Statistical methods for estimation of biochemical kinetic parameters

This thesis consists of four original pieces of work contained in chapters 2,3,4 and 5. These cover four topics within the area of statistical methods for parameter estimation of biochemical kinetic models. Emphasis is put on integrating single-cell reporter gene data with stochastic dynamic models. Chapter 2 introduces a modelling framework based on stochastic and ordinary differential equations that addresses the problem of reconstructing transcription time course profiles and associated degradation rates from fluorescent and luminescent reporter genes. We present three case studies where the methodology is used to reconstruct unobserved transcription profiles and to estimate associated degradation rates. In Chapter 3 we use the linear noise approximation to model biochemical reactions through a stochastic dynamic model and derive an explicit formula for the likelihood function which allows for computationally efficient parameter estimation. The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly techniques of data augmentation are not necessary. In Chapter 4 we present an inference framework for interpretation of fluorescent reporter gene data. The method takes into account stochastic variability in a fluorescent signal resulting from intrinsic noise of gene expression, extrinsic noise and kinetics of fluorescent protein maturation. Chapter 5 presents a Bayesian hierarchical model, that allows us to infer distributions of fluorescent reporter degradation rates. All methods are embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo

Information-theoretic analysis of multivariate single - cell signaling responses using SLEMI

Mathematical methods of information theory constitute essential tools to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm to analyze signaling data within the framework of information theory. Our approach enables robust as well as statistically and computationally efficient analysis of signaling systems with high-dimensional outputs and a large number of input values. Analysis of the NF-kB single - cell signaling responses to TNF-a uniquely reveals that the NF-kB signaling dynamics improves discrimination of high concentrations of TNF-a with a modest impact on discrimination of low concentrations. Our readily applicable R-package, SLEMI - statistical learning based estimation of mutual information, allows the approach to be used by computational biologists with only elementary knowledge of information theory

Statistical methods for estimation of biochemical kinetic parameters

This thesis consists of four original pieces of work contained in chapters 2,3,4 and 5. These cover four topics within the area of statistical methods for parameter estimation of biochemical kinetic models. Emphasis is put on integrating single-cell reporter gene data with stochastic dynamic models. Chapter 2 introduces a modelling framework based on stochastic and ordinary differential equations that addresses the problem of reconstructing transcription time course profiles and associated degradation rates from fluorescent and luminescent reporter genes. We present three case studies where the methodology is used to reconstruct unobserved transcription profiles and to estimate associated degradation rates. In Chapter 3 we use the linear noise approximation to model biochemical reactions through a stochastic dynamic model and derive an explicit formula for the likelihood function which allows for computationally efficient parameter estimation. The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly techniques of data augmentation are not necessary. In Chapter 4 we present an inference framework for interpretation of fluorescent reporter gene data. The method takes into account stochastic variability in a fluorescent signal resulting from intrinsic noise of gene expression, extrinsic noise and kinetics of fluorescent protein maturation. Chapter 5 presents a Bayesian hierarchical model, that allows us to infer distributions of fluorescent reporter degradation rates. All methods are embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo.EThOS - Electronic Theses Online ServiceUniversity of Warwick. Dept. of Statistics (UoW)GBUnited Kingdo

Insanity as a Defense to the Civil Fraud Penalty

Most neurological diseases are associated with chronic inflammation initiated by the activation of microglia, which produce cytotoxic and inflammatory factors. Signal transducers and activators of transcription (STATs) are potent regulators of gene expression but contribution of particular STAT to inflammatory gene expression and STAT-dependent transcriptional networks underlying brain inflammation need to be identified. In the present study, we investigated the genomic distribution of Stat binding sites and the role of Stats in the gene expression in lipopolysaccharide (LPS)-activated primary microglial cultures. Integration of chromatin immunoprecipitation-promoter microarray data and transcriptome data revealed novel Stat-target genes including Jmjd3, Ccl5, Ezr, Ifih1, Irf7, Uba7, and Pim1. While knockdown of individual Stat had little effect on the expression of tested genes, knockdown of both Stat1 and Stat3 inhibited the expression of Jmjd3 and inflammatory genes. Transcriptional regulation of Jmjd3 by Stat1 and Stat3 is a novel mechanism crucial for launching inflammatory responses in microglia. The effects of Jmjd3 on inflammatory gene expression were independent of its H3K27me3 demethylase activity. Forced expression of constitutively activated Stat1 and Stat3 induced the expression of Jmjd3, inflammation-related genes, and the production of proinflammatory cytokines as potently as lipopolysacharide. Gene set enrichment and gene function analysis revealed categories linked to the inflammatory response in LPS and Stat1C + Stat3C groups. We defined upstream pathways that activate STATs in response to LPS and demonstrated contribution of Tlr4 and Il-6 and interferon-. signaling. Our findings define novel direct transcriptional targets of Stat1 and Stat3 and highlight their contribution to inflammatory gene expression

ToFFi – Toolbox for frequency-based fingerprinting of brain signals

Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel & Gross, 2016). SFs allow for accurate ROI identification and can serve as biomarkers of differences exhibited by non-neurotypical groups. At present, there are no open-source, versatile tools to calculate spectral fingerprints. We have filled this gap by creating a modular, highly-configurable MATLAB Toolbox for Frequency-based Fingerprinting (ToFFi). It can transform magnetoencephalographic and electroencephalographic signals into unique spectral representations using ROIs provided by anatomical (AAL, Desikan-Killiany), functional (Schaefer), or other custom volumetric brain parcellations. Toolbox design supports reproducibility and parallel computations

ToFFi - Toolbox for Frequency-based Fingerprinting of Brain Signals

Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel & Gross, 2016). SFs allow for accurate ROI identification and can serve as biomarkers of differences exhibited by non-neurotypical groups. At present, there are no open-source, versatile tools to calculate spectral fingerprints. We have filled this gap by creating a modular, highly-configurable MATLAB Toolbox for Frequency-based Fingerprinting (ToFFi). It can transform MEG/EEG signals into unique spectral representations using ROIs provided by anatomical (AAL, Desikan-Killiany), functional (Schaefer), or other custom volumetric brain parcellations. Toolbox design supports reproducibility and parallel computations.Comment: 21 pages, 10 figure

A First Summarization System of a Video in a Target Language

International audienceIn this paper, we present the first results of the project AMIS (Access Multilingual Information opinionS) funded by Chist-Era. The main goal of this project is to understand the content of a video in a foreign language. In this work, we consider the understanding process, such as the aptitude to capture the most important ideas contained in a media expressed in a foreign language. In other words, the understanding will be approached by the global meaning of the content of a support and not by the meaning of each fragment of a video. Several stumbling points remain before reaching the fixed goal. They concern the following aspects: Video summarization, Speech recognition, Machine translation and Speech segmentation. All these issues will be discussed and the methods used to develop each of these components will be presented. A first implementation is achieved and each component of this system is evaluated on a representative test data. We propose also a protocol for a global subjective evaluation of AMIS

Combinatorial identification of DNA methylation patterns over age in the human brain

Background: DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways. Results: We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites. Our rule-based classifier reports combinations of CpG sites, together with changes in their methylation status in the form of easy-to-read IF-THEN rules, which allows for identification of the genes associated with the underlying sites. Conclusion: We utilized machine learning and statistical methods to discretize decision class (age) values to get a general pattern of methylation changes over the lifespan. The CpG sites present in the significant rules were annotated to genes involved in brain formation, general development, as well as genes linked to cancer and Alzheimer's disease