Viewpoint Cite This: Biochemistry 2018, 57, 9−10
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Computational Systems Biochemistry: Beyond the Static Interactome Sarvenaz Sarabipour* and Feilim Mac Gabhann Institute for Computational Medicine, Department of Biomedical Engineering, and Institute for NanoBio Technology, Johns Hopkins University, Baltimore, Maryland 21218, United States
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comprehensive mechanistic understanding of cell signaling networks is crucial for elucidating biological function in human health and disease. The observed diversity of cell and tissue phenotypes results from the integration of subcellular components and molecular constituents, including subnetworks that each are predictable in isolation but cooperate in complex ways. Unraveling the dynamic role of each protein and pathway is experimentally challenging. Quantifying the individual protein−protein interaction events (e.g., binding kinetics, thermodynamics, and post-translational modifications) and structure (receptors adopt multiple conformations to enhance or silence signaling activity) is invaluable in understanding recognition (specificity or promiscuity). However to understand whole network complexity, typical analytical assays that assess binary interactions and individual molecular states are insufficient, or resource-prohibitive at scale. High-throughput systems-level in vitro or in vivo experiments provide global and often quantitative insights into key signaling proteins, with results usually depicted as static signaling pathway connection maps. Spatiotemporal dynamics of protein signaling and gene regulatory networks need more than traditional biochemical measurements or systems-scale high-dimensional data alone. Computational systems biochemistry aims to integrate mathematical modeling and quantitative experimentation to understand the biochemical signaling networks in action inside the cell. This is done by simulating formal mathematical representations of biophysical−biochemical reactions and generates experimentally testable hypotheses. The models can incorporate cell surface proteins and signaling lipids, interacting with a multitude of growth factors, ions, and small molecule peptides (Figure 1). Each protein can have multiple specific post-translational modifications, and each has cell-specific, temporally controlled, and molecularly regulated expression patterns. Many of the modifications (e.g., phosphorylation) are perturbed in disease, causing dysregulated signaling. All of these complex features can be reproduced in the models. Mathematical models have been critically important in the identification and quantification of mechanisms of action and network dynamics, of key proteins and pathways, e.g., the nuclear factor κB (NF-κB) transcription factor system. The NFκBs regulate cell fate decisions such as differentiation and proliferation. Inactive NF-κBs reside in the cytosol; the activation and subsequent nuclear translocation of NF-κB upon extracellular stimulation result in DNA binding and regulation of multiple gene expression programs. This activity is largely controlled by three cytosolic inhibitory proteins (IκB isoforms) that bind to NF-κB. NF-κB translocation is also regulated by phosphorylation of IKKs (IκB kinases) (IKKα and IKKβ). Activated IKK isoforms control post-translational modifications (phosphorylation) of NF-κB, causing release and degradation of IκBs in response to extracellular stimulation © 2017 American Chemical Society
Figure 1. Emergent cellular phenotypes are regulated by an intricate system of fast biochemical interactions with different dynamics. Probing signaling pathways experimentally is challenging. Computational systems biochemistry brings high-dimensional experimental measurements together with mechanistic computational modeling to provide in-depth analysis of cell signaling networks in action. Illustration by Sarvenaz Sarabipour and Martin Rietveld.
of specific cell surface receptors. Mathematical models have enabled quantification of the dynamics of active NF-κB and the specific role of the three IκBs in NF-κB activation.1,2 This is not feasible using experimental measurements that can probe only a limited number of interactions. Hoffmann et al. built a mathematical model that described how the NF-κB system produces time-dependent oscillations. This model showed that the combined action of IκBβ