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In the past decade, trustworthy Artificial Intelligence (AI) has emerged as a focus for the AI community to ensure better adoption of AI models, and explainable AI is a cornerstone in this area. Over the years, the focus has shifted from building transparent AI methods to making recommendations on how to make black-box or opaque machine learning models and their results more understandable by experts and non-expert users. In our previous work, to address the goal of supporting user-centered explanations that make model recommendations more explainable, we developed an Explanation Ontology (EO). The EO is a general-purpose representation that was designed to help system designers connect explanations to their underlying data and knowledge. This paper addresses the apparent need for improved interoperability to support a wider range of use cases. We expand the EO, mainly in the system attributes contributing to explanations, by introducing new classes and properties to support a broader range of state-of-the-art explainer models. We present the expanded ontology model, highlighting the classes and properties that are important to model a larger set of fifteen literature-backed explanation types that are supported within the expanded EO. We build on these explanation type descriptions to show how to utilize the EO model to represent explanations in five use cases spanning the domains of finance, food, and healthcare. We include competency questions that evaluate the EO's capabilities to provide guidance for system designers on how to apply our ontology to their own use cases. This guidance includes allowing system designers to query the EO directly and providing them exemplar queries to explore content in the EO represented use cases. We have released this significantly expanded version of the Explanation Ontology at https://purl.org/heals/eo and updated our resource website, https://tetherless-world.github.io/explanation-ontology , with supporting documentation. Overall, through the EO model, we aim to help system designers be better informed about explanations and support these explanations that can be composed, given their systems' outputs from various AI models, including a mix of machine learning, logical and explainer models, and different types of data and knowledge available to their systems. [ABSTRACT FROM AUTHOR]

Objective: To examine changes in use patterns, cost of healthcare services before and after the outbreak of the COVID-19 pandemic, and their impacts on expenditures for patients receiving treatment for depression, anxiety, eating disorders, and substance use. Methods: This cross-sectional study employed statistical tests to analyze claims in MarketScan® Commercial Database in March 2020-February 2021 and quarterly from March 2020 to August 2021, compared to respective pre-pandemic periods. The analysis is based on medical episodes created by the Merative™ Medical Episode Grouper (MEG). MEG is a methodology that groups medical and prescription drug claims to create clinically relevant episodes of care. Results: Comparing year-over-year changes, proportion of patients receiving anxiety treatment among all individuals obtaining healthcare services grew 13.7% in the first year of the pandemic (3/2020–2/2021) versus 10.0% in the year before the pandemic (3/2019–2/2020). This, along with a higher growth in price per episode (5.5% versus 4.3%) resulted in a greater increase in per claimant expenditure ($0.61 versus $0.41 per month). In the same periods, proportion of patients receiving treatment for depression grew 3.7% versus 6.9%, but per claimant expenditure grew by same amount due to an increase in price per episode (4.8%). Proportion of patients receiving treatment for anorexia started to increase 21.1% or more in the fall of 2020. Patient proportion of alcohol use in age group 18–34 decreased 17.9% during the pandemic but price per episode increased 26.3%. Patient proportion of opioid use increased 11.5% in March–May 2020 but decreased or had no significant changes in subsequent periods. Conclusions: We investigated the changes in use patterns and expenditures of mental health patients before and after the outbreak of the COVID-19 pandemic using claims data in MarketScan®. We found that the changes and their financial impacts vary across mental health conditions, age groups, and periods of the pandemic. Some changes are unexpected from previously reported prevalence increases among the general population and could underlie unmet treatment needs. Therefore, mental health providers should anticipate the use pattern changes in services with similar COVID-19 pandemic disruptions and payers should anticipate cost increases due, in part, to increased price and/or service use. [ABSTRACT FROM AUTHOR]

