A knowledge model for analysis and simulation of regulatory networks

MOTIVATION: In order to aid in hypothesis-driven experimental gene discovery, we are designing a computer application for the automatic retrieval of signal transduction data from electronic versions of scientific publications using natural language processing (NLP) techniques, as well as for visualizing and editing representations of regulatory systems. These systems describe both signal transduction and biochemical pathways within complex multicellular organisms, yeast, and bacteria. This computer application in turn requires the development of a domain-specific ontology, or knowledge model. RESULTS: We introduce an ontological model for the representation of biological knowledge related to regulatory networks in vertebrates. We outline a taxonomy of the concepts, define their 'whole-to-part' relationships, describe the properties of major concepts, and outline a set of the most important axioms. The ontology is partially realized in a computer system designed to aid researchers in biology and medicine in visualizing and editing a representation of a signal transduction system.     

Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning

INTRODUCTION: Intracellular signaling/synthetic pathways are being increasingly extensively characterized. However, while these pathways can be displayed in static diagrams, in reality they exist with a degree of dynamic complexity that is responsible for heterogeneous cellular behavior. Multiple parallel pathways exist and interact concurrently, limiting the ability to integrate the various identified mechanisms into a cohesive whole. Computational methods have been suggested as a means of concatenating this knowledge to aid in the understanding of overall system dynamics. Since the eventual goal of biomedical research is the identification and development of therapeutic modalities, computational representation must have sufficient detail to facilitate this 'engineering' process. Adding to the challenge, this type of representation must occur in a perpetual state of incomplete knowledge. We present a modeling approach to address this challenge that is both detailed and qualitative. This approach is termed 'dynamic knowledge representation,' and is intended to be an integrated component of the iterative cycle of scientific discovery. METHODS: BioNetGen (BNG), a software platform for modeling intracellular signaling pathways, was used to model the toll-like receptor 4 (TLR-4) signal transduction cascade. The informational basis of the model was a series of reference papers on modulation of (TLR-4) signaling, and some specific primary research papers to aid in the characterization of specific mechanistic steps in the pathway. This model was detailed with respect to the components of the pathway represented, but qualitative with respect to the specific reaction coefficients utilized to execute the reactions. Responsiveness to simulated lipopolysaccharide (LPS) administration was measured by tumor necrosis factor (TNF) production. Simulation runs included evaluation of initial dose-dependent response to LPS administration at 10, 100, 1000 and 10,000, and a subsequent examination of preconditioning behavior with increasing LPS at 10, 100, 1000 and 10,000 and a secondary dose of LPS at 10,000 administered at approximately 27h of simulated time. Simulations of 'knockout' versions of the model allowed further examination of the interactions within the signaling cascade. RESULTS: The model demonstrated a dose-dependent TNF response curve to increasing stimulus by LPS. Preconditioning simulations demonstrated a similar dose-dependency of preconditioning doses leading to attenuation of response to subsequent LPS challenge - a 'tolerance' dynamic. These responses match dynamics reported in the literature. Furthermore, the simulated 'knockout' results suggested the existence and need for dual negative feedback control mechanisms, represented by the zinc ring-finger protein A20 and inhibitor kappa B proteins (IkappaB), in order for both effective attenuation of the initial stimulus signal and subsequent preconditioned 'tolerant' behavior. CONCLUSIONS: We present an example of detailed, qualitative dynamic knowledge representation using the TLR-4 signaling pathway, its control mechanisms and overall behavior with respect to preconditioning. The intent of this approach is to demonstrate a method of translating the extensive mechanistic knowledge being generated at the basic science level into an executable framework that can provide a means of 'conceptual model verification.' This allows for both the 'checking' of the dynamic consequences of a mechanistic hypothesis and the creation of a modular component of an overall model directed at the engineering goal of biomedical research. It is hoped that this paper will increase the use of knowledge representation and communication in this fashion, and facilitate the concatenation and integration of community-wide knowledge.     

The molecule role ontology: an ontology for annotation of signal transduction pathway molecules in the scientific literature

In general, it is not easy to specify a single sequence identity for each molecule name that appears in a pathway in the scientific literature. A molecule name may stand for concepts of various granularities, from concrete objects such as H-Ras and ERK1 to abstract concepts or categories such as Ras and MAPK. Typically, the relations among molecule names derive a hierarchical structure; without a proper way to handle this knowledge, it becomes ever more difficult to develop a reliable pathway database. This paper describes an ontology that is designed to annotate molecules in the scientific literature on signal transduction pathways.     

