Bridging the gap between in silico and cell-based analysis of the nuclear factor-kappaB signaling pathway by in vitro studies of IKK2

Regulation of signal transduction by spatial parameters: a case in NF-κB oscillation

NF-κB is a transcription factor regulating expression of more than 500 genes, and its dysfunction leads to the autoimmune and inflammatory diseases. In malignant cancer cells, NF-κB is constitutively activated. Thus the elucidation of mechanisms for NF-κB regulation is important for the establishment of therapeutic treatment caused by incorrect NF-κB responses. Cytoplasmic NF-κB translocates to the nucleus by the application of extracellular stimuli such as cytokines. Nuclear NF-κB is known to oscillate with the cycle of 1.5-4.5 h, and it is thought that the oscillation pattern regulates the expression profiles of genes. In this review, first we briefly describe regulation mechanisms of NF-κB. Next, published computational simulations on the oscillation of NF-κB are summarised. There are at least 60 reports on the computational simulation and analysis of NF-κB oscillation. Third, the importance of a 'space' for the regulation of oscillation pattern of NF-κB is discussed, showing altered oscillation pattern by the change in spatial parameters such as diffusion coefficient, nuclear to cytoplasmic volume ratio (N/C ratio), and transport through nuclear membrane. Finally, simulations in a true intracellular space (TiCS), which is an intracellular 3D space reconstructed in a computer with organelles such as nucleus and mitochondria are discussed.

Integrated signaling pathway and gene expression regulatory model to dissect dynamics of Escherichia coli challenged mammary epithelial cells

Cells transform external stimuli, through the activation of signaling pathways, which in turn activate gene regulatory networks, in gene expression. As more omics data are generated from experiments, eliciting the integrated relationship between the external stimuli, the signaling process in the cell and the subsequent gene expression is a major challenge in systems biology. The complex system of non-linear dynamic protein interactions in signaling pathways and gene networks regulates gene expression. The complexity and non-linear aspects have resulted in the study of the signaling pathway or the gene network regulation in isolation. However, this limits the analysis of the interaction between the two components and the identification of the source of the mechanism differentiating the gene expression profiles. Here, we present a study of a model of the combined signaling pathway and gene network to highlight the importance of integrated modeling. Based on the experimental findings we developed a compartmental model and conducted several simulation experiments. The model simulates the mRNA expression of three different cytokines (RANTES, IL8 and TNFα) regulated by the transcription factor NFκB in mammary epithelial cells challenged with E. coli. The analysis of the gene network regulation identifies a lack of robustness and therefore sensitivity for the transcription factor regulation. However, analysis of the integrated signaling and gene network regulation model reveals distinctly different underlying mechanisms in the signaling pathway responsible for the variation between the three cytokine's mRNA expression levels. Our key findings reveal the importance of integrating the signaling pathway and gene expression dynamics in modeling. Modeling infers valid research questions which need to be verified experimentally and can assist in the design of future biological experiments.

Communicating oscillatory networks: frequency domain analysis

Background

Constructing predictive dynamic models of interacting signalling networks remains one of the great challenges facing systems biology. While detailed dynamical data exists about individual pathways, the task of combining such data without further lengthy experimentation is highly nontrivial. The communicating links between pathways, implicitly assumed to be unimportant and thus excluded, are precisely what become important in the larger system and must be reinstated. To maintain the delicate phase relationships between signals, signalling networks demand accurate dynamical parameters, but parameters optimised in isolation and under varying conditions are unlikely to remain optimal when combined. The computational burden of estimating parameters increases exponentially with increasing system size, so it is crucial to find precise and efficient ways of measuring the behaviour of systems, in order to re-use existing work.

Results

Motivated by the above, we present a new frequency domain-based systematic analysis technique that attempts to address the challenge of network assembly by defining a rigorous means to quantify the behaviour of stochastic systems. As our focus we construct a novel coupled oscillatory model of p53, NF-kB and the mammalian cell cycle, based on recent experimentally verified mathematical models. Informed by online databases of protein networks and interactions, we distilled their key elements into simplified models containing the most significant parts. Having coupled these systems, we constructed stochastic models for use in our frequency domain analysis. We used our new technique to investigate the crosstalk between the components of our model and measure the efficacy of certain network-based heuristic measures.

