Introducing spatial information into predictive NF-kappa B modelling - an agent-based approach

Nature is governed by local interactions among lower-level sub-units, whether at the cell, organ, organism, or colony level. Adaptive system behaviour emerges via these interactions, which integrate the activity of the sub-units. To understand the system level it is necessary to understand the underlying local interactions. Successful models of local interactions at different levels of biological organisation, including epithelial tissue and ant colonies, have demonstrated the benefits of such 'agent-based' modelling [1-4]. Here we present an agent-based approach to modelling a crucial biological system the intracellular NF-kappa B signalling pathway. The pathway is vital to immune response regulation, and is fundamental to basic survival in a range of species [5-7]. Alterations in pathway regulation underlie a variety of diseases, including atherosclerosis and arthritis. Our modelling of individual molecules, receptors and genes provides a more comprehensive outline of regulatory network mechanisms than previously possible with equation-based approaches [8]. The method also permits consideration of structural parameters in pathway regulation; here we predict that inhibition of NF-kappa B is directly affected by actin filaments of the cytoskeleton sequestering excess inhibitors, therefore regulating steady-state and feedback behaviour

Advocating the need of a systems biology approach for personalised prognosis and treatment of B-CLL patients

The clinical course of B-CLL is heterogeneous. This heterogeneity leads to a clinical dilemma: can we identify those patients who will benefit from early treatment and predict the survival? In recent years, mathematical modelling has contributed significantly in understanding the complexity of diseases. In order to build a mathematical model for determining prognosis of B-CLL one has to identify, characterise and quantify key molecules involved in the disease. Here we discuss the need and role of mathematical modelling in predicting B-CLL disease pathogenesis and suggest a new systems biology approach for a personalised therapy of B-CLL patients

Circuitry of nuclear factor κB signaling

Over the past few years, the transcription factor nuclear factor (NF)-κB and the proteins that regulate it have emerged as a signaling system of pre-eminent importance in human physiology and in an increasing number of pathologies. While NF-κB is present in all differentiated cell types, its discovery and early characterization were rooted in understanding B-cell biology. Significant research efforts over two decades have yielded a large body of literature devoted to understanding NF-κB's functioning in the immune system. NF-κB has been found to play roles in many different compartments of the immune system during differentiation of immune cells and development of lymphoid organs and during immune activation. NF-κB is the nuclear effector of signaling pathways emanating from many receptors, including those of the inflammatory tumor necrosis factor and Toll-like receptor superfamilies. With this review, we hope to provide historical context and summarize the diverse physiological functions of NF-κB in the immune system before focusing on recent advances in elucidating the molecular mechanisms that mediate cell type-specific and stimulus-specific functions of this pleiotropic signaling system. Understanding the genetic regulatory circuitry of NF-κB functionalities involves system-wide measurements, biophysical studies, and computational modeling

Model simplification of signal transduction pathway networks via a hybrid inference strategy

A full-scale mathematical model of cellular networks normally involves a large number of variables and parameters. How to effectively develop manageable and reliable models is crucial for effective computation, analysis and design of such systems. The aim of model simplification is to eliminate parts of a model that are unimportant for the properties of interest. In this work, a model reduction strategy via hybrid inference is proposed for signal pathway networks. It integrates multiple techniques including conservation analysis, local sensitivity analysis, principal component analysis and flux analysis to identify the reactions and variables that can be considered to be eliminated from the full-scale model. Using an I·B-NF-·B signalling pathway model as an example, simulation analysis demonstrates that the simplified model quantitatively predicts the dynamic behaviours of the network

Identification of complex biological network classes using extended correlation analysis

Modeling and analysis of complex biological networks necessitates suitable handling of data on a parallel scale. Using the IkB-NF-kB pathway model and a basis of sensitivity analysis, analytic methods are presented, extending correlation from the network kinetic reaction rates to that of the rate reactions. Alignment of correlated processed components, vastly outperforming correlation of the data source, advanced sets of biological classes possessing similar network activities. Additional construction generated a naturally structured, cardinally based system for component-specific investigation. The computationally driven procedures are described, with results demonstrating viability as mechanisms useful for fundamental oscillatory network activity investigation

Unique reporter-based sensor platforms to monitor signalling in cells

Introduction: In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level. <p/>Methods: Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis. <p/>Findings: We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation. <p/>Conclusions: We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters