Decomposing Noise in Biochemical Signaling Systems Highlights the Role of Protein Degradation

AbstractStochasticity is an essential aspect of biochemical processes at the cellular level. We now know that living cells take advantage of stochasticity in some cases and counteract stochastic effects in others. Here we propose a method that allows us to calculate contributions of individual reactions to the total variability of a system’s output. We demonstrate that reactions differ significantly in their relative impact on the total noise and we illustrate the importance of protein degradation on the overall variability for a range of molecular processes and signaling systems. With our flexible and generally applicable noise decomposition method, we are able to shed new, to our knowledge, light on the sources and propagation of noise in biochemical reaction networks; in particular, we are able to show how regulated protein degradation can be employed to reduce the noise in biochemical systems

A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and—more importantly—identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome

A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number

Motivation: cis-regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis-regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown. Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer–promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

Background Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data. Results We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo. Conclusion The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods

Wspomaganie i rozszerzanie funkcjonalności debugowania IntelliTrace

IntelliTrace is a historic debugger for the .NET platform. It has many capabilities but also some serious limitations: it significantly affects the performance of applications or provides only basic methods for analysis of collected data. In this paper, IntelliTrace is examined and a set of tools called IntelliTrace Toolkit, which allows these problems to be overcome, is proposed.IntelliTrace to debuger historyczny dla platformy .NET. Ma wiele możliwości, ale również kilka ograniczeń, na przykład wpływa negatywnie na wydajności aplikacji i udostępnia tylko podstawowe sposoby analizowania zgromadzonych danych. W tym artykule możliwości IntelliTrace zostały poddane analizie i na tej podstawie zaproponowano zestaw narzędzi IntelliTrace Toolkit, który rozwiązuje wspomniane problemy

Zastosowanie wykresów rekurencyjnych w analizie programów komputerowych

Recurrence plots are a tool which allow one to analyze and visualize recurrence in non-linear dynamical systems. In this paper it was examined whether recurrence plots could be used to analyze data collected by historic debuggers. Besides a tool that facilitates this kind of analysis was proposed.Wykresy rekurencyjne to narzędzie pozwalające wizualizować oraz analizować powtarzające się stany w nieliniowych systemach dynamicznych. Artykuł ten opisuje eksperymenty, w których sprawdzono, czy wykresy rekurencyjne nadają się do analizy programów komputerowych. Zaproponowano również narzędzie wspomagające taką analizę

NK Cells as Potential Targets for Immunotherapy in Endometriosis

Endometriosis is a common gynecological disease defined by the presence of endometrial-like tissue outside the uterus, most frequently on the pelvic viscera and ovaries, which is associated with pelvic pains and infertility. It is an inflammatory disorder with some features of autoimmunity. It is accepted that ectopic endometriotic tissue originates from endometrial cells exfoliated during menstruation and disseminating into the peritoneum by retrograde menstrual blood flow. It is assumed that the survival of endometriotic cells in the peritoneal cavity may be partially due to their abrogated elimination by natural killer (NK) cells. The decrease of NK cell cytotoxic activity in endometriosis is associated with an increased expression of some inhibitory NK cell receptors. It may be also related to the expression of human leukocyte antigen G (HLA-G), a ligand for inhibitory leukocyte immunoglobulin-like receptor subfamily B member 1 (LILRB1) receptors. The downregulated cytotoxic activity of NK cells may be due to inhibitory cytokines present in the peritoneal milieu of patients with endometriosis. The role of NK cell receptors and their ligands in endometriosis is also confirmed by genetic association studies. Thus, endometriosis may be a subject of immunotherapy by blocking NK cell negative control checkpoints including inhibitory NK cell receptors. Immunotherapies with genetically modified NK cells also cannot be excluded