Two-Exponential Models of Gene Expression Patterns for Noisy Experimental Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Alexandrov T
PROVIDER: S-EPMC6247991 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundAn important analysis performed on microarray gene-expression data is to discover biclusters, which denote groups of genes that are coherently expressed for a subset of conditions. Various biclustering algorithms have been proposed to find different types of biclusters from these real-valued gene-expression data sets. However, these algorithms suffer from several limitations such as inability to explicitly handle errors/noise in the data; difficulty in discovering small bicliusters due to their top-down approach; inability of some of the approaches to find overlapping biclusters, which is crucial as many genes participate in multiple biological processes. Association pattern mining also produce biclusters as their result and can naturally address some of these limitations. However, traditional association mining only finds exact biclusters, which limits its applicability in real-life data sets where the biclusters may be fragmented due to random noise/errors. Moreover, as they only work with binary or boolean attributes, their application on gene-expression data require transforming real-valued attributes to binary attributes, which often results in loss of information. Many past approaches have tried to address the issue of noise and handling real-valued attributes independently but there is no systematic approach that addresses both of these issues together.ResultsIn this paper, we first propose a novel error-tolerant biclustering model, 'ET-bicluster', and then propose a bottom-up heuristic-based mining algorithm to sequentially discover error-tolerant biclusters directly from real-valued gene-expression data. The efficacy of our proposed approach is illustrated by comparing it with a recent approach RAP in the context of two biological problems: discovery of functional modules and discovery of biomarkers. For the first problem, two real-valued S.Cerevisiae microarray gene-expression data sets are used to demonstrate that the biclusters obtained from ET-bicluster approach not only recover larger set of genes as compared to those obtained from RAP approach but also have higher functional coherence as evaluated using the GO-based functional enrichment analysis. The statistical significance of the discovered error-tolerant biclusters as estimated by using two randomization tests, reveal that they are indeed biologically meaningful and statistically significant. For the second problem of biomarker discovery, we used four real-valued Breast Cancer microarray gene-expression data sets and evaluate the biomarkers obtained using MSigDB gene sets.ConclusionsThe results obtained for both the problems: functional module discovery and biomarkers discovery, clearly signifies the usefulness of the proposed ET-bicluster approach and illustrate the importance of explicitly incorporating noise/errors in discovering coherent groups of genes from gene-expression data.
| S-EPMC3247082 | biostudies-literature
Project description:A key goal in molecular evolution is to extract mechanistic insights from signatures of selection. A case study is codon usage, where despite many recent advances and hypotheses, two longstanding problems remain: the relative contribution of selection and mutation in determining codon frequencies and the relative contribution of translational speed and accuracy to selection. The relevant targets of selection--the rate of translation and of mistranslation of a codon per unit time in the cell--can only be related to mechanistic properties of the translational apparatus if the number of transcripts per cell is known, requiring use of gene expression measurements. Perhaps surprisingly, different gene-expression data sets yield markedly different estimates of selection. We show that this is largely due to measurement noise, notably due to differences between studies rather than instrument error or biological variability. We develop an analytical framework that explicitly models noise in expression in the context of the population-genetic model. Estimates of mutation and selection strength in budding yeast produced by this method are robust to the expression data set used and are substantially higher than estimates using a noise-blind approach. We introduce per-gene selection estimates that correlate well with previous scoring systems, such as the codon adaptation index, while now carrying an evolutionary interpretation. On average, selection for codon usage in budding yeast is weak, yet our estimates show that genes range from virtually unselected to average per-codon selection coefficients above the inverse population size. Our analytical framework may be generally useful for distinguishing biological signals from measurement noise in other applications that depend upon measurements of gene expression.
| S-EPMC3649678 | biostudies-literature
Project description:Cell death/survival following traumatic brain injury (TBI) may be a result of alterations in the intracellular ratio of death and survival factors. Bcl-2 family genes mediate both cell survival and the initiation of cell death. Using lysate RNase protection assays, mRNA expression of the anti-cell death genes Bcl-2 and Bcl-xL, and the pro-cell death gene Bax, was evaluated following experimental brain injuries in adult male Sprague-Dawley rats. Both the lateral fluid-percussion (LFP) and the lateral controlled cortical impact (LCI) models of TBI showed similar patterns of gene expression. Anti-cell death bcl-2 and bcl-xL mRNAs were attenuated early and tended to remain depressed for at least 3 days after injury in the cortex and hippocampus ipsilateral to injury. Pro-cell death bax mRNA was elevated in these areas, usually following the decrease in anti-cell death genes. These common patterns of gene expression suggest an important role for Bcl-2 genes in cell death and survival in the injured brain. Understanding the regulation of these genes may facilitate the development of new therapeutic strategies for a condition that currently has no proven pharmacologic treatments.
