Biclustering‐based association rule mining approach for predicting cancer‐associated protein interactions

Protein–protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer‐related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer‐associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering‐based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering‐based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer‐related diseases.Inspec keywords: cancer, medical computing, data mining, proteins, genetics, pattern clusteringOther keywords: biological processes, cancer‐related diseases, cancer research, cancer‐related protein interaction databases, protein–protein interactions, cancer‐associated protein interactions, biclustering‐based association rule mining approach, negative data set, annotation type, human proteins, cancer‐associated proteins, PPI database, cancer disease     

Construction and characterization of rectal cancer‐related lncRNA‐mRNA ceRNA network reveals prognostic biomarkers in rectal cancer

Rectal cancer is an important cause of cancer‐related deaths worldwide. In this study, the differentially expressed (DE) lncRNAs/mRNAs were first identified and the correlation level between DE lncRNAs and mRNAs were calculated. The results showed that genes of highly correlated lncRNA‐mRNA pairs presented strong prognosis effects, such as GPM6A, METTL24, SCN7A, HAND2‐AS1 and PDZRN4. Then, the rectal cancer‐related lncRNA‐mRNA network was constructed based on the ceRNA theory. Topological analysis of the network revealed that the network was maintained by hub nodes and a hub subnetwork was constructed, including the hub lncRNA MIR143HG and MBNL1‐SA1. Further analysis indicated that the hub subnetwork was highly related to cancer pathways, such as ‘Focal adhesion’ and ‘Wnt signalling pathway’. Hub subnetwork also had significant prognosis capability. A closed lncRNA‐mRNA module was identified by bilateral network clustering. Genes in modules also showed high prognosis effects. Finally, a core lncRNA‐TF crosstalk network was identified to uncover the crosstalk and regulatory mechanisms of lncRNAs and TFs by integrating ceRNA crosstalks and TF binding affinities. Some core genes, such as MEIS1, GLI3 and HAND2‐AS1 were considered as the key regulators in tumourigenesis. Based on the authors’ comprehensive analysis, all these lncRNA‐mRNA crosstalks provided promising clues for biological prognosis of rectal cancer.     

Construction and investigation of breast‐cancer‐specific ceRNA network based on the mRNA and miRNA expression data

It has been proved and widely acknowledged that messenger RNAs can talk to each other by competing for a limited pool of miRNAs. The competing endogenous RNAs are called as ceRNAs. Although some researchers have recently used ceRNAs to do biological function annotations, few of them have investigated the ceRNA network on specific disease systematically. In this work, using both miRNA expression data and mRNA expression data of breast cancer patient as well as the miRNA target relations, the authors proposed a computational method to construct a breast‐cancer‐specific ceRNA network by checking whether the shared miRNA sponges between the gene pairs are significant. The ceRNA network is shown to be scale‐free, thus the topological characters such as hub nodes and communities may provide important clues for the biological mechanism. Through investigation on the communities (the dense clusters) in the network, it was found that they are related to cancer hallmarks. In addition, through function annotation of the hub genes in the network, it was found that they are related to breast cancer. Moreover, classifiers based on the discriminative hubs can significantly distinguish breast cancer patients’ risks of distant metastasis in all the three independent data sets.Inspec keywords: cancer, genetics, medical computing, molecular biophysics, RNAOther keywords: breast‐cancer specific ceRNA network construction, miRNA expression data, mRNA expression data, gene pairs, computational method, dense clusters, cancer hallmarks, biological mechanism, discriminative hub genes     

Identifying driver modules based on multi‐omics biological networks in prostate cancer

The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi‐omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors’ method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.

Abbreviations

CNV

Copy number variation

FN

False Negative

FP

False Positive

HPRD

Human protein reference database

JS

Jensen–Shannon

KL

Kullback–Leibler

NCG

A comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens.

PRAD

Prostate adenocarcinoma

PPI

Protein–protein interaction

TCGA

The cancer genome Atlas

TN

True Negative

TP

True Positive

     

