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Task C: GREET Hydro-mechanical-chemical-biological processes during groundwater recovery in crystalline rock.
Abstract not provided.
Combustion studies of n-butyl acetate synthesized by a new biological process and comparisons with neat n-butyl acetate
Blending petroleum fuels with biofuels is a common approach for stemming the depletion of crude oil while also mitigating the impact of their combustion on the environment. It has recently been considered that n-butyl acetate (BA, C6H12O2, boiling point of 399K) could be a viable biofuel additive to diesel fuel. This paper reports the combustion dynamics of BA droplets in the absence of external convection. Two grades of BA were examined: one synthesized by a new process that uses a solventogenic Clostridium strain through an extractive fermentation process using n-hexadecane as the extractant (SBA); the other commercially available as a high-purity (99.9%) 'neat' BA grade produced by Fischer esterification (NBA). The mass concentration of by-products from the synthesis process amounted to approximately 6% of the total composition and included n-butanol, n-hexadecane, iso-propyl alcohol and ethyl acetate. The platform used to study the combustion of commercial and synthesized BA was an isolated droplet burning with spherical symmetry. Initial droplet diameters were fixed at 0.6 mm. Experiments were carried out in a drop tower to promote one-dimensional transport dynamics, in the standard atmosphere, and with ignition accomplished by spark discharge. Differences in droplet burning rates and flame structures were undetectable between NBA and SBA, despite the differences in burning among the SBA components individually. The results presented show that the new synthesis process for butyl acetate yields a sustainable alternative to conventional methodologies with burning characteristics that are identical to NBA in stagnant gas transport fields.
Technical Note: Implementation of biological washout processes within GATE/GEANT4—A Monte Carlo study in the case of carbon therapy treatments
Purpose: The imaging of positron emitting isotopes produced during patient irradiation is the only in vivo method used for hadrontherapy dose monitoring in clinics nowadays. However, the accuracy of this method is limited by the loss of signal due to the metabolic decay processes (biological washout). In this work, a generic modeling of washout was incorporated into the GATE simulation platform. Additionally, the influence of the washout on the β{sup +} activity distributions in terms of absolute quantification and spatial distribution was studied. Methods: First, the irradiation of a human head phantom with a {sup 12}C beam, so that a homogeneous dose distribution was achieved in the tumor, was simulated. The generated {sup 11}C and {sup 15}O distribution maps were used as β{sup +} sources in a second simulation, where the PET scanner was modeled following a detailed Monte Carlo approach. The activity distributions obtained in the presence and absence of washout processes for several clinical situations were compared. Results: Results show that activity values are highly reduced (by a factor of 2) in the presence of washout. These processes have a significant influence on the shape of the PET distributions. Differences in the distal activity falloff position of 4 mm are observed for a tumor dose deposition of 1 Gy (T{sub ini} = 0 min). However, in the case of high doses (3 Gy), the washout processes do not have a large effect on the position of the distal activity falloff (differences lower than 1 mm). The important role of the tumor washout parameters on the activity quantification was also evaluated. Conclusions: With this implementation, GATE/GEANT 4 is the only open-source code able to simulate the full chain from the hadrontherapy irradiation to the PET dose monitoring including biological effects. Results show the strong impact of the washout processes, indicating that the development of better models and measurement of biological washout data are essential.
