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IET Systems Biology

Ming Shi, Weiming Shen, Hong-Qiang Wang, Yanwen Chong
Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)-guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l1-norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual...
December 2016: IET Systems Biology
Zhi-Tong Bing, Guang-Hui Yang, Jie Xiong, Ling Guo, Lei Yang
Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival-related individual genes (or miRNAs) and miRNA -mRNA pairs in GBM using a multi-step approach. First, the weighted gene co-expression network analysis and survival analysis are applied to identify survival-related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis...
December 2016: IET Systems Biology
Hyeygjeon Chang, Claude Moog, Alessandro Astolfi
The authors examine the human immunodeficiency virus (HIV) eradication in this study using a mathematical model and analyse the occurrence of virus eradication during the early stage of infection. To this end they use a deterministic HIV-infection model, modify it to describe the pharmacological dynamics of antiretroviral HIV drugs, and consider the clinical experimental results of preexposure prophylaxis HIV treatment. They also use numerical simulation to model the experimental scenario, thereby supporting the clinical results with a model-based explanation...
December 2016: IET Systems Biology
Hossein Sharifi Noghabi, Majid Mohammadi, Yao-Hua Tan
One of the most important needs in the post-genome era is providing the researchers with reliable and efficient computational tools to extract and analyse this huge amount of biological data, in which DNA copy number variation (CNV) is a vitally important one. Array-based comparative genomic hybridisation (aCGH) is a common approach in order to detect CNVs. Most of methods for this purpose were proposed for one-dimensional profiles. However, slightly this focus has moved from one- to multi-dimensional signals...
December 2016: IET Systems Biology
Esra Gov, Kazim Yalcin Arga
Transcriptional regulation of gene expression is an essential cellular process that is arranged by transcription factors (TFs), microRNAs (miRNA) and their target genes through a variety of mechanisms. Here, we set out to reconstruct a comprehensive transcriptional regulatory network of Homo sapiens consisting of experimentally verified regulatory information on miRNAs, TFs and their target genes. We have performed topological analyses to elucidate the transcriptional regulatory roles of miRNAs and TFs. When we thoroughly investigated the network motifs, different gene regulatory scenarios were observed; whereas, mutual TF-miRNA regulation (interactive cooperation) and hierarchical operation where miRNAs were the upstream regulators of TFs came into prominence...
December 2016: IET Systems Biology
Ivan Kondofersky, Fabian J Theis, Christiane Fuchs
In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data...
December 2016: IET Systems Biology
Yuan Zhang, Haihong Liu, Jin Zhou
The primary objective of this study is to study oscillatory expression of gene regulatory network in Escherichia coli mediated by microRNAs (sRNAs) with transcriptional and translational time delays. Motivated by the regulation of gene expression proposed by Shimoni et al. (Molecular Systems Biology, 2007), a general model of delayed gene regulatory network by sRNAs is formulated. This model can well describe many practical architectures of gene regulatory network by sRNAs, particularly when both transcriptional and translational time delays are introduced...
December 2016: IET Systems Biology
Peter J Gawthrop, Edmund J Crampin
Bond graphs can be used to build thermodynamically-compliant hierarchical models of biomolecular systems. As bond graphs have been widely used to model, analyse and synthesise engineering systems, this study suggests that they can play the same rôle in the modelling, analysis and synthesis of biomolecular systems. The particular structure of bond graphs arising from biomolecular systems is established and used to elucidate the relation between thermodynamically closed and open systems. Block diagram representations of the dynamics implied by these bond graphs are used to reveal implicit feedback structures and are linearised to allow the application of control-theoretical methods...
October 2016: IET Systems Biology
Alessandro Borri, Pasquale Palumbo, Abhyudai Singh
Synthetic biology combines different branches of biology and engineering aimed at designing synthetic biological circuits able to replicate emergent properties useful for the biotechnology industry, human health and environment. The role of negative feedback in noise propagation for a basic enzymatic reaction scheme is investigated. Two feedback control schemes on enzyme expression are considered: one from the final product of the pathway activity, the other from the enzyme accumulation. Both schemes are designed to provide the same steady-state average values of the involved players, in order to evaluate the feedback performances according to the same working mode...
