Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-κB signaling-induced gene expression responses in inflammation
© Peng et al; licensee BioMed Central Ltd. 2010
Received: 23 December 2009
Accepted: 8 June 2010
Published: 8 June 2010
Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli.
We proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-κB signaling pathway with the inflammatory gene regulatory responses because NF-κB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets) to construct the NF-κB signaling pathway and reverse engineering (Network Components Analysis) to build a gene regulatory network (GRN). Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-α, IL-1, IL-6, CXCL1, CXCL2 and CCL3) is concordant with the NF-κB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-κB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome.
We successfully identified and distinguished the corresponding signaling profiles among three microarray datasets with different stimuli strengths. In our model, the crucial genes of the NF-κB regulatory network were also identified to reflect the biological consequences of inflammation. With the experimental validation, our strategy is thus an effective solution to decipher cross-talk effects when attempting to integrate new kinetic parameters from other signal transduction pathways. The strategy also provides new insight for systems biology modeling to link any signal transduction pathways with the responses of downstream genes of interest.
Signal transduction is a complex process in which a cell converts environmental signals to a series of intracellular biochemical reactions. Diverse cellular stimuli can create a wide variety of transcription factor activities through signal transduction pathways, resulting in differential gene expression that dictates subsequent cellular behaviors. Although a great deal of effort has been made in modeling signal transduction pathways or gene regulatory networks independently, a strategy to link the signaling pathway with downstream gene expression responses seems to be lacking. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses, to identify the physiological consequences of environmental stimuli.
Over the past few years, a considerable number of studies have reported various systematic modeling protocols for the reconstruction of large-scale cellular signaling networks [1–8]. Besides the qualitative analysis of the signaling networks, mathematical approaches for the quantitative modeling and simulation of signal transduction pathways have also been developed [9–15]. Most of these quantitative signaling models are kinetic reactions represented by the assemblage of ordinary differential equations (ODE) . The ODEs are designed to simulate dynamic changes throughout continuous but limited time points that define a function of rate changes for one independent variable and one or more of its derivatives with respect to that variable. Given the determined kinetic parameters, such ODE models provide a "forward-engineering" framework to simulate the spatiotemporal dynamics of the system. The time profiles of the target transcription factor activation in response to various stimuli can be obtained by this established approach.
In gene regulatory network modeling, some wet-bench experimental approaches have been used to detect the gene expression and transcriptional activities. Microarray technology is a powerful high-throughput technique enabling biologists to simultaneously measure the expression profile of tens of thousands of genes under prescribed conditions [17, 18]. As for transcriptional activities, the electrophoretic mobility shift assay (EMSA) [19, 20] is an affinity electrophoresis technique that can determine single protein or protein-DNA complex binding activity at a time. However, the ability to broadly assess the activities of transcription factors is still much limited. Therefore, most computational transcription activity and gene regulatory network are reconstructed from genome wide gene expression data.
Such a data-driven approach to gene expression analysis provides systematic information of underlying gene regulatory systems and offers the possibility to infer the dynamics and mechanisms of transcription control by reverse engineering [21–28]. There are two kinds of reverse engineering strategies for modeling gene regulatory networks based on DNA microarray data, namely the "influence" and "physical" approaches . The "influence approach" produces the genetic network that illustrates regulatory influences between RNA transcripts. This strategy can integrate information pertaining to the relationships between regulated genes, protein-protein interactions, and enzyme catalysis to establish network based on transcript profiling data when the expression of certain transcripts is highly correlated. However, influence models are difficult to interpret in the context of location and modification in the cell. The second strategy, known as the "physical approach", seeks to construct a physical interaction model between transcription factors and gene promoters. The transcriptional activities can be often predicted from gene expression data by a sigmoid function . Moreover, factor analysis is another methodology to construct a physical regulation model which is represented as bipartite graph with transcription factors in the first layer and regulated genes in the second layer for reducing the dimensionality of the reverse engineering problem. In previous studies, principal component analysis (PCA) , independent component analysis (ICA) , and network component analysis (NCA)  have been applied to reconstruct transcription factor activities using gene expression profiles. PCA and ICA are traditional dimensionality reduction technologies. The transcription factor activity reconstructed by PCA and ICA is constrained, respectively, to be mutually orthogonal and statistical independent. These statistical assumptions do not match the real biological systems. However, NCA contrasts with traditional PCA and ICA in that it does not make any aforementioned statistical assumptions.