Language is often thought as being poorly adapted to precisely describe or quantify smell and olfactory attributes. In this work, we show that semantic descriptors of odors can be implemented in a model to successfully predict odor mixture discriminability, an olfactory attribute. We achieved this by taking advantage of the structure-to-percept model we previously developed for monomolecular odorants, using chemical descriptors to predict pleasantness, intensity and 19 semantic descriptors such as "fish," "cold," "burnt," "garlic," "grass," and "sweet" for odor mixtures, followed by a metric learning to obtain odor mixture discriminability. Through this expansion of the representation of olfactory mixtures, our Semantic model outperforms state of the art methods by taking advantage of the intermediary semantic representations learned from human perception data to enhance and generalize the odor discriminability/similarity predictions. As 10 of the semantic descriptors were selected to predict discriminability/similarity, our approach meets the need of rapidly obtaining interpretable attributes of odor mixtures as illustrated by the difficulty of finding olfactory metamers. More fundamentally, it also shows that language can be used to establish a metric of discriminability in the everyday olfactory space. [ABSTRACT FROM AUTHOR]

Nicotiana gandarela Augsten & Stehmann (Solanaceae), sp. nov., a small 'tobacco' known only from one locality at Serra do Gandarela, in the state of Minas Gerais, Brazil, is described and illustrated. It is morphologically characterized by its rosulate basal leaves, red corolla with a short tube not inflated at the apex, and the peculiar habitat, a shaded site under a rocky outcrop ledge along a forested stream. Phylogenetic analyses based on a combined dataset of nuclear (ITS) and plastid (ndhF, trnLF, and trnSG) DNA sequences revealed that the species belongs to the Nicotiana sect. Alatae and is sister to the clade with the remaining species in the section. A key for the identification of Brazilian species of the section is given. The unusual habitat, the small population size, and the intense pressure of mining activities in the surroundings made the species assessed as Critically Endangered (CR), needing conservation efforts to avoid its extinction. [ABSTRACT FROM AUTHOR]

Sudden olfactory loss in the absence of concurrent nasal congestion is now a well‐recognized symptom of COVID‐19. We examined olfaction using standardized objective tests of odour detection, identification and hedonics collected from asymptomatic university students before and as SARS‐CoV‐2 emerged locally. Olfactory performance of students who were tested when the virus is known to be endemic (n = 22) was compared to students tested in the month prior to viral circulation (n = 25), a normative sample assessed during the previous 4 years (n = 272) and those tested in prior years during the same time period. Analyses showed significantly reduced odour detection for the virus exposed cohort compared to students tested before (t = 2.60; P =.01; d = 0.77; CI 0.17, 1.36) and to the normative sample (D = 0.38; P =.005). Odour identification scores were similar, but the exposed cohort rated odours as less unpleasant (P <.001, CLES = 0.77). Hyposmia increased 4.4‐fold for students tested 2 weeks before school closure (N = 22) and increased 13.6‐fold for students tested in the final week (N = 11). While the unavailability of COVID‐19 testing is a limitation, this naturalistic study demonstrates week‐by‐week increase in hyposmia in asymptomatic students as a virus was circulating on campus, consistent with increasing airborne viral loads. The specific hedonic deficit in unpleasantness appraisal suggests a deficit in the TAAR olfactory receptor class, which conveys the social salience of odours. Assessment of odour detection and hedonic ratings may aid in early detection of SARS‐CoV‐2 exposure in asymptomatic and pre‐symptomatic persons. [ABSTRACT FROM AUTHOR]

Ontogenetic shifts in plants are ubiquitous, but their ecological and evolutionary significance remain largely unknown. Juveniles have few opportunities to accumulate sufficient biomass to withstand damage, whereas adults allocate resources to reproduction. Thus, age‐specific environmental filters are expected to drive selection towards specific resource‐investment strategies for each developmental stage.We tested whether species exhibited strategies that favour faster rates of biomass accumulation during early developmental stages to cope with vulnerability, shifting towards habitat‐dependent strategies to optimise resource acquisition and use later on. We also hypothesised that juveniles exhibit greater intraspecific variability of strategies than adults, as a result of continuous filtering of traits throughout the plant's development towards adulthood.We measured key leaf traits—leaf area (LA), leaf dry matter content (LDMC) and specific leaf area (SLA)—and calculated scores for competitor (C), stress‐tolerator (S), ruderal (R) strategies of the CSR system for juveniles of 54 species and conspecific adults of 27 species naturally occurring either in unproductive (grassland) or productive (forest) habitats.Juveniles exhibited higher SLA and LA and lower LDMC, and thus a more R‐strategy in both habitats when compared with adults, but were not necessarily "R" in absolute terms. We also found smaller intraspecific variation for the R‐strategy in adults compared with juveniles in both habitats, whereas the variation of the other strategies was habitat dependent.Ontogenetic shifts in ecological strategies appear to have been selected as a response to ontogeny‐dependent filters. Thus, strategies that favour less costly leaves and faster growth rates (relatively R‐selected) in juveniles shift towards C and/or S strategies in adulthood, depending on habitat productivity. Nevertheless, habitat‐dependent specialisation seems to be a major driver of ecological strategy selection in juveniles. Our study reveals ontogeny‐dependent strategies, offering a new approach to integrate plant development and functional specialisation. A plain language summary is available for this article. Plain Language Summary [ABSTRACT FROM AUTHOR]