October 2016: IET Systems Biology
Hasseeb Azzawi, Jingyu Hou, Yong Xiang, Russul Alanni
Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models...
October 2016: IET Systems Biology
Diego di Bernardo
No abstract text is available yet for this article.
October 2016: IET Systems Biology
E Fabian Cardozo, Michael J Piovoso, Ryan Zurakowski
Combined antiretroviral therapy (cART) suppress HIV-1 viral replication, such that viral load in plasma remains below the limit of detection in standard assays. However, intermittent episodes of transient viremia (blips) occur in a set of HIV-patients. Given that follicular hyperplasia occurs during lymphoid inflammation as a normal response to infection, it is hypothesised that when the diameter of the lymph node follicle (LNF) increases and crosses a critical size, a viral blip occurs due to cryptic viremia...
August 2016: IET Systems Biology
Zhenshen Bao, Xianbin Li, Xiangzhen Zan, Liangzhong Shen, Runnian Ma, Wenbin Liu
Signalling pathway analysis is a popular approach that is used to identify significant cancer-related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (-1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary...
August 2016: IET Systems Biology
Korosh Rouhollahi, Mehran Emadi Andani, Seyed Mahdi Karbassi, Iman Izadi
In this study, a model of basal ganglia (BG) is applied to develop a deep brain stimulation controller to reduce Parkinson's tremor. Conventionally, one area in BG is stimulated, with no feedback, to control Parkinson's tremor. In this study, a new architecture is proposed to develop feedback controller as well as to stimulate two areas of BG simultaneously. To this end, two controllers are designed and implemented in globus pallidus internal (GPi) and subthalamic nucleus (STN) in the brain. A proportional controller and a backstepping controller are designed and implemented in GPi and STN, respectively...
August 2016: IET Systems Biology
Jae Kyoung Kim
Circadian (∼24 h) clocks are self-sustained endogenous oscillators with which organisms keep track of daily and seasonal time. Circadian clocks frequently rely on interlocked transcriptional-translational feedback loops to generate rhythms that are robust against intrinsic and extrinsic perturbations. To investigate the dynamics and mechanisms of the intracellular feedback loops in circadian clocks, a number of mathematical models have been developed. The majority of the models use Hill functions to describe transcriptional repression in a way that is similar to the Goodwin model...
August 2016: IET Systems Biology
Lin Wu, Min Li, Jianxin Wang, Fang-Xiang Wu
Many systems of interests in practices can be represented as complex networks. For biological systems, biomolecules do not perform their functions alone but interact with each other to form so-called biomolecular networks. A system is said to be controllable if it can be steered from any initial state to any other final state in finite time. The network controllability has become essential to study the dynamics of the networks and understand the importance of individual nodes in the networks. Some interesting biological phenomena have been discovered in terms of the structural controllability of biomolecular networks...
June 2016: IET Systems Biology
Cong Jin, Shu-Wei Jin
A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes...
June 2016: IET Systems Biology
Gerasimos G Rigatos
It is proven that the model of the p53-mdm2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p53 protein at the desirable levels. To estimate the non-measurable elements of the state vector describing the p53-mdm2 system dynamics, the derivative-free non-linear Kalman filter is used...
June 2016: IET Systems Biology
Duygu Dede Şener, Hasan Oğul
Understanding time-course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta-analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data...
June 2016: IET Systems Biology
Haizhou Wang, Ming Leung, Angela Wandinger-Ness, Laurie G Hudson, Mingzhou Song
Integrating prior molecular network knowledge into interpretation of new experimental data is routine practice in biology research. However, a dilemma for deciphering interactome using Bayes' rule is the demotion of novel interactions with low prior probabilities. Here the authors present constrained generalised logical network (CGLN) inference to predict novel interactions in dynamic networks, respecting previously known interactions and observed temporal coherence. It encodes prior interactions as probabilistic logic rules called local constraints, and forms global constraints using observed dynamic patterns...
April 2016: IET Systems Biology
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