On‐chip label‐free impedance‐based detection of antibiotic permeation

Biosensors are analytical tools used for the analysis of biomaterial samples and provide an understanding about the biocomposition, structure, and function of biomolecules and/or biomechanisms by converting the biological response into an electrical and/or optical signal. In particular, with the rise in antibiotic resistance amongst pathogenic bacteria, the study of antibiotic activity and transport across cell membranes in the field of biosensors has been gaining widespread importance. Herein, for the rapid and label‐free detection of antibiotic permeation across a membrane, a microelectrode integrated microfluidic device is presented. The integrated chip consists of polydimethylsiloxane based microfluidic channels bonded onto microelectrodes on‐glass and enables us to recognize the antibiotic permeation across a membrane into the model membranes based on electrical impedance measurement, while also allowing optical monitoring. Impedance testing is label free and therefore allows the detection of both fluorescent and non‐fluorescent antibiotics. As a model membrane, Giant Unilamellar Vesicles (GUVs) are used and impedance measurements were performed by a precision inductance, capacitance, and resistance metre. The measured signal recorded from the device was used to determine the change in concentration inside and outside of the GUVs. We have found that permeation of antibiotic molecules can be easily monitored over time using the proposed integrated device. The results also show a clear difference between bilayer permeation that occurs through the lipidic bilayer and porin‐mediated permeation through the porin channels inserted in the lipid bilayer.     

Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.Inspec keywords: fuzzy control, variable structure systems, particle swarm optimisation, neurocontrollers, fuzzy neural nets, blood, genetic algorithms, closed loop systems, medical control systems, fuzzy reasoning, diseases, nonlinear control systems, sugarOther keywords: data fusion, adaptive neuro‐fuzzy inference systems, particle swarm optimisation, hybrid controller, desired BG‐level tracking, chattering reduction, daily glucose‐level disturbances, closed‐loop control scheme, glucose–insulin regulatory system, type‐1 diabetic mellitus patients, innovative hybrid glucose–insulin regulators, artificial intelligence, fuzzy logic, genetic algorithm, Palumbo model, blood glucose level, T1DM patients, glucose reference tracking, insulin injection, mode controllers, glucose–insulin mechanism, chattering‐free hybrid adaptive neuro‐fuzzy inference system, particle swarm optimisation data fusion‐based BG‐level control     

Investigation of graphene‐on‐metal substrates for SPR‐based sensor using finite‐difference time domain

In this study, the authors investigated the effects of a single layer graphene as a coating layer on top of metal thin films such as silver, gold, aluminum and copper using finite‐difference time domain method. To enhance the resolution of surface plasmon resonance (SPR) sensor, it is necessary to increase the SPR reflectivity and decrease the full‐width‐half maximum (FWHM) of the SPR curve so that there is minimum uncertainty in the determination of the resonance dip. Numerical data was verified with analytical and experimental data where all the data were in good agreement with resonance angle differing in <10% due to noise present in components such as humidity and temperature. In further analysis, reflectivity and FWHM were compared among four types of metal with various thin film thicknesses where graphene was applied on top of the metal layers, and data was compared against pure conventional metal thin films. A 60 nm‐thick Au thin film results in higher performance with reflectivity of 92.4% and FWHM of 0.88° whereas single layer graphene‐on‐60 nm‐thick Au gave reflectivity of 91.7% and FWHM of 1.32°. However, a graphene‐on‐40 nm‐thick Ag also gave good performance with narrower FWHM of 0.88° and reflection spectra of 89.2%.Inspec keywords: graphene, surface plasmon resonance, finite difference time‐domain analysis, reflectivity, metallic thin films, silver, gold, aluminium, copper, chemical sensors, biological techniquesOther keywords: graphene‐on‐metal substrates, SPR‐based sensor, finite‐difference time domain, metal thin films, surface plasmon resonance sensor, SPR curve, resonance angles, reflectivity, C, Ag, Au, Al, Cu     

Anti‐triangle centrality‐based community detection in complex networks

Community detection has been extensively studied in the past decades largely because of the fact that community exists in various networks such as technological, social and biological networks. Most of the available algorithms, however, only focus on the properties of the vertices, ignoring the roles of the edges. To explore the roles of the edges in the networks for community discovery, the authors introduce the novel edge centrality based on its antitriangle property. To investigate how the edge centrality characterises the community structure, they develop an approach based on the edge antitriangle centrality with the isolated vertex handling strategy (EACH) for community detection. EACH first calculates the edge antitriangle centrality scores for all the edges of a given network and removes the edge with the highest score per iteration until the scores of the remaining edges are all zero. Furthermore, EACH is characterised by being free of the parameters and independent of any additional measures to determine the community structure. To demonstrate the effectiveness of EACH, they compare it with the state‐of‐the art algorithms on both the synthetic networks and the real world networks. The experimental results show that EACH is more accurate and has lower complexity in terms of community discovery and especially it can gain quite inherent and consistent communities with a maximal diameter of four jumps.Inspec keywords: biology computing, complex networks, graph theory, social sciences computingOther keywords: antitriangle centrality‐based community detection, complex networks, technological networks, social networks, biological networks, vertex properties, edge roles, community discovery, antitriangle property, community structure, edge antitriangle centrality, isolated vertex handling strategy, EACH, antitriangle centrality scores, synthetic networks, real world networks     

