Retrieving relevant time‐course experiments: a study on Arabidopsis microarrays

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. In this study, the authors address this problem in the context of time‐course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time‐course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time‐course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.Inspec keywords: botany, lab‐on‐a‐chip, genetics, bioinformatics, information retrieval, data mining, data analysis, associative processingOther keywords: relevant time‐course experiment retrieval, time‐course Arabidopsis microarray, time‐course gene regulation, stimulus response, systems biology, computational method, gene behaviour model, gene networked interaction, latent parameter, model parameter estimation, meta‐analysis, data analysis, time‐course gene expression experiment, information retrieval, computational framework, time‐course experiment query, relevant experiment list, repository, environmental factor, query experiment, experimental content similarity     

Gene regulatory network discovery using pairwise Granger causality

Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time‐series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pairwisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full‐model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell‐cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.Inspec keywords: biology computing, cancer, causality, cellular biophysics, genetics, genomics, time seriesOther keywords: gene regulatory network discovery, pairwise Granger causality, gene expression data, drug development, time‐series data, synthetic data, spurious causalities, full‐model Granger causality detection, vector autoregressive model, real human HeLa cell‐cycle dataset, Akaike information criterion, degree distributions, network hubs     

Properties of sparse penalties on inferring gene regulatory networks from time‐course gene expression data

Genes regulate each other and form a gene regulatory network (GRN) to realise biological functions. Elucidating GRN from experimental data remains a challenging problem in systems biology. Numerous techniques have been developed and sparse linear regression methods become a promising approach to infer accurate GRNs. However, most linear methods are either based on steady‐state gene expression data or their statistical properties are not analysed. Here, two sparse penalties, adaptive least absolute shrinkage and selection operator and smoothly clipped absolute deviation, are proposed to infer GRNs from time‐course gene expression data based on an auto‐regressive model and their Oracle properties are proved under mild conditions. The effectiveness of those methods is demonstrated by applications to in silico and real biological data.Inspec keywords: genetics, autoregressive processesOther keywords: sparse penalties, gene regulatory networks, time‐course gene expression data, GRN, biological functions, systems biology, sparse linear regression methods, steady‐state gene expression data, adaptive least absolute shrinkage, selection operator, smoothly clipped absolute deviation, autoregressive model, Oracle properties     

Investigating receptor enzyme activity using time‐scale analysis

At early drug discovery, purified protein‐based assays are often used to characterise compound potency. In the context of dose response, it is often perceived that a time‐independent inhibitor is reversible and a time‐dependent inhibitor is irreversible. The legitimacy of this argument is investigated using a simple kinetics model, where it is revealed by model‐based analytical analysis and numerical studies that dose response of an irreversible inhibitor may appear time‐independent under certain parametric conditions. Hence, the observation of time‐independence cannot be used as sole evidence for identification of inhibitor reversibility. It has also been discussed how the synthesis and degradation of a target receptor affect drug inhibition in an in vitro cell‐based assay setting. These processes may also influence dose response of an irreversible inhibitor in such a way that it appears time‐independent under certain conditions. Furthermore, model‐based steady‐state analysis reveals the complexity nature of the drug–receptor process.Inspec keywords: enzymes, molecular biophysics, drugs, biochemistry, reaction kinetics, cellular biophysicsOther keywords: receptor enzyme activity, time‐scale analysis, drug discovery, purified protein‐based assays, compound potency, dose response, reversible time‐independent inhibitor, irreversible time‐dependent inhibitor, kinetics model, target receptor degradation, drug inhibition, in vitro cell‐based assay setting, model‐based steady‐state analysis, drug‐receptor process     

Multi‐scale approach for simulating time‐delay biochemical reaction systems

This study presents a multi‐scale approach for simulating time‐delay biochemical reaction systems when there are wide ranges of molecular numbers. The authors construct a new efficient approach based on partitioning into slow and fast subsets in conjunction with predictor–corrector methods. This multi‐scale approach is shown to be much more efficient than existing methods such as the delay stochastic simulation algorithm and the modified next reaction method. Numerical testing on several important problems in systems biology confirms the accuracy and computational efficiency of this approach.Inspec keywords: biochemistry, delays, biological techniques, predictor‐corrector methodsOther keywords: multiscale approach, time‐delay biochemical reaction systems, predictor–corrector methods, delay stochastic simulation algorithm, modified next reaction method, numerical testing, systems biology, method accuracy, computational efficiency     

Lung cancer prediction from microarray data by gene expression programming

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. Prediction performance evaluations and comparisons between the authors’ GEP models and three representative machine learning methods, support vector machine, multi‐layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross‐data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.Inspec keywords: lung, cancer, medical diagnostic computing, patient diagnosis, genetic algorithms, feature selection, learning (artificial intelligence), support vector machines, multilayer perceptrons, radial basis function networks, reliability, sensitivity analysisOther keywords: lung cancer prediction, cancer‐related death, cancer diagnosis, gene profiles, gene expression programming‐based model, gene selection, GEP‐based prediction models, prediction performance evaluations, representative machine learning methods, support vector machine, multilayer perceptron, radial basis function neural network, real microarray lung cancer datasets, cross‐data set validation, reliability, receiver operating characteristic curve     

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     

Discovery of significant pathways in breast cancer metastasis via module extraction and comparison

Discovering significant pathways rather than single genes or small gene sets involved in metastasis is becoming more and more important in the study of breast cancer. Many researches have shed light on this problem. However, most of the existing works are relying on some priori biological information, which may bring bias to the models. The authors propose a new method that detects metastasis‐related pathways by identifying and comparing modules in metastasis and non‐metastasis gene co‐expression networks. The gene co‐expression networks are built by Pearson correlation coefficients, and then the modules inferred in these two networks are compared. In metastasis and non‐metastasis networks, 36 and 41 significant modules are identified. Also, 27.8% (metastasis) and 29.3% (non‐metastasis) of the modules are enriched significantly for one or several pathways with p ‐value <0.05. Many breast cancer genes including RB1, CCND1 and TP53 are included in these identified pathways. Five significant pathways are discovered only in metastasis network: glycolysis pathway, cell adhesion molecules, focal adhesion, stathmin and breast cancer resistance to antimicrotubule agents, and cytosolic DNA‐sensing pathway. The first three pathways have been proved to be closely associated with metastasis. The rest two can be taken as a guide for future research in breast cancer metastasis.Inspec keywords: cancer, genetics, genomics, DNA, molecular biophysics, adhesion, cellular biophysicsOther keywords: breast cancer metastasis, module extraction, gene sets, metastasis‐related pathways, nonmetastasis gene coexpression networks, Pearson correlation coefflcients, breast cancer genes, RB1, CCND1, TP53, glycolysis pathway, cell adhesion molecules, focal adhesion, stathmin, breast cancer resistance, antimicrotubule agents, cytosolic DNA‐sensing pathway, breast cancer metastasis     

Gaussian graphical model for identifying significantly responsive regulatory networks from time course high‐throughput data

With rapid accumulation of functional relationships between biological molecules, knowledge‐based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge‐based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge‐based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.Inspec keywords: Bayes methods, biology computing, circadian rhythms, Gaussian processes, genetics, genomics, graphs, molecular biophysics, proteinsOther keywords: Gaussian graphical model, responsive regulatory networks, time course high‐throughput data, biological molecules, dynamic gene expression proflling, circadian rhythm, consistency measurement, matching network structure, simulated time course microarray data, true time course microarray data, dynamic Bayesian network model, time course gene expression proflles, network architectures, documented regulatory networks, speciflc gene expression proflling data, phenotypes, proteins, functional linkages, databases, knowledge‐based networks     

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     

Network‐based method for detecting dysregulated pathways in glioblastoma cancer

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. First, the authors construct a gene network based on mutual exclusivity between each pair of genes and the interaction between gene mutations and expression changes. Then they detect all complete subgraphs using CFinder clustering algorithm in the constructed gene network. Next, the two gene sets whose overlapping scores are above a specific threshold are merged. Finally, they obtain two dysregulated pathways in which there are glioblastoma‐related multiple genes which are closely related to the two subtypes of glioblastoma. The results show that one dysregulated pathway revolving around epidermal growth factor receptor is likely to be associated with the primary subtype of glioblastoma, and the other dysregulated pathway revolving around TP53 is likely to be associated with the secondary subtype of glioblastoma.Inspec keywords: cancer, tumours, drugs, brain, neurophysiology, genetic algorithms, genetics, skin, proteins, molecular biophysics, genomics, patient diagnosis, molecular configurationsOther keywords: network‐based method, dysregulated pathways detection, glioblastoma cancer, biological molecular mechanisms, precise diagnosis, cancer patient treatment, drug targets, mutual exclusivity, mutated genes, gene mutations, expression changes, expression data, CFinder clustering algorithm, constructed gene network, gene sets, overlapping scores, glioblastoma‐related multiple genes, epidermal growth factor receptor, TP53, secondary subtype     

Signalling pathway impact analysis based on the strength of interaction between genes

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. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer‐related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.Inspec keywords: genetics, cancer, biology computing, perturbation theory, biological organs, data analysisOther keywords: gene interaction strength, cancer‐related pathways, differentially expressed genes, biological experiments, signal perturbation propagation, signalling pathway impact analysis methods, Pearson correlation coefficient, mutual information, colorectal cancer dataset analysis, pancreatic cancer dataset, PSPIA, MSPIA     

Non‐fragile reliable control synthesis of the sugarcane borer

This study considers the problem of non‐fragile reliable control synthesis for mathematical model of interaction between the sugarcane borer (Diatraea saccharalis) and its egg parasitoid Trichogramma galloi. In particular, the control could be substituted by periodic releases of a small population of natural enemies and hence it is important to propose the time‐varying controller in sugarcane borer. The main aim of this study is to design a state feedback non‐fragile (time‐varying) reliable controller such that the states of the sugarcane borer system reach the equilibrium point within the desired period. A novel approach is proposed to deal with the uncertain matrices which appear in non‐fragile reliable control. Finally, simulations based on sugarcane borer systems are conducted to illustrate the advantages and effectiveness of the proposed design technique. The result reveals that the proposed non‐fragile control provides good performance in spite of periodic releases of a small population of natural enemies occurs.Inspec keywords: microorganisms, plant diseases, biology computing, state feedback, biocontrol, control system synthesisOther keywords: nonfragile reliable control synthesis, sugarcane borer, mathematical model, Diatraea saccharalis, egg parasitoid, Trichogramma galloi, periodic releases, natural enemies, state feedback nonfragile time‐varying reliable controller, equilibrium point, design technique     

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     

System‐based strategies for p53 recovery

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. Attractor point analysis is carried out to select the suitable domain of attraction. A two‐loop negative feedback control strategy is devised to drag the system trajectories to the attractor point and to regulate cellular concentration of Nutlin, respectively. An integrated framework is constituted to incorporate the pharmacokinetic effects of Nutlin in the cancerous cells. Bifurcation analysis is also performed on the p53 model to see the conditions for p53 oscillation.Inspec keywords: proteins, tumours, cancer, cellular biophysics, molecular biophysics, molecular configurations, biochemistry, differential equations, closed loop systems, bifurcation, biology computingOther keywords: system‐based strategies, p53 recovery, systems theory‐based novel drug design approach, dynamic system, ordinary differential equations‐based mathematical model, control engineering practices, subsequent controller design, wild‐type p53, p53 revival, oscillatory, control system paradigm, mathematical model, Nutlin effect, attractor point analysis, domain‐of‐attraction, two‐loop negative feedback control strategy, cellular concentration, pharmacokinetic effects, cancerous cells, bifurcation analysis, p53 oscillation, anomalous cell     

Comprehensive study of tumour single nucleotide polymorphism array data reveals significant driver aberrations and disrupted signalling pathways in human hepatocellular cancer

The authors describe an integrated method for analysing cancer driver aberrations and disrupted pathways by using tumour single nucleotide polymorphism (SNP) arrays. The authors new method adopts a novel statistical model to explicitly quantify the SNP signals, and therefore infers the genomic aberrations, including copy number alteration and loss of heterozygosity. Examination on the dilution series dataset shows that this method can correctly identify the genomic aberrations even with the existence of severe normal cell contamination in tumour sample. Furthermore, with the results of the aberration identification obtained from multiple tumour samples, a permutation‐based approach is proposed for identifying the statistically significant driver aberrations, which are further incorporated with the known signalling pathways for pathway enrichment analysis. By applying the approach to 286 hepatocellular tumour samples, they successfully uncover numerous driver aberration regions across the cancer genome, for example, chromosomes 4p and 5q, which harbour many known hepatocellular cancer related genes such as alpha‐fetoprotein (AFP) and ectodermal‐neural cortex (ENC1). In addition, they identify nine disrupted pathways that are highly enriched by the driver aberrations, including the systemic lupus erythematosus pathway, the vascular endothelial growth factor (VEGF) signalling pathway and so on. These results support the feasibility and the utility of the proposed method on the characterisation of the cancer genome and the downstream analysis of the driver aberrations and the disrupted signalling pathways.Inspec keywords: cancer, DNA, genetics, genomics, liver, molecular biophysics, molecular configurations, physiological models, polymorphism, statistical analysis, tumoursOther keywords: tumour single nucleotide polymorphism array data, disrupted signalling pathways, human hepatocellular cancer, cancer driver aberrations, statistical model, SNP signals, genomic aberrations, heterozygosity, dilution series dataset, normal cell contamination, permutation‐based approach, statistical significant driver aberrations, hepatocellular tumour samples, cancer genome, hepatocellular cancer related genes, systemic lupus erythematosus pathway, VEGF signalling pathway     

Effect of the polyelectrolyte coating on the photothermal efficiency of gold nanorods and the photothermal induced cancer cell damage

Coating gold nanorods (GNRs) with polyelectrolytes is an effective approach to make them biocompatible for potential use in photothermal treatment (PTT) of cancer. The authors report the effect of coating of the GNRs with polystyrene sulphonate (PSS‐GNRs) and PSS plus poly di‐allyl di‐methyl ammonium chloride (PDDAC‐GNRs) on its photothermal conversion efficiency (PTE), cellular uptake and subsequently the photothermal induced cytotoxicity in human oral cancer cells (NT8e). Coating of GNRs with PSS led to decrease in PTE by ∼30% and further coating it with PDDAC led to its increase to similar level, with respect to as‐ prepared GNRs. The cellular uptake of PDDAC‐GNRs in cancer cells was double than that for PSS‐GNRs. PTT of cancer cells after treatment with 60 pM of either PDDAC‐GNRs or PSS‐GNRs resulted in cytotoxicty of ∼90%. At higher concentration of 120 pM, while PSS‐GNRs showed no further change, for PDDAC‐GNR the photothermal induced cytotoxicity decreased to ∼50%. The broadening of longitudinal surface plasmon peak of PDDAC‐GNRs and appearance of dark clusters in cells under bright‐field microscope suggested intracellular clustering of PDDAC‐GNRs. In conclusion, despite high PTE and cellular uptake of PDDAC‐GNRs, its intracellular clustering (due to acidic pH) adversely affect the PTT of cancer cells.Inspec keywords: polymer electrolytes, gold, nanorods, cancer, nanomedicine, cellular biophysics, toxicology, radiation therapy, polymer filmsOther keywords: polyelectrolyte coating, photothermal efficiency, gold nanorods, photothermal induced cancer cell damage, cancer photothermal treatment, polystyrene sulphonate, poly di‐allyl di‐methyl ammonium chloride, photothermal conversion efficiency, cellular uptake, photothermal‐induced cytotoxicity, human oral cancer cells, dark clusters, bright‐field microscope, PDDAC‐GNR intracellular clustering, acidic pH, intracellular compartment, Au     

Emerging microfluidic devices for cancer cells/biomarkers manipulation and detection

Circulating tumour cells (CTCs) are active participants in the metastasis process and account for ∼90% of all cancer deaths. As CTCs are admixed with a very large amount of erythrocytes, leukocytes, and platelets in blood, CTCs are very rare, making their isolation, capture, and detection a major technological challenge. Microfluidic technologies have opened‐up new opportunities for the screening of blood samples and the detection of CTCs or other important cancer biomarker‐proteins. In this study, the authors have reviewed the most recent developments in microfluidic devices for cells/biomarkers manipulation and detection, focusing their attention on immunomagnetic‐affinity‐based devices, dielectrophoresis‐based devices, surface‐plasmon‐resonance microfluidic sensors, and quantum‐dots‐based sensors.Inspec keywords: microfluidics, bioMEMS, cancer, cellular biophysics, biomedical equipment, patient diagnosis, tumours, proteins, molecular biophysics, electrophoresis, surface plasmon resonance, quantum dotsOther keywords: quantum‐dot‐based sensors, surface‐plasmon‐resonance microfluidic sensors, dielectrophoresis‐based devices, immunomagnetic‐affinity‐based devices, cancer biomarker‐proteins, CTC detection, blood samples, microfluidic technology, platelets, leukocytes, leukocytes, erythrocytes, cancer deaths, metastasis process, circulating tumour cells, cancer cell‐biomarker detection, cancer cell‐biomarker manipulation, microfluidic devices