Conclusions

We find that the interactions between the networks we study are highly complex and not intuitive: (i) points of maximum perturbation do not necessarily correspond to points of maximum proximity to influence; (ii) increased coupling strength does not necessarily increase perturbation; (iii) different perturbations do not necessarily sum and (iv) overall, susceptibility to perturbation is amplitude and frequency dependent and cannot easily be predicted by heuristic measures.

Our methodology is particularly relevant for oscillatory systems, though not limited to these, and is most revealing when applied to the results of stochastic simulation. The technique is able to characterise precisely the distance in behaviour between different models, different systems and different parts within the same system. It can also measure the difference between different simulation algorithms used on the same system and can be used to inform the choice of dynamic parameters. By measuring crosstalk between subsystems it can also indicate mechanisms by which such systems may be controlled in experiments and therapeutics. We have thus found our technique of frequency domain analysis to be a valuable benchmark systems-biological tool.

On the architecture of cell regulation networks

BACKGROUND

With the rapid development of high-throughput experiments, detecting functional modules has become increasingly important in analyzing biological networks. However, the growing size and complexity of these networks preclude structural breaking in terms of simplest units. We propose a novel graph theoretic decomposition scheme combined with dynamics consideration for probing the architecture of complex biological networks.

RESULTS

Our approach allows us to identify two structurally important components: the "minimal production unit"(MPU) which responds quickly and robustly to external signals, and the feedback controllers which adjust the output of the MPU to desired values usually at a larger time scale. The successful application of our technique to several of the most common cell regulation networks indicates that such architectural feature could be universal. Detailed illustration and discussion are made to explain the network structures and how they are tied to biological functions.

CONCLUSIONS

The proposed scheme may be potentially applied to various large-scale cell regulation networks to identify functional modules that play essential roles and thus provide handles for analyzing and understanding cell activity from basic biochemical processes.

Network inference from time-dependent Omics data

We provide a commented overview of the available databases for the systematic collection of pathway information and biological models essential for the interpretation of Omics data. Then, we present both the state of the art and the future challenges of network inference, a research area dealing with the deduction of reaction mechanisms from experimental Omics data. This approach represents one of the most challenging instances for making use of the huge amount of information gathered in the Omics era.

High-throughput, single-cell NF-κB dynamics

Single cells in a population often respond differently to perturbations in the environment. Live-cell microscopy has enabled scientists to observe these differences at the single-cell level. Some advantages of live-cell imaging over population-based methods include better time resolution, higher sensitivity, automation, and richer datasets. One specific area where live-cell microscopy has made a significant impact is the field of NF-κB signaling dynamics, and recent efforts have focused on making live-cell imaging of these dynamics more high-throughput. We highlight the major aspects of increasing throughput and describe a current system that can monitor, image and analyze the NF-κB activation of thousands of single cells in parallel.

High-throughput and single-cell imaging of NF-kappaB oscillations using monoclonal cell lines

Background

The nuclear factor-κB (NF-κB) family of transcription factors plays a role in a wide range of cellular processes including the immune response and cellular growth. In addition, deregulation of the NF-κB system has been associated with a number of disease states, including cancer. Therefore, insight into the regulation of NF-κB activation has crucial medical relevance, holding promise for novel drug target discovery. Transcription of NF-κB-induced genes is regulated by differential dynamics of single NF-κB subunits, but only a few methods are currently being applied to study dynamics. In particular, while oscillations of NF-κB activation have been observed in response to the cytokine tumor necrosis factor α (TNFα), little is known about the occurrence of oscillations in response to bacterial infections.

Results

To quantitatively assess NF-κB dynamics we generated human and murine monoclonal cell lines that stably express the NF-κB subunit p65 fused to GFP. Furthermore, a high-throughput assay based on automated microscopy coupled to image analysis to quantify p65-nuclear translocation was established. Using this assay, we demonstrate a stimulus- and cell line-specific temporal control of p65 translocation, revealing, for the first time, oscillations of p65 translocation in response to bacterial infection. Oscillations were detected at the single-cell level using real-time microscopy as well as at the population level using high-throughput image analysis. In addition, mathematical modeling of NF-κB dynamics during bacterial infections predicted masking of oscillations on the population level in asynchronous activations, which was experimentally confirmed.

Conclusions

Taken together, this simple and cost effective assay constitutes an integrated approach to infer the dynamics of NF-κB kinetics in single cells and cell populations. Using a single system, novel factors modulating NF-κB can be identified and analyzed, providing new possibilities for a wide range of applications from therapeutic discovery and understanding of disease to host-pathogen interactions.