From Data towards Knowledge: Revealing the Architecture of Signaling Systems by Unifying Knowledge Mining and Data Mining of Systematic Perturbation Data

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as “if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed.”     

Power-law models of signal transduction pathways

The mathematical modelling of signal transduction pathways has become a valuable aid to understanding the complex interactions involved in intracellular signalling mechanisms. An important aspect of the mathematical modelling process is the selection of the model type and structure. Until recently, the convention has been to use a standard kinetic model, often with the Michaelis-Menten steady state assumption. However this model form, although valuable, is only one of a number of choices, and the aim of this article is to consider the mathematical structure and essential features of an alternative model form--the power-law model. Specifically, we analyse how power-law models can be applied to increase our understanding of signal transduction pathways when there may be limited prior information. We distinguish between two kinds of power law models: a) Detailed power-law models, as a tool for investigating pathways when the structure of protein-protein interactions is completely known, and; b) Simplified power-law models, for the analysis of systems with incomplete structural information or insufficient quantitative data for generating detailed models. If sufficient data of high quality are available, the advantage of detailed power-law models is that they are more realistic representations of non-homogenous or crowded cellular environments. The advantages of the simplified power-law model formulation are illustrated using some case studies in cell signalling. In particular, the investigation on the effects of signal inhibition and feedback loops and the validation of structural hypotheses are discussed.     

植物中丛枝菌根形成的信号途径研究进展

丛枝菌根(arbuscular mycorrhizal, AM)共生是丛枝菌根真菌与大多数陆地植物的根系之间形成的一种互利共生关系。植物给菌根真菌提供碳水化合物; 作为回报, 菌根真菌能够增强植物对矿质营养元素(尤其是磷)的吸收。菌根的形成过程是一系列信号交换和转导的结果, 具有严格并且一致的顺序。本文以植物中菌根形成的信号途径为主线, 对菌根真菌的形成过程和信号转导途径及其方式进行了分析和讨论。高等植物中菌根形成的信号途径与豆科植物的结瘤信号途径部分共享, 并且与钙离子信号途径相关, 但前者更为广泛。尽管该途径中很多过程目前还不十分清楚, 但是相信在不久的将来就可以揭开菌根形成过程中的众多谜团。     

植物中丛枝菌根形成的信号途径研究进展

丛枝菌根(arbuscular mycorrhizal,AM)共生是丛枝菌根真菌与大多数陆地植物的根系之间形成的一种互利共生关系。植物给菌根真菌提供碳水化合物;作为回报,菌根真菌能够增强植物对矿质营养元素(尤其是磷)的吸收。菌根的形成过程是一系列信号交换和转导的结果,具有严格并且一致的顺序。本文以植物中菌根形成的信号途径为主线,对菌根真菌的形成过程和信号转导途径及其方式进行了分析和讨论。高等植物中菌根形成的信号途径与豆科植物的结瘤信号途径部分共享,并且与钙离子信号途径相关,但前者更为广泛。尽管该途径中很多过程目前还不十分清楚,但是相信在不久的将来就可以揭开菌根形成过程中的众多谜团。     

CADLIVE for constructing a large-scale biochemical network based on a simulation-directed notation and its application to yeast cell cycle

The further understanding of the mechanisms of gene regulatory networks requires comprehensive tools for both the representation of complicated signal transduction pathways and the in silico identification of genomic signals that govern the regulation of gene expression. Consequently, sophisticated notation must be developed to represent the signal transduction pathways in a form that can be readily processed by both computers and humans. We propose the regulator–reaction equations combined with detailed attributes including the associated cellular component, molecular function, and biological process and present the simulation-directed graphical notation that is derived from modification of Kohn’s method. We have developed the software suite, CADLIVE (Computer-Aided Design of LIVing systEms), which features a graphical user interface (GUI) to edit large-scale maps of complicated signal transduction pathways using a conventional XML-based representation. The regulator–reaction equations represent not only mechanistic reactions, but also semantic models containing ambiguous and incomplete processes. In order to demonstrate the feasibility of CADLIVE, we constructed a detailed map of the budding yeast cell cycle, which consists of 184 molecules and 152 reactions, in a really compact space. CADLIVE enables one to look at the whole view of a large-scale map, to integrate postgenomic data into the map, and to computationally simulate the signal transduction pathways, which greatly facilitates exploring novel or unexpected interactions.     

