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Here we provide a brief perspective on the role of biology in NASA’s planetary science goals, and its spacecraft missions, past, present, and future. We argue that while biology - via astrobiology - generates much interest and excitement for NASA, biology is vastly under-represented as a science within NASA Planetary Science Division missions.
To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.
The Biological Upgrading of Sugars (BUS) project directly targets the anaerobic conversion of lignocellulosic feedstocks into intermediate molecules readily upgradeable to fuel precursors. Recent efforts on the BUS project have a particular emphasis on the biological production of butyric acid, an intermediate that can be readily upgraded to sustainable aviation fuel, diesel blend-stocks, and high value chemicals. The BUS project approaches this direction through a combination of strain engineering, fermentation process engineering, development of novel separations technologies, and the design and build of pilot scale systems. Our ultimate project goal is to develop an integrated cost-effective process at pilot scale to achieve DOE's MYPP targets of $2.50/GGE. The major thrust of the BUS project over the last project cycle was on the development of integrated processes surrounding the anaerobic production of carboxylic acids using diverse Clostridium species. We developed and expanded genetic tools for several Clostridium species and rewired microbial metabolism in an attempt to maximize substrate utilization and flux towards butyric acid. We designed and built novel bioreactors with an in situ product recovery system enabling the biological production and recovery of highly purified acids. We leveraged this system to generate 100s of grams of acid from corn stover hydrolysate. In this presentation we highlight data surrounding our proposed process and accompanying results from technoeconomic and life-cycle analyses of our integrated process. Finally, we detail plans of our pilot scale reactor system that is in process and discuss our future routes towards achieving economically viable and sustainable diesel and jet blendstocks.
This disclosure provides methods, systems, and compositions of matter for studying solvent accessibility and three-dimensional structure of biological molecules. A plasma can be used to generate marker radicals, which can interact with a biological molecule and mark the solvent-accessible portions of the biological molecule.
Abstract Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.
Biological computer-aided design and manufacturing (bioCAD/CAM) tools facilitate the design and build processes of engineering biological systems using iterative design-build-test-learn (DBTL) cycles. In this book chapter, we highlight some of the bioCAD/CAM tools developed and used at the US Department of Energy (DOE) Joint Genome Institute (JGI), Joint BioEnergy Institute (JBEI), and Agile BioFoundry (ABF). We demonstrate the use of these bioCAD/CAM tools on a common workflow for designing and building a multigene pathway in a hierarchical fashion. Additionally, each tool presented in this book chapter is specifically tailored to support one or more specific steps in a workflow, can be integrated with the others into design and build workflows, and can be deployed at academic, government, or commercial entities.
High-resolution imaging with secondary ion mass spectrometry (nanoSIMS) has become a standard method in systems biology and environmental biogeochemistry and is broadly used to decipher ecophysiological traits of environmental microorganisms, metabolic processes in plant and animal tissues, and cross-kingdom symbioses. When combined with stable isotope-labeling—an approach we refer to as nanoSIP—nanoSIMS imaging offers a distinctive means to quantify net assimilation rates and stoichiometry of individual cell-sized particles in both low- and high-complexity environments. While the majority of nanoSIP studies in environmental and microbial biology have focused on nitrogen and carbon metabolism (using 15 N and 13 C tracers), multiple advances have pushed the capabilities of this approach in the past decade. The development of a high-brightness oxygen ion source has enabled high-resolution metal analyses that are easier to perform, allowing quantification of metal distribution in cells and environmental particles. New preparation methods, tools for automated data extraction from large data sets, and analytical approaches that push the limits of sensitivity and spatial resolution have allowed for more robust characterization of populations ranging from marine archaea to fungi and viruses. Further, NanoSIMS studies continue to be enhanced by correlation with orthogonal imaging and ‘omics approaches; when linked to molecular visualization methods, such as in situ hybridization and antibody labeling, these techniques enable in situ function to be linked to microbial identity and gene expression. Here we present an updated description of the primary materials, methods, and calculations used for nanoSIP, with an emphasis on recent advances in nanoSIMS applications, key methodological steps, and potential pitfalls.
In this study, the reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other’s work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.
When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.
Filamentous fungi are critical in the transition to a more sustainable food system. While genetic modification of these organisms has promise for enhancing the nutritional value, sensory appeal, and scalability of fungal foods, genetic tools and demonstrated use cases for bioengineered food production by edible strains are lacking. Here, we develop a modular synthetic biology toolkit for Aspergillus oryzae, an edible fungus used in fermented foods, protein production, and meat alternatives. Our toolkit includes a CRISPR-Cas9 method for gene integration, neutral loci, and tunable promoters. We use these tools to elevate intracellular levels of the nutraceutical ergothioneine and the flavor-and color molecule heme in the edible biomass. The strain overproducing heme is red in color and is readily formulated into imitation meat patties with minimal processing. These findings highlight the promise of synthetic biology to enhance fungal foods and provide useful genetic tools for applications in food production and beyond.
Biological accelerator mass spectrometry (AMS) provides ultrasensitive carbon-14 isotopic analysis enabling a deeper understanding of human health concerns by enabling quantification of pharmacokinetics and other molecular endpoints directly in humans. It enables environmentally and human relevant studies of metabolic pathways through the use of very low concentrations of labeled metabolic substrates in cells and organisms. Here, we discuss why AMS is an important tool for the biosciences, the development and evolution of biological AMS at Livermore and discuss technical refinements that will improve the efficiency of operation for the measurement of ultra-trace levels of 14 C, which, long term, will enable greater ease of use and sample throughput.
Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This manuscript proposes a general approach for querying a causal knowledge graph with a causal question and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. Here, we demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm and performing counterfactual inference to predict the causal effect of medical countermeasures for severely ill COVID-19 patients.
