Identification of self‐regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data

Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self‐regulatory interactions. The values of gene co‐expressions could help in identifying co‐regulations among them. The proposed approach aims at computing the differences in variances of co‐expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co‐variances using principal component analysis (PCA) to predict an asymmetric and non‐diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time‐series RTX therapy, Arabidopsis thaliana, DREAM‐3, and DREAM‐8 datasets, in comparison with existing state‐of‐the‐art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self‐regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.Inspec keywords: molecular biophysics, principal component analysis, genetics, biology computing, reverse engineeringOther keywords: controlling regulatory agents, interacting genes, unlinked network components, self‐regulatory interactions, gene pair regulatory interactions, self‐regulatory network motifs, reverse engineering gene regulatory networks, microarray gene expression data, diversified computational approaches, symmetric interaction, diagonal interaction, gene pairs, gene co‐expressions, co‐expressed genes, mean expressions, gene regulatory expressions, asymmetric gene regulatory interactions     

Efficient co‐cultivation of human fibroblast cells (HFCs) and adipose‐derived stem cells (ADSs) on gelatin/PLCL nanofiber

In this study, we investigated whether the nanofibers produced by natural‐synthetic polymers can probably promote the proliferation of co‐cultured adipose‐derived stem cells/human fibroblast cells (ADSs/HFCs) and synthesis of collagen. Nanofiber was fabricated by blending gelatin and poly (L‐lactide co‐ɛ‐caprolactone) (PLCL) polymer nanofiber (Gel/PLCL). Cell morphology and the interaction between cells and Gel/PLCL nanofiber were evaluated by FESEM and fluorescent microscopy. MTS assay and quantitative real‐time polymerase chain reaction were applied to assess the proliferation of co‐cultured ADSs/HFCs and the collagen type I and III synthesis, respectively. The concentrations of two cytokines including fibroblast growth factor‐basic and transforming growth factor‐β1 were also measured in culture medium of co‐cultured ADSs/HDCs using enzyme‐linked immunosorbent assay assay. Actually, nanofibers exhibited proper structural properties in terms of stability in cell proliferation and toxicity analysis processes. Gel/PLCL nanofiber promoted the growth and the adhesion of HFCs. Our results showed in contact co‐culture of ADSs/HFCs on the Gel/PLCL nanofiber increased cellular adhesion and proliferation synergistically compared to non‐coated plate. Also, synthesis of collagen and cytokines secretion of co‐cultured ADSs/HFCs on Gel/PLCL scaffolds is significantly higher than non‐coated plates. To conclude, the results suggest that Gel/PLCL nanofiber can imitate physiological characteristics in vivo and enhance the efficacy of co‐cultured ADSs/HFCs in wound healing process.Inspec keywords: biomedical materials, enzymes, adhesion, fluorescence, polymer fibres, tissue engineering, wounds, nanofibres, cellular biophysics, molecular biophysics, gelatin, biochemistry, nanomedicine, field emission scanning electron microscopy, nanofabricationOther keywords: cell morphology, cell proliferation, efficient cocultivation, HFCs, ADSs, gelatin‐PLCL nanofiber, natural‐synthetic polymers, cocultured adipose‐derived stem cells‐human fibroblast cells, FESEM, fluorescent microscopy, MTS assay, quantitative real‐time polymerase chain reaction, collagen type I synthesis, collagen type III synthesis, cytokines, transforming growth factor‐β1, fibroblast growth factor‐basic growth factor‐β1, culture medium, enzyme‐linked immunosorbent assay assay, structural properties, toxicity analysis, cellular adhesion, physiological characteristics in vivo, wound healing     

Overlapping functional modules detection in PPI network with pair‐wise constrained non‐negative matrix tri‐factorisation