Chemical-Biological Cyclic Process for Hexavalent Chromium Reduction to Trivalent Chromium in Aqueous Medium - 20224
Conventional treatment of chromium contamination focuses on immobilization of the highly toxic and water-soluble form Cr(VI), by reducing it to the relatively less toxic and less mobile trivalent form, Cr(III), using reducing compounds like ferrous sulfate or ferrous iron, Fe(II). The Cr(VI) also respond to biodegradation and subsequent reduction. During the reduction reaction, Fe(II) is oxidized to ferric, Fe(III), which, being at the highest oxidation state, has no ability to reduce Cr(VI), and therefore remediation process terminates. The good news is that metal-reducing bacteria, S. oneidensis MR-1, can regenerate Fe(II) from Fe(III) with the help of an organic electron donor in an appropriate bacterial media. Thus, in the Cr(VI) chemical remediation process, Fe(II) to Fe(III) conversion is reversed by the biological process, regenerating Fe(II) and the Cr(VI) chemical reduction process continues in a cyclic fashion. This study presents the results of regeneration of Fe(II) from fresh Fe(III) as well as from the reaction products of Cr(VI) and Fe(II). Both fresh Fe(III) and reaction product Fe(III) exhibited the same reduction behavior. Fe(II) production increased with the increase of initial concentration of Fe(III), resulting in 87.5% Fe(II) regeneration. The extent of Fe(III) reduction (Fe(II)t/Fe(III)0.hour) was 21 times higher than the extent of Cr(VI) reduction (Cr(VI)t/Cr(VI)0.hour) by S. oneidensis MR-1. In a cyclic process of sequential Cr(VI) addition, the amount of Fe(II) regeneration decreased with the increase in cycle number, a fixed quantity of starting S. oneidensis MR-1 could regenerate Fe(II) at least five times in a ten-hour period. More importantly, regenerated Fe(II) instantly reduced Cr(VI) to Cr(III). So, Fe(II) regeneration with metal-reducing bacteria has a good potential for application in groundwater and wastewater remediation in reducing Cr(VI) to Cr(III). As a comparison of chemical to biological transformation, chemical transformation of Cr(VI) by Fe(II) was almost 600 times greater than that of biological transformation. Therefore a chemical-biological cyclic process offers potential applications for remediation of groundwater and hexavalent chromium contaminated waste sites. (authors)
Quantitative DNA Stable Isotope Probing Identifies Active Microorganisms Assimilating Volatile Fatty Acids in Full-Scale Enhanced Biological Phosphorus Removal Processes
Enhanced biological phosphorus removal (EBPR) systems often rely on exogenous carbon sources, such as volatile fatty acids (VFAs), to achieve higher P removal. Here, we employed DNA quantitative stable isotope probing (qSIP) using two VFAs, acetate and propionate, in cyclic anaerobic/aerobic incubations to assess their effects on P cycling and microbial activity with biomass from two full-scale EBPR water resource-recovery facilities that utilize VFA addition. We found that anaerobic VFA uptake preferences differed within known groups of PAOs, such as Candidatus Accumulibacter and Tetrasphaera-affiliated members (e.g., Ca. Phosphoribacter), between the two biomasses. The combination of qSIP with metagenomics identified isotopically labeled phages that were linked to active PAOs, highlighting their potential roles in modulating EBPR community composition and activity. The highest levels of anaerobic labeling from acetate were in genomes belonging to Saccharimonadales and Rickettsiales, which are generally host-associated with bacteria and eukaryotes, respectively. Furthermore, this finding highlights the possibility of cross-feeding between PAO hosts and their parasites or predators, as well as the role of so-far uncharacterized organisms participating in carbon cycling under EBPR conditions. Collectively, these results expand our understanding of the ecological interactions involved in communities anaerobically uptaking VFAs and cycling P that are central to EBPR.
Computationally efficient Bayesian estimation of graphical networks for omics data
Graphical networks are useful, widely-used modeling approaches to represent complex biological processes with biological measurements generated by platforms such as mass spectrometry. Bayesian analyses of graphical networks for omics data have several advantages over their frequentist counterparts, such as the inclusion of prior knowledge in the estimation of models. However, Bayesian approaches to date have only been feasible for data with a couple hundred biomolecules due to prohibitive computational time, but omics data often contains tens of thousands of biomolecules. Here, we present and illustrate a more computationally efficient approach named BPlane (Bayesian PseudoLikelihood-based Algorithm for Network Estimation) to extend Bayesian modeling capabilities for larger-sized datasets, such as most untargeted proteomics data. Via simulation, we demonstrate that BPlane produces substantial computational savings over a current state-of-the-art Bayesian algorithm while maintaining competitive edge detection accuracy. On a SARS-CoV2 proteomics data with 7000 proteins, the competing algorithm takes three times as long to complete the first iteration as BPlane takes to converge after over 100 iterations.