In vitro evaluation of biodegradable nHAP‐Chitosan‐Gelatin‐based scaffold for tissue engineering application

The present study focuses on fabrication and characterisation of porous composite scaffold containing hydroxyapatite (HAP), chitosan, and gelatin with an average pore size of 250–1010 nm for improving wound repair and regeneration by Electrospinning method. From the results of X ‐Ray Diffraction (XRD) study, the peaks correspond to crystallographic structure of HAP powder. The presence of functional group bonds of HAP powder, Chitosan and scaffold was studied using Fourier Transform Infrared Spectroscopy (FTIR). The surface morphology of the scaffold was observed using Scanning Electron Microscope (SEM). The Bioactivity of the Nano composite scaffolds was studied using simulated body fluid solution at 37 ± 1°C. The biodegradability test was studied using Tris‐Buffer solution for the prepared nanocomposites [nano Chitosan, nano Chitosan gelatin, Nano based Hydroxyapatite Chitosan gelatin]. The cell migration and potential biocompatibility of nHAP‐chitosan‐gelatin scaffold was assessed via wound scratch assay and were compared to povedeen as control. Cytocompatibility evaluation for Vero Cells using wound scratch assay showed that the fabricated porous nanocomposite scaffold possess higher cell proliferation and growth than that of povedeen. Thus, the study showed that the developed nanocomposite scaffolds are potential candidates for regenerating damaged cell tissue in wound healing process.Inspec keywords: nanofabrication, tissue engineering, electrospinning, wounds, cellular biophysics, scanning electron microscopy, surface morphology, X‐ray diffraction, biomedical materials, nanomedicine, porosity, biodegradable materials, nanoporous materials, calcium compounds, gelatin, nanocomposites, Fourier transform infrared spectra, nanoparticles, precipitation (physical chemistry)Other keywords: average pore size, wound repair, crystallographic structure, HAP powder, functional group bonds, simulated body fluid solution, biodegradability test, Tris‐Buffer solution, cell migration, wound scratch assay, tissue engineering, electrospinning method, X‐ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, biocompatibility, cytocompatibility, porous nanocomposite scaffold, cell tissue, nHAP‐chitosan‐gelatin scaffold composites, wet chemical precipitation method, surface morphology, nanohydroxyapatite‐nanochitosan‐gelatin scaffold composites, cell proliferation, wound healing, (Ca₁₀ (PO₄)₆ (OH)₂)     

Adaptive fractional‐order blood glucose regulator based on high‐order sliding mode observer

Type I diabetes is described by the destruction of the insulin‐producing beta‐cells in the pancreas. Hence, exogenous insulin administration is necessary for Type I diabetes patients. In this study, to estimate the states that are not directly available from the Bergman minimal model a high‐order sliding mode observer is proposed. Then fractional calculus is combined with sliding mode control (SMC) for blood glucose regulation to create more robustness performance and make more degree of freedom and flexibility for the proposed method. Then an adaptive fractional‐order SMC is proposed. The adaptive SMC protect controller against disturbance and uncertainties while the fractional calculus provides robust performance. Numerical simulation verifies that the proposed controllers have better performance in the presence of disturbance and uncertainties without chattering.Inspec keywords: variable structure systems, biochemistry, blood, robust control, medical control systems, observers, sugar, diseases, calculus, adaptive control, cellular biophysicsOther keywords: fractional‐order SMC, adaptive SMC, fractional calculus, robust performance, adaptive fractional‐order blood glucose regulator, insulin‐producing beta‐cells, exogenous insulin administration, diabetes patients, Bergman minimal model, mode control, blood glucose regulation, pancreas, type I diabetes, state estimation, high‐order sliding mode observer, sliding mode control, degree of freedom, numerical simulation     

Observer‐based resilient finite‐time control of blood gases model during extra‐corporeal circulation

This study aims at designing an observer‐based resilient controller to regulate the amount of oxygen and carbon dioxide in the blood of patients during the extra‐corporeal blood circulation process. More precisely, in this study, a suitable observer‐based resilient controller is constructed to regulate the levels of patient blood gases in a finite interval of time. The finite‐time boundedness with the prescribed H∞ performance index of the considered blood gases control system against modelling uncertainty and external disturbances is ensured by using Lyapunov stability analysis. Moreover, a set of sufficient conditions for obtaining the controller gain is developed in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed robust finite‐time control scheme is verified through simulation results. The result reveals that the blood gases are maintained in their physiological ranges during a stable extra‐corporeal circulation process via the proposed observer‐based resilient controller.Inspec keywords: blood, haemodynamics, oxygen, carbon compounds, controllers, medical control systems, biomedical equipment, Lyapunov methods, linear matrix inequalitiesOther keywords: observer‐based resilient finite‐time control, observer‐based resilient controller, oxygen amount, carbon dioxide amount, extracorporeal blood circulation process, patient blood gas levels, finite time interval, finite‐time boundedness, H_∞ performance index, blood gases control system, Lyapunov stability analysis, controller gain, linear matrix inequalities, physiological ranges, LMIs, CO₂ , O₂      

