Expanding genotype/phenotype of neuromuscular diseases by comprehensive target capture/NGS

published_or_final_versio

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

Prognostic and therapeutic significance of carbohydrate antigen 19-9 as tumor marker in patients with pancreatic cancer

In pancreatic cancer ( PC) accurate determination of treatment response by imaging often remains difficult. Various efforts have been undertaken to investigate new factors which may serve as more appropriate surrogate parameters of treatment efficacy. This review focuses on the role of carbohydrate antigen 19- 9 ( CA 19- 9) as a prognostic tumor marker in PC and summarizes its contribution to monitoring treatment efficacy. We undertook a Medline/ PubMed literature search to identify relevant trials that had analyzed the prognostic impact of CA 19- 9 in patients treated with surgery, chemoradiotherapy and chemotherapy for PC. Additionally, relevant abstract publications from scientific meetings were included. In advanced PC, pretreatment CA 19- 9 levels have a prognostic impact regarding overall survival. Also a CA 19- 9 decline under chemotherapy can provide prognostic information for median survival. A 20% reduction of CA 19- 9 baseline levels within the first 8 weeks of chemotherapy appears to be sufficient to define a prognostic relevant subgroup of patients ('CA 19- 9 responder'). It still remains to be defined whether the CA 19- 9 response is a more reliable method for evaluating treatment efficacy compared to conventional imaging. Copyright (c) 2006 S. Karger AG, Basel

From cellular attractor selection to adaptive signal control for traffic networks

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains

Response of Soil Respiration to Soil Temperature and Moisture in a 50-Year-Old Oriental Arborvitae Plantation in China

China possesses large areas of plantation forests which take up great quantities of carbon. However, studies on soil respiration in these plantation forests are rather scarce and their soil carbon flux remains an uncertainty. In this study, we used an automatic chamber system to measure soil surface flux of a 50-year-old mature plantation of Platycladus orientalis at Jiufeng Mountain, Beijing, China. Mean daily soil respiration rates (Rs) ranged from 0.09 to 4.87 µmol CO2 m−2s−1, with the highest values observed in August and the lowest in the winter months. A logistic model gave the best fit to the relationship between hourly Rs and soil temperature (Ts), explaining 82% of the variation in Rs over the annual cycle. The annual total of soil respiration estimated from the logistic model was 645±5 g C m−2 year−1. The performance of the logistic model was poorest during periods of high soil temperature or low soil volumetric water content (VWC), which limits the model's ability to predict the seasonal dynamics of Rs. The logistic model will potentially overestimate Rs at high Ts and low VWC. Seasonally, Rs increased significantly and linearly with increasing VWC in May and July, in which VWC was low. In the months from August to November, inclusive, in which VWC was not limiting, Rs showed a positively exponential relationship with Ts. The seasonal sensitivity of soil respiration to Ts (Q10) ranged from 0.76 in May to 4.38 in October. It was suggested that soil temperature was the main determinant of soil respiration when soil water was not limiting

Monomeric Bistability and the Role of Autoloops in Gene Regulation

Genetic toggle switches are widespread in gene regulatory networks (GRN). Bistability, namely the ability to choose among two different stable states, is an essential feature of switching and memory devices. Cells have many regulatory circuits able to provide bistability that endow a cell with efficient and reliable switching between different physiological modes of operation. It is often assumed that negative feedbacks with cooperative binding (i.e. the formation of dimers or multimers) are a prerequisite for bistability. Here we analyze the relation between bistability in GRN under monomeric regulation and the role of autoloops under a deterministic setting. Using a simple geometric argument, we show analytically that bistability can also emerge without multimeric regulation, provided that at least one regulatory autoloop is present