Simple mutual-inhibition networks are frequently occurring motifs in transcriptional regulatory networks for cell lineage commitment. Stable attractors represent cell commitment states. However, how progenitor-specific transcription factors stabilise progenitor cells and commit them to different cell fates remains unexplained. In this study, the authors represent the cell commitment motifs composed of mutual-inhibition regulation and progenitor-specific transcription factors, and develop associated mathematical model to understand how specific cell fate decisions are made. Bifurcation analysis and numerical simulation show that the model could exhibit multiple stable steady states corresponding to progenitor and committed cell states. The transitions between different cell states correspond to different commitment processes. Furthermore, the authors demonstrate that different commitment patterns, for example, haematopoietic and neural fate decisions fall within the scope of proposed framework.

A number of biological systems can be modelled by Markov chains. Recently, there has been an increasing concern about when biological systems modelled by Markov chains will perform a dynamic phenomenon called overshoot. In this study, the authors found that the steady-state behaviour of the system will have a great effect on the occurrence of overshoot. They showed that overshoot in general cannot occur in systems that will finally approach an equilibrium steady state. They further classified overshoot into two types, named as simple overshoot and oscillating overshoot. They showed that except for extreme cases, oscillating overshoot will occur if the system is far from equilibrium. All these results clearly show that overshoot is a non-equilibrium dynamic phenomenon with energy consumption. In addition, the main result in this study is validated with real experimental data.

This study provides a timely comparative genomic and transcriptomic analysis of the terpene synthase (TPS) gene family in Medicago truncatula (bears glandular and non-glandular trichomes) and Arabidopsis thaliana (bears non-glandular trichomes). The authors’ efforts aimed to gain insight into TPS function, phylogenetic relationships and the role of trichomes in terpene biosynthesis and function. In silico analysis identified 33 and 23 putative full-length TPS genes in Arabidopsis and Medicago, respectively. All AtTPS and MtTPS fall into the five established angiosperm TPS subfamilies, with lineage-specific expansion of Subfamily A in Arabidopsis and Subfamily G in Medicago. Large amounts of tandem duplication have occurred in both species, but only one syntenic duplication seems to have occurred in Arabidopsis, with no such duplication apparent in Medicago. Expression analysis indicates that there is much more trichome-localised TPS expression in Medicago than in Arabidopsis. However, TPS genes were expressed in non-glandular trichomes in both species. One trichome-specific gene has been identified in each Medicago and Arabidopsis along with flower-, seed-, stem- and root-specific genes. Of these, MtTPS11 is a promising candidate for trichome-specific genetic engineering, a technology that may be possible for both plants according to the findings of this manuscript. These results suggest that non-glandular trichomes may play a role in plant chemical defense and/or ecological communication instead of only in physical defence. Finally, the general lack of correlation between expression patterns and phylogenetic relationships in both species suggests that phylogenetic analysis alone is insufficient to predict gene function even for phylogenetically close paralogs.

G-protein couple receptor (GPCR) is one of the most striking examples of signalling proteins and it is only observed in eukaryotes. Based on various GPCR identification methods and classification systems, several evolutionary presumptions of different GPCR families have been reported. However, the prototype of GPCR still limits our knowledge. By investigating its structure and domain variance, the authors propose that GPCR might be evolved from prokaryotic world. The results given by the authors indicate that metabotropic glutamate receptor family would be the ancestor of GPCR. Phylogenetic analysis hints that one of metabotropic glutamate receptor GABA is possibly formed and evolved from the ancient chemical union of bacteriorhodopsin and periplasmic binding protein. The results obtained by the authors also unprecedentedly demonstrate that specific domains and identical structures are shown in each type of GPCR, which provides unique opportunities for future strategies on GPCR orphans’ prediction and classification.

Pancreatic cancer is a devastating disease and predicting the status of the patients becomes an important and urgent issue. The authors explore the applicability of inductive logic programming (ILP) method in the disease and show that the accumulated clinical laboratory data can be used to predict disease characteristics, and this will contribute to the selection of therapeutic modalities of pancreatic cancer. The availability of a large amount of clinical laboratory data provides clues to aid in the knowledge discovery of diseases. In predicting the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer, using the ILP model, three rules are developed that are consistent with descriptions in the literature. The rules that are identified are useful to detect the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer and therefore contributed significantly to the decision of therapeutic strategies. In addition, the proposed method is compared with the other typical classification techniques and the results further confirm the superiority and merit of the proposed method.

Alzheimer's disease (AD) is a severe neurodegenerative disorder without curative treatment. Extensive data on pathological molecular processes have been accumulated over the last years. These data combined allows a systems biology approach to identify key regulatory elements of AD and to establish a model descriptive of the disease process which can be used for the development of therapeutic agents. In this study, the authors propose a closed network that uses a set of nodes (amyloid beta, tau, beta-secretase, glutamate, cyclin-dependent kinase 5, phosphoinositide 3-kinase and hypoxia-induced factor 1 alpha) as key elements of importance to the pathogenesis of AD. The proposed network, in total 39 nodes, is able to become a novel tool capable of providing new insights into AD, such as feedback loops. Further, it highlights interconnections between pathways and identifies their combination for therapy of AD.

Single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs) play different roles in biological processes when they bind to single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA). However, the underlying binding mechanisms of SSBs and DSBs have not yet been fully understood. Here, the authors firstly constructed two groups of ssDNA and dsDNA specific binding sites from two non-redundant sets of SSBs and DSBs. They further analysed the relationship between the two classes of binding sites and a newly proposed set of features (residue charge distribution, secondary structure and spatial shape). To assess and utilise the predictive power of these features, they trained a classification model using support vector machine to make predictions about the ssDNA and the dsDNA binding sites. The author's analysis and prediction results indicated that the two classes of binding sites can be distinguishable by the three types of features, and the final classifier using all the features achieved satisfactory performance. In conclusion, the proposed features will deepen their understanding of the specificity of proteins which bind to ssDNA or dsDNA.

The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming increasingly important for the research on protein–protein interaction and drug design. For each interface residue or target residue to be predicted, the authors extract hybrid features which incorporate a wide range of information of the target residue and its spatial neighbor residues, that is, the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). Here, feature selection is performed using random forests to avoid over-fitting. Thereafter, the extreme learning machine is employed to effectively integrate these hybrid features for predicting hot spots in protein interfaces. By the 5-fold cross validation in the training set, their method can achieve accuracy (ACC) of 82.1% and Matthew's correlation coefficient (MCC) of 0.459, and outperforms some alternative machine learning methods in the comparison study. Furthermore, their method achieves ACC of 76.8% and MCC of 0.401 in the independent test set, and is more effective than the major existing hot spot predictors. Their prediction method offers a powerful tool for uncovering candidate residues in the studies of alanine scanning mutagenesis for functional protein interaction sites.