| S-EPMC2590760 | biostudies-literature
Project description:ObjectiveTraditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.MethodsWe use a list of keywords specific to the phenotype of interest to generate noisy labeled training data. We train L1 penalized logistic regression models for a chronic and an acute disease and evaluate the performance of the models against a gold standard.ResultsOur models for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.90, 0.89, and 0.86, 0.89, respectively. Local implementations of the previously validated rule-based definitions for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.96, 0.92 and 0.84, 0.87, respectively.We have demonstrated feasibility of learning phenotype models using imperfectly labeled data for a chronic and acute phenotype. Further research in feature engineering and in specification of the keyword list can improve the performance of the models and the scalability of the approach.ConclusionsOur method provides an alternative to manual labeling for creating training sets for statistical models of phenotypes. Such an approach can accelerate research with large observational healthcare datasets and may also be used to create local phenotype models.
| S-EPMC5070523 | biostudies-literature
Project description:This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data. Our learning method is evaluated on the motion of a pendulum-a well studied baseline for nonlinear model identification and control with continuous states and control inputs-measured via high-dimensional noisy RGB images. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models, as well as identify and quantify modeling uncertainties.
| S-EPMC9747975 | biostudies-literature
Project description:To perform parametric identification of mathematical models of biological events, experimental data are rare to be sufficient to estimate target behaviors produced by complex non-linear systems. We performed parameter fitting to a cell cycle model with experimental data as an in silico experiment. We calibrated model parameters with the generalized least squares method with randomized initial values and checked local and global sensitivity of the model. Sensitivity analyses showed that parameter optimization induced less sensitivity except for those related to the metabolism of the transcription factors c-Myc and E2F, which are required to overcome a restriction point (R-point). We performed bifurcation analyses with the optimized parameters and found the bimodality was lost. This result suggests that accumulation of c-Myc and E2F induced dysfunction of R-point. We performed a second parameter optimization based on the results of sensitivity analyses and incorporating additional derived from recent in vivo data. This optimization returned the bimodal characteristics of the model with a narrower range of hysteresis than the original. This result suggests that the optimized model can more easily go through R-point and come back to the gap phase after once having overcome it. Two parameter space analyses showed metabolism of c-Myc is transformed as it can allow cell bimodal behavior with weak stimuli of growth factors. This result is compatible with the character of the cell line used in our experiments. At the same time, Rb, an inhibitor of E2F, can allow cell bimodal behavior with only a limited range of stimuli when it is activated, but with a wider range of stimuli when it is inactive. These results provide two insights; biologically, the two transcription factors play an essential role in malignant cells to overcome R-point with weaker growth factor stimuli, and theoretically, sparse time-course data can be used to change a model to a biologically expected state.
| S-EPMC3983526 | biostudies-literature
Project description:Normal pregnancy requires adaptations of the maternal vasculature. During preeclampsia these adaptations are not well established, which may be related to maternal hypertension and proteinuria. The effects of preeclampsia on the maternal vasculature are not yet fully understood. We aimed to evaluate gene expression in aortas of pregnant rats with experimental preeclampsia using a genome wide microarray. Aortas were isolated from pregnant Wistar outbred rats with low-dose LPS-induced preeclampsia (ExpPE), healthy pregnant (Pr), non-pregnant and low-dose LPS-infused non-pregnant rats. Gene expression was measured by microarray and validated by real-time quantitative PCR. Gene Set Enrichment Analysis was performed to compare the groups. Functional analysis of the aorta was done by isotonic contraction measurements while stimulating aortic rings with potassium chloride. 526 genes were differentially expressed, and positive enrichment of "potassium channels", "striated muscle contraction", and "neuronal system" gene sets were found in ExpPE vs. Pr. The potassium chloride-induced contractile response of ExpPE aortic rings was significantly decreased compared to this response in Pr animals. Our data suggest that potassium channels, neuronal system and (striated) muscle contraction in the aorta may play a role in the pathophysiology of experimental preeclampsia. Whether these changes are also present in preeclamptic women needs further investigation.
| S-EPMC5665945 | biostudies-literature
Project description:Npas4 has recently been identified as an important factor in brain plasticity, particularly in mechanisms of inhibitory control. Little is known about Npas4 expression in terms of cortical plasticity. In the present study expressions of Npas4 and the archetypal immediate early gene (IEG) c-Fos were investigated in the barrel cortex of mice after sensory deprivation (sparing one row of whiskers for 7 days) or sensory conditioning (pairing stimulation of one row of whiskers with aversive stimulus). Laser microdissection of individual barrel rows allowed for analysis of IEGs expression precisely in deprived and nondeprived barrels (in deprivation study) or stimulated and nonstimulated barrels (in conditioning study). Cortex activation by sensory conditioning was found to upregulate the expression of both Npas4 and c-Fos. Reorganization of cortical circuits triggered by removal of selected rows of whiskers strongly affected c-Fos but not Npas4 expression. We hypothesize that increased inhibitory synaptogenesis observed previously after conditioning may be mediated by Npas4 expression.
| S-EPMC4345254 | biostudies-literature
Project description:The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO's superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.
| S-EPMC7359369 | biostudies-literature
Project description:Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO(2) operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the dose-response curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimental-computational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts.
| S-EPMC1931564 | biostudies-literature