The recently discovered bactericidal properties of nanostructures on wings of insects such as cicadas and dragonflies have inspired the development of similar nanostructured surfaces for antibacterial applications. Since most antibacterial applications require nanostructures covering a considerable amount of area, a practical fabrication method needs to be cost-effective and scalable. However, most reported nanofabrication methods require either expensive equipment or a high temperature process, limiting cost efficiency and scalability. Here, we report a simple, fast, lowcost, and scalable antibacterial surface nanofabrication methodology. Our method is based on metal-assisted chemical etching that only requires etching a single crystal silicon substrate in a mixture of silver nitrate and hydrofluoric acid for several minutes. We experimentally studied the effects of etching time on the morphology of the silicon nanospikes and the bactericidal properties of the resulting surface. We discovered that 6minutes of etching results in a surface containing silicon nanospikes with optimal geometry. The bactericidal properties of the silicon nanospikes were supported by bacterial plating results, fluorescence images, and scanning electron microscopy images. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) play a key role in many, if not all, cellular processes. Disease is often caused by perturbation of PPIs, as recently indicated by studies of missense mutations. To understand the associations of proteins and to unravel the global picture of PPIs in the cell, different experimental detection techniques for PPIs have been established. Genetic and biochemical methods such as the yeast two-hybrid system or affinity purification-based approaches are well suited to high-throughput, proteome-wide screening and are mainly used to obtain qualitative results. However, they have been criticized for not reflecting the cellular situation or the dynamic nature of PPIs. In this review, we provide an overview of various genetic methods that go beyond qualitative detection and allow quantitative measuring of PPIs in mammalian cells, such as dual luminescence-based co-immunoprecipitation, Förster resonance energy transfer or luminescence-based mammalian interactome mapping with bait control. We discuss the strengths and weaknesses of different techniques and their potential applications in biomedical research. [ABSTRACT FROM AUTHOR]

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation. [ABSTRACT FROM AUTHOR]

Motivation: Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli.Results: We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli.Availability: Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h.Contact: christoph.hafemeister@nyu.edu or atarca@med.wayne.edu.Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating’ those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species.Contact: pmeyerr@us.ibm.com or Julia.Hoeng@pmi.comSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue.Results: Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins.Availability and implementation: Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively.Contact: gyanbhanot@gmail.com or luofeng@clemson.edu or atarca@med.wayne.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict.Contact: ebilal@us.ibm.com or gustavo@us.ibm.com.Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data.Results: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species.Availability and implementation: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip.Contact: meikelbiehl@gmail.com [ABSTRACT FROM AUTHOR]

Motivation: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development. However, in many cases, a naive ‘extrapolation’ between the two species has not succeeded. As a result, clinical trials of new drugs sometimes fail even after considerable success in the mouse or rat stage of development. In addition to in vitro studies, inter-species translation requires analytical tools that can predict the enriched gene sets in human cells under various stimuli from corresponding measurements in animals. Such tools can improve our understanding of the underlying biology and optimize the allocation of resources for drug development.Results: We developed an algorithm to predict differential gene set enrichment as part of the sbv IMPROVER (systems biology verification in Industrial Methodology for Process Verification in Research) Species Translation Challenge, which focused on phosphoproteomic and transcriptomic measurements of normal human bronchial epithelial (NHBE) primary cells under various stimuli and corresponding measurements in rat (NRBE) primary cells. We find that gene sets exhibit a higher inter-species correlation compared with individual genes, and are potentially more suited for direct prediction. Furthermore, in contrast to a similar cross-species response in protein phosphorylation states 5 and 25 min after exposure to stimuli, gene set enrichment 6 h after exposure is significantly different in NHBE cells compared with NRBE cells. In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods.Availability and implementation: Implementation of all algorithms is available as source code (in Matlab) at http://bhanot.biomaps.rutgers.edu/wiki/codes_SC3_Predicting_GeneSets.zip, along with the relevant data used in the analysis. Gene sets, gene expression and protein phosphorylation data are available on request.Contact: hormoz@kitp.ucsb.edu [ABSTRACT FROM AUTHOR]

