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IET Systems Biology

Yingjun Sheng, Jilei Tang, Kewei Ren, Lydia C Manor, Hongbao Cao
In recent years, numerous studies reported over a hundred of genes playing roles in the etiology of postmenopausal osteoporosis (PO). However, many of these candidate genes were lack of replication and results were not always consistent. Here, the authors proposed a computational workflow to curate and evaluate PO related genes. They integrate large-scale literature knowledge data and gene expression data (PO case/control: 10/10) for the marker evaluation. Pathway enrichment, sub-network enrichment, and gene-gene interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene...
June 2018: IET Systems Biology
Adam Kozak, Dorota Formanowicz, Piotr Formanowicz
Atherosclerosis is a complex process of gathering sub-endothelial plaques decreasing lumen of the blood vessels. This disorder affects people of all ages, but its progression is asymptomatic for many years. It is regulated by many typical and atypical factors including the immune system response, a chronic kidney disease, a diet rich in lipids, a local inflammatory process and a local oxidative stress that is here one of the key factors. In this study, a Petri net model of atherosclerosis regulation is presented...
June 2018: IET Systems Biology
Muhammad Rizwan Azam, Sahar Fazal, Mukhtar Ullah, Aamer I Bhatti
The authors have proposed a systems theory-based novel drug design approach for the p53 pathway. The pathway is taken as a dynamic system represented by ordinary differential equations-based mathematical model. Using control engineering practices, the system analysis and subsequent controller design is performed for the re-activation of wild-type p53. p53 revival is discussed for both modes of operation, i.e. the sustained and oscillatory. To define the problem in control system paradigm, modification in the existing mathematical model is performed to incorporate the effect of Nutlin...
June 2018: IET Systems Biology
Abhishek Dey, Shaunak Sen
Mathematical methods provide useful framework for the analysis and design of complex systems. In newer contexts such as biology, however, there is a need to both adapt existing methods as well as to develop new ones. Using a combination of analytical and computational approaches, the authors adapt and develop the method of describing functions to represent the input-output responses of biomolecular signalling systems. They approximate representative systems exhibiting various saturating and hysteretic dynamics in a way that is better than the standard linearisation...
June 2018: IET Systems Biology
Snehal B Shinde, Manish P Kurhekar
The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling...
June 2018: IET Systems Biology
Ehsan Shakeri, Gholamreza Latif-Shabgahi, Amir Esmaeili Abharian
In recent years, many efforts have been made to present optimal strategies for cancer therapy through the mathematical modelling of tumour-cell population dynamics and optimal control theory. In many cases, therapy effect is included in the drift term of the stochastic Gompertz model. By fitting the model with empirical data, the parameters of therapy function are estimated. The reported research works have not presented any algorithm to determine the optimal parameters of therapy function. In this study, a logarithmic therapy function is entered in the drift term of the Gompertz model...
April 2018: IET Systems Biology
Mohsen Bakouri
In this study, the physiological control algorithm using sliding mode control method is implemented to track the reference input signal. The controller is developed using feed-forward part, reference model, and steady-state flow estimator. The proposed control method is evaluated using a dynamic heart-pump interaction model incorporating descriptions of the cardiovascular system - rotary blood pump. The immediate response of the controller to preload as well as afterload was studied. Stability and feasibility of the control system were demonstrated through the tests...
April 2018: IET Systems Biology
Omid Aghajanzadeh, Mojtaba Sharifi, Shabnam Tashakori, Hassan Zohoor
A new robust adaptive controller is developed for the control of the hepatitis B virus (HBV) infection inside the body. The non-linear HBV model has three state variables: uninfected cells, infected cells and free viruses. A control law is designed for the antiviral therapy such that the volume of infected cells and the volume of free viruses are decreased to their desired values which are zero. One control input represents the efficiency of drug therapy in inhibiting viral production and the other control input represents the efficiency of drug therapy in blocking new infection...
April 2018: IET Systems Biology
Lixin Cheng, Pengfei Liu, Kwong-Sak Leung
Computational clustering methods help identify functional modules in protein-protein interaction (PPI) network, in which proteins participate in the same biological pathways or specific functions. Subcellular localisation is crucial for proteins to implement biological functions and each compartment accommodates specific portions of the protein interaction structure. However, the importance of protein subcellular localisation is often neglected in the studies of module identification. In this study, the authors propose a novel procedure, subcellular module identification with localisation expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments...
April 2018: IET Systems Biology
Guangming Liu, Bianfang Chai, Kuo Yang, Jian Yu, Xuezhong Zhou
A large amount of available protein-protein interaction (PPI) data has been generated by high-throughput experimental techniques. Uncovering functional modules from PPI networks will help us better understand the underlying mechanisms of cellular functions. Numerous computational algorithms have been designed to identify functional modules automatically in the past decades. However, most community detection methods (non-overlapping or overlapping types) are unsupervised models, which cannot incorporate the well-known protein complexes as a priori...
April 2018: IET Systems Biology
Hao Wu, Jihua Dong, Jicheng Wei
The knowledge on the biological molecular mechanisms underlying cancer is important for the precise diagnosis and treatment of cancer patients. Detecting dysregulated pathways in cancer can provide insights into the mechanism of cancer and help to detect novel drug targets. Based on the wide existing mutual exclusivity among mutated genes and the interrelationship between gene mutations and expression changes, this study presents a network-based method to detect the dysregulated pathways from gene mutations and expression data of the glioblastoma cancer...
February 2018: IET Systems Biology
Gökhan Demirkıran, Güleser Kalaycı Demir, Cüneyt Güzeliş
This study proposes a two-dimensional (2D) oscillator model of p53 network, which is derived via reducing the multidimensional two-phase dynamics model into a model of ataxia telangiectasia mutated (ATM) and Wip1 variables, and studies the impact of p53-regulators on cell fate decision. First, the authors identify a 6D core oscillator module, then reduce this module into a 2D oscillator model while preserving the qualitative behaviours. The introduced 2D model is shown to be an excitable relaxation oscillator...
February 2018: IET Systems Biology
Prova Biswas, Ashoke Sutradhar, Pallab Datta
In this study, the authors propose a methodology for the estimation of glucose masses in stomach (both in solid and liquid forms), intestine, plasma and tissue; insulin masses in portal vein, liver, plasma and interstitial fluid using only plasma glucose measurement. The proposed methodology fuses glucose-insulin homoeostasis model (in the presence of meal intake) and plasma glucose measurement with a Bayesian non-linear filter. Uncertainty of the model over individual variations has been incorporated by adding process noise to the homoeostasis model...
February 2018: IET Systems Biology
Sabrina Siebert, Katja Ickstadt, Martin Schäfer, Yvonne Radon, Peter J Verveer
Cells communicate with their environment via proteins, located at the plasma membrane separating the interior of a cell from its surroundings. The spatial distribution of these proteins in the plasma membrane under different physiological conditions is of importance, since this may influence their signal transmission properties. In this study, the authors compare different methods such as hierarchical clustering, extensible Markov models and the gammics method for analysing such a spatial distribution. The methods are examined in a simulation study to determine their optimal use...
February 2018: IET Systems Biology
Pooja A Dnyane, Shraddha S Puntambekar, Chetan J Gadgil
Biological systems are often represented as Boolean networks and analysed to identify sensitive nodes which on perturbation disproportionately change a predefined output. There exist different kinds of perturbation methods: perturbation of function, perturbation of state and perturbation in update scheme. Nodes may have defects in interpretation of the inputs from other nodes and calculation of the node output. To simulate these defects and systematically assess their effect on the system output, two new function perturbations, referred to as 'not of function' and 'function of not', are introduced...
February 2018: IET Systems Biology
Yu-Jia Hu, Chun-Liang Lin, Wei-Xian Li
In electronic systems, dynamic random access memory (DRAM) is one of the core modules in the modern silicon computer. As for a bio-computer, one would need a mechanism for storage of bio-information named 'data', which, in binary logic, has two levels, logical high and logical low, or in the normalised form, '1' and '0'. This study proposes a possible genetic DRAM based on the modified electronic configuration, which uses the biological reaction to fulfil an equivalent RC circuit constituting a memory cell...
December 2017: IET Systems Biology
Ming Shi, Weiming Shen, Yanwen Chong, Hong-Qiang Wang
Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators...
December 2017: IET Systems Biology
Runxia Wang, Haihong Liu, Fei Feng, Fang Yan
In this study, the authors first discuss the existence of Bogdanov-Takens and triple zero singularity of a five neurons neutral bidirectional associative memory neural networks model with two delays. Then, by utilising the centre manifold reduction and choosing suitable bifurcation parameters, the second-order and the third-order normal forms of the Bogdanov-Takens bifurcation for the system are obtained. Finally, the obtained normal form and numerical simulations show some interesting phenomena such as the existence of a stable fixed point, a pair of stable non-trivial equilibria, a stable limit cycles, heteroclinic orbits, homoclinic orbits, coexistence of two stable non-trivial equilibria and a stable limit cycles in the neighbourhood of the Bogdanov-Takens bifurcation critical point...
December 2017: IET Systems Biology
Chung-Dann Kan, Wei-Ling Chen, Chia-Hung Lin, Ying-Shin Chen
Extracorporeal membrane oxygenation system is used for rescue treatment strategies for temporary cardiopulmonary function support to facilitate adequately oxygenated blood to return into the systemic and pulmonary circulation systems. Therefore, a servo flow regulator is used to adjust the roller motor speed, while support blood flow can match the sweep gas flow (GF) in a membrane oxygenator. A generalised regression neural network is designed as an estimator to automatically estimate the desired roller pump speed and control parameters...
December 2017: IET Systems Biology
Yazdan Batmani
In this study, a closed-loop treatment strategy is proposed for the control of blood glucose levels in type 1 diabetic patients. Toward this end, a non-linear technique for designing suboptimal tracking controllers, called the state-dependent Riccati equation tracker, is used based on a mathematical model of the glucose-insulin regulatory system. Since two state variables of the utilised model are not available to the controller, a non-linear filter is also designed to estimate these variables using the measured blood glucose concentration...
August 2017: IET Systems Biology
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