Dynamical Model of Drug Accumulation in Bacteria: Sensitivity Analysis and Experimentally Testable Predictions.
Ontology highlight
ABSTRACT:
SUBMITTER: Vesselinova N
PROVIDER: S-EPMC5100933 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We found a startling correlation (Pearson ρ > 0.97) between a single event in daily sea surface temperatures each spring, and peak fish egg abundance measurements the following summer, in 7 years of approximately weekly fish egg abundance data collected at Scripps Pier in La Jolla California. Even more surprising was that this event-based result persisted despite the large and variable number of fish species involved (up to 46), and the large and variable time interval between trigger and response (up to ~3 months). To mitigate potential over-fitting, we made an out-of-sample prediction beyond the publication process for the peak summer egg abundance observed at Scripps Pier in 2020 (available on bioRxiv). During peer-review, the prediction failed, and while it would be tempting to explain this away as a result of the record-breaking toxic algal bloom that occurred during the spring (9x higher concentration of dinoflagellates than ever previously recorded), a re-examination of our methodology revealed a potential source of over-fitting that had not been evaluated for robustness. This cautionary tale highlights the importance of testable true out-of-sample predictions of future values that cannot (even accidentally) be used in model fitting, and that can therefore catch model assumptions that may otherwise escape notice. We believe that this example can benefit the current push towards ecology as a predictive science and support the notion that predictions should live and die in the public domain, along with the models that made them.
| S-EPMC7723288 | biostudies-literature
Project description:Recent advances in microscopy, computing power and image processing have enabled the analysis of ever larger datasets of movies of microorganisms to study their behaviour. However, techniques for analysing the dynamics of individual cells from such datasets are not yet widely available in the public domain. We recently demonstrated significant phenotypic heterogeneity in the adhesion of Escherichia coli bacteria to glass surfaces using a new method for the high-throughput analysis of video microscopy data. Here, we present an in-depth analysis of this method and its limitations, and make public our algorithms for following the positions and orientations of individual rod-shaped bacteria from time-series of 2D images to reconstruct their trajectories and characterise their dynamics. We demonstrate in detail how to use these algorithms to identify different types of adhesive dynamics within a clonal population of bacteria sedimenting onto a surface. The effects of measurement errors in cell positions and of limited trajectory durations on our results are discussed.
| S-EPMC6553751 | biostudies-literature
Project description:Individuals' dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.
| S-EPMC2722684 | biostudies-literature
Project description:Skin ulcer is a common type of disease affecting patients' health and quality of life, and bacterial infection increases the difficulty of its management.The present study collected the results of bacterial culture sampled from the surface of 110 cases of skin ulcers at our hospital from January 2011 to December 2012. We analyzed the constituent ratios of ulcer surface bacteria, the change in the main infectious bacteria and the results of drug-sensitivity testing for common bacteria. In addition, the characteristics of bacterial infection of skin ulcers were summarized.Of the 110 samples, 90 isolated bacteria were cultured. Sixty-one were Gram-negative bacteria, mainly comprising Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterobacter cloacae and Escherichia coli. In addition, 23 isolates were Gram-positive bacteria, mainly comprising Staphylococcus aureus and Enterococcus faecalis. The probability of a negative bacterial culture in 2012 was significantly lower than that in 2011 (16.7% vs. 40.0%, p < 0.01). Moreover, the probability of P. aeruginosa infection in 2012 was significantly higher than that in 2011 (31.7% vs. 14.0%, p < 0.01). P. aeruginosa was resistant to seven commonly used antibiotics. Both K. pneumoniae and E. coli had higher resistance to ampicillin. E. cloacae were not sensitive to piperacillin/tazobactam. Acinetobacter baumannii was resistant to all the tested drugs. S. aureus, E. faecalis and Staphylococcus epidermidis had high resistance to clindamycin. There was other drug resistance to reflect the higher rate of skin bacterial resistance.Skin bacterial resistance rate is high. Gram-negative bacteria gradually account for the majority, and P. aeruginosa becomes the most important skin infection pathogen. These characteristics of bacterial infections of skin ulcers provide a significant reference for guiding the selection of antibiotics, better controlling infections of skin ulcers and accelerating the healing of skin ulcers.
| S-EPMC5555241 | biostudies-other
Project description:Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman's ρ from a range of 0.43-0.62 to 0.30-0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.
