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     

Hierarchical parameter estimation of GRN based on topological analysis

Reverse engineering of gene regulatory network (GRN) is an important and challenging task in systems biology. Existing parameter estimation approaches that compute model parameters with the same importance are usually computationally expensive or infeasible, especially in dealing with complex biological networks.In order to improve the efficiency of computational modeling, the paper applies a hierarchical estimation methodology in computational modeling of GRN based on topological analysis. This paper divides nodes in a network into various priority levels using the graph‐based measure and genetic algorithm. The nodes in the first level, that correspond to root strongly connected components(SCC) in the digraph of GRN, are given top priority in parameter estimation. The estimated parameters of vertices in the previous priority level ARE used to infer the parameters for nodes in the next priority level. The proposed hierarchical estimation methodology obtains lower error indexes while consuming less computational resources compared with single estimation methodology. Experimental outcomes with insilico networks and a realistic network show that gene networks are decomposed into no more than four levels, which is consistent with the properties of inherent modularity for GRN. In addition, the proposed hierarchical parameter estimation achieves a balance between computational efficiency and accuracy.Inspec keywords: biology computing, network theory (graphs), reverse engineering, graph theory, genetics, genetic algorithms, directed graphs, parameter estimationOther keywords: hierarchical parameter estimation, GRN, topological analysis, gene regulatory network, important task, computational systems biology, compute model parameters, complex biological networks, efficient information, model quality, parameter reliability, computational modelling, study divides nodes, priority levels, graph‐based measure, previous priority level, hierarchical estimation methodology obtains, computational resources, single time estimation, insilico network, realistic network show, computational efficiency     

Bond graph modelling of chemoelectrical energy transduction

Energy‐based bond graph modelling of biomolecular systems is extended to include chemoelectrical transduction thus enabling integrated thermodynamically compliant modelling of chemoelectrical systems in general and excitable membranes in particular. Our general approach is illustrated by recreating a well‐known model of an excitable membrane. This model is used to investigate the energy consumed during a membrane action potential thus contributing to the current debate on the trade‐off between the speed of an action potential event and energy consumption. The influx of Na+ is often taken as a proxy for energy consumption; in contrast, this study presents an energy‐based model of action potentials. As the energy‐based approach avoids the assumptions underlying the proxy approach it can be directly used to compute energy consumption in both healthy and diseased neurons. These results are illustrated by comparing the energy consumption of healthy and degenerative retinal ganglion cells using both simulated and in vitro data.Inspec keywords: molecular biophysics, biochemistry, bioelectric potentials, biomembrane transport, sodium, neurophysiology, eyeOther keywords: energy‐based bond graph modelling, chemoelectrical energy transduction, biomolecular systems, integrated thermodynamically compliant modelling, chemoelectrical systems, excitable membranes, membrane action potential, energy consumption, healthy neurons, diseased neurons, healthy retinal ganglion cells, degenerative retinal ganglion cells, Na     

Describing function‐based approximations of biomolecular systems

Mathematical methods provide useful framework for the analysis and design of complex systems. In newer contexts such as biology, however, there is a need to both adapt existing methods as well as to develop new ones. Using a combination of analytical and computational approaches, the authors adapt and develop the method of describing functions to represent the input–output responses of biomolecular signalling systems. They approximate representative systems exhibiting various saturating and hysteretic dynamics in a way that is better than the standard linearisation. Furthermore, they develop analytical upper bounds for the computational error estimates. Finally, they use these error estimates to augment the limit cycle analysis with a simple and quick way to bound the predicted oscillation amplitude. These results provide system approximations that can add more insight into the local behaviour of these systems than standard linearisation, compute responses to other periodic inputs and to analyse limit cycles.Inspec keywords: molecular biophysics, physiological models, approximation theoryOther keywords: describing‐function‐based approximations, mathematical methods, computational approaches, biomolecular signalling systems, hysteretic dynamics, saturating dynamics, analytical upper bounds, computational error estimates, oscillation amplitude     

Transient response characteristics in a biomolecular integral controller

The cellular behaviour of perfect adaptation is achieved through the use of an integral control element in the underlying biomolecular circuit. It is generally unclear how integral action affects the important aspect of transient response in these biomolecular systems, especially in light of the fact that it typically deteriorates the transient response in engineering contexts. To address this issue, the authors investigated the transient response in a computational model of a simple biomolecular integral control system involved in bacterial signalling. They find that the transient response can actually speed up as the integral gain parameter increases. On further analysis, they find that the underlying dynamics are composed of slow and fast modes and the speed‐up of the transient response is because of the speed‐up of the slow‐mode dynamics. Finally, they note how an increase in the integral gain parameter also leads to a decrease in the amplitude of the transient response, consistent with the overall improvement in the transient response. These results should be useful in understanding the overall effect of integral action on system dynamics, particularly for biomolecular systems.Inspec keywords: transient response, molecular biophysics, cellular biophysics, microorganisms, biocontrolOther keywords: transient response characteristics, biomolecular integral controller, cellular behaviour, integral control element, biomolecular circuit, integral action, computational model, biomolecular integral control system, bacterial signalling, integral gain parameter     

