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     

Algorithm to identify the optimal perturbation based on the net basin‐of‐state of perturbed states in Boolean network

Boolean networks are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long‐term behavior of systems. Here, the authors investigate the 1 bit perturbation, which falls under the category of structural intervention. The authors’ idea is that, if and only if a perturbed state evolves from a desirable attractor to an undesirable attractor or from an undesirable attractor to a desirable attractor, then the size of basin of attractor of a desirable attractor may decrease or increase. In this case, if the authors obtain the net BOS of the perturbed states, they can quickly obtain the optimal 1 bit perturbation by finding the maximum value of perturbation gain. Results from both synthetic and real biological networks show that the proposed algorithm is not only simpler and but also performs better than the previous basin‐of‐states (BOS)‐based algorithm by Hu et al..Inspec keywords: perturbation theory, genetics, Boolean functionsOther keywords: optimal perturbation, perturbed states, Boolean network, gene regulatory networks, basin‐of‐states‐based algorithm, state‐transition diagram, structural intervention, perturbation gain, synthetic biological networks, real biological networks, 1 bit perturbation     

Bogdanov–Takens bifurcation in a neutral BAM neural networks model with delays

In this study, the authors first discuss the existence of Bogdanov–Takens and triple zero singularity of a five neurons neutral bidirectional associative memory neural networks model with two delays. Then, by utilising the centre manifold reduction and choosing suitable bifurcation parameters, the second‐order and the third‐order normal forms of the Bogdanov–Takens bifurcation for the system are obtained. Finally, the obtained normal form and numerical simulations show some interesting phenomena such as the existence of a stable fixed point, a pair of stable non‐trivial equilibria, a stable limit cycles, heteroclinic orbits, homoclinic orbits, coexistence of two stable non‐trivial equilibria and a stable limit cycles in the neighbourhood of the Bogdanov–Takens bifurcation critical point.Inspec keywords: neurophysiology, neural nets, bifurcation, delays, critical pointsOther keywords: Bogdanov‐Takens bifurcation critical point, neutral BAM neural networks, bidirectional associative memory, delays, triple zero singularity, neurons, centre manifold reduction, bifurcation parameters, second‐order normal forms, third‐order normal forms, numerical simulations, stable fixed point, stable nontrivial equilibria, stable limit cycles, heteroclinic orbits, homoclinic orbits     

Two faces of competition: target‐mediated reverse signalling in microRNA and mitogen‐activated protein kinase regulatory networks

Biomolecular regulatory networks are organised around hubs, which can interact with a large number of targets. These targets compete with each other for access to their common hubs, but whether the effect of this competition would be significant in magnitude and in function is not clear. In this review, the authors discuss recent in vivo studies that analysed the system level retroactive effects induced by target competition in microRNA and mitogen‐activated protein kinase regulatory networks. The results of these studies suggest that downstream targets can regulate the overall state of their upstream regulators, and thus cannot be ignored in analysing biomolecular networks.Inspec keywords: reviews, RNA, molecular biophysics, enzymesOther keywords: target‐mediated reverse signalling, mitogen‐activated protein kinase regulatory networks, biomolecular regulatory networks, microRNA regulatory networks, review, in vivo study     

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     

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     

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     

Modelling epigenetic regulation of gene expression in 12 human cell types reveals combinatorial patterns of cell‐type‐specific genes

The maintenance of the diverse cell types in a multicellular organism is one of the fundamental mysteries of biology. Modelling the dynamic regulatory relationships between the histone modifications and the gene expression across the diverse cell types is essential for the authors to understand the mechanisms of the epigenetic regulation. Here, the authors thoroughly assessed the histone modification enrichment profiles at the promoters and constructed quantitative models between the histone modification abundances and the gene expression in 12 human cell types. The author''s results showed that the histone modifications at the promoters exhibited remarkably cell‐type‐dependent variability in the cell‐type‐specific (CTS) genes. They demonstrated that the variable profiles of the modifications are highly predictive for the dynamic changes of the gene expression across all the cell types. Their findings revealed the close relationship between the combinatorial patterns of the histone modifications and the CTS gene expression. They anticipate that the findings and the methods they used in this study could provide useful information for the future studies of the regulatory roles of the histone modifications in the CTS genes.Inspec keywords: cellular biophysics, genetics, genomics, physiological models, proteinsOther keywords: CTS gene expression, variable profiles, cell‐type‐dependent variability, histone modification abundances, constructed quantitative models, promoters, histone modification enrichment profiles, dynamic regulatory relationship modelling, biology, multicellular organism, cell‐type‐specific genes, combinatorial patterns, human cell types, epigenetic regulation modelling     

In situ synthesised TiO2 ‐chitosan‐chondroitin 4–sulphate nanocomposites for bone implant applications

The artificial materials for bone implant applications are gaining more importance in the recent years. The series titania‐chitosan‐chondroitin 4–sulphate nanocomposites of three different concentrations (2:1:x, where x ‐ 0.125, 0.25, 0.5) have been synthesised by in situ sol–gel method and characterised by various techniques. The particle size of the nanocomposites ranges from 30–50 nm. The bioactivity, swelling nature, and the antimicrobial nature of the nanocomposites were investigated. The swelling ability and bioactivity of the composites is significantly greater and they possess high zone of inhibition against the microorganisms such as Staphylococcus aureus and Escherichia coli. The cell viability of the nanocomposites were evaluated by using MG‐63 and observed the composites possess high cell viability at low concentration. The excellent bioactivity and biocompatibility makes these nanocomposites a promising biomaterial for bone implant applications.Inspec keywords: titanium compounds, filled polymers, nanocomposites, bone, orthopaedics, biomedical materials, sol‐gel processing, nanofabrication, particle size, swelling, microorganisms, cellular biophysics, nanomedicine, prostheticsOther keywords: in situ synthesised TiO₂ ‐chitosan‐chondroitin 4‐sulphate nanocomposites, bone implant applications, artificial materials, in situ sol‐gel method, particle size, swelling nature, antimicrobial nature, microorganisms, Staphylococcus aureus, Escherichia coli, cell viability, MG‐63, biomaterial, size 30 nm to 50 nm, TiO₂      

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     

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     

DOC‐LS,a new liposome for dermal delivery,and its endocytosis by HaCaT and CCC‐ESF‐1 cells

The main objective of this work was to investigate the uptake channels of skin cells through which coumarin 6, transported by deoxycholate‐mediated liposomes (DOC‐LS), was internalised; this was also compared against the action of conventional LS. Coumarin 6‐loaded DOC‐LS and LS were characterised for size distribution, zeta potential, and shape, and analysed in vitro in human epidermal immortal keratinocyte (HaCaT) (epidermal) and human embryonic skin fibroblast (CCC‐ESF‐1) (dermal) cell lines. Various endocytosis inhibitors were incubated with cells treated with the nanocarriers. Flow cytometry results indicated that HaCaT and CCC‐ESF‐1 cells internalise the tested preparations through pinocytotic vesicles, macropinocytosis, clathrin‐mediated endocytic pathways, and via lysosomes, which consume a considerable amount of energy. The endocytosis pathways of DOC‐LS and LS showed no difference. This study provides a basis for the application of LS being combined with a microneedle system for efficient intracellular drug delivery, targeting cutaneous histocyte disorders.Inspec keywords: drugs, nanoparticles, lipid bilayers, nanomedicine, biomedical materials, electrokinetic effects, biomembrane transport, drug delivery systems, skin, organic compoundsOther keywords: dermal delivery, CCC‐ESF‐1 cells, skin cells, deoxycholate‐mediated liposomes, coumarin 6‐loaded DOC‐LS, endocytosis inhibitors, clathrin‐mediated endocytic pathways, endocytosis pathways, HaCaT cell lines, size distribution, zeta potential, nanocarriers, flow cytometry, pinocytotic vesicles, macropinocytosis, microneedle system, efficient intracellular drug delivery, targeting cutaneous histocyte disorders     

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