Microelectromechanical system‐based biosensor for label‐free detection of human cytomegalovirus

The human cytomegalovirus (HCMV) is an asymptomatic common virus that is typically harmless, but in some cases, it can be life threatening. Thus, there is an urgent need to develop novel diagnostic methods and strengthen the efforts to combat this virus. A microcantilever‐based biosensor functionalised with the UL83‐antibody of HCMV (UL83‐HCMV antibody) has been developed to detect the UL83‐antigen of HCMV (UL83‐HCMV antigen) at different concentrations ranging from 0.3 to 300 ng/ml. The response of the biosensor to the presence of UL83‐HCMV antigen was measured through the shift in resonance frequency before and after antigen–antibody binding. The system shows a low detection limit of 84 pg/ml, which is comparable to traditional sensors, and a detection time of less than 15 min was achieved. The selectivity of the sensor was demonstrated using three different proteins with and without the UL83‐HCMV antigen. The biosensor shows high selectivity for the UL83‐HCMV antigen. Mass loading by the UL83‐HCMV antigen was roughly estimated with a sensitivity of ∼30 fg/Hz. This technique is crucial for the fabrication of portable and low‐cost biosensors that can be used in real‐time monitoring and enables early medical diagnosis.     

Fuzzy logic and Lyapunov‐based non‐linear controllers for HCV infection

Hepatitis C is the liver disease caused by the Hepatitis C virus (HCV) which can lead to serious health problems such as liver cancer. In this research work, the non‐linear model of HCV having three state variables (uninfected hepatocytes, infected hepatocytes and virions) and two control inputs has been taken into account, and four non‐linear controllers namely non‐linear PID controller, Lyapunov Redesign controller, Synergetic controller and Fuzzy Logic‐Based controller have been proposed to control HCV infection inside the human body. The controllers have been designed for the anti‐viral therapy in order to control the amount of uninfected hepatocytes to the desired safe limit and to track the amount of infected hepatocytes and virions to their reference value which is zero. One control input is the Pegylated interferon (peg‐IFN‐α) which acts in reducing the infected hepatocytes and the other input is ribavirin which blocks the production of virions. By doing so, the uninfected hepatocytes increase and achieve the required safe limit. Lyapunov stability analysis has been used to prove the stability of the whole system. The comparative analysis of the proposed nonlinear controllers using MATLAB/Simulink have been done with each other and with linear PID. These results depict that the infected hepatocytes and virions are reduced to the desired level, enhancing the rate of sustained virologic response (SVR) and reducing the treatment period as compared with previous strategies introduced in the literature.     

M‐matrix‐based stability conditions for genetic regulatory networks with time‐varying delays and noise perturbations

Stability is essential for designing and controlling any dynamic systems. Recently, the stability of genetic regulatory networks has been widely studied by employing linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high‐dimensional LMIs. In the previous study, the authors present several stability conditions for genetic regulatory networks with time‐varying delays, based on M ‐matrix theory and using the non‐smooth Lyapunov function, which results in determining whether a low‐dimensional matrix is a non‐singular M ‐matrix. However, the previous approach cannot be applied to analyse the stability of genetic regulatory networks with noise perturbations. Here, the authors design a smooth Lyapunov function quadratic in state variables and employ M ‐matrix theory to derive new stability conditions for genetic regulatory networks with time‐varying delays. Theoretically, these conditions are less conservative than existing ones in some genetic regulatory networks. Then the results are extended to genetic regulatory networks with time‐varying delays and noise perturbations. For genetic regulatory networks with n genes and n proteins, the derived conditions are to check if an n × n matrix is a non‐singular M ‐matrix. To further present the new theories proposed in this study, three example regulatory networks are analysed.Inspec keywords: genetics, linear matrix inequalities, Lyapunov matrix equations, molecular biophysics, noise, proteinsOther keywords: M‐matrix‐based stability condition, genetic regulatory networks, time‐varying delays, noise perturbations, linear matrix inequality approach, high‐dimensional LMI, Lyapunov function, state variables, M‐matrix theory, proteins, nonsingular M‐matrix     