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     

Experimental evidence for constraints in amplitude‐timescale co‐variation of a biomolecular pulse generating circuit design

Understanding constraints on the functional properties of biomolecular circuit dynamics, such as the possible variations of amplitude and timescale of a pulse, is an important part of biomolecular circuit design. While the amplitude‐timescale co‐variations of the pulse in an incoherent feedforward loop have been investigated computationally using mathematical models, experimental support for any such constraints is relatively unclear. Here, the authors address this using experimental measurement of an existing pulse generating incoherent feedforward loop circuit realisation in the context of a standard mathematical model. They characterise the trends of co‐variation in the pulse amplitude and rise time computationally by randomly exploring the parameter space. They experimentally measured the co‐variation by varying inducers and found that larger amplitude pulses have a slower rise time. They discuss the gap between the experimental measurements and predictions of the standard model, highlighting model additions and other biological factors that might bridge the gap.Inspec keywords: pulse generators, molecular electronics, integrated circuit designOther keywords: biomolecular pulse, functional properties, biomolecular circuit dynamics, biomolecular circuit design, incoherent feedforward loop, loop circuit realisation, pulse amplitude, amplitude‐timescale covariation, biomolecular pulse generating circuit, biological factors     

Minimum steering node set of complex networks and its applications to biomolecular networks

Many systems of interests in practices can be represented as complex networks. For biological systems, biomolecules do not perform their functions alone but interact with each other to form so‐called biomolecular networks. A system is said to be controllable if it can be steered from any initial state to any other final state in finite time. The network controllability has become essential to study the dynamics of the networks and understand the importance of individual nodes in the networks. Some interesting biological phenomena have been discovered in terms of the structural controllability of biomolecular networks. Most of current studies investigate the structural controllability of networks in notion of the minimum driver node sets (MDSs). In this study, the authors analyse the network structural controllability in notion of the minimum steering node sets (MSSs). They first develop a graph‐theoretic algorithm to identify the MSS for a given network and then apply it to several biomolecular networks. Application results show that biomolecules identified in the MSSs play essential roles in corresponding biological processes. Furthermore, the application results indicate that the MSSs can reflect the network dynamics and node importance in controlling the networks better than the MDSs.Inspec keywords: molecular biophysics, biocontrol, graph theoryOther keywords: graph‐theoretic algorithm, MSS, minimum driver node sets, structural controllability, network dynamics, network controllability, biological systems, biomolecular networks, complex networks, minimum steering node set     

Period–amplitude co‐variation in biomolecular oscillators

The period and amplitude of biomolecular oscillators are functionally important properties in multiple contexts. For a biomolecular oscillator, the overall constraints in how tuning of amplitude affects period, and vice versa, are generally unclear. Here, the authors investigate this co‐variation of the period and amplitude in mathematical models of biomolecular oscillators using both simulations and analytical approximations. The authors computed the amplitude–period co‐variation of 11 benchmark biomolecular oscillators as their parameters were individually varied around a nominal value, classifying the various co‐variation patterns such as a simultaneous increase/decrease in period and amplitude. Next, the authors repeated the classification using a power norm‐based amplitude metric, to account for the amplitudes of the many biomolecular species that may be part of the oscillations, finding largely similar trends. Finally, the authors calculate ‘scaling laws’ of period–amplitude co‐variation for a subset of these benchmark oscillators finding that as the approximated period increases, the upper bound of the amplitude increases, or reaches a constant value. Based on these results, the authors discuss the effect of different parameters on the type of period–amplitude co‐variation as well as the difficulty in achieving an oscillation with large amplitude and small period.Inspec keywords: molecular biophysics, oscillations, biology computing, circadian rhythmsOther keywords: period‐amplitude co‐variation, biomolecular oscillators, mathematical models, analytical approximations, co‐variation patterns, power norm‐based amplitude metric, scaling laws     

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     

Effective implicit finite‐difference method for sensitivity analysis of stiff stochastic discrete biochemical systems