Cancer cells are highly variable and largely resistant to therapeutic intervention. Recently, the use of the tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induced treatment is gaining momentum due to TRAIL’s ability to specifically target cancers with limited effect on normal cells. Nevertheless, several malignant cancer types still remain non-sensitive to TRAIL. Previously, we developed a dynamic computational model, based on perturbation-response differential equations approach, and predicted protein kinase C (PKC) as the most effective target, with over 95% capacity to kill human fibrosarcoma (HT1080) in TRAIL stimulation (1). Here, to validate the model prediction, which has significant implications for cancer treatment, we conducted experiments on two TRAIL-resistant cancer cell lines (HT1080 and HT29). Using PKC inhibitor bisindolylmaleimide I, we demonstrated that cell viability is significantly impaired with over 95% death of both cancer types, in consistency with our previous model. Next, we measured caspase-3, Poly (ADP-ribose) polymerase (PARP), p38, and JNK activations in HT1080, and confirmed cell death occurs through apoptosis with significant increment in caspase-3 and PARP activations. Finally, to identify a crucial PKC isoform, from 10 known members, we analyzed each isoform mRNA expressions in HT1080 cells and shortlisted the highest 4 for further siRNA knock-down (KD) experiments. From these KDs, PKCδ produced the most cancer cell death in conjunction with TRAIL. Overall, our approach combining model predictions with experimental validation holds promise for systems biology based cancer therapy. [ABSTRACT FROM AUTHOR]

Background: Travelers receive medical advice from a variety of sources, including travel agencies. The aim of this study is to describe the quality of pre-travel advice provided by travel agencies in Cuzco to travelers interested in visiting malaria and yellow fever endemic areas.Methods: Trained medical students posed as tourists and visited travel agencies in Cuzco requesting travel advice for a trip to the southern Amazon of Peru, recording advice regarding risk and prevention of malaria and yellow fever.Results: A total of 163 registered travel agencies were included in the study. The mean proposed tour duration was 6.8 days (±1.4 days) with a median time to departure of 3 days and a median tour cost of 805 US dollars (USD) [interquartile range (IQR) 580-1,095]. Overall, 45% employees failed to mention the risk for any illness. Eighteen percent of the employees acknowledged risk of malaria and 53% risk of yellow fever. However, 36% denied malaria risk and 2% denied risk of yellow fever in the region. The price of tours from travel agencies that did not mention any health risk was significantly lower [1,009.6 ± 500.5 vs 783.9 ± 402 USD, t (152) = 3, p < 0.01] compared with the price from agencies that did mention health risks. Almost all who acknowledged malaria (97%) and/or yellow fever (100%) were able to provide at least one recommendation for prevention. However, advice was not always accurate or spontaneously volunteered. Only 7% of the employees provided both correct scheduling and location information for administration of the yellow fever vaccine.Conclusions: The majority of registered travel agencies in Cuzco did not provide sufficient and accurate information regarding risk and prevention of malaria and yellow fever to travelers inquiring about trips to the southern Amazon of Peru. [ABSTRACT FROM AUTHOR]