Detecting small attractors of large Boolean networks by function‐reduction‐based strategy

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. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2.Inspec keywords: Boolean functions, genetics, cellular biophysicsOther keywords: detecting small attractors, function‐reduction‐based strategy, model gene regulatory networks, therapeutic intervention strategies, Boolean‐network analysis, cellular states, NP‐hard, singleton attractor, Boolean functions, partial gene activity profile, cell differentiation     

Ketoconazole‐conjugated ZnO nanoparticles based semi‐solid formulation and study their impacts on skin disease

In this study, the ketoconazole‐conjugated zinc oxide (ZnO) nanoparticles were prepared in a single‐step approach using dextrose as an intermediate compound. The physical parameters confirmed the drug conjugation with ZnO and their size was around 70–75 nm. The drug loading and in vivo drug release studies indicated that the –CHO group from the dextrose increase the drug loading up to 65% and their release kinetics were also studied. The anti‐fungal studies indicated that the prepared nanoparticles exhibit strong anti‐fungal activity and the minimum concentration needed is 10 mg/ml. The nanoparticles loaded semi‐solid gel was prepared using carbopol, methylparaben, propyl paraben and propylene glycol. The in vitro penetration of the ketoconazole‐conjugated nanoparticles was studied using the skin. The results indicated that the semi‐solid gel preparations influenced the penetration and also favoured the accumulation into the skin membrane. The veterinary clinical studies indicated that the prepared gel is highly suitable for treatment of Malassezia.Inspec keywords: II‐VI semiconductors, skin, biomedical materials, antibacterial activity, wide band gap semiconductors, drug delivery systems, nanomedicine, drugs, diseases, gels, nanofabrication, nanoparticles, zinc compounds, biomembranes, veterinary medicineOther keywords: strong anti‐fungal activity, propyl paraben, propylene glycol, semisolid gel preparations, skin membrane, veterinary clinical studies, semisolid formulation, skin disease, ketoconazole‐conjugated zinc oxide nanoparticles, single‐step approach, physical parameters, drug conjugation, drug loading, release kinetics, dextrose, in vivo drug release studies, carbopol, methylparaben, in vitro penetration, Malassezia, ZnO     

Hilbert transform‐based time‐series analysis of the circadian gene regulatory network

In this work, the authors propose the Hilbert transform (HT)‐based numerical method to analyse the time series of the circadian rhythms. They demonstrate the application of HT by taking both deterministic and stochastic time series that they get from the simulation of the fruit fly model Drosophila melanogaster and show how to extract the period, construct phase response curves, determine period sensitivity of the parameters to perturbations and build Arnold tongues to identify the regions of entrainment. They also derive a phase model that they numerically simulate to capture whether the circadian time series entrains to the forcing period completely (phase locking) or only partially (phase slips) or neither. They validate the phase model, and numerics with the experimental time series forced under different temperature cycles. Application of HT to the circadian time series appears to be a promising tool to extract the characteristic information about circadian rhythms.Inspec keywords: time series, genetics, Hilbert transforms, stochastic processes, circadian rhythms, signal processing, medical signal processingOther keywords: phase model, experimental time series, circadian time series, circadian rhythms, circadian gene regulatory network, deterministic time series, stochastic time series, fruit fly model, phase response curves, period sensitivity, phase locking, phase slips, Hilbert transform, time‐series analysis, signal processing     

On the design of human immunodeficiency virus treatment based on a non‐linear time‐delay model

Mathematical modelling and methods from control theory can be employed to find appropriate drug regimens in human immunodeficiency virus (HIV) treatment. In this study, using a non‐linear time‐delay model, the authors design some suboptimal highly active antiretroviral therapy (HAART) [http://www.en.wikipedia.org/wiki/Protease_inhibitor_%28pharmacology%29] regimens for patients with HIV. The non‐linear delayed model is used to describe the dynamical interactions between HIV and human immune system in the presence of HAART. Based on the model, a set point tracking problem is defined in order to set the number of susceptible CD4⁺ T cells to a desired value. To solve this set point tracking problem in a suboptimal way, the authors introduce a new method which is able to consider constraints on the amount of drug dosage. It is proved that the proposed method is able to set the number of susceptible CD4⁺ T cells to the desired value. Simulation results confirm that the method is efficient even in the presence of parametric uncertainties.Inspec keywords: control theory, delays, diseases, patient treatmentOther keywords: human immunodeficiency virus treatment design, nonlinear time delay model, control theory, HIV treatment, suboptimal highly active antiretroviral therapy, suboptimal HAART, human immune system, set point tracking problem, CD4+T cells, drug dosage