System-based identification of toxicity pathways associated with multi-walled carbon nanotube-induced pathological responses
The fibrous shape and biopersistence of multi-walled carbon nanotubes (MWCNT) have raised concern over their potential toxicity after pulmonary exposure. As in vivo exposure to MWCNT produced a transient inflammatory and progressive fibrotic response, this study sought to identify significant biological processes associated with lung inflammation and fibrosis pathology data, based upon whole genome mRNA expression, bronchoaveolar lavage scores, and morphometric analysis from C57BL/6J mice exposed by pharyngeal aspiration to 0, 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Using a novel computational model employing non-negative matrix factorization and Monte Carlo Markov Chain simulation, significant biological processes with expression similar to MWCNT-induced lung inflammation and fibrosis pathology data in mice were identified. A subset of genes in these processes was determined to be functionally related to either fibrosis or inflammation by Ingenuity Pathway Analysis and was used to determine potential significant signaling cascades. Two genes determined to be functionally related to inflammation and fibrosis, vascular endothelial growth factor A (vegfa) and C-C motif chemokine 2 (ccl2), were confirmed by in vitro studies of mRNA and protein expression in small airway epithelial cells exposed to MWCNT as concordant with in vivo expression. This study identified that the novel computational model was sufficient to determine biological processes strongly associated with the pathology of lung inflammation and fibrosis and could identify potential toxicity signaling pathways and mechanisms of MWCNT exposure which could be used for future animal studies to support human risk assessment and intervention efforts. - Highlights: • A novel computational model identified toxicity pathways matching in vivo pathology. • Systematic identification of MWCNT-induced biological processes in mouse lungs • MWCNT-induced functional networks of lung inflammation and fibrosis were revealed. • Two functional, representative genes, ccl2 and vegfa, were validated in vitro.
Outer membrane vesicles catabolize lignin-derived aromatic compounds in Pseudomonas putida KT2440
Lignin is an abundant and recalcitrant component of plant cell walls. While lignin degradation in nature is typically attributed to fungi, growing evidence suggests that bacteria also catabolize this complex biopolymer. However, the spatiotemporal mechanisms for lignin catabolism remain unclear. Improved understanding of this biological process would aid in our collective knowledge of both carbon cycling and microbial strategies to valorize lignin to value-added compounds. Here, we examine lignin modifications and the exoproteome of three aromatic–catabolic bacteria: Pseudomonas putida KT2440, Rhodoccocus jostii RHA1, and Amycolatopsis sp. ATCC 39116. P. putida cultivation in lignin-rich media is characterized by an abundant exoproteome that is dynamically and selectively packaged into outer membrane vesicles (OMVs). Interestingly, many enzymes known to exhibit activity toward lignin-derived aromatic compounds are enriched in OMVs from early to late stationary phase, corresponding to the shift from bioavailable carbon to oligomeric lignin as a carbon source. In vivo and in vitro experiments demonstrate that enzymes contained in the OMVs are active and catabolize aromatic compounds. Taken together, this work supports OMV-mediated catabolism of lignin-derived aromatic compounds as an extracellular strategy for nutrient acquisition by soil bacteria and suggests that OMVs could pote
Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns
Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.
Hypoxia-Independent Downregulation of Hypoxia-Inducible Factor 1 Targets by Androgen Deprivation Therapy in Prostate Cancer
Purpose: We explored changes in hypoxia-inducible factor 1 (HIF1) signaling during androgen deprivation therapy (ADT) of androgen-sensitive prostate cancer xenografts under conditions in which no significant change in immunostaining of the hypoxia marker pimonidazole had occurred. Methods and Materials: Gene expression profiles of volume-matched androgen-exposed and androgen-deprived CWR22 xenografts, with similar pimonidazole-positive fractions, were compared. Direct targets of androgen receptor (AR) and HIF1 transcription factors were identified among the differentially expressed genes by using published lists. Biological processes affected by ADT were determined by gene ontology analysis. HIF1α protein expression in xenografts and biopsy samples from 35 patients receiving neoadjuvant ADT was assessed by immunohistochemistry. Results: A total of 1344 genes showed more than 2-fold change in expression by ADT, including 35 downregulated and 5 upregulated HIF1 targets. Six genes were shared HIF1 and AR targets, and their downregulation was confirmed with quantitative RT-PCR. Significant suppression of the biological processes proliferation, metabolism, and stress response in androgen-deprived xenografts was found, consistent with tumor regression. Nineteen downregulated HIF1 targets were involved in those significant biological processes, most of them in metabolism. Four of these were shared AR and HIF1 targets, including genes encoding the regulatory glycolytic proteins HK2, PFKFB3, and SLC2A1. Most of the downregulated HIF1 targets were induced by hypoxia in androgen-responsive prostate cancer cell lines, confirming their role as hypoxia-responsive HIF1 targets in prostate cancer. Downregulation of HIF1 targets was consistent with the absence of HIF1α protein in xenografts and downregulation in patients by ADT (P<.001). Conclusions: AR repression by ADT may lead to downregulation of HIF1 signaling independently of hypoxic fraction, and this may contribute to tumor regression. HIF1α expression is probably not a useful hypoxia biomarker during ADT in prostate cancer.
