Motivation: An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions. Results: On Gaussian observational data, BNs and GGMs were found to outperform RNs. The difference in performance was not significant for the non-linear simulated data and the cytoflow data, though. Also, we did not observe a significant difference between BNs and GGMs on observational data in general. However, for interventional data, BNs outperform GGMs and RNs, especially when taking the edge directions rather than just the skeletons of the graphs into account. This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs. Availability: Data, software and supplementary material are available from . Contact:adriano@bioss.ac.uk, dirk@bioss.ac.uk, Grzegorc@statistik.uni-dortmund.de

There have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited amount of independent experimental conditions and noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. In the present paper, we consider a Bayesian approach to systematically integrate expression data with multiple sources of prior knowledge. Each source is encoded via a separate energy function, from which a prior distribution over network structures in the form of a Gibbs distribution is constructed. The hyperparameters associated with the different sources of prior knowledge, which measure the influence of the respective prior relative to the data, are sampled from the posterior distribution with MCMC. We have evaluated the proposed scheme on the yeast cell cycle and the Raf signalling pathway. Our findings quantify to what extent the inclusion of independent prior knowledge improves the network reconstruction accuracy, and the values of the hyperparameters inferred with the proposed scheme were found to be close to optimal with respect to minimizing the reconstruction error.

The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio reriowhen compared with VDAC1-Mus musculusand VDAC1-Homo sapiens(p < 0.05). In addition,asignificant correlation (0.66 > r² > 0.97) was observed betweenn-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R²of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available athttps://doi.org/10.6084/m9.figshare.4802320.v2.

β-glucosidases are enzymes that catalyze the hydrolysis of oligosaccharides and disaccharides, such as cellobiose. These enzymes play a key role in cellulose degrading, such as alleviating product inhibition of cellulases. Consequently, they have been considered essential for the biofuel industry. However, the majority of the characterized β-glucosidases is inhibited by glucose. Hence, glucose-tolerant β-glucosidases have been targeted to improve the production of second-generation biofuels. In this paper, we proceeded a systematic literature review (SLR), collected protein structures and constructed a database of glucose-tolerant β-glucosidases, called betagdb. SLR was performed at PubMed, ScienceDirect and Scopus Library databases and conducted according to PRISMA framework. It was conducted in five steps: i) analysis of duplications, ii) title reading, iii) abstract reading, iv) diagonal reading, and v) full-text reading. The second, third, fourth, and fifth steps were performed independently by two researchers. Besides, we performed bioinformatics analysis on the collected data, such as structural and multiple alignments to detect the most conserved residues in the catalytic pocket, and molecular docking to characterize essential residues for substrate recognizing, glucose tolerance, and the β-glucosidase activity. We selected 27 papers, 23 sequences, and 5 PDB files of glucose-tolerant β-glucosidases. We characterized 11 highly conserved residues: H121, W122, N166, E167, N297, Y299, E355, W402, E409, W410, and F418. The presence of these residues may be essential for β-glucosidases. We also discussed the importance of residues W169, C170, L174, H181, and T226. Furthermore, we proposed that the number of contacts for each residue in the catalytic pocket might be a metric that could be used to suggest mutations. We believe that the herein propositions, together with the sequence and structural data collection, might be helpful for effective engineering of β-glucosidases for biofuel production and may help to shed some light on the degradation of cellulosic biomass.