NF-κB is a transcription factor that has long been recognized as the "master switch" in regulating the expression of various cytokines and host response effectors, as well as a wide array of genes to control inflammation, cell survival, apoptosis, and immune defense responses . NF-κB signaling can be initiated from membrane receptors, such as Toll-like receptors, tumor necrosis factor alpha (TNF-α) receptors, and interleukin-1 (IL-1) receptors, either individually or synergistically. In the past few decades, many studies have tried to resolve the complex NF-κB dependent protein-protein and DNA-protein interactions, and significant progress has since been made on modeling signal transduction pathways and gene regulatory networks of the inflammatory response based on both biochemical and microarray data [35–40]. However, a systemic and dynamic view of how external stimuli evoke NF-κB-dependent signal transduction activities to the downstream gene expression still remained unclear.
To overcome this challenge, we proposed a new computational modeling approach by connecting transcription activities derived from the reverse engineering of gene expression profiling to the transcription activities simulated from a forward engineering signaling model, using the NF-κB signaling pathway with the corresponding gene responses as the case study. In this work, the NCA model was applied to reconstruct the regulatory activity of NF-κB using gene expression profile data obtained in response to specific external stimuli . A kinetic model was used to simulate the IKK-NF-κB signaling pathway . By mapping the NF-κB profiles generated from the reverse engineering gene expression profiling data and the ones from the forward simulation, the bridging IKK activities induced by external stimuli were inferred. Features of this inferred signaling process were then confirmed by independent experiments using similar stimuli. This strategy successfully linked the initial signaling pathway with the relevant gene regulatory network. It also successfully inferred and distinguished the corresponding stimuli from gene responses under different inflammation conditions. Taken together, the strategy discussed in the present study can help enhance our understanding of inflammatory responses during the infection process; it is also applicable to other cellular processes.
Results and Discussion
The reconstruction of the transcriptional activity of NF-κB by NCA
The Network Component Analysis  is a natural model with a reverse-engineering approach that has been used to deduce transcription factor activities and regulatory control strengths using temporal gene expression data and transcriptional regulatory relationships. The NCA framework regards the gene regulatory network as a bipartite network model involving transcription factors and their target genes. The details of the NCA model are described in the Methods.
Lipopolysaccharide (LPS) is a kind of bacterial endotoxin widely used to stimulate inflammatory responses in vitro. The gene expression datasets in which cell cultures were treated with different doses of LPS from Boldrick et al.  were used. Briefly, Boldrick et al. added LPS to human peripheral blood mononuclear cells (PBMCs) at concentrations of 0.01 μg/ml, 0.1 μg/ml, and 1 μg/ml. The transcript profiles of the PBMCs were determined by microarray and evaluated before the infection and at 0.5, 2, 4, 6, and 12 hours after infection.
It should be pointed out that even though the LPS dose changes over two orders of magnitude, the heights of the initial peaks of the reconstructed NF-κB activities differ by less than 50%. On the other hand, the increasing LPS dose produced profiles with different features, as described below during the later stage after two hours. Figure 3 illustrates that the activation intensities of all the three NF-κB profiles decrease after the peak at two hours. However, after four hours, the NF-κB activity decreases more slowly (Figure 3A and 3B) in the conditions of both of the lower doses of LPS (0.01 μg/ml, 0.1 μg/ml), whereas NF-κB maintains its activation intensity and results in a shoulder shape (Figure 3C) at six hours under the high dose of LPS (1 μg/ml). These patterns of reconstructed NF-κB activity were used in identification of corresponding IKK activity profiles.
IKK-NF-κB signaling simulation
The nuclear transcription factor NF-κB is a protein complex that regulates numerous physiological processes [44, 46] and plays a pivotal role in regulating the innate and adaptive immune responses [47–49]. Inactive NF-κB is retained in the cytoplasm while bound to an inhibitory factor, IκB. Upon stimulation by TNF-α, bacterial LPS or IL-1, NF-κB can be released from IκB and activated through a series of signal transduction pathways. Among these pathways, IKK proteins are the converging key regulators of NF-κB signaling. For example, when bacterial pathogens present virulent factors into host cells, IκB is phosphorylated by activated IKK and subsequently degraded by the ubiquitin-proteasome complex . Upon IκB degradation, the activated NF-κB is then translocated into the nucleus where it regulates transcription from a large number of genes encoding components of the immune system, including a wide range of pro-inflammatory cytokines, chemokines, adhesion molecules, and inducible enzymes . Accordingly, a multitude of signals transmitted from various receptors in the cell results in IKK activation, thus initiating the IKK-IκB-NF-κB signaling cascade that regulates NF-κB activity.
