Architecture‐dependent robustness in a class of multiple positive feedback loops

Many types of multiple positive feedbacks with each having potentials to generate bistability exist extensively in natural, raising the question of why a particular architecture is present in a cell. In this study, the authors investigate multiple positive feedback loops across three classes: one‐loop class, two‐loop class and three‐loop class, where each class is composed of double positive feedback loop (DPFL) or double negative feedback loop (DNFL) or both. Through large‐scale sampling and robustness analysis, the authors find that for a given class, the homogeneous DPFL circuit (i.e. the coupled circuit that is composed of only DPFLs) is more robust than all the other circuits in generating bistable behaviour. In addition, stochastic simulation shows that the low stable state is more robust than the high stable state in homogeneous DPFL whereas the high‐stable state is more robust than the low‐stable state in homogeneous DNFL circuits. It was argued that this investigation provides insight into the relationship between robustness and network architecture.Inspec keywords: cellular biophysics, feedback, sampling methods, stochastic processesOther keywords: network architecture, low stable state, stochastic simulation, bistable behaviour, homogeneous DPFL circuit, robustness analysis, large‐scale sampling, DNFL, double negative feedback loop, double positive feedback loop, three‐loop class, two‐loop class, one‐loop class, cell architecture, bistability, multiple positive feedback loops, architecture‐dependent robustness     

Time‐invariant biological networks with feedback loops: structural equation models and structural identifiability

Quantitative analyses of biological networks such as key biological parameter estimation necessarily call for the use of graphical models. While biological networks with feedback loops are common in reality, the development of graphical model methods and tools that are capable of dealing with feedback loops is still in its infancy. Particularly, inadequate attention has been paid to the parameter identifiability problem for biological networks with feedback loops such that unreliable or even misleading parameter estimates may be obtained. In this study, the structural identifiability analysis problem of time‐invariant linear structural equation models (SEMs) with feedback loops is addressed, resulting in a general and efficient solution. The key idea is to combine Mason''s gain with Wright''s path coefficient method to generate identifiability equations, from which identifiability matrices are then derived to examine the structural identifiability of every single unknown parameter. The proposed method does not involve symbolic or expensive numerical computations, and is applicable to a broad range of time‐invariant linear SEMs with or without explicit latent variables, presenting a remarkable breakthrough in terms of generality. Finally, a subnetwork structure of the C. elegans neural network is used to illustrate the application of the authors’ method in practice.Inspec keywords: matrix algebra, least squares approximations, statistical analysis, parameter estimation, biologyOther keywords: structural identifiability analysis problem, time‐invariant linear structural equation models, feedback loops, identifiability equations, time‐invariant linear SEMs, time‐invariant biological networks, graphical model methods, parameter identifiability problem, biological parameter estimation, subnetwork structure, C. elegans neural network     

Ferulic acid encapsulated chitosan‐tripolyphosphate nanoparticles attenuate quorum sensing regulated virulence and biofilm formation in Pseudomonas aeruginosa PAO1

Pseudomonas aeruginosa is an opportunistic nosocomial pathogenic microorganism causing majority of acute hospital‐acquired infections and poses a serious public health concern. The persistence of bacterial infection can be attributed to the highly synchronised cell‐to‐cell communication phenomenon, quorum sensing (QS) which regulates the expression of a number of virulence factors and biofilm formation which eventually imparts resistance to the conventional antimicrobial therapy. In this study, the anti‐quorum sensing and anti‐biofilm potential of ferulic acid encapsulated chitosan‐tripolyphosphate nanoparticles (FANPs) was investigated against P. aeruginosa PAO1 and compared with native ferulic acid. Dynamic light scattering and transmission electron microscopic analysis confirmed the synthesis of FANPs with mean diameter of 215.55 nm. FANPs showed significant anti‐quorum sensing activity by downregulating QS‐regulated virulence factors. In addition, FANPs also significantly attenuate the swimming and swarming motility of P. aeruginosa PAO1. The anti‐biofilm efficacy of FANPs as compared to native ferulic acid was established by light and confocal laser scanning microscopic analysis. The promising results of FANPs in attenuating QS highlighted the slow and sustained release of ferulic acid at the target sites with greater efficacy suggesting its application towards the development of anti‐infective agents.Inspec keywords: microorganisms, nanofabrication, nanoparticles, nanomedicine, light scattering, cellular biophysics, drugs, antibacterial activity, drug delivery systems, filled polymers, materials preparationOther keywords: size 215.55 nm, ferulic acid encapsulated chitosan‐tripolyphosphate nanoparticles, dynamic light scattering, QS‐regulated virulence factors, cell‐to‐cell communication phenomenon, nosocomial pathogenic microorganism, anti‐quorum sensing activity, Pseudomonas aeruginosa PAO1, anti‐infective agents, confocal laser scanning microscopic analysis, anti‐biofilm efficacy, transmission electron microscopic analysis, native ferulic acid, FANPs, anti‐biofilm potential, conventional antimicrobial therapy, bacterial infection, acute hospital‐acquired infections, biofilm formation     

Non‐linear feedback control of the p 53 protein–mdm 2 inhibitor system using the derivative‐free non‐linear Kalman filter