| S-EPMC7906305 | biostudies-literature
Project description:Beliefs about the state of the world are an important influence on both normal behavior and psychopathology. However, understanding of the neural basis of belief processing remains incomplete, and several aspects of belief processing have only recently been explored. Specifically, different types of beliefs may involve fundamentally different inferential processes and thus recruit distinct brain regions. Additionally, neural processing of truth and falsity may differ from processing of certainty and uncertainty. The purpose of this study was to investigate the neural underpinnings of assessment of testable and non-testable propositions in terms of truth or falsity and the level of certainty in a belief. Functional magnetic resonance imaging (fMRI) was used to study 14 adults while they rated propositions as true or false and also rated the level of certainty in their judgments. Each proposition was classified as testable or non-testable. Testable propositions activated the DLPFC and posterior cingulate cortex, while non-testable statements activated areas including inferior frontal gyrus, superior temporal gyrus, and an anterior region of the superior frontal gyrus. No areas were more active when a proposition was accepted, while the dorsal anterior cingulate was activated when a proposition was rejected. Regardless of whether a proposition was testable or not, certainty that the proposition was true or false activated a common network of regions including the medial prefrontal cortex, caudate, posterior cingulate, and a region of middle temporal gyrus near the temporo-parietal junction. Certainty in the truth or falsity of a non-testable proposition (a strong belief without empirical evidence) activated the insula. The results suggest that different brain regions contribute to the assessment of propositions based on the type of content, while a common network may mediate the influence of beliefs on motivation and behavior based on the level of certainty in the belief.
| S-EPMC4420500 | biostudies-literature
Project description:Statistical inference and mechanistic, process-based modelling represent two philosophically different streams of research whose primary goal is to make predictions. Here, we merge elements from both approaches to keep the theoretical power of process-based models while also considering their predictive uncertainty using Bayesian statistics. In environmental and biological sciences, the predictive uncertainty of process-based models is usually reduced to parametric uncertainty. Here, we propose a practical approach to tackle the added issue of structural sensitivity, the sensitivity of predictions to the choice between quantitatively close and biologically plausible models. In contrast to earlier studies that presented alternative predictions based on alternative models, we propose a probabilistic view of these predictions that include the uncertainty in model construction and the parametric uncertainty of each model. As a proof of concept, we apply this approach to a predator-prey system described by the classical Rosenzweig-MacArthur model, and we observe that parametric sensitivity is regularly overcome by structural sensitivity. In addition to tackling theoretical questions about model sensitivity, the proposed approach can also be extended to make probabilistic predictions based on more complex models in an operational context. Both perspectives represent important steps towards providing better model predictions in biology, and beyond.
| S-EPMC6303789 | biostudies-literature
Project description:It has been proposed that the brain specializes in predicting future states of the environment. These predictions are probabilistic, and must be continuously updated on the basis of their mismatch with actual evidence. Although electrophysiological data disclose neural activity patterns in relation to predictive processes, little is known about how this activity supports prediction build-up through evidence accumulation. Here we addressed this gap. Participants were required to make moment-by-moment predictions about stimuli presented in sequences in which gathering evidence from previous items as they were presented was either possible or not. Two event-related potentials (ERP), a frontocentral P2 and a central P3, were sensitive to information accumulation throughout the sequence. Time-frequency (TF) analyses revealed that prediction build-up process also modulated centrally distributed theta activity, and that alpha power was suppressed in anticipation to fully predictable stimuli. Results are in agreement with the notion of predictions as probability distributions and highlight the ability of observers to extract those probabilities in a changing environment and to adjust their predictions consequently.
| S-EPMC5765034 | biostudies-literature
Project description:The palladium-catalyzed enantioselective allylic substitution by carbon or nitrogen nucleophiles is a key transformation that is particularly useful for the synthesis of bioactive compounds. Unfortunately, the selection of a suitable ligand/substrate combination often requires significant screening effort. Here, we show that a transition state force field (TSFF) derived by the quantum-guided molecular mechanics (Q2MM) method can be used to rapidly screen ligand/substrate combinations. Testing of this method on 77 literature reactions revealed several cases where the computationally predicted major enantiomer differed from the one reported. Interestingly, experimental follow-up led to a reassignment of the experimentally observed configuration. This result demonstrates the power of mechanistically based methods to predict and, where necessary, correct the stereochemical outcome.
| S-EPMC8602308 | biostudies-literature
Project description:Tumor cell lines and drug-resistant counterparts. These data support the publication Gyorffy et al, Oncogene 2005 (July), Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. We contrasted the expression profiles of 13 different human tumor cell lines of gastric (EPG85-257), pancreatic (EPP85-181), colon (HT29) and breast (MCF7 and MDA-MB-231) origin and their counterparts resistant to the topoisomerase inhibitors daunorubicin, doxorubicin or mitoxantrone. We interrogated cDNA arrays with 43 000 cDNA clones ( approximately 30 000 unique genes) to study the expression pattern of these cell lines. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Using regression correlation
2005-07-30 | E-GEOD-3034 | biostudies-arrayexpress