Elucidation of functional consequences of signalling pathway interactions

BACKGROUND

A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information, which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein - protein interactions.

RESULTS

A computational framework is presented which allows for the description of embedded networks, and identification of common shared components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B (NF-kappaB) and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases were identified as connecting proteins most likely responsible for propagation of transduction signals across the networks were determined. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks.

CONCLUSION

It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request.

Sustained oscillations of NF-kappaB produce distinct genome scanning and gene expression profiles

NF-kappaB is a prototypic stress-responsive transcription factor that acts within a complex regulatory network. The signaling dynamics of endogenous NF-kappaB in single cells remain poorly understood. To examine real time dynamics in living cells, we monitored NF-kappaB activities at multiple timescales using GFP-p65 knock-in mouse embryonic fibroblasts. Oscillations in NF-kappaB were sustained in most cells, with several cycles of transient nuclear translocation after TNF-alpha stimulation. Mathematical modeling suggests that NF-kappaB oscillations are selected over other non-oscillatory dynamics by fine-tuning the relative strengths of feedback loops like IkappaBalpha. The ability of NF-kappaB to scan and interact with the genome in vivo remained remarkably constant from early to late cycles, as observed by fluorescence recovery after photobleaching (FRAP). Perturbation of long-term NF-kappaB oscillations interfered with its short-term interaction with chromatin and balanced transcriptional output, as predicted by the mathematical model. We propose that negative feedback loops do not simply terminate signaling, but rather promote oscillations of NF-kappaB in the nucleus, and these oscillations are functionally advantageous.

Calibration of dynamic models of biological systems with KInfer

Methods for parameter estimation that are robust to experimental uncertainties and to stochastic and biological noise and that require a minimum of a priori input knowledge are of key importance in computational systems biology. The new method presented in this paper aims to ensure an inference model that deduces the rate constants of a system of biochemical reactions from experimentally measured time courses of reactants. This new method was applied to some challenging parameter estimation problems of nonlinear dynamic biological systems and was tested both on synthetic and real data. The synthetic case studies are the 12-state model of the SERCA pump and a model of a genetic network containing feedback loops of interaction between regulator and effector genes. The real case studies consist of a model of the reaction between the inhibitor kappaB kinase enzyme and its substrate in the signal transduction pathway of NF-kappaB, and a stiff model of the fermentation pathway of Lactococcus lactis.

Computational systems biology of the cell cycle

One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.

High content cell screening in a microfluidic device

A comprehensive, systems level understanding of cell signaling networks requires methods to efficiently assay multiple signaling species, at the level of single cells, responding to a variety of stimulation protocols. Here we describe a microfluidic device that enables quantitative interrogation of signaling networks in thousands of individual cells using immunofluorescence-based readouts. The device is especially useful for measuring the signaling activity of kinases, transcription factors, and/or target genes in a high throughput, high content manner. We demonstrate how the device may be used to measure detailed time courses of signaling responses to one or more soluble stimuli and/or chemical inhibitors as well as responses to a complex temporal pattern of multiple stimuli. Furthermore we show how the throughput and resolution of the device may be exploited in investigating the differences, if any, of signaling at the level of a single cell versus at the level of the population. In particular, we show that NF-kappaB activity dynamics in individual cells are not asynchronous and instead resemble the dynamics of the population average in contrast to studies of cells overexpressing p65-EGFP.

Understanding NF-kappaB signaling via mathematical modeling

Mammalian inflammatory signaling, for which NF-kappaB is a principal transcription factor, is an exquisite example of how cellular signaling pathways can be regulated to produce different yet specific responses to different inflammatory insults. Mathematical models, tightly linked to experiment, have been instrumental in unraveling the forms of regulation in NF-kappaB signaling and their underlying molecular mechanisms. Our initial model of the IkappaB-NF-kappaB signaling module highlighted the role of negative feedback in the control of NF-kappaB temporal dynamics and gene expression. Subsequent studies sparked by this work have helped to characterize additional feedback loops, the input-output behavior of the module, crosstalk between multiple NF-kappaB-activating pathways, and NF-kappaB oscillations. We anticipate that computational techniques will enable further progress in the NF-kappaB field, and the signal transduction field in general, and we discuss potential upcoming developments.