Solutions and case studies for thermally driven reactive transport and porosity evolution in geothermal systems (reactive Lauwerier problem)

Subsurface non-isothermal fluid injection is a ubiquitous scenario in energy and water resource applications, which can lead to geochemical disequilibrium and thermally driven solubility changes and reactions. Depending on the nature of the solubility of a mineral, the thermal change can lead to either mineral dissolution or precipitation (due to undersaturation or supersaturation conditions). Here, by considering this thermo-hydro-chemical (THC) scenario and by calculating the temperature-dependent solubility using a non-isothermal solution (the so-called Lauwerier solution), thermally driven reactive transport solutions are derived for a confined aquifer. The coupled solutions, hereafter termed the “reactive Lauwerier problem”, are developed for axisymmetric and Cartesian symmetries and additionally provide the porosity evolution in the aquifer. The solutions are then used to study two common cases: (I) hot CO2-rich water injection into a carbonate aquifer and (II) hot silica-rich water injection into a sandstone aquifer, leading to mineral dissolution and precipitation, respectively. We discuss the timescales of such fluid–rock interactions and the changes in hydraulic system properties. The solutions and findings contribute to the understanding and management of subsurface energy and water resources, such as aquifer thermal energy storage, aquifer storage and recovery and reinjection of used geothermal water. The solutions are also useful for developing and benchmarking complex coupled numerical codes.</p

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species' lifestyle (specialized/generalist); the second, environmental-independent, is associated (correlation = ∼0.6) with the intrinsic metabolic capacities of a species—higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets

Transcriptional Regulation by CHIP/LDB Complexes

It is increasingly clear that transcription factors play versatile roles in turning genes “on” or “off” depending on cellular context via the various transcription complexes they form. This poses a major challenge in unraveling combinatorial transcription complex codes. Here we use the powerful genetics of Drosophila combined with microarray and bioinformatics analyses to tackle this challenge. The nuclear adaptor CHIP/LDB is a major developmental regulator capable of forming tissue-specific transcription complexes with various types of transcription factors and cofactors, making it a valuable model to study the intricacies of gene regulation. To date only few CHIP/LDB complexes target genes have been identified, and possible tissue-dependent crosstalk between these complexes has not been rigorously explored. SSDP proteins protect CHIP/LDB complexes from proteasome dependent degradation and are rate-limiting cofactors for these complexes. By using mutations in SSDP, we identified 189 down-stream targets of CHIP/LDB and show that these genes are enriched for the binding sites of APTEROUS (AP) and PANNIER (PNR), two well studied transcription factors associated with CHIP/LDB complexes. We performed extensive genetic screens and identified target genes that genetically interact with components of CHIP/LDB complexes in directing the development of the wings (28 genes) and thoracic bristles (23 genes). Moreover, by in vivo RNAi silencing we uncovered novel roles for two of the target genes, xbp1 and Gs-alpha, in early development of these structures. Taken together, our results suggest that loss of SSDP disrupts the normal balance between the CHIP-AP and the CHIP-PNR transcription complexes, resulting in down-regulation of CHIP-AP target genes and the concomitant up-regulation of CHIP-PNR target genes. Understanding the combinatorial nature of transcription complexes as presented here is crucial to the study of transcription regulation of gene batteries required for development

Constraint-based Functional Similarity of Metabolic Genes: Going Beyond Network Topology

Motivation: Several recent studies attempted to establish measures for the similarity between genes that are based on the topological properties of metabolic networks. However, these approaches offer only a static description of the properties of interest and offer moderate (albeit significant) correlations with pertinent experimental data. Results: Using a constraint-based large-scale metabolic model, we present two effectively computable measures of functional gene similarity, one based on the response of the metabolic network to gene knock-outs and the other based on the metabolic flux activity across a variety of growth media. We applied these measures to 750 genes comprising the metabolic network of the budding yeast. Comparing the in silico computed functional similarities to Gene Ontology (GO) annotations and gene expression data, we show that our computational method captures functional similarities between metabolic genes that go beyond those obtained by the topological analysis of metabolic networks alone, thus revealing dynamic characteristics of gene function. Interestingly, the measure based on the network response to different growth environments markedly outperforms the measure based on its response to gene knockouts, though both have some added synergistic value in depicting the functional relationships between metabolic genes. Contact

A method for inferring medical diagnoses from patient similarities

BACKGROUND: Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools. METHODS: Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses. RESULTS: Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses. CONCLUSIONS: Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks