Integer programming‐based method for observability of singleton attractors in Boolean networks

Boolean network (BN) is a popular mathematical model for revealing the behaviour of a genetic regulatory network. Furthermore, observability, an important network feature, plays a significant role in understanding the underlying network. Several studies have been done on analysis of observability of BNs and complex networks. However, the observability of attractor cycles, which can serve as biomarker detection, has not yet been addressed in the literature. This is an important, interesting and challenging problem that deserves a detailed study. In this study, a novel problem was first proposed on attractor observability in BNs. Identification of the minimum set of consecutive nodes can be used to discriminate different attractors. Furthermore, it can serve as a biomarker for different disease types (represented as different attractor cycles). Then a novel integer programming method was developed to identify the desired set of nodes. The proposed approach is demonstrated and verified by numerical examples. The computational results further illustrates that the proposed model is effective and efficient.Inspec keywords: integer programming, Boolean algebra, complex networks, diseasesOther keywords: disease, consecutive nodes, biomarker detection, attractor cycles, complex networks, genetic regulatory network, mathematical model, Boolean networks, singleton attractors, integer programming‐based method     

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     

Multi‐bit Boolean model for chemotactic drift of Escherichia coli

Dynamic biological systems can be modelled to an equivalent modular structure using Boolean networks (BNs) due to their simple construction and relative ease of integration. The chemotaxis network of the bacterium Escherichia coli (E. coli ) is one of the most investigated biological systems. In this study, the authors developed a multi‐bit Boolean approach to model the drifting behaviour of the E. coli chemotaxis system. Their approach, which is slightly different than the conventional BNs, is designed to provide finer resolution to mimic high‐level functional behaviour. Using this approach, they simulated the transient and steady‐state responses of the chemoreceptor sensory module. Furthermore, they estimated the drift velocity under conditions of the exponential nutrient gradient. Their predictions on chemotactic drifting are in good agreement with the experimental measurements under similar input conditions. Taken together, by simulating chemotactic drifting, they propose that multi‐bit Boolean methodology can be used for modelling complex biological networks. Application of the method towards designing bio‐inspired systems such as nano‐bots is discussed.Inspec keywords: cell motility, microorganisms, Boolean functionsOther keywords: multibit Boolean approach, conventional BNs, high‐level functional behaviour, steady‐state responses, chemoreceptor sensory module, drift velocity, chemotactic drifting, multibit Boolean methodology, complex biological networks, bio‐inspired systems, multibit Boolean model, chemotactic drift, dynamic biological systems, equivalent modular structure, Boolean networks, simple construction, chemotaxis network, bacterium Escherichia coli, biological systems     

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     

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     

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     

Dynamics and robustness of the cardiac progenitor cell induced pluripotent stem cell network during cell phenotypes transition

Robustness is a fundamental characteristic of biological systems since all living systems need to adapt to internal or external perturbations, unpredictable environments, stochastic events and unreliable components, and so on. A long‐term challenge in systems biology is to reveal the origin of robustness underlying molecular regulator network. In this study, a simple Boolean model is used to investigate the global dynamic properties and robustness of cardiac progenitor cell (CPC) induced pluripotent stem cell network that governs reprogramming and directed differentiation process. It is demonstrated that two major attractors correspond to source and target cell phenotypes, respectively, and two dominating attracting trajectories characterise the biological pathways between two major cell phenotypes. In particular, the experimentally observed transition between different cell phenotypes can be reproduced and explained theoretically. Furthermore, the robustness of major attractors and trajectories is largely maintained with respect to small perturbations to the network. Taken together, the CPC‐induced pluripotent stem cell network is extremely robustly designed for their functions.Inspec keywords: cellular biophysics, Boolean functions, perturbation theory, molecular biophysics, cardiologyOther keywords: cardiac progenitor cell induced pluripotent stem cell network, cell phenotypes transition, biological systems, living systems, internal perturbations, external perturbations, unpredictable environments, stochastic events, unreliable components, long‐term challenge, systems biology, molecular regulator network, Boolean model, global dynamic properties, directed differentiation process, CPC‐induced pluripotent stem cell network     

Small RNA‐mediated switch‐like regulation in bacterial quorum sensing

Quorum sensing (QS) is a signalling mechanism by which bacteria produce, release and then detect and respond to changes in their density and biosignals called autoinducers (AIs). There are multiple feedback loops in the QS system of Vibrio harveyi. However, how these feedback loops function to control signal processing remains unclear. In this study, the authors present a computational model for the switch‐like regulation of signal transduction by small regulatory RNA‐mediated QS based on intertwined network involving AIs, LuxO, LuxU, Qrr sRNAs and LuxR. In agreement with experimental observations, the model suggests that different feedbacks play critical roles in the switch‐like regulation. The authors results reveal that V. harveyi uses multiple feedbacks to precisely control signal transduction.Inspec keywords: biocommunications, biocontrol, biology computing, cellular biophysics, physiological models, RNAOther keywords: RNA‐mediated switch‐like regulation, bacterial quorum sensing, signaling mechanism, autoinducers, Vibrio harveyi, feedback loops function, signal processing control, switch‐like regulation     

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     

Structural and practical identifiability analysis of S‐system

In the field of systems biology, biological reaction networks are usually modelled by ordinary differential equations. A sub‐class, the S‐systems representation, is a widely used form of modelling. Existing S‐systems identification techniques assume that the system itself is always structurally identifiable. However, due to practical limitations, biological reaction networks are often only partially measured. In addition, the captured data only covers a limited trajectory, therefore data can only be considered as a local snapshot of the system responses with respect to the complete set of state trajectories over the entire state space. Hence the estimated model can only reflect partial system dynamics and may not be unique. To improve the identification quality, the structural and practical identifiablility of S‐system are studied. The S‐system is shown to be identifiable under a set of assumptions. Then, an application on yeast fermentation pathway was conducted. Two case studies were chosen; where the first case is based on a larger state trajectories and the second case is based on a smaller one. By expanding the dataset which span a relatively larger state space, the uncertainty of the estimated system can be reduced. The results indicated that initial concentration is related to the practical identifiablity.Inspec keywords: biochemistry, differential equations, microorganisms, cellular biophysics, fermentationOther keywords: structural identifiability analysis, practical identifiability analysis, S‐system, system biology, biological reaction networks, ordinary differential equations, local snapshot, state trajectories, estimated model, partial system dynamics, identification quality, yeast fermentation pathway, relatively larger state space     

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     

Adaptive modelling of gene regulatory network using Bayesian information criterion‐guided sparse regression approach

Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)‐guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l ₁ ‐norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual. Especially, the proposed method provides a clear interpretation of combinatorial regulations of gene expression by optimally extracting regulation coordination for a given target gene. Experimental results on both simulation data and real‐world microarray data demonstrate the competent performance of discovering regulatory relationships in GRN reconstruction.Inspec keywords: genetics, Bayes methods, genomics, regression analysis, inference mechanisms, bioinformaticsOther keywords: adaptive modelling, gene regulatory network, Bayesian information criterion‐guided sparse regression approach, GRN, microarray expression data, systems biology, GRN reconstruction, optimisation, l₁ ‐norm regularisation     

Inferring non‐synonymous single‐nucleotide polymorphisms‐disease associations via integration of multiple similarity networks

Detecting associations between human genetic variants and their phenotypic effects is a significant problem in understanding genetic bases of human‐inherited diseases. The focus is on a typical type of genetic variants called non‐synonymous single nucleotide polymorphisms (nsSNPs), whose occurrence may potentially alter the structures of proteins, affecting functions of proteins, and thereby causing diseases. Most of the existing methods predict associations between nsSNPs and diseases based on features derived from only protein sequence and/or structure information, and give no information about which specific disease an nsSNP is associated with. To cope with these problems, the identification of nsSNPs that are associated with a specific disease from a set of candidate nsSNPs as a binary classification problem has been formulated. A new approach has been adopted for predicting associations between nsSNPs and diseases based on multiple nsSNP similarity networks and disease phenotype similarity networks. With a series of comprehensive validation experiments, it has been demonstrated that the proposed method is effective in both recovering the nsSNP‐disease associations and inferring suspect disease‐associated nsSNPs for both diseases with known genetic bases and diseases of unknown genetic bases.Inspec keywords: diseases, genetics, polymorphism, proteinsOther keywords: nonsynonymous single‐nucleotide polymorphisms, disease associations, multiple similarity network integration, human genetic variants, phenotypic effects, human‐inherited diseases, nonsynonymous single nucleotide polymorphisms, proteins, protein sequence, structure information, candidate nsSNP, binary classification problem, disease phenotype similarity networks, unknown genetic bases     