Previously, a computational model describing the inflammatory signal transduction dynamics in mammals from IKK to NF-κB has been proposed and experimentally validated by Hoffmann and colleagues [42, 45, 51, 52]. Their model allows temporal simulations of the dynamic profile of NF-κB in the nucleus upon in the input of different profiles of IKK activity. In this study, we adapted the Hoffmann model [45, 51, 52] and expressed it in ODEs representing the mass balances of 24 components involving 72 reactions. The Hybrid Functional Petri-Net (HFPN) [53, 54] was then used to carry out simulations of the kinetic model. HFPN uses uncomplicated graph annotations to define kinetic functions. The architecture of the model are shown in the supplementary data, and a detailed explanation is provided in the Materials and Methods section.
Based on this simulation model of IKK-NF-κB signaling, we can infer that IKK activity arises from the LPS stimuli by mapping the simulated NF-κB profiles to those derived from the gene expression datasets. Moreover, because the simulated IKK profiles have consistent activity with the derived NF-κB profiles, we would expect that the inferred IKK profiles would have similar features to the reconstructed NF-κB profiles from the gene expression data.
Linking the NF-κB signaling pathway to the corresponding changes in gene expression
The computational model of the forward simulation for the NF-κB signaling pathway provides NF-κB profiles under different IKK stimuli. Given the reconstructed NF-κB profiles from the gene expression data and the computational model of the forward simulation under different IKK stimuli, we can generate IKK activity curves by matching the NCA-derived NF-κB profiles and the forward simulated NF-κB profiles initially. Then, the IKK activity curve that corresponds to the best-matched forward-simulated NF-κB profile will be selected.
Validations and model interpretation
TNF-α and IL-1 are the main pro-inflammatory mediators of inflammation and tissue damage that are induced by NF-κB and associated with septic shock in acute severe infection. They play a synergistic role in orchestrating the inflammatory response and enhance the NF-κB activities by positive feedback loops. This positive effect of increasing TNFα and IL-1 on the NF-κB activity might explain the second wave of the NF-κB profile predicted by our integrative model in the presence of the highest LPS dose. Large amounts of TNF-α and IL-1 would result in a systemic inflammatory response syndrome (SIRS). This phenomenon leads to increased vascular permeability and the generation of thrombi in many small vessels; it also consumes massive amounts of blood-clotting proteins, thus deregulating hemostasis in the patient. The phenomenon also frequently results in the failure of several organs in the body; thus, septic shock often results in a high mortality rate [58, 59].
IL-6 is also one of the most important mediators of the acute inflammatory response that is released in response to IL-1. IL-6 is responsible for the induction of fever; it stimulates energy mobilization, which then leads to an increased body temperature in the muscle and fatty tissue [60, 61]. Moreover, in acute injury, high-dose bacterial invasion would induce acute inflammatory responses. The aberrant secretion of pro-inflammatory cytokines alters fibrin deposition and degradation and results in the abnormal cell-cell adhesion formation. CXCL1, a cytokine-induced neutrophil chemoattractant, has been revealed to be involved in cell-cell adhesion formation in sepsis , which is a serious challenge in acute infections and leads to high morbidity.
CXCL2 is also a critical chemokine for neutrophils. CXCL2 has been implicated in responses to both TNF-α and LPS via the NF-κB-dependent pathway. During infection or sepsis, increased CXCL2 would recruit mucosal neutrophils and amplify the inflammatory response. This might give rise to intestinal injury . Besides the cytokines and chemokines described above, the expression of CCL3 also correlates with NF-κB activity, and NF-κB also strongly influences CCL3 expression. CCL3, a β-chemokine, regulates the migration and recruitment of various effector cells, such as monocytes, neutrophils, and natural killer cells, into infected sites. Previous studies provide evidence that CCL3 is involved in the degranulation and recruitment of mast cells . The key roles of mast cells in the innate immunity against acute inflammation include the release of TNF-α-histamine and other inflammatory mediators, as well as bacterial phagocytosis. CCL3 can activate neutrophils and enhance the phagocytic antibacterial functions of macrophages. CCL3, therefore, is considered to play a crucial role in mast cell-associated host resistance against sepsis or other acute inflammation.
In summary, we have linked a signaling pathway with the resulting gene responses by mapping the transcription factor activity (e.g., NF-κB) inferred from forward and reverse engineering for three different stimuli strengths. The corresponding signal profiles (e.g., IKK) were successfully reconstructed by our proposed method. Furthermore, the genes that highly correlated with transcription factor activity and had relatively strong influence strengths were also identified to reflect the biological consequences. This method is a practical and effective solution for linking exogenous cellular stimuli with signal transduction dynamics and gene expression profiling. It provides an overall systematic view of the inflammation process. In the near future, this strategy may be applied to more systems, as genome-wide regulatory data (such as ChIP-chip, ChIP-PET) involving transcription factors in mammals become available. For example, some inflammatory and autoimmune diseases are caused by the aberrant regulation of NF-κB, e.g., rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, atherosclerosis, and asthma [65, 66]. Therefore, the application of our approach, which utilizes the dynamic simulation of a signaling pathway and reverse engineering, will be beneficial in the development of an in silico model for drug design and effective therapeutic strategies.
Network component analysis
The matrix [E]I × Jis the logarithm of the signals of the microarray data containing I genes and J time points. [S]I × Krepresents the regulatory relationship and influence strength between K transcription factors and I regulated genes. [A]K × Irepresents the activities of the K transcription factors at the J time points.
Given [E]I × J, [S]I × Kand [A]K × Jcan be obtained using a bipartite decomposition technique that requires knowledge of the position of the nonzero elements of [S]I × K, i.e., the prior knowledge of the regulatory relationships between the transcription factors and genes. The regulatory information was obtained from the TRANSFAC database (version 11.2) and MetaCore™ analytical suite, as shown in Figure 2. All of the NF-κB/RelA:P50-regulated genes in our network were expected to be significantly upregulated or downregulated.
Forward simulation by HFPN
HFPN is an extension of Petri Net that can handle continuous events of real numbers . It has the advantage of an intuitive representation for modeling a dynamic biological system. There are two kinds of places and transition components in HFPN, discrete/continuous places and discrete/continuous transitions. Discrete places and transitions hold nonnegative integer number tokens as their content. A continuous place can contain nonnegative real numbers. A continuous transition fires continuously according to a given speed function and values in the places. In addition, three kinds of arcs are defined. A normal arc makes the tokens in a place increase or decrease and triggers the next transition. An inhibitory arc is used to stop the action of transitions, and a test arc does not decrease tokens but triggers a connected transition.
In this study, the NF-κB signaling model was implemented by HFPN using the revised ODE model proposed by Hoffmann and colleagues [45, 51, 52]. This ODE model was modified from an earlier version  to include IKK time-course generator, nuclear-cytoplasmic volume ratio and other important factors as suggested by Lipniacki et al .
We created building blocks of HFPN according to a reaction categorized by the NF-κB signaling model, as shown in Figure S1a (Additional file 2). They include association, dissociation, nuclear import/export, and protein/RNA synthesis and degradation. The proteins are represented by continuous places, and the reactions are represented by continuous transitions. Based on these building blocks, the NF-κB signaling model was implemented in Cell Illustrator 3.0  as shown in Figure S1b (Additional file 2).
We obtained NF-κB profiles by using different IKK profiles in this simulation model. To map the NF-κB profile from gene expression data, we developed an IKK generator, which generated the possible IKK profiles that had different peak times and intensities by adjusting the slope between each time point. The NF-κB profile was mapped in the order of time points at 0.5 h, 2 h, 4 h, 6 h, and 12 h.
Normalizing and mapping the pattern of transcription factor profiles
After the transcription factor profiles from the NCA and signal simulation were normalized, we mapped them by calculating the Pearson correlation between the obtained using different IKK stimuli profiles and the reconstructed from the gene expression to determine which IKK stimulus profile could induce the gene response. Further, the actual activity profile of a transcription factor can be obtained from the signaling simulation, and the connectivity strength can be derived from the NCA equation using the actual activity profile of the transcription factor.
