From biological pathways to regulatory networks

This paper presents a general theoretical framework for generating Boolean networks whose state transitions realize a set of given biological pathways or minor variations thereof. This ill-posed inverse problem, which is of crucial importance across practically all areas of biology, is solved by using Karnaugh maps which are classical tools for digital system design. It is shown that the incorporation of prior knowledge, presented in the form of biological pathways, can bring about a dramatic reduction in the cardinality of the network search space. Constraining the connectivity of the network, the number and relative importance of the attractors, and concordance with observed time-course data are additional factors that can be used to further reduce the cardinality of the search space. The networks produced by the approaches developed here should facilitate the understanding of multivariate biological phenomena and the subsequent design of intervention approaches that are more likely to be successful in practice. As an example, the results of this paper are applied to the widely studied p53 pathway and it is shown that the resulting network exhibits dynamic behavior consistent with experimental observations from the published literature.     

MicroRNAs与疾病和发育

作为模式生物实验系统,线虫可用于研究控制动物发育和人类疾病遗传机制。研究发育缺陷的线虫突变体有助于在动物中发现对发育和生理过程有重要调控作用的基因。其中一些基因编码一类小RNA,如microRNA(miRNA),通过作用于特定基因信使RNA来调控其蛋白质表达。一些在线虫发育过程中有功能的miRNA在人体中也存在。它们参与调控与疾病相关的生物学过程,如癌症、糖尿病和神经退行性疾病。通过分析miRNA在临床样品、哺乳动物细胞和模式生物线虫中的表达,从而揭示miRNA调控途径在相关人类疾病中的功能。     

Network-based prediction and analysis of HIV dependency factors

HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.     

Splenic reflex modulation of central cardiovascular regulatory pathways

The splenorenal reflex induces changes in mean arterial pressure (MAP) and renal function. We hypothesized that, in addition to spinal pathways previously identified, these effects are also mediated through central pathways. We investigated the effect of elevated splenic venous pressure on central neural activation in intact, renal-denervated, and renal + splenic-denervated rats. Fos-labeled neurons were quantified in the nucleus of the tractus solitarius (NTS), paraventricular nucleus (PVN), supraoptic nucleus (SON), and subfornical organ (SFO) after 1-h partial splenic vein occlusion (SVO) in conscious rats bearing balloon occluders around the splenic vein, telemetric pressure transducers in the gastric vein (splenic venous pressure), and abdominal aorta catheters (MAP). SVO stimulated Fos expression in the PVN and SON, but not NTS or SFO of intact rats. Renal denervation abolished this response in the parvocellular PVN, while renal + splenic denervation abolished activation in the magnocellular PVN and the SON. In renal-denervated animals, SVO depressed Fos expression in the NTS and increased expression in the SFO, responses that were abolished by renal + splenic denervation. In intact rats, SVO also induced a fall in right atrial pressure, an increase in renal afferent nerve activity, and an increase in MAP. We conclude that elevated splenic venous pressure does induce hypothalamic activation and that this is mediated through both splenic and renal afferent nerves. However, in the absence of renal afferent input, SVO depressed NTS activation, probably as a result of the accompanying fall in cardiac preload and reduced afferent signaling from the cardiopulmonary receptors.     

Reconstructing the regulatory kinase pathways of myogenesis from phosphopeptide data

Multiple kinase activities are required for skeletal muscle differentiation. However, the mechanisms by which these kinase pathways converge to coordinate the myogenic process are unknown. Using multiple phosphoprotein and phosphopeptide enrichment techniques we obtained phosphopeptides from growing and differentiating C2C12 muscle cells and determined specific peptide sequences using LC-MS/MS. To place these phosphopeptides into a rational context, a bioinformatics approach was used. Phosphorylation sites were matched to known site-specific and to site non-specific kinase-substrate interactions, and then other substrates and upstream regulators of the implicated kinases were incorporated into a model network of protein-protein interactions. The model network implicated several kinases of known relevance to myogenesis including AKT, GSK3, CDK5, p38, DYRK, and MAPKAPK2 kinases. This combination of proteomics and bioinformatics technologies should offer great utility as the volume of protein-protein and kinase-substrate information continues to increase.     

Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property

Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) “Metabolism”, (ii) “Genetic Information Processing”, (iii) “Environmental Information Processing”, (iv) “Cellular Processes”, (v) “Organismal Systems”, and (vi) “Human Diseases”. The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area.     

Network-based diffusion analysis: a new method for detecting social learning

Social learning has been documented in a wide diversity of animals. In free-living animals, however, it has been difficult to discern whether animals learn socially by observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyses the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then be identified using model selection based on the Akaike information criterion. We tested our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison with diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.