Synthetic soils for ecological and synthetic biology applications
Soils are heterogeneous and dynamic systems characterized by complex physical, chemical, and biological interactions. Understanding these interactions is critical, as they influence plant productivity, global biogeochemical cycles, and ecosystem resilience. While ecologists have long studied soils in field, greenhouse, and laboratory settings, their complexity and heterogeneity make it challenging to pinpoint key properties driving biological processes and derive mechanistic insights. Advancements in synthetic biology, which seeks to engineer and control biological processes in soils, have increased the demand for standardized and controllable experimental platforms. These platforms, referred to here as ‘synthetic soils’, are systems designed to reproduce selected physicochemical characteristics of natural soils in a simplified and defined format, allowing scientists to systematically change soil physicochemical properties (i.e. texture, mineralogy, pH) to study how biological components (i.e. microbes, plants, soil fauna, etc.) respond to, modify, or interact within these controlled environments. This review explores existing synthetic soils, their advantages, limitations, and applications in ecology and synthetic biology, and discusses potential directions for their future development.
Characterization of Local and Systemic Impact of Whitefly (Bemisia tabaci) Feeding and Whitefly-Transmitted Tomato Mottle Virus Infection on Tomato Leaves by Comprehensive Proteomics
Tomato mottle virus (ToMoV) is a ssDNA plant begomovirus transmitted to solanaceous crops by the whitefly species complex (Bemisia tabaci), causing stunted growth, leaf mottling, and reduced fruit yield. Using a genetic repertoire of 7 genes, ToMoV pathogenesis includes manipulation of multiple plant biological processes to circumvent antiviral defenses. To further understand the effects of whitefly feeding and whitefly-transmitted ToMoV infection on tomato plants (Solanum lycopersicum ‘Florida Lanai’), we generated comprehensive protein profiles of leaves subjected to feeding by either (1) viruliferous whiteflies harboring ToMoV, or (2) non-viruliferous whiteflies, or (3) a no-feeding control. In each condition (1-3), whiteflies were confined to clip cages on a mature leaf for 3 days. The effects of whitefly feeding and ToMoV infection were measured both locally and systemically by sampling either a mature leaf directly from the site of whitefly feeding, or from a newly formed leaf 10 days post feeding (dpf). At 3 dpf, relatively few biological processes were affected specifically by ToMoV at the site of feeding, and include proteins associated with translation initiation and elongation, and plasmodesmata dynamics. In contrast, the systemic impacts of ToMoV on younger leaves at 10 dpf were more pronounced and included a virus-specific change in plant proteins associated with mRNA maturation and export, RNA-dependent DNA methylation, and other plant anti-viral transcriptional gene silencing systems. Our analysis supports previous findings and provides novel insight into biological processes in tomato altered locally and systemically by whitefly feeding and ToMoV infection.
Nonadiabatic rate constants for proton transfer and proton-coupled electron transfer reactions in solution: Effects of quadratic term in the vibronic coupling expansion
Rate constant expressions for vibronically nonadiabatic proton transfer and proton-coupled electron transfer reactions are presented and analyzed. The regimes covered include electronically adiabatic and nonadiabatic reactions, as well as high-frequency and low-frequency proton donor-acceptor vibrational modes. These rate constants differ from previous rate constants derived with the cumulant expansion approach in that the logarithmic expansion of the vibronic coupling in terms of the proton donor-acceptor distance includes a quadratic as well as a linear term. The analysis illustrates that inclusion of this quadratic term in the framework of the cumulant expansion framework may significantly impact the rate constants at high temperatures for proton transfer interfaces with soft proton donor-acceptor modes that are associated with small force constants and weak hydrogen bonds. The effects of the quadratic term may also become significant in these regimes when using the vibronic coupling expansion in conjunction with a thermal averaging procedure for calculating the rate constant. In this case, however, the expansion of the coupling can be avoided entirely by calculating the couplings explicitly for the range of proton donor-acceptor distances sampled. The effects of the quadratic term for weak hydrogen-bonding systems are less significant for more physically realistic models that prevent the sampling of unphysical short proton donor-acceptor distances. Additionally, the rigorous relation between the cumulant expansion and thermal averaging approaches is clarified. In particular, the cumulant expansion rate constant includes effects from dynamical interference between the proton donor-acceptor and solvent motions and becomes equivalent to the thermally averaged rate constant when these dynamical effects are neglected. This analysis identifies the regimes in which each rate constant expression is valid and thus will be important for future applications to proton transfer and proton-coupled electron transfer in chemical and biological processes.
Industrial biomanufacturing: The future of chemical production
The current model for industrial chemical manufacturing employs large-scale megafacilities that benefit from economies of unit scale. However, this strategy faces environmental, geographical, political, and economic challenges associated with energy and manufacturing demands. We review how exploiting biological processes for manufacturing (i.e., industrial biomanufacturing) addresses these concerns while also supporting and benefiting from economies of unit number. Key to this approach is the inherent small scale and capital efficiency of bioprocesses and the ability of engineered biocatalysts to produce designer products at high carbon and energy efficiency with adjustable output, at high selectivity, and under mild process conditions. The biological conversion of single-carbon compounds represents a test bed to establish this paradigm, enabling rapid, mobile, and widespread deployment, access to remote and distributed resources, and adaptation to new and changing markets.
An analytical phytoplankton model and its application in the tidal freshwater James River
Here, this study constructs an analytical model to understand the phytoplankton dynamics in a one-dimensional system by integrating physical and biological processes. We present an analytical solution of phytoplankton distribution along the longitudinal direction of a tidal river. The solution has two components: water age and accumulative growth. The water age represents the overall effect of physical transport due to advection, while the accumulative growth represents the accumulation of phytoplankton biomass due to biological processes during the transport. In addition, an alternative solution with nonlinear reaction term is given to account for the feedback mechanism of nonlinear processes that affect phytoplankton growth rate. The nonlinear reaction term can be also used to approximate the effect of physical mixing on phytoplankton distribution. The analytical solutions can be further used to predict the temporal variation and spatial distribution of phytoplankton along the tidal river. They also serve as a mathematical tool to unite different field phytoplankton observations. We applied the model in the Tidal Freshwater Region (TFR) of James River under different dynamic conditions and the model results match well with the observations, which validates the theory on different time scales. Furthermore, an analysis is conducted for the Local Chlorophyll-a Maximum (LCM) in the TFR. It shows that the condition for LCM is biologically controlled, but its location is regulated by river flow and river geomorphology.
2025 Workshop on Envisioning Frontiers in AI and Computing for Biological Research: Position Papers
This workshop aims to identify key research directions for transforming biology using artificial intelligence (AI), machine learning (ML) and computational methods to facilitate the discovery of new behaviors, mechanisms, and designs of biological processes relevant to DOE missions, underpinning a broader U.S. bioeconomy. By developing novel AI/ML technologies to analyze and interpret complex biological data, researchers can organize and simulate biological processes at various scales as well as advance predictive understanding and manipulation of biological systems. This integration of computation, experimentation, and next-generation experimental technologies can lead to discoveries in new biological behaviors and mechanisms relevant to DOE missions. The focus is on how advanced computational and mathematical methods can impact this mission by exploring digital twins, foundation models, automated laboratory experiments, modeling of complex living systems, and data-driven approaches for the biodesign of plants and microbial systems. While data management is important, it is not the primary focus of this workshop, which will assess the current state, trends, and AI/ML challenges at the interface between biology and computational science to identify opportunities for high-impact research at their intersection. The goal is to define research needs and opportunities that align with biological sciences, computational sciences, and applied mathematics research.
Chromatin structures from integrated AI and polymer physics model
The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure fromindirectmeasures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.