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     

Identification of self‐regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data

Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self‐regulatory interactions. The values of gene co‐expressions could help in identifying co‐regulations among them. The proposed approach aims at computing the differences in variances of co‐expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co‐variances using principal component analysis (PCA) to predict an asymmetric and non‐diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time‐series RTX therapy, Arabidopsis thaliana, DREAM‐3, and DREAM‐8 datasets, in comparison with existing state‐of‐the‐art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self‐regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.Inspec keywords: molecular biophysics, principal component analysis, genetics, biology computing, reverse engineeringOther keywords: controlling regulatory agents, interacting genes, unlinked network components, self‐regulatory interactions, gene pair regulatory interactions, self‐regulatory network motifs, reverse engineering gene regulatory networks, microarray gene expression data, diversified computational approaches, symmetric interaction, diagonal interaction, gene pairs, gene co‐expressions, co‐expressed genes, mean expressions, gene regulatory expressions, asymmetric gene regulatory interactions     

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     

Genetic programming‐based approach to elucidate biochemical interaction networks from data

Biochemical systems are characterised by cyclic/reversible reciprocal actions, non‐linear interactions and a mixed relationship structures (linear and non‐linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive‐based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming‐based causality detection (GPCD) methodology which blends evolutionary computation‐based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the ‘interaction gaps’ which were missed by other methods.Inspec keywords: autoregressive processes, biochemistry, biology computing, genetic algorithms, parameter estimation, transfer functionsOther keywords: elucidate biochemical interaction networks, biochemical systems, cyclic‐reversible reciprocal actions, nonlinear interactions, autoregressive‐based modelling, granger causality, transfer function, canonical variate analysis, genetic programming‐based causality detection methodology, GPCD methodology, mathematical model, parameter estimation methods     

Polypyrrole‐modified magnetic nanoparticles and high‐performance liquid chromatography for determination of glibenclamide from biological fluids

In this research, the successful application of polypyrrole (PPy)‐modified magnetic nanoparticles (NPs) is described as an efficient adsorbent for the extraction and the preconcentration of glibenclamide (GB). To measure it in biological fluids samples, HPLC‐UV detection was used. First, iron oxide NPs were prepared by coprecipitation procedure and then their surface was modified by PPy monomers. Characteristics of Fe₃ O₄ @PPy NPs were investigated by FTIR technique and NP size studied with scanning electron microscopy. The vibrating sample magnetometer was used to characterise the magnetic properties of the prepared modified NPs. The affecting parameters in extraction including analyte sorption time, analyte desorption time, ionic strength, sample volume, pH, eluent type, eluent amount, and amount of Fe₃ O₄ @PPy NPs were investigated and optimised. The linear range of the proposed method is 0.2–700.0 μg l⁻¹ and the limit of detection is 0.1 μg l ^{− 1}. The relative standard deviation for five replicate analyses was 3.9. Finally, the proposed procedure was successfully employed for preconcentration and determination of GB in biological fluids.Inspec keywords: nanofabrication, pH, nanomagnetics, desorption, iron compounds, chromatography, adsorption, spectrochemical analysis, nanoparticles, magnetic particles, scanning electron microscopy, Fourier transform infrared spectraOther keywords: polypyrrole‐modified magnetic nanoparticles, high‐performance liquid chromatography, glibenclamide, preconcentration, biological fluids samples, HPLC‐UV detection, iron oxide NPs, coprecipitation procedure, PPy monomers, scanning electron microscopy, vibrating sample magnetometer, magnetic properties, analyte desorption time, analyte sorption time, biological fluids     

Development of a sustainable route for the production of high‐fructose syrup from the polyfructan inulin

The authors used mesoporous silica microspheres as a support for the immobilization of inulinase from Aspergillus brasiliensis MTCC 1344 by the process of cross‐linking. Under optimized operating conditions of pH 6.0, particle/enzyme ratio of 2.0:1.0 and glutaraldehyde concentration of 7 mM, a maximum immobilization yield of 90.7% was obtained after a cross‐linking time of 12.25 h. Subsequently, the cross‐linked inulinase was utilized for the hydrolysis of 5% inulin, and a maximum fructose concentration of 31.7 g/L was achieved under the optimum conditions of pH 6.0 and temperature 60°C in 3 h. Furthermore, on performing reusability studies during inulin hydrolysis, it was observed that the immobilized inulinase could be reused up to 10 subsequent cycles of hydrolysis, thus providing a facile and commercially attractive process of high‐fructose syrup production.     

A variable‐selection control chart via penalized likelihood and Gaussian mixture model for multimodal and high‐dimensional processes

With the development of the sensor network and manufacturing technology, multivariate processes face a new challenge of high‐dimensional data. However, traditional statistical methods based on small‐ or medium‐sized samples such as T² monitoring statistics may not be suitable because of the “curse of dimensionality” problem. To overcome this shortcoming, some control charts based on the variable‐selection (VS) algorithms using penalized likelihood have been suggested for process monitoring and fault diagnosis. Although there has been much effort to improve VS‐based control charts, there is usually a common distributional assumption that in‐control observations should follow a single multivariate Gaussian distribution. However, in current manufacturing processes, processes can have multimodal properties. To handle the high‐dimensionality and multimodality, in this study, a VS‐based control chart with a Gaussian mixture model (GMM) is proposed. We extend the VS‐based control chart framework to the process with multimodal distributions, so that the high‐dimensionality and multimodal information in the process can be better considered.     

Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data

The effect of meal on blood glucose concentration is a key issue in diabetes mellitus because its estimation could be very useful in therapy decisions. In the case of type 1 diabetes mellitus (T1DM), the therapy based on automatic insulin delivery requires a closed‐loop control system to maintain euglycaemia even in the postprandial state. Thus, the mathematical modelling of glucose metabolism is relevant to predict the metabolic state of a patient. Moreover, the eating habits are characteristic of each person, so it is of interest that the mathematical models of meal intake allow to personalise the glycaemic state of the patient using therapy historical data, that is, daily measurements of glucose and records of carbohydrate intake and insulin supply. Thus, here, a model of glucose metabolism that includes the effects of meal is analysed in order to establish criteria for data‐based personalisation. The analysis includes the sensitivity and identifiability of the parameters, and the parameter estimation problem was resolved via two algorithms: particle swarm optimisation and evonorm. The results show that the mathematical model can be a useful tool to estimate the glycaemic status of a patient and personalise it according to her/his historical data.Inspec keywords: medical control systems, closed loop systems, particle swarm optimisation, parameter estimation, biochemistry, diseases, patient monitoring, patient diagnosis, blood, sugar, patient treatment, medical computingOther keywords: meal intake, metabolic state, mathematical modelling, postprandial state, closed‐loop control system, automatic insulin delivery, T1DM, type 1 diabetes mellitus, therapy decisions, blood glucose concentration, TIDM patients, meal glucose–insulin model, mathematical model, parameter estimation problem, data‐based personalisation, glucose metabolism, insulin supply, carbohydrate intake, glucose records, therapy historical data, glycaemic state