Background Although much is understood about the enzymatic cascades that underlie cellular biosynthesis, comparatively little is known about the rules that determine their cellular organization. We performed a detailed analysis of the localization of E.coli GFP-tagged enzymes for cells growing exponentially Results We found that out of 857 globular enzymes, at least 219 have a discrete punctuate localization in the cytoplasm and catalyze the first or the last reaction in 60% of biosynthetic pathways. A graph-theoretic analysis of E.coli's metabolic network shows that localized enzymes, in contrast to non-localized ones, form a tree-like hierarchical structure, have a higher within-group connectivity, and are traversed by a higher number of feed-forward and feedback loops than their non-localized counterparts. A Gene Ontology analysis of these enzymes reveals an enrichment of terms related to essential metabolic functions in growing cells. Given that these findings suggest a distinct metabolic role for localization, we studied the dynamics of cellular localization of the cell wall synthesizing enzymes in B. subtilis and found that enzymes localize during exponential growth but not during stationary growth. Conclusions We conclude that active biochemical pathways inside the cytoplasm are organized spatially following a rule where their first or their last enzymes localize to effectively connect the different active pathways and thus could reflect the activity state of the cell's metabolic network. [ABSTRACT FROM AUTHOR]

Many proteins are observed to localize to specific subcellular regions within bacteria. Recent experiments have shown that proteins that have self-interactions that lead them to aggregate tend to localize to the poles. Theoretical modeling of the localization of aggregating protein within bacterial cell geometries shows that aggregates can spontaneously localize to the pole due to nucleoid occlusion. The resulting polar localization, whether it be to a single pole or to both was shown to depend on the rate of protein addition. Motivated by these predictions we selected a set of genes from Escherichia coli, whose protein products have been reported to localize when tagged with green fluorescent protein (GFP), and explored the dynamics of their localization.We induced protein expression from each gene at different rates and found that in all cases unipolar patterning is favored at low rates of expression whereas bipolar is favored at higher rates of expression. Our findings are consistent with the predictions of the model, suggesting that localization may be due to aggregation plus nucleoid occlusion. When we expressed GFP by itself under the same conditions, no localization was observed. These experiments highlight the potential importance of protein aggregation, nucleoid occlusion and rate of protein expression in driving polar localization of functional proteins in bacteria. [ABSTRACT FROM AUTHOR]

Many proteins are observed to localize to specific subcellular regions within bacteria. Recent experiments have shown that proteins that have self-interactions that lead them to aggregate tend to localize to the poles. Theoretical modeling of the localization of aggregating protein within bacterial cell geometries shows that aggregates can spontaneously localize to the pole due to nucleoid occlusion. The resulting polar localization, whether it be to a single pole or to both was shown to depend on the rate of protein addition. Motivated by these predictions we selected a set of genes from E. coli, whose protein products have been reported to localize when tagged with GFP, and explored the dynamics of their localization. We induced protein expression from each gene at different rates and found that in all cases unipolar patterning is favored at low rates of expression whereas bipolar is favored at higher rates of expression. Our findings are consistent with the predictions of the model, suggesting that localization may be due to aggregation plus nucleoid occlusion. When we expressed GFP by itself under the same conditions, no localization was observed. These experiments highlight the potential importance of protein aggregation, nucleoid occlusion and rate of protein expression in driving polar localization of functional proteins in bacteria. [ABSTRACT FROM AUTHOR]

Background Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission. [ABSTRACT FROM AUTHOR]

Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein.Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team.The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams.Availability: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.Contact: gustavo@us.ibm.comSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Motivation: Analyses and algorithmic predictions based on high-throughput data are essential for the success of systems biology in academic and industrial settings. Organizations, such as companies and academic consortia, conduct large multi-year scientific studies that entail the collection and analysis of thousands of individual experiments, often over many physical sites and with internal and outsourced components. To extract maximum value, the interested parties need to verify the accuracy and reproducibility of data and methods before the initiation of such large multi-year studies. However, systematic and well-established verification procedures do not exist for automated collection and analysis workflows in systems biology which could lead to inaccurate conclusions.Results: We present here, a review of the current state of systems biology verification and a detailed methodology to address its shortcomings. This methodology named ‘Industrial Methodology for Process Verification in Research’ or IMPROVER, consists on evaluating a research program by dividing a workflow into smaller building blocks that are individually verified. The verification of each building block can be done internally by members of the research program or externally by ‘crowd-sourcing’ to an interested community. www.sbvimprover.comImplementation: This methodology could become the preferred choice to verify systems biology research workflows that are becoming increasingly complex and sophisticated in industrial and academic settings.Contact: gustavo@us.ibm.com [ABSTRACT FROM PUBLISHER]

The article proposes that systems biology results can be verified using community-wide challenges that test specific methodologies using the power of crowds. The researchers argue that if challenge-based verification process become routine, it is likely that industrial research workflows will see increased efficiency in the generation of applied scientific results and decreased expense per verified result. Specific solutions to avoid false steps are elaborated.

During Bacillus subtilis sporulation, an endocytic-like process called engulfment results in one cell being entirely encased in the cytoplasm of another cell. The driving force underlying this process of membrane movement has remained unclear, although components of the machinery have been characterized. Here we provide evidence that synthesis of peptidoglycan, the rigid, strength bearing extracellular polymer of bacteria, is a key part of the missing force-generating mechanism for engulfment. We observed that sites of peptidoglycan synthesis initially coincide with the engulfing membrane and later with the site of engulfment membrane fission. Furthermore, compounds that block muropeptide synthesis or polymerization prevented membrane migration in cells lacking a component of the engulfment machinery (SpoIIQ), and blocked the membrane fission event at the completion of engulfment in all cells. In addition, these compounds inhibited bulge and vesicle formation that occur in spoIID mutant cells unable to initiate engulfment, as did genetic ablation of a protein that polymerizes muropeptides. This is the first report to our knowledge that peptidoglycan synthesis is necessary for membrane movements in bacterial cells and has implications for the mechanism of force generation during cytokinesis. [ABSTRACT FROM AUTHOR]

Circadian behaviors in the animal kingdom are regulated by a small set of conserved genes. Starting with a historical perspective focused on Drosophila, the authors describe how the recurrent discovery of circadian clock genes uncovered a molecular mechanism associated with cycling gene expression. These molecular cycles appear to emerge from delayed negative and positive feedback. The authors will then introduce a novel timing mechanism uncovered by a single cell-based assay, with the new ideas and prospects for future research that it has raised. [ABSTRACT FROM AUTHOR]

Individual cells in clonal populations often respond differently to environmental changes; for binary phenotypes, such as cell death, this can be measured as a fractional response. These types of responses have been attributed to cell-intrinsic stochastic processes and variable abundances of biochemical constituents, such as proteins, but the influence of organelles is still under investigation. We use the response to TNF-related apoptosis inducing ligand (TRAIL) and a new statistical framework for determining parameter influence on cell-to-cell variability through the inference of variance explained, DEPICTIVE, to demonstrate that variable mitochondria abundance correlates with cell survival and determines the fractional cell death response. By quantitative data analysis and modeling we attribute this effect to variable effective concentrations at the mitochondria surface of the pro-apoptotic proteins Bax/Bak. Further, our study suggests that inhibitors of anti-apoptotic Bcl-2 family proteins, used in cancer treatment, may increase the diversity of cellular responses, enhancing resistance to treatment. Phenotypic cell-to-cell variability contributes to fractional killing, but the mechanisms are incompletely understood. Here the authors show that mitochondrial density correlates with cell survival in response to TRAIL, and that variable effective concentrations of Bax/Bak contribute to the effect. [ABSTRACT FROM AUTHOR]

There has been recent progress in predicting whether common verbal descriptors such as “fishy”, “floral” or “fruity” apply to the smell of odorous molecules. However, accurate predictions have been achieved only for a small number of descriptors. Here, we show that applying natural-language semantic representations on a small set of general olfactory perceptual descriptors allows for the accurate inference of perceptual ratings for mono-molecular odorants over a large and potentially arbitrary set of descriptors. This is noteworthy given that the prevailing view is that humans’ capacity to identify or characterize odors by name is poor. We successfully apply our semantics-based approach to predict perceptual ratings with an accuracy higher than 0.5 for up to 70 olfactory perceptual descriptors, a ten-fold increase in the number of descriptors from previous attempts. These results imply that the semantic distance between descriptors defines the equivalent of an odorwheel. It is now possible to predict what a chemical smells like based on its chemical structure, however to date, this has only been done for a small number of odor descriptors. Here, using natural-language semantic representations, the authors demonstrate prediction of a much wider range of descriptors. [ABSTRACT FROM AUTHOR]