Graphics processing unit‐based alignment of protein interaction networks

Network alignment is an important bridge to understanding human protein–protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large‐scale networks via sequential computing. In this study, the typical Hungarian‐Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2‐nearest neighbours (HGA‐2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA‐2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA‐2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large‐scale networks are considered. By using HGA‐2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods.Inspec keywords: graphics processing units, proteins, molecular biophysics, genetics, microorganisms, medical computing, bioinformaticsOther keywords: graphics processing unit‐based alignment, protein interaction networks, network alignment, human protein–protein interactions, Hungarian‐Greedy algorithm, GPU acceleration, gene ontology terms, phylogenetic trees reconstruction, herpes viruses     

Synthesis of silver nanoparticles by Bacillus subtilis T‐1 growing on agro‐industrial wastes and producing biosurfactant

In this study, culture supernatnats of Bacillus subtilis T‐1 growing on brewery effluents and molasses was used for silver nanoparticles (Ag‐NPs) synthesis. The biosurfactant production of B. subtilis T‐1 was confirmed by the detection of genes in the genome and by the identification of the product in the supernatants. The genes for synthesis of surfactin (sfp, srfAA) and iturin (ituC) were noted by PCR reactions. Also, in examined culture supernatants the presence of C₁₃, C₁₄ and C₁₅ surfactin homologues with the sodiated molecules [M + Na]⁺ at m /z 1030, 1044 and 1058 was confirmed using LC/MS/MS analysis. The formation of NPs in the culture supernatants was confirmed by UV–vis spectroscopy. The dynamic light scattering measurements and transmission electron microscopy images showed the nanometric sizes of the biosynthesised Ag‐NPs which ranged from several nm to several tens of nm depending on the used culture supernatant. Biological properties of Ag‐NPs were evaluated by binding of Ag‐NPs with DNA isolated from the Escherichia coli ATCC 25922 and B. subtilis ATCC 6633. Biogenic Ag‐NPs were actively bound to DNA in increased concentration which could be the one important mode of antibacterial action of the Ag‐NPs.Inspec keywords: silver, nanoparticles, nanofabrication, materials preparation, microorganisms, antibacterial activity, industrial waste, agrochemicals, surfactants, breweries, genomics, genetics, chromatography, mass spectroscopic chemical analysis, ultraviolet spectroscopy, visible spectroscopy, spectrochemical analysis, light scattering, transmission electron microscopy, DNA, bonds (chemical), biochemistry, molecular biophysics, nanobiotechnology, biological techniques, particle size, enzymesOther keywords: silver nanoparticle synthesis, Bacillus subtilis T‐1 growth, agro‐industrial waste, biosurfactant production, brewery effluent, molasses, Ag‐NP synthesis, B. subtilis T‐1, gene detection, genome, supernatant product identification, surfactin synthesis, sfp, srfAA, iturin synthesis, ituC, PCR reaction, C₁₃ surfactin homologue, C₁₄ surfactin homologue, C₁₅ surfactin homologue, sodiated molecules, LC‐MS‐MS analysis, UV‐vis spectroscopy, dynamic light scattering measurement, transmission electron microscopy image, Ag‐NP nanometric size range, Ag‐NP biosynthesis, used culture supernatant dependence, biological properties, DNA isolation, Escherichia coli ATCC 25922, B. subtilis ATCC 6633, biogenic Ag‐NP‐DNA binding, Ag‐NP antibacterial action, Ag     

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      

Photo‐induced Leishmania DNA degradation by silver‐doped zinc oxide nanoparticle: an in‐vitro approach

Recently, the authors reported newly synthesised polyethylene glycol (PEG)ylated silver (9%)‐doped zinc oxide nanoparticle (doped semiconductor nanoparticle (DSN)) which has high potency for killing Leishmania tropica by producing reactive oxygen species on exposure to sunlight. The current report is focused on Leishmania DNA interaction and damage caused by the DSN. Here, we showed that the damage to Leishmania DNA was indirect, as the DSN was unable to interact with the DNA in intact Leishmania cell, indicating the incapability of PEGylated DSN to cross the nucleus barrier. The DNA damage was the result of high production of singlet oxygen on exposure to sunlight. The DNA damage was successfully prevented by singlet oxygen scavenger (sodium azide) confirming involvement of the highly energetic singlet oxygen in the DNA degradation process.Inspec keywords: silver, zinc compounds, nanoparticles, nanomedicine, DNA, microorganisms, cellular biophysics, biomedical engineeringOther keywords: photo‐induced Leishmania DNA degradation, PEGylated silver‐doped zinc oxide nanoparticle, Leishmania tropica, reactive oxygen species, sunlight, Leishmania DNA interaction, Leishmania cell, DNA damage, singlet oxygen scavenger, sodium azide, DNA degradation process, ZnO:Ag