PredCRP: predicting and analysing the regulatory roles of CRP from its binding sites in Escherichia coli.

Cyclic AMP receptor protein (CRP), a global regulator in Escherichia coli, regulates more than 180 genes via two roles: activation and repression. Few methods are available for predicting the regulatory roles from the binding sites of transcription factors. This work proposes an accurate method PredCRP to derive an optimised model (named PredCRP-model) and a set of four interpretable rules (named PredCRP-ruleset) for predicting and analysing the regulatory roles of CRP from sequences of CRP-binding sites. A dataset consisting of 169 CRP-binding sites with regulatory roles strongly supported by evidence was compiled. The PredCRP-model, using 12 informative features of CRP-binding sites, and cooperating with a support vector machine achieved a training and test accuracy of 0.98 and 0.93, respectively. PredCRP-ruleset has two activation rules and two repression rules derived using the 12 features and the decision tree method C4.5. This work further screened and identified 23 previously unobserved regulatory interactions in Escherichia coli. Using quantitative PCR for validation, PredCRP-model and PredCRP-ruleset achieved a test accuracy of 0.96 (=22/23) and 0.91 (=21/23), respectively. The proposed method is suitable for designing predictors for regulatory roles of all global regulators in Escherichia coli. PredCRP can be accessed at https://github.com/NctuICLab/PredCRP .

Robustness and adaptation reveal plausible cell cycle controlling subnetwork in Saccharomyces cerevisiae.

Biological systems are often organized spatially and temporally by multi-scale functional subsystems (modules). A specific subcellular process often corresponds to a subsystem composed of some of these interconnected modules. Accurate identification of system-level modularity organization from the large scale networks can provide valuable information on subsystem models of subcellular processes or physiological phenomena. Computational identification of functional modules from the large scale network is the key approach to solve the complexity of modularity in the past decade, but the overlapping and multi-scale nature of modules often renders unsatisfactory results in these methods. Most current methods for modularity detection are optimization-based and suffered from the drawback of size resolution limit. It is difficult to trace the origin of the unsatisfactory results, which may be due to poor data, inappropriate objective function selection or simply resulted from natural evolution, and hence no system-level accurate modular models for subcellular processes can be offered. Motivated by the idea of evolution with robustness and adaption as guiding principles, we propose a novel approach that can identify significant multi-scale overlapping modules that are sufficiently accurate at the system and subsystem levels, giving biological insights for subcellular processes. The success of our evolution strategy method is demonstrated by applying to the yeast protein-protein interaction network. Functional subsystems of important physiological phenomena can be revealed. In particular, the cell cycle controlling network is selected for detailed discussion. The cell cycle subcellular processes in yeast can be successfully dissected into functional modules of cell cycle control, cell size check point, spindle assembly checkpoint, and DNA damage check point in G2/M and S phases. The interconnections between check points and cell cycle control modules provide clues on the signal stimulus entries of check points into the cell cycle, which are consistent with experimental findings. This evolution strategy method can be applied adequately to extract the plausible yeast cell cycle subnetworks from the whole network. Connections between modules in the obtained cell cycle subnetworks reveal significant cell cycle control mechanisms. This method can also be useful when applied to other biological systems at various temporal and spatial scales for example, the gene transcription networks, and biological systems from mesoscopic scale, e.g cortical network in brain, to subcellular molecular networks.

Accurate and fast computational method for identifying protein function using protein-protein interaction data.

A novel quantity called functional correlation was proposed to evaluate functional closeness of a protein with its neighbors in a PPI network. Each unclassified protein was assigned a functional probability distribution which specified the likelihood of an unclassified protein belonged to a specific function group. The functional probability distribution for all unclassified proteins were adjusted iteratively until the functional correlation reached optimum. A function was assigned to an unclassified protein if its corresponding functional probability was higher than a chosen threshold. Our results showed that the functional correlation optimization method (FCOM) is more robust to false protein interactions and insensitive to the amount of known function proteins in a PPI network than other methods. FCOM method can be easily and usefully applied to organisms with rare known function proteins, disease genes, protein complexes, overlapped modular structures prediction and so on.

Evaluation of antinociceptive, anti-inflammatory and antipyretic effects of Strobilanthes cusia leaf extract in male mice and rats.

The leaf of Strobilanthes cusia (Acanthaceae), popularly known as Da-Ching-Yeh, has been commonly used in traditional Chinese medicine. It is used for influenza, epidemic cerebrospinal meningitis, encephalitis B, viral pneumonia and mumps. It is also used to treat sore throat, aphthae and inflammatory diseases with redness of skin, etc. In this study, we evaluated the antinociceptive, anti-inflammatory and antipyretic effects of methanol extract of Strobilanthes cusia leaf. The results showed that the extract significantly inhibited the writhing responses of mice and decreased the licking time on both the early and late phases of the formalin test in a dose-dependent manner. It also reduced the paw edema induced by carrageenan in rats. In addition, it potently attenuated pyrexia induced by lipopolysaccharide.