history-dependent dynamics. This method produces a phenomenological measure of cellular memory without regard to the specific cellular mechanisms encoding it. We then apply this framework to a strain of Bacillus subtilis engineered to report on commitment to sporulation and degradative enzyme (AprE) synthesis and estimate the capacity of these systems and growth dynamics to ‘remember’ 10 distinct cell histories prior to application of a common stressor. The analysis suggests that B. subtilis remembers, both in short and long term, aspects of its cell history, and that this memory is distributed differently among the observables. While this study does not examine the mechanistic bases for memory, it presents a framework for quantifying memory in cellular behaviors and is thus a starting point for studying new questions about cellular regulation and evolutionary strategy

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings