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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
Chien-Hung Huang, Teng-Hung Chen, Ka-Lok Ng
Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical approach to reveal hidden properties and features of four species PCINs. Two main issues are addressed, (i) the global and local network topological properties, and (ii) the stability of the networks under 12 types of perturbations...
April 2016: IET Systems Biology
Shaunak Sen
The cellular behaviour of perfect adaptation is achieved through the use of an integral control element in the underlying biomolecular circuit. It is generally unclear how integral action affects the important aspect of transient response in these biomolecular systems, especially in light of the fact that it typically deteriorates the transient response in engineering contexts. To address this issue, the authors investigated the transient response in a computational model of a simple biomolecular integral control system involved in bacterial signalling...
April 2016: IET Systems Biology
Qiben Zheng, Liangzhong Shen, Xuequn Shang, Wenbin Liu
Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behaviour of systems. A central aim of Boolean-network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP-hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table...
April 2016: IET Systems Biology
Henry Han, Ying Liu
The big omics data are challenging translational bioinformatics in an unprecedented way for its complexities and volumes. How to employ big omics data to achieve a rivalling-clinical, reproducible disease diagnosis from a systems approach is an urgent problem to be solved in translational bioinformatics and machine learning. In this study, the authors propose a novel transcriptome marker diagnosis to tackle this problem using big RNA-seq data by viewing whole transcriptome as a profile marker systematically...
February 2016: IET Systems Biology
Pingmei Cai, Guinan Wang, Shiwei Yu, Hongjuan Zhang, Shuxue Ding, Zikai Wu
The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two-stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance...
February 2016: IET Systems Biology
Yuzhen Guo, Zikai Wu, Ying Wang, Yong Wang
In this study, the authors studied the protein structure prediction problem by the two-dimensional hydrophobic-polar model on triangular lattice. Particularly the non-compact conformation was modelled to fold the amino acid sequence into a relatively larger triangular lattice, which is more biologically realistic and significant than the compact conformation. Then protein structure prediction problem was abstracted to match amino acids to lattice points. Mathematically, the problem was formulated as an integer programming and they transformed the biological problem into an optimisation problem...
February 2016: IET Systems Biology
Wenyi Qin, Guijun Zhao, Matthew Carson, Caiyan Jia, Hui Lu
A structure-based statistical potential is developed for transcription factor binding site (TFBS) prediction. Besides the direct contact between amino acids from TFs and DNA bases, the authors also considered the influence of the neighbouring base. This three-body potential showed better discriminate powers than the two-body potential. They validate the performance of the potential in TFBS identification, binding energy prediction and binding mutation prediction.
February 2016: IET Systems Biology
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