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. The authors propose a novel semi‐supervised model named pairwise constrains nonnegative matrix tri‐factorisation (PCNMTF), which takes full advantage of the well‐known protein complexes to find overlapping functional modules based on protein module indicator matrix and module correlation matrix simultaneously from PPI networks. PCNMTF determinately models and learns the mixed module memberships of each protein by considering the correlation among modules simultaneously based on the non‐negative matrix tri‐factorisation. The experiment results on both synthetic and real‐world biological networks demonstrate that PCNMTF gains more precise functional modules than that of state‐of‐the‐art methods.Inspec keywords: proteins, molecular biophysics, cellular biophysics, matrix algebraOther keywords: overlapping functional module detection, PPI network, pair‐wise constrained nonnegative matrix trifactorisation, protein–protein interaction data, cellular functions, protein complexes, real‐world biological networks, synthetic biological networks     

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non‐smokers with lung adenocarcinoma

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non‐coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non‐smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co‐expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA–mRNA pairs which were significantly dysregulated in non‐smokers with lung adenocarcinoma. Also two pairs (miR‐133b/Protein Kinase C Zeta (PRKCZ) and miR‐557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors’ understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.Inspec keywords: cancer, molecular biophysics, patient diagnosis, tumours, RNA, proteins, lung, genetics, medical diagnostic computing, molecular configurationsOther keywords: miRNAs expression profiles, mRNAs expression profiles, smokers, nonsmokers, integrative analysis, lung adenocarcinoma, microRNAs, disease specific biomarkers, noncoding regulators, genetic abnormalities, weighted gene coexpression network analysis     

Identifying genuine protein–protein interactions within communities of gene co‐expression networks using a deconvolution method

Direct relationships between biological molecules connected in a gene co‐expression network tend to reflect real biological activities such as gene regulation, protein–protein interactions (PPIs), and metabolisation. As correlation‐based networks contain numerous indirect connections, those direct relationships are always ‘hidden’ in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co‐expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a ‘deconvolution filter’ contain more genuine PPIs than before deconvolution.Inspec keywords: proteins, deconvolution, genetics, bioinformatics, biology computing, molecular biophysicsOther keywords: identifying genuine protein–protein interactions, gene co‐expression network, deconvolution method, direct relationships, biological molecules, biological activities, gene regulation, correlation‐based networks, numerous indirect connections, global network, network communities, biological significance, protein function, protein complexes, studying network evolution, biological sciences, different gene co‐expression networks     

Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co‐expression analysis

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. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival‐related miRNA ‐mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient''s survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin‐1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA ‐mRNA regulatory networks that comprised 38 survival‐related miRNA ‐mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.Inspec keywords: RNA, molecular biophysics, genetics, cancerOther keywords: signature regulatory network, glioblastoma prognosis, mRNA coexpression analysis, miRNA coexpression analysis, glioblastoma multiforme, brain tumour, microRNAs, pathogenesis, genome‐wide regulatory networks, miRNA‐mRNA pairs, weighted gene coexpression network analysis, survival analysis, GBM prognosis, integration analysis, neuropilin‐1 gene, module turquoise, molecular regulatory mechanisms     

Systems analysis utilising pathway interactions identifies sonic hedgehog pathway as a primary biomarker and oncogenic target in hepatocellular carcinoma

The development and progression of cancer is associated with disruption of biological networks. Historically studies have identified sets of signature genes involved in events ultimately leading to the development of cancer. Identification of such sets does not indicate which biologic processes are oncogenic drivers and makes it difficult to identify key networks to target for interventions. Using a comprehensive, integrated computational approach, the authors identify the sonic hedgehog (SHH) pathway as the gene network that most significantly distinguishes tumour and tumour‐adjacent samples in human hepatocellular carcinoma (HCC). The analysis reveals that the SHH pathway is commonly activated in the tumour samples and its activity most significantly differentiates tumour from the non‐tumour samples. The authors experimentally validate these in silico findings in the same biologic material using Western blot analysis. This analysis reveals that the expression levels of SHH, phosphorylated cyclin B1, and CDK7 levels are much higher in most tumour tissues as compared to normal tissue. It is also shown that siRNA‐mediated silencing of SHH gene expression resulted in a significant reduction of cell proliferation in a liver cancer cell line, SNU449 indicating that SHH plays a major role in promoting cell proliferation in liver cancer. The SHH pathway is a key network underpinning HCC aetiology which may guide the development of interventions for this most common form of human liver cancer.Inspec keywords: bioinformatics, cancer, cellular biophysics, genetics, liver, molecular biophysics, RNA, systems analysis, tumoursOther keywords: biomedical informatics, human liver cancer, network underpinning HCC aetiology, liver cancer cell line, cell proliferation, SHH gene expression, siRNA‐mediated silencing, CDK7 levels, phosphorylated cyclin B1, Western blot analysis, in silico findings, SHH pathway, human hepatocellular carcinoma, tumour‐adjacent samples, gene network, integrated computational approach, oncogenic drivers, biologic processes, cancer development, biological networks, cancer progression, oncogenic target, primary biomarker, sonic hedgehog pathway, pathway interactions, systems analysis     

Inferring catalysis in biological systems

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. This may be due to unknown but present catalytic reactions. From a modelling perspective, the question of whether a certain reaction is catalysed leads to a large increase of model candidates. For large networks the calibration of all possible models becomes computationally infeasible. We propose a method which determines a substantially reduced set of appropriate model candidates and identifies the catalyst of each reaction at the same time. This is incorporated in a multiple‐step procedure which first extends the network by additional latent variables and subsequently identifies catalyst candidates using similarity analysis methods. Results from synthetic data examples suggest a good performance even for non‐informative data with few observations. Applied on CD95 apoptotic pathway our method provides new insights into apoptosis regulation.Inspec keywords: catalysis, catalysts, biochemistry, genetics, enzymes, biology computing, calibration, molecular clustersOther keywords: inferring catalysis, biological systems, systems biology, communication patterns, genes, enzymes, proteins, time‐resolved experiments, time‐resolved communication, reaction networks, gene regulatory networks, biochemical networks, signalling pathways, mathematical data modelling, catalytic reactions, calibration, catalyst, multiple‐step procedure, latent variables, similarity analysis methods, noninformative data, differentiation apoptotic pathway, cluster     

Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms

In this study, three non‐linear indices consist of compression, one‐dimensional (1D) and two‐dimensional (2D) fractal dimensions are used for the determination of the malignancy or benignity of cancer tumours in breast thermograms. On the other hand, by developing the high‐precision infrared cameras as well as new methods of image processing, biomedical thermography images have found a prominent position among the others. Furthermore, cancerous tissue can be affected by the laser. In this study, in order to treat the cancerous lesion identified by breast thermograms, the laser parameters are designed. The basis of controller designing is the obtained non‐linear indices. If the indices are moved from the chaotic behaviour to normal condition, the treating tissue is going from cancerous to a healthy condition and the treatment process is completed. Radiation frequency and the energy density of laser are designed as two key elements in the cancer treatment. In this study, the type I and type II fuzzy controllers are employed for the control strategies. Using the proposed closed‐loop control, the non‐linear indices of the cancerous lesion will be reduced during the treatment process. The simulation results on two datasets of breast thermograms indicate the superiority of type II fuzzy controller.Inspec keywords: closed loop systems, fractals, infrared imaging, tumours, fuzzy control, medical image processing, cancer, biological tissues, gynaecology, patient treatmentOther keywords: cancer tumours, breast thermograms, high‐precision infrared cameras, image processing, cancerous tissue, cancerous lesion, nonlinear indices, type II fuzzy controller, closed‐loop control, fuzzy controller design, breast cancer treatment, 2D fractal dimensions     

Chitosan nanoparticles loaded with aspirin and 5‐fluororacil enable synergistic antitumour activity through the modulation of NF‐κB/COX‐2 signalling pathway

Based on the enhancement of synergistic antitumour activity to treat cancer and the correlation between inflammation and carcinogenesis, the authors designed chitosan nanoparticles for co‐delivery of 5‐fluororacil (5‐Fu: an as anti‐cancer drug) and aspirin (a non‐steroidal anti‐inflammatory drug) and induced synergistic antitumour activity through the modulation of the nuclear factor kappa B (NF‐κB)/cyclooxygenase‐2 (COX‐2) signalling pathways. The results showed that aspirin at non‐cytotoxic concentrations synergistically sensitised hepatocellular carcinoma cells to 5‐Fu in vitro. It demonstrated that aspirin inhibited NF‐κB activation and suppressed NF‐κB regulated COX‐2 expression and prostaglandin E2 (PGE2) synthesis. Furthermore, the proposed results clearly indicated that the combination of 5‐Fu and aspirin by chitosan nanoparticles enhanced the intracellular concentration of drugs and exerted synergistic growth inhibition and apoptosis induction on hepatocellular carcinoma cells by suppressing NF‐κB activation and inhibition of expression of COX‐2.Inspec keywords: proteins, molecular biophysics, cellular biophysics, biomedical materials, cancer, nanoparticles, drug delivery systems, enzymes, tumours, nanomedicine, drugsOther keywords: chitosan nanoparticles, aspirin, 5‐fluororacil, synergistic antitumour activity, anticancer drug, nonsteroidal antiinflammatory drug, hepatocellular carcinoma cells, NF‐κB activation, NF‐κB regulated COX‐2 expression, PGE2, synergistic growth inhibition, apoptosis induction, prostaglandin E2 synthesis, intracellular concentration, noncytotoxic concentrations, NF‐κB‐cyclooxygenase‐2 signalling pathways, cyclooxygenase‐2, nuclear factor kappa B     

In silico identification of potential targets and drugs for non‐small cell lung cancer

Lung cancer is one of the leading causes of death in both the USA and Taiwan, and it is thought that the cause of cancer could be because of the gain of function of an oncoprotein or the loss of function of a tumour suppressor protein. Consequently, these proteins are potential targets for drugs. In this study, differentially expressed genes are identified, via an expression dataset generated from lung adenocarcinoma tumour and adjacent non‐tumour tissues. This study has integrated many complementary resources, that is, microarray, protein‐protein interaction and protein complex. After constructing the lung cancer protein‐protein interaction network (PPIN), the authors performed graph theory analysis of PPIN. Highly dense modules are identified, which are potential cancer‐associated protein complexes. Up‐ and down‐regulated communities were used as queries to perform functional enrichment analysis. Enriched biological processes and pathways are determined. These sets of up‐ and down‐regulated genes were submitted to the Connectivity Map web resource to identify potential drugs. The authors'' findings suggested that eight drugs from DrugBank and three drugs from NCBI can potentially reverse certain up‐ and down‐regulated genes'' expression. In conclusion, this study provides a systematic strategy to discover potential drugs and target genes for lung cancer.Inspec keywords: cellular biophysics, lung, cancer, drugs, genetics, tumours, lab‐on‐a‐chip, proteins, molecular biophysics, graph theory, query processing, medical computingOther keywords: down‐regulated gene expression, up‐regulated gene expression, potential target genes, DrugBank, potential drugs, connectivity map Web resource, biological processes, functional enrichment analysis, up‐regulated communities, down‐regulated communities, cancer‐associated protein complexes, k‐communities, highly‐dense modules, PPIN, graph theory analysis, lung cancer protein‐protein interaction network, MIPS, BioGrid, ArrayExpress, microarray, nontumour tissues, human lung adenocarcinoma tumour, bioconductor package, tumour suppressor protein, oncoprotein, nonsmall cell lung cancer, in silico identification     

Identification of specific microRNA–messenger RNA regulation pairs in four subtypes of breast cancer