Uncovering signal transduction networks from high-throughput data by integer linear programming

Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system.     

Developmental signaling in Hydra: what does it take to build a "simple" animal?

Developmental processes in multicellular animals depend on an array of signal transduction pathways. Studies of model organisms have identified a number of such pathways and dissected them in detail. However, these model organisms are all bilaterians. Investigations of the roles of signal transduction pathways in the early-diverging metazoan Hydra have revealed that a number of the well-known developmental signaling pathways were already in place in the last common ancestor of Hydra and bilaterians. In addition to these shared pathways, it appears that developmental processes in Hydra make use of pathways involving a variety of peptides. Such pathways have not yet been identified as developmental regulators in more recently diverged animals. In this review I will summarize work to date on developmental signaling pathways in Hydra and discuss the future directions in which such work will need to proceed to realize the potential that lies in this simple animal.     

Melatonin receptors and signal transduction mechanisms

The ovine pars tuberalis (PT) still offers the best model for the study of signal transduction pathways regulated by the melatonin receptor. From the evidence accumulated so far, it seems likely that the cAMP signal transduction pathway will be a major effector of a stimulatory signal to the PT which can be regulated by melatonin. Thus a principal action of melatonin in the PT may be the repression of biochemical processes driven by cAMP. However, through the phenomenon of sensitization, melatonin may also act to amplify a stimulatory input to the cAMP signal transduction pathway in the PT. These events are mediated via the melatonin receptor, which is itself a target for regulation by the melatonin signal. Studies using the PT have identified several signalling pathways that may serve to positively or negatively regulate the expression of the melatonin receptor. These and other studies in the PT have alluded to cAMP-independent pathways regulated by the melatonin receptor.     

Molecular mechanisms of arsenic carcinogenesis

Arsenic is a metalloid compound that is widely distributed in the environment. Human exposure of this compound has been associated with increased cancer incidence. Although the exact mechanisms remain to be investigated, numerous carcinogenic pathways have been proposed. Potential carcinogenic actions for arsenic include oxidative stress, genotoxic damage, DNA repair inhibition, epigenetic events, and activation of certain signal transduction pathways leading to abberrant gene expression. In this article, we summarize current knowledge on the molecular mechanisms of arsenic carcinogenesis with an emphasis on ROS and signal transduction pathways.     

一类信号转导通路模型参数辨识中最小应测量状态的确定

建立信号转导通路的数学模型是系统生物学的一个重要目标．但是信号转导通路本身的复杂结构及其所表现出的强非线性特征,使得对该类模型的参数辨识十分困难．参数辨识所需要的测量数据的选择对于辨识结果有重要影响．本文研究了一类信号转导通路模型参数辨识中最小应测量状态的计算问题．给出了用于确定该最小应测量状态的目标函数,通过对目标函数进行简单的运算,确定了用来估计未知参数的最小应测量状态的数量,同时给出了一种计算系统状态对应于未知参数的敏感性系统曲线的新方法．以TNFα诱导的NF-κB信号转导通路为例进行了仿真研究,并给出了仿真结果．     

Integrin signalling in neutrophils and macrophages.

Integrins have been characterized extensively as adhesion receptors capable of transducing signals inside the cell. In myelomonocytic cells, integrin-mediated adhesive interactions regulate different selective cell responses, such as transmigration into the inflammatory site, cytokine secretion, production or reactive oxygen intermediates, degranulation and phagocytosis. In the last few years, great progress has been made in elucidating mechanisms of signal transduction by integrins in neutrophils and macrophages. This review summarises the current information on the role of integrins in regulating myelomonocytic cell functions and highlights the signalling pathways activated by integrin engagement in these cells. Also, exploiting the current knowledge of mechanisms of integrin signal transduction in other cell types, we propose a model to explain how integrins transduce signals inside neutrophils and macrophages, and how signaling pathways leading to regulation of selective cell functions may be coordinated.