IKK Kinase Assay
The stimulation was performed with the application of LPS at 1, 0.1, and 0.01 μg/ml to the THP-1 human monocytic cell line (American Type Culture Collection), which is used as a cell model system for PBMC . At the indicated time points, before infection and at 0.5, 2, 4, 6, and 12 hours after infection, cells were harvested from the dish, washed twice by PBS, pelleted at 500 × g for 3 min, and snap-frozen in liquid nitrogen. The cell pellet was resuspended in lysis buffer containing 50 mM Tris-HCl, pH 7.6, 250 mM NaCl, 3 mM EDTA, 3 mM EGTA, 1% (v/v) Triton X-100, 0.5% (v/v) Igepal CA-630, 10% glycerol, 20 mM NaF, 40 mM β-glycero-3-phosphate, 2 mM DTT, 1 mM PMSF, 2 mM pNPP, 1 mM Na3VO4, and 10 μg/mL each of aprotinin, bestatin, leupeptin, and pepstatin.
The immuno-precipitation of the cell extract was performed using an anti-IKKγ Ab (BD). Protein A agarose beads were added to pull down the immuno-precipitated IKK complex. The immuno-pellets were washed and resuspended in 20 μl of kinase buffer containing 200 mM HEPES, pH 7.5, 100 mM MgCl2, 5 μCi of [γ-32P]-ATP and 1 μg of GST-IκBα(1-54) substrate and incubated for 30 min at 30°C. The reactions were stopped by the addition of SDS-PAGE loading buffer, heated at 95°C for 5 minutes, and resolved on 10% SDS-PAGE gels by standard procedures. The bottom section of the gel containing the phosphorylated GST-IκBα(1-54) as mounted, and the isotope intensity was detected by film. Proteins in upper section of the gel were transferred onto PVDF membrane (Millipore) and subjected to immunoblotting techniques to detect IKKα (Santa Cruz). The data were analysis by Image Q (GE Healthcare).
network component analysis
tumor necrosis factor alpha
Hybrid Functional Petri-Net
Pearson correlation coefficient.
This study is supported by National Science Council, ROC, under grant NSC96-2627-B-007-007.
- Bhalla US, Iyengar R: Emergent properties of networks of biological signaling pathways. Science 1999, 283(5400):381–387. 10.1126/science.283.5400.381View ArticlePubMed
- Cho KH, Wolkenhauer O: Analysis and modelling of signal transduction pathways in systems biology. Biochem Soc Trans 2003, 31(Pt 6):1503–1509. 10.1042/BST0311503View ArticlePubMed
- Janes KA, Yaffe MB: Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006, 7(11):820–828. 10.1038/nrm2041View ArticlePubMed
- Levchenko A: Dynamical and integrative cell signaling: challenges for the new biology. Biotechnol Bioeng 2003, 84(7):773–782. 10.1002/bit.10854View ArticlePubMed
- Liu Y, Zhao H: A computational approach for ordering signal transduction pathway components from genomics and proteomics Data. BMC Bioinformatics 2004, 5: 158. 10.1186/1471-2105-5-158View ArticlePubMedPubMed Central
- Neves SR, Iyengar R: Modeling of signaling networks. Bioessays 2002, 24(12):1110–1117. 10.1002/bies.1154View ArticlePubMed
- Steffen M, Petti A, Aach J, D'Haeseleer P, Church G: Automated modelling of signal transduction networks. BMC Bioinformatics 2002, 3: 34. 10.1186/1471-2105-3-34View ArticlePubMedPubMed Central
- Suresh Babu CV, Joo Song E, Yoo YS: Modeling and simulation in signal transduction pathways: a systems biology approach. Biochimie 2006, 88(3–4):277–283. 10.1016/j.biochi.2005.08.006View ArticlePubMed
- Forsten-Williams K, Chua CC, Nugent MA: The kinetics of FGF-2 binding to heparan sulfate proteoglycans and MAP kinase signaling. J Theor Biol 2005, 233(4):483–499. 10.1016/j.jtbi.2004.10.020View ArticlePubMed
- Goldstein B, Faeder JR, Hlavacek WS: Mathematical and computational models of immune-receptor signalling. Nat Rev Immunol 2004, 4(6):445–456. 10.1038/nri1374View ArticlePubMed
- Kitano H: International alliances for quantitative modeling in systems biology. Mol Syst Biol 2005, 1: 2005 0007. 10.1038/msb4100011View ArticlePubMedPubMed Central
- Meng TC, Somani S, Dhar P: Modeling and simulation of biological systems with stochasticity. Silico Biol 2004, 4(3):293–309.