It is proven that the model of the p 53–mdm 2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p 53 protein at the desirable levels. To estimate the non‐measurable elements of the state vector describing the p 53–mdm 2 system dynamics, the derivative‐free non‐linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p 53–mdm 2 system, the derivative‐free non‐linear Kalman filter is re‐designed as a disturbance observer. The derivative‐free non‐linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non‐linear model. The proposed non‐linear feedback control and perturbations compensation method for the p 53–mdm 2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.Inspec keywords: proteins, molecular biophysics, biochemistry, Kalman filters, inverse problems, perturbation theoryOther keywords: nonlinear feedback control, p53 protein‐mdm2 inhibitor system, derivative‐free nonlinear Kalman filter, differential flatness theory, protein synthesis loop, diffeomorphism, protein synthesis model, feedback control law, nonmeasurable elements, modelling uncertainties, inverse transformation, nonlinear model, perturbation compensation method, chemotherapy schemes, medication infusion     

Biological pest control using a model‐based robust feedback

Biological control is the artificial manipulation of natural enemies of a pest for its regulation to densities below a threshold for economic damage. The authors address the biological control of a class of pest population models using a model‐based robust feedback approach. The proposed control framework is based on a recursive cascade control scheme exploiting the chained form of pest population models and the use of virtual inputs. The robust feedback is formulated considering the non‐linear model uncertainties via a simple and intuitive control design. Numerical results on three pest biological control problems show that the proposed model‐based robust feedback can regulate the pest population at the desired reference via the manipulation of a biological control action despite model uncertainties.Inspec keywords: cascade control, control system synthesis, nonlinear control systems, feedback, robust control, pest control, manipulatorsOther keywords: biological pest control, artificial manipulation, natural enemies, pest population models, robust feedback approach, recursive cascade control scheme, nonlinear model uncertainties, simple control design, intuitive control design, pest biological control problems, biological control action     

Signal amplification strategy using gold/N ‐trimethyl chitosan/iron oxide magnetic composite nanoparticles as a tracer tag for high‐sensitive electrochemical detection

This study presents a novel signal amplification method for high‐sensitive electrochemical immunosensing. Gold (Au)/N ‐trimethyl chitosan (TMC)/iron oxide (Fe₃ O₄) (shell/shell/core) nanocomposite was used as a tracing tag to label antibody. The tag was shown to be capable of amplifying the recognition signal by high‐density assembly of Au nanoparticles (NPs) on TMC/Fe₃ O₄ particles. The remarkable conductivity of AuNPs provides a feasible pathway for electron transfer. The method was found to be simple, reliable and capable of high‐sensitive detection of human serum albumin as a model, down to 0.2 pg/ml in the range of 0.25–1000 pg/ml. Findings of the present study would create new opportunities for sensitive and rapid detection of various analytes.Inspec keywords: gold, filled polymers, conducting polymers, iron compounds, magnetic particles, nanoparticles, nanocomposites, nanosensors, electrochemical sensors, proteins, molecular biophysics, biomagnetism, biosensorsOther keywords: signal amplification strategy, gold‐N‐trimethyl chitosan‐iron oxide magnetic composite nanoparticles, tracer tag, high‐sensitive electrochemical detection, high‐sensitive electrochemical immunosensing, antibody, high‐density assembly, AuNP conductivity, electron transfer, human serum albumin, FeO‐Au     

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     

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     

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     

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     

Non‐normality can facilitate pulsing in biomolecular circuits

Non‐normality can underlie pulse dynamics in many engineering contexts. However, its role in pulses generated in biomolecular contexts is generally unclear. Here, the authors address this issue using the mathematical tools of linear algebra and systems theory on simple computational models of biomolecular circuits. They find that non‐normality is present in standard models of feedforward loops. They used a generalised framework and pseudospectrum analysis to identify non‐normality in larger biomolecular circuit models, finding that it correlates well with pulsing dynamics. Finally, they illustrate how these methods can be used to provide analytical support to numerical screens for pulsing dynamics as well as provide guidelines for design.Inspec keywords: linear algebra, feedforward, eigenvalues and eigenfunctions, network analysis, molecular biophysicsOther keywords: nonnormality, biomolecular circuits, pulse dynamics, engineering contexts, biomolecular contexts, linear algebra, systems theory, simple computational models, standard models, larger biomolecular circuit models     

Effect of GNP functionalisation and multiple N ‐methylation of β ‐amyloid residue (32–37) on Gram‐positive bacterium

In the previous report, the authors showed the gold nanoparticle (GNP) functionalised multiple N ‐methylated fragments of the residue (32–37) of beta (β)‐amyloid protein (1–42), CGGIGLMVG and CGGGGGIGLMVG toward disruption of β ‐amyloid (1–42), the predominant component of senile plaques. Herein the in vitro antimicrobial activities of both normal and multiple N ‐methylated sequences of CGGIGLMVG and CGGGGGIGLMVG were screened and it was found that all the eight sequences including four (non‐functionalised with GNP) to possess activity against both Gram‐positive [Staphylococcus aureus (ATCC 43300) and Enterococcus faecalis (ATCC 5129)] and Gram‐negative [Escherichia coli (ATCC 35218), Pseudomonas aeruginosa (ATCC 27853) and Klebsiella pneumoniae (ATCC 700603)] bacteria. Among them, N ‐methylated sequences CGGIGLMVG and CGGGGGIGLMVG shown remarkable activity against Gram‐positive bacteria.Inspec keywords: microorganisms, gold, nanoparticles, nanomedicineOther keywords: GNP functionalisation, N‐methylation, β‐amyloid residue, Gram‐positive bacterium, gold nanoparticle functionalised multiple N‐methylated fragments, beta β‐amyloid protein, CGGGGGIGLMVG, Staphylococcus aureus, ATCC 43300, Enterococcus faecalis, ATCC 5129, Escherichia coli, ATCC 35218, Pseudomonas aeruginosa, ATCC 27853, Klebsiella pneumoniae, ATCC 700603, Au     

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     

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     

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