Simulation of cellular processes is achieved through a range of mathematical modelling approaches. Deterministic differential equation models are a commonly used first strategy. However, because many biochemical processes are inherently probabilistic, stochastic models are often called for to capture the random fluctuations observed in these systems. In that context, the Chemical Master Equation (CME) is a widely used stochastic model of biochemical kinetics. Use of these models relies on estimates of kinetic parameters, which are often poorly constrained by experimental observations. Consequently, sensitivity analysis, which quantifies the dependence of systems dynamics on model parameters, is a valuable tool for model analysis and assessment. A number of approaches to sensitivity analysis of biochemical models have been developed. In this study, the authors present a novel method for estimation of sensitivity coefficients for CME models of biochemical reaction systems that span a wide range of time‐scales. They make use of finite‐difference approximations and adaptive implicit tau‐leaping strategies to estimate sensitivities for these stiff models, resulting in significant computational efficiencies in comparison with previously published approaches of similar accuracy, as evidenced by illustrative applications.Inspec keywords: biochemistry, sensitivity analysis, stochastic processes, cellular biophysics, probability, fluctuations, master equation, reaction kinetics, finite difference methodsOther keywords: effective implicit finite‐difference method, sensitivity analysis, stiff stochastic discrete biochemical systems, cellular processes, mathematical modelling, deterministic differential equation models, inherently probabilistic‐stochastic models, random fluctuations, Chemical Master Equation, biochemical kinetics, kinetic parameter estimation, systems dynamics, CME models, biochemical reaction systems, finite‐difference approximations, adaptive implicit tau‐leaping strategies, computational efficiencies     

Design of binary subtractor using actin quantum cellular automata

Actin is a biological protein that provides support to the cellular structure and plays a crucial role in cytoskeletal and intra‐cellular signalling events. Logic circuits can be designed with actin filaments with the help of actin quantum automata. The authors use a rule (4,27) to implement some novel designs of logic subtractor circuits on this automata to achieve the difference in two binary bits. Logic design of both half and full binary subtractors is proposed in this study. Actin‐based quantum cellular automata can be used in different combinations of input to get optimised results from the circuits. The authors focus on consolidating the designs inside single automata block to generate output in a less number of timesteps and less overheads. The designs are simulated with Simulink and this way output is verified for these different design approaches. Reliability and fault‐tolerance check is another interesting part of this study. To get a better idea of the optimisation achieved, the authors have also presented a comparative study between the proposed designs in terms of circuit size and efficiency. With all these parameters involved, this study explores opportunities for future implementation of unconventional computing in nano‐scale and cost‐effective bio‐molecular networks.Inspec keywords: cellular automata, molecular biophysics, molecular configurations, biology computing, proteins, logic circuits, biomolecular electronics, cellular biophysics, fault toleranceOther keywords: binary subtractor, actin quantum cellular automata, biological protein, cellular structure, cytoskeletal signalling events, intracellular signalling events, actin filaments, logic subtractor circuits, binary bits, logic design, half binary subtractors, full binary subtractors, optimised results, single automata block, Simulink, design approaches, reliability, fault‐tolerance check, circuit size, circuit efficiency, unconventional computing, nanoscale biomolecular networks, cost‐effective biomolecular networks     

MPGraph: multi‐view penalised graph clustering for predicting drug–target interactions

Identifying drug–target interactions has been a key step for drug repositioning, drug discovery and drug design. Since it is expensive to determine the interactions experimentally, computational methods are needed for predicting interactions. In this work, the authors first propose a single‐view penalised graph (SPGraph) clustering approach to integrate drug structure and protein sequence data in a structural view. The SPGraph model does clustering on drugs and targets simultaneously such that the known drug–target interactions are best preserved in the clustering results. They then apply the SPGraph to a chemical view with drug response data and gene expression data in NCI‐60 cell lines. They further generalise the SPGraph to a multi‐view penalised graph (MPGraph) version, which can integrate the structural view and chemical view of the data. In the authors'' experiments, they compare their approach with some comparison partners, and the results show that the SPGraph could improve the prediction accuracy in a small scale, and the MPGraph can achieve around 10% improvements for the prediction accuracy. They finally give some new targets for 22 Food and Drug Administration approved drugs for drug repositioning, and some can be supported by other references.Inspec keywords: graphs, drug delivery systems, drugs, proteins, molecular biophysics, molecular configurations, optimisation, eigenvalues and eigenfunctions, Laplace equations, cancer, cellular biophysics, gene therapy, medical computingOther keywords: MPGraph, multiview penalised graph clustering, drug‐target interactions, drug repositioning, drug discovery, drug design, computational methods, single‐view penalized graph clustering approach, drug structure, protein sequence data, SPGraph model, optimisation problem, spectral clustering, eigenvalue decomposition, Laplacian model, gene expression data, NCI‐60 cell lines     

One‐step method for the preparation of poly(methyl methacrylate) modified titanium‐bioactive glass three‐dimensional scaffolds for bone tissue engineering

A novel three‐dimensional (3D) titanium (Ti)‐doping meso‐macroporous bioactive glasses (BGs)/poly(methyl methacrylate) (PMMA) composite was synthesised using PMMA and EO₂₀ PO₇₀ EO₂₀ (P₁₂₃) as the macroporous and mesoporous templates, respectively. Unlike the usual calcination method, the acid steam technique was used to improve the polycondensation of Ti‐BGs, and then PMMA was partially extracted via chloroform to induce the macroporous structure. Simultaneously, the residual PMMA which remained in the wall enhanced the compressive strength to 2.4 MPa (0.3 MPa for pure BGs). It is a simple and green method to prepare the macro‐mesoporous Ti‐BGs/PMMA. The materials showed the 3D interconnected hierarchical structure (250 and 3.4 nm), making the fast inducing‐hydroxyapatite growth and the controlled drug release. Besides mentioned above, the good antimicrobial property and biocompatible of the scaffold also ensure it is further of clinical use. Herein, the fabricated materials are expected to have potential application on bone tissue regeneration.Inspec keywords: titanium, bone, tissue engineering, glass, materials preparation, biomedical materials, polymers, porous materials, drug delivery systems, nanomedicineOther keywords: poly(methyl methacrylate), PMMA preparation, 3D titanium‐bioactive glass scaffold, bone tissue engineering, titanium‐doping mesomacroporous bioactive glass, bioactive glass‐PMMA composite, macroporous template, mesoporous template, calcination method, acid steam technique, titanium‐bioactive glass polycondensation, macroporous structure, green method, macromesoporous titanium‐bioactive glass‐PMMA, 3D interconnected hierarchical structure, fast inducing‐hydroxyapatite growth, controlled drug release, bone tissue regeneration, Ti     

Identifying latent dynamic components in biological systems

In computational systems biology, the general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. In the past, many such models have been formulated for different biological applications. The authors consider the scenario where a given model fails to predict a set of observations with acceptable accuracy and ask the question whether this is because of the model lacking important external regulations. Real‐world examples for such entities range from microRNAs to metabolic fluxes. To improve the prediction, they propose an algorithm to systematically extend the network by an additional latent dynamic variable which has an exogenous effect on the considered network. This variable''s time course and influence on the other species is estimated in a two‐step procedure involving spline approximation, maximum‐likelihood estimation and model selection. Simulation studies show that such a hidden influence can successfully be inferred. The method is also applied to a signalling pathway model where they analyse real data and obtain promising results. Furthermore, the technique can be employed to detect incomplete network structures.Inspec keywords: biology computing, RNA, splines (mathematics), maximum likelihood estimation, approximation theory, biochemistryOther keywords: latent dynamic components, biological systems, computational system biology, regulatory models, multivariate readouts, biological applications, external regulations, real‐world examples, microRNA, metabolic fluxes, latent dynamic variables, variable time course, two‐step procedure, spline approximation, maximum‐likelihood estimation, model selection, signalling pathway model, real data, incomplete network structures     

Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors

Dopamine (DA) is an important neurotransmitter for multiple brain functions, and dysfunctions of the dopaminergic system are implicated in neurological and neuropsychiatric disorders. Although the dopaminergic system has been studied at multiple levels, an integrated and efficient computational model that bridges from molecular to neuronal circuit level is still lacking. In this study, the authors aim to develop a realistic yet efficient computational model of a dopaminergic pre‐synaptic terminal. They first systematically perturb the variables/substrates of an established computational model of DA synthesis, release and uptake, and based on their relative dynamical timescales and steady‐state changes, approximate and reduce the model into two versions: one for simulating hourly timescale, and another for millisecond timescale. They show that the original and reduced models exhibit rather similar steady and perturbed states, whereas the reduced models are more computationally efficient and illuminate the underlying key mechanisms. They then incorporate the reduced fast model into a spiking neuronal model that can realistically simulate the spiking behaviour of dopaminergic neurons. In addition, they successfully include autoreceptor‐mediated inhibitory current explicitly in the neuronal model. This integrated computational model provides the first step toward an efficient computational platform for realistic multiscale simulation of dopaminergic systems in in silico neuropharmacology.Inspec keywords: neurophysiology, organic compounds, brain, medical disordersOther keywords: integrated dopaminergic neuronal model, reduced intracellular processes, inhibitory autoreceptors, neurotransmitter, multiple brain functions, dysfunctions, neurological disorders, neuropsychiatric disorders, computational model, molecular level, neuronal‐circuit level, dopaminergic presynaptic terminal, relative dynamical timescales, steady perturbed states, reduced fast model, spiking neuronal model, autoreceptor‐mediated inhibitory current, integrated computational model, efficient computational platform, realistic multiscale simulation, in silico neuropharmacology