Reverse genetics approaches have revolutionized plant biology and agriculture. Phenomics has the prospect of bridging plant phenotypes with genes, including transgenes, to transform agricultural fields. Genetically encoded fluorescent proteins (FPs) have revolutionized plant biology paradigms in gene expression, protein trafficking and plant physiology. While the first instance of plant canopy imaging of green fluorescent protein (GFP) was performed over 25 years ago, modern phenomics has largely ignored fluorescence as a transgene expression device despite the burgeoning FP colour palette available to plant biologists. Here, we show a new platform for stand-off imaging of plant canopies expressing a wide variety of FP genes. The platform-the fluorescence-inducing laser projector (FILP)-uses an ultra-low-noise camera to image a scene illuminated by compact diode lasers of various colours, coupled with emission filters to resolve individual FPs, to phenotype transgenic plants expressing FP genes. Each of the 20 FPs screened in plants were imaged at >3 m using FILP in a laboratory-based laser range. We also show that pairs of co-expressed fluorescence proteins can be imaged in canopies. The FILP system enabled a rapid synthetic promoter screen: starting from 2000 synthetic promoters transfected into protoplasts to FILP-imaged agroinfiltrated Nicotiana benthamiana plants in a matter of weeks, which was useful to characterize a water stress-inducible synthetic promoter. FILP canopy imaging was also accomplished for stably transformed GFP potato and in a split-GFP assay, which illustrates the flexibility of the instrument for analysing fluorescence signals in plant canopies.
As science culture gravitates toward a more holistic inclusion of both males and females in research design, the outlining of sex differences and their respective intersections with disease physiology and pathophysiology should see reciprocal expansion. Melanoma skin cancer, for example, has observed a female advantage in incidence, mortality, and overall survival since the early 1970s. The exact biological mechanism of this trend, however, is unclear and further complicated by a layering of clinical variables such as skin phototype, age, and body mass index. In this perspective, we highlight epidemiological evidence of sex differences in melanoma and summarize the landscape of their potential origin. Among several biological hallmarks, we make a note of sex-specific immune profiles—along with divergent hormonal regulation, social practices, DNA damage and oxidative stress responses, body composition, genetic variants, and X-chromosome expression—as probable drivers of disparity in melanoma initiation and progression. This review further focuses the conversation of sex as an influencing factor in melanoma development and its potential implication for disease management and treatment strategies.
We present a new strategy for the production of a δ-lactam from glucose that integrates biological production of triacetic acid lactone (TAL, 4-hydroxy-6-methyl-2H-2-one) with catalytic transformation of TAL into 6-methylpiperidin-2-one (MPO) through metabolic engineering, isomerization, amination, and catalytic hydrogenation/hydrogenolysis. We developed a sustainable and antibiotic-free fed-batch fermentation using genetically modified Rhodotorula toruloides IFO0880. This process achieved a yield of 2-hydroxy-6-methyl-4H-pyran-4-one (2H4P) at 0.05 g/g of glucose, corresponding to a 9.9 g/L titer. By adjusting the pH of the fermentation broth to 2, 2H4P was quantitatively converted into TAL. The TAL in the fermentation broth was directly converted by aminolysis into 4-hydroxy-6-methylpyridin-2(1H)-one (HMPO), which achieved an 18.5% yield with 94.3% purity. The HMPO yield was lower in the fermentation broth than in a clean feedstock (32.2%), suggesting that the biological impurities are inhibitors in this reaction. Further investigation revealed that lower pH levels and reduced TAL concentrations in the fermentation broth significantly decreased HMPO yields. Subsequently, the precipitated HMPO was filtered and dried and then subjected to the final catalytic conversion in H2O solvent, achieving a MPO yield of 91.8%. This integrated approach demonstrated the direct use of TAL in the filtered aqueous fermentation broth without the need to isolate TAL.
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
The 7th FOSBE conference was held August 5-8 at the Hilton Chicago Oak Brook Hills Resort & Conference Center, in Chicago, Illinois. The conference was sponsored by CACHE, with technical co-sponsorship from IFAC Biosystems and Bioprocesses (TC 8.4), AIChE’s Society of Biological Engineering, and IEEE’s Control Systems Society. 83 participants from 11 countries met for the event; approximately 5% of participants were from industry and 43% were from countries outside the U.S. The meeting opened with a welcome reception Sunday evening, which was followed by Dr. Nathan Price, Systems Biology Institute, delivering the conference-opening lecture, “Mining Personal, Dense, Dynamic Data Clouds to Enhance Health and Drive Discovery”.
Fatty acids can be produced biosynthetically in microbes and these compounds can serve as precursors to biodiesels and other high value oleochemicals. However, progress on engineering fatty acid biosynthesis, and biofuel synthesis more generally, has been hindered by current quantification methods that are either indirect or not amenable to high-throughput or single-cell resolution screening. In this project, we assembled an interdisciplinary team with complimentary expertise in synthetic biology and microscopy, metabolic engineering, and chemical imaging to address these challenges. We used chemical imaging to directly measure lipid biosynthesis in Escherichia coli engineered to produce fatty acids, obtaining detailed single-cell resolution measurements. We deployed stimulated Raman scattering (SRS) microscopy in concert with multiplexed genome engineering and gene circuit design strategies from synthetic biology to optimize production of fatty acids. These results provided novel insight into cell-to-cell heterogeneity present in biofuel production strains. In addition, we introduced new chemical imaging methods which are label-free and do not require fluorescent reporters. These efforts were complemented by other studied developing foundational tools for regulation and control, which offer excellent potential for advancing researchers’ ability to rapidly design, build, and test strains for enhanced biofuel synthesis.