Four subtypes of breast cancer, luminal A, luminal B, basal‐like, human epidermal growth factor receptor‐enriched, have been identified based on gene expression profiles of human tumours. The goal of this study is to find whether the same groups'' genes would exhibit different networks among the four subtypes. Differential expressed genes between each of the four subtypes and the normal samples were identified. The overlaps between the four groups of differentially expressed genes were used to construct regulations networks for each of the four subtypes. Univariate and multivariate Cox regressions were employed to test the genes in the four regulation networks. This study demonstrated that the common genes in four subtypes showed different regulation. Also, the hsa‐miR‐182 and decorin pair performs different functions among the four subtypes of breast cancer. The result indicated that heterogeneity of breast cancer is not only reflected in the different expression patterns among different genes, but also in the different regulatory networks of the same group of genes.Inspec keywords: genetics, cellular biophysics, tumours, molecular biophysics, RNA, biochemistry, cancer, proteins, biology computingOther keywords: decorin pair performs different functions, breast cancer heterogeneity, regulatory networks, specific microRNA–messenger, regulation pairs, human epidermal growth factor receptor, gene expression profiles, differentially expressed genes, regulations networks, hsa‐miR‐182, decorin pair, human tumours     

Crosstalk between pathways enhances the controllability of signalling networks

The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow‐tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross‐pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors’ work provides a comprehensive understanding of the impact of network structures and properties on controllability.Inspec keywords: genetics, numerical analysis, control theoryOther keywords: signalling network controllability, interconnected pathways, bow‐tie architecture, structure controllability, biological mechanisms, cross‐pathway interactions, numerical simulations, biological networks, control theory, gene regulatory network     

Analysis and classification of DNA‐binding sites in single‐stranded and double‐stranded DNA‐binding proteins using protein information

Single‐stranded DNA‐binding proteins (SSBs) and double‐stranded DNA‐binding proteins (DSBs) play different roles in biological processes when they bind to single‐stranded DNA (ssDNA) or double‐stranded DNA (dsDNA). However, the underlying binding mechanisms of SSBs and DSBs have not yet been fully understood. Here, the authors firstly constructed two groups of ssDNA and dsDNA specific binding sites from two non‐redundant sets of SSBs and DSBs. They further analysed the relationship between the two classes of binding sites and a newly proposed set of features (residue charge distribution, secondary structure and spatial shape). To assess and utilise the predictive power of these features, they trained a classification model using support vector machine to make predictions about the ssDNA and the dsDNA binding sites. The author''s analysis and prediction results indicated that the two classes of binding sites can be distinguishable by the three types of features, and the final classifier using all the features achieved satisfactory performance. In conclusion, the proposed features will deepen their understanding of the specificity of proteins which bind to ssDNA or dsDNA.Inspec keywords: biology computing, DNA, molecular biophysics, molecular configurations, pattern classification, proteins, support vector machinesOther keywords: dsDNA binding sites, ssDNA binding sites, support vector machine, classiflcation model, spatial shape, secondary structure, residue charge distribution, binding mechanisms, biological process, protein information, double‐stranded DNA‐binding proteins, single‐stranded DNA‐binding proteins     

Bifurcation analysis of bistable and oscillatory dynamics in biological networks using the root‐locus method

Most of the biological systems including gene regulatory networks can be described well by ordinary differential equation models with rational non‐linearities. These models are derived either based on the reaction kinetics or by curve fitting to experimental data. This study demonstrates the applicability of the root‐locus‐based bifurcation analysis method for studying the complex dynamics of such models. The effectiveness of the bifurcation analysis in determining the exact parameter regions in each of which the system shows a certain dynamical behaviour, such as bistability, oscillation, and asymptotically equilibrium dynamics is shown by considering two mostly studied gene regulatory networks, namely Gardner''s genetic toggle switch and p53 gene network possessing two‐phase (mono‐stable/oscillation) dynamics.Inspec keywords: oscillations, curve fitting, differential equations, bifurcation, genetics, nonlinear dynamical systemsOther keywords: nonlinearities, reaction kinetics, root‐locus‐based bifurcation analysis method, complex dynamics, exact parameter regions, dynamical behaviour, equilibrium dynamics, studied gene regulatory networks, p53 gene network, bistable dynamics, oscillatory dynamics, biological networks, root‐locus method, biological systems, ordinary differential equation models     

Pseudo‐spectral method for controlling the drug dosage in cancer

A mixed chemotherapy–immunotherapy treatment protocol is developed for cancer treatment. Chemotherapy pushes the trajectory of the system towards the desired equilibrium point, and then immunotherapy alters the dynamics of the system by affecting the parameters of the system. A co‐existing cancerous equilibrium point is considered as the desired equilibrium point instead of the tumour‐free equilibrium. Chemotherapy protocol is derived using the pseudo‐spectral (PS) controller due to its high convergence rate and simple implementation structure. Thus, one of the contributions of this study is simplifying the design procedure and reducing the controller computational load in comparison with Lyapunov‐based controllers. In this method, an infinite‐horizon optimal control problem is proposed for a non‐linear cancer model. Then, the infinite‐horizon optimal control of cancer is transformed into a non‐linear programming problem. The efficient Legendre PS scheme is suggested to solve the proposed problem. Then, the dynamics of the system is modified by immunotherapy is another contribution. To restrict the upper limit of the chemo‐drug dose based on the age of the patients, a Mamdani fuzzy system is designed, which is not present yet. Simulation results on four different dynamics cases how the efficiency of the proposed treatment strategy.Inspec keywords: patient treatment, cancer, convergence, linear programming, optimal control, nonlinear programming, nonlinear control systems, Lyapunov methods, drugs, tumoursOther keywords: nonlinear programming problem, efficient Legendre PS scheme, chemo‐drug dose, Mamdani fuzzy system, treatment strategy, pseudospectral method, drug dosage, mixed chemotherapy–immunotherapy treatment protocol, cancer treatment, desired equilibrium point, immunotherapy alters, cancerous equilibrium point, tumour‐free equilibrium, chemotherapy protocol, pseudospectral controller, high convergence rate, simple implementation structure, controller computational load, Lyapunov‐based controllers, infinite‐horizon optimal control problem, nonlinear cancer model     

Multi‐threshold and multi‐input DNA logic design style for profiling the microRNA biomarkers of real cancers

Early detection of cancer is very critical because it can reduce the treatment risk and cost. MicroRNAs (miRNAs) have been introduced in recent years as an efficient class of biomarkers for cancer early detection. Now, real‐time polymerase chain reaction has been used to profile the miRNA expression, which is costly, time consuming and low accuracy. Most recently, DNA logic gates are used to detect the miRNA expression level that is more accurate and faster than previous methods. The DNA‐based logic gates face with serious challenges such as the large complexity and low scalability. In this study, the authors proposed a methodology to design multi‐threshold and multi‐input DNA‐based logic gates in response to specific miRNA inputs in live mammalian cells. The proposed design style can simultaneously recognise multiple miRNAs with different rising and falling thresholds. The design style has been evaluated on the lung cancer biomarkers and the experimental results show the efficiency of the proposed method in terms of accuracy, efficiency and speed.Inspec keywords: DNA, logic design, biocomputing, RNA, molecular biophysics, logic gates, lung, genetics, cellular biophysics, cancer, biology computing, enzymes, biosensorsOther keywords: falling thresholds, specific miRNA inputs, multiinput DNA‐based logic gates, low scalability, DNA‐based logic gates face, miRNA expression level, DNA logic gates, low accuracy, time consuming, real‐time polymerase chain reaction, cancer early detection, treatment risk, cancers, microRNA biomarkers, multiinput DNA logic design style, multithreshold, lung cancer biomarkers