- Snoep JL: The Silicon Cell initiative: working towards a detailed kinetic description at the cellular level. Curr Opin Biotechnol 2005, 16(3):336–343. 10.1016/j.copbio.2005.05.003View ArticlePubMed
- Stucki JW, Simon HU: Mathematical modeling of the regulation of caspase-3 activation and degradation. J Theor Biol 2005, 234(1):123–131. 10.1016/j.jtbi.2004.11.011View ArticlePubMed
- Yang CR, Shapiro BE, Hung SP, Mjolsness ED, Hatfield GW: A mathematical model for the branched chain amino acid biosynthetic pathways of Escherichia coli K12. J Biol Chem 2005, 280(12):11224–11232. 10.1074/jbc.M411471200View ArticlePubMed
- Cateau H, Tanaka S: Kinetic analysis of multisite phosphorylation using analytic solutions to Michaelis-Menten equations. J Theor Biol 2002, 217(1):1–14. 10.1006/jtbi.2002.3024View ArticlePubMed
- Chee M, Yang R, Hubbell E, Berno A, Huang XC, Stern D, Winkler J, Lockhart DJ, Morris MS, Fodor SP: Accessing genetic information with high-density DNA arrays. Science 1996, 274(5287):610–614. 10.1126/science.274.5287.610View ArticlePubMed
- Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995, 270(5235):467–470. 10.1126/science.270.5235.467View ArticlePubMed
- Garner MM, Revzin A: A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the Escherichia coli lactose operon regulatory system. Nucleic Acids Res 1981, 9(13):3047–3060. 10.1093/nar/9.13.3047View ArticlePubMedPubMed Central
- Fried M, Crothers DM: Equilibria and kinetics of lac repressor-operator interactions by polyacrylamide gel electrophoresis. Nucleic Acids Res 1981, 9(23):6505–6525. 10.1093/nar/9.23.6505View ArticlePubMedPubMed Central
- D'Haeseleer P, Liang S, Somogyi R: Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 2000, 16(8):707–726. 10.1093/bioinformatics/16.8.707View ArticlePubMed
- Husmeier D: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 2003, 19(17):2271–2282. 10.1093/bioinformatics/btg313View ArticlePubMed
- Liang S, Fuhrman S, Somogyi R: Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 1998, 18–29.
- Repsilber D, Liljenstrom H, Andersson SG: Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses. Biosystems 2002, 66(1–2):31–41. 10.1016/S0303-2647(02)00019-9View ArticlePubMed
- Soranzo N, Bianconi G, Altafini C: Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics 2007, 23(13):1640–1647. 10.1093/bioinformatics/btm163View ArticlePubMed
- Tegner J, Yeung MK, Hasty J, Collins JJ: Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 2003, 100(10):5944–5949. 10.1073/pnas.0933416100View ArticlePubMedPubMed Central
- Wang Y, Joshi T, Zhang XS, Xu D, Chen L: Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 2006, 22(19):2413–2420. 10.1093/bioinformatics/btl396View ArticlePubMed
- Zak DE, Gonye GE, Schwaber JS, Doyle FJ: Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network. Genome Res 2003, 13(11):2396–2405. 10.1101/gr.1198103View ArticlePubMedPubMed Central
- Gardner TS, Faith JJ: Reverse-engineering transcription control networks. Phys Life Rev 2005, 2: 65–88. 10.1016/j.plrev.2005.01.001View ArticlePubMed
- Chen HC, Lee HC, Lin TY, Li WH, Chen BS: Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle. Bioinformatics 2004, 20(12):1914–1927. 10.1093/bioinformatics/bth178View ArticlePubMed
- Raychaudhuri S, Stuart JM, Altman RB: Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 2000, 455–466.
- Liebermeister W: Linear modes of gene expression determined by independent component analysis. Bioinformatics 2002, 18(1):51–60. 10.1093/bioinformatics/18.1.51View ArticlePubMed
- Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP: Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci USA 2003, 100(26):15522–15527. 10.1073/pnas.2136632100View ArticlePubMedPubMed Central
- Hayden MS, West AP, Ghosh S: NF-kappaB and the immune response. Oncogene 2006, 25(51):6758–6780. 10.1038/sj.onc.1209943View ArticlePubMed
- Berardi M, Canonica GW: The inflammatory network. Monaldi Arch Chest Dis 2002, 57(2):147.PubMed
- Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, Chen RO, Brownstein BH, Cobb JP, Tschoeke SK, et al.: A network-based analysis of systemic inflammation in humans. Nature 2005, 437(7061):1032–1037. 10.1038/nature03985View ArticlePubMed
- Hirsch E: Signal transduction in inflammation. Perspective clues from the leukocyte-endothelium interface. Thromb Haemost 2006, 95(1):3–4.PubMed
- Jayapal M, Tay HK, Reghunathan R, Zhi L, Chow KK, Rauff M, Melendez AJ: Genome-wide gene expression profiling of human mast cells stimulated by IgE or FcepsilonRI-aggregation reveals a complex network of genes involved in inflammatory responses. BMC Genomics 2006, 7: 210. 10.1186/1471-2164-7-210View ArticlePubMedPubMed Central
- Kuwano K, Hara N: Signal transduction pathways of apoptosis and inflammation induced by the tumor necrosis factor receptor family. Am J Respir Cell Mol Biol 2000, 22(2):147–149.View ArticlePubMed
- Saban R, D'Andrea MR, Andrade-Gordon P, Derian CK, Dozmorov I, Ihnat MA, Hurst RE, Simpson C, Saban MR: Regulatory network of inflammation downstream of proteinase-activated receptors. BMC Physiol 2007, 7: 3. 10.1186/1472-6793-7-3View ArticlePubMedPubMed Central
- Boldrick JC, Alizadeh AA, Diehn M, Dudoit S, Liu CL, Belcher CE, Botstein D, Staudt LM, Brown PO, Relman DA: Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci USA 2002, 99(2):972–977. 10.1073/pnas.231625398View ArticlePubMedPubMed Central
- Cheong R, Hoffmann A, Levchenko A: Understanding NF-kappaB signaling via mathematical modeling. Mol Syst Biol 2008, 4: 192. 10.1038/msb.2008.30View ArticlePubMedPubMed Central
- Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, et al.: TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 2006, (34 Database):D108–110. 10.1093/nar/gkj143
- Moynagh PN: The NF-kappaB pathway. J Cell Sci 2005, 118(Pt 20):4589–4592. 10.1242/jcs.02579View ArticlePubMed
- Werner SL, Barken D, Hoffmann A: Stimulus specificity of gene expression programs determined by temporal control of IKK activity. Science 2005, 309(5742):1857–1861. 10.1126/science.1113319View ArticlePubMed
- Gerondakis S, Grossmann M, Nakamura Y, Pohl T, Grumont R: Genetic approaches in mice to understand Rel/NF-kappaB and IkappaB function: transgenics and knockouts. Oncogene 1999, 18(49):6888–6895. 10.1038/sj.onc.1203236View ArticlePubMed
- Ghosh S, May MJ, Kopp EB: NF-kappa B and Rel proteins: evolutionarily conserved mediators of immune responses. Annu Rev Immunol 1998, 16: 225–260. 10.1146/annurev.immunol.16.1.225View ArticlePubMed
- Li Q, Verma IM: NF-kappaB regulation in the immune system. Nat Rev Immunol 2002, 2(10):725–734. 10.1038/nri910View ArticlePubMed
- Liou HC: Regulation of the immune system by NF-kappaB and IkappaB. J Biochem Mol Biol 2002, 35(6):537–546.View ArticlePubMed
- Karin M, Delhase M: The I kappa B kinase (IKK) and NF-kappa B: key elements of proinflammatory signalling. Semin Immunol 2000, 12(1):85–98. 10.1006/smim.2000.0210View ArticlePubMed
- Cheong R, Bergmann A, Werner SL, Regal J, Hoffmann A, Levchenko A: Transient IkappaB kinase activity mediates temporal NF-kappaB dynamics in response to a wide range of tumor necrosis factor-alpha doses. J Biol Chem 2006, 281(5):2945–2950. 10.1074/jbc.M510085200View ArticlePubMed
- Kearns JD, Basak S, Werner SL, Huang CS, Hoffmann A: IκBε provides negative feedback to control NF-kappaB oscillations, signaling dynamics, and inflammatory gene expression. J Cell Biol 2006, 173(5):659–664. 10.1083/jcb.200510155View ArticlePubMedPubMed Central
- Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S: Biopathways representation and simulation on hybrid functional Petri net. Silico Biol 2003, 3(3):389–404.
- Peng SC, Chang HM, Hsu DF, Tang CY: Modeling Signal Transduction of Neural System by Hybrid Petri Net Representation. In Operations Research Proceedings: 2004; Tilburg. Springer; 2004:271–279.
- Dunne A, O'Neill LA: The interleukin-1 receptor/Toll-like receptor superfamily: signal transduction during inflammation and host defense. Sci STKE 2003, 2003(171):re3. 10.1126/stke.2003.171.re3PubMed
- Guergnon J, Chaussepied M, Sopp P, Lizundia R, Moreau MF, Blumen B, Werling D, Howard CJ, Langsley G: A tumour necrosis factor alpha autocrine loop contributes to proliferation and nuclear factor-kappaB activation of Theileria parva-transformed B cells. Cell Microbiol 2003, 5(10):709–716. 10.1046/j.1462-5822.2003.00314.xView ArticlePubMed
- Netea MG, van der Meer JW, van Deuren M, Kullberg BJ: Proinflammatory cytokines and sepsis syndrome: not enough, or too much of a good thing? Trends Immunol 2003, 24(5):254–258. 10.1016/S1471-4906(03)00079-6View ArticlePubMed
- Durocher A, Becq MC, Gosset P, Saulnier F, Lefebvre MC, Tonnel AB, Capron A, Wattel F: [TNF and sepsis]. Ann Med Interne (Paris) 1991, 142(2):91–94.
- Dinarello CA: Proinflammatory and anti-inflammatory cytokines as mediators in the pathogenesis of septic shock. Chest 1997, 112(6 Suppl):321S-329S. 10.1378/chest.112.6_Supplement.321SView ArticlePubMed
- Makino T, Noguchi Y, Yoshikawa T, Doi C, Nomura K: Circulating interleukin 6 concentrations and insulin resistance in patients with cancer. Br J Surg 1998, 85(12):1658–1662. 10.1046/j.1365-2168.1998.00938.xView ArticlePubMed
- Febbraio MA, Pedersen BK: Contraction-induced myokine production and release: is skeletal muscle an endocrine organ? Exerc Sport Sci Rev 2005, 33(3):114–119. 10.1097/00003677-200507000-00003View ArticlePubMed
- Wagner TH, Drewry AM, Macmillan S, Dunne WM, Chang KC, Karl IE, Hotchkiss RS, Cobb JP: Surviving sepsis: bcl-2 overexpression modulates splenocyte transcriptional responses in vivo. Am J Physiol Regul Integr Comp Physiol 2007, 292(4):R1751–1759.View ArticlePubMed
- De Plaen IG, Han XB, Liu X, Hsueh W, Ghosh S, May MJ: Lipopolysaccharide induces CXCL2/macrophage inflammatory protein-2 gene expression in enterocytes via NF-kappaB activation: independence from endogenous TNF-alpha and platelet-activating factor. Immunology 2006, 118(2):153–163. 10.1111/j.1365-2567.2006.02344.xView ArticlePubMedPubMed Central
- Takahashi H, Tashiro T, Miyazaki M, Kobayashi M, Pollard RB, Suzuki F: An essential role of macrophage inflammatory protein 1alpha/CCL3 on the expression of host's innate immunities against infectious complications. J Leukoc Biol 2002, 72(6):1190–1197.PubMed
- Tak PP, Firestein GS: NF-kappaB: a key role in inflammatory diseases. J Clin Invest 2001, 107(1):7–11. 10.1172/JCI11830View ArticlePubMedPubMed Central
- Yamamoto Y, Gaynor RB: Therapeutic potential of inhibition of the NF-kappaB pathway in the treatment of inflammation and cancer. J Clin Invest 2001, 107(2):135–142. 10.1172/JCI11914View ArticlePubMedPubMed Central
- Hoffmann A, Levchenko A, Scott ML, Baltimore D: The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation. Science 2002, 298(5596):1241–1245. 10.1126/science.1071914View ArticlePubMed
- Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M: Mathematical model of NF-kappaB regulatory module. J Theor Biol 2004, 228(2):195–215. 10.1016/j.jtbi.2004.01.001View ArticlePubMed
- Nagasaki M, Doi A, Matsuno H, Miyano S: Genomic Object Net: I. A platform for modelling and simulating biopathways. Appl Bioinformatics 2003, 2(3):181–184.PubMed
- Sharif O, Bolshakov VN, Raines S, Newham P, Perkins ND: Transcriptional profiling of the LPS induced NF-kappaB response in macrophages. BMC Immunol 2007, 8: 1. 10.1186/1471-2172-8-1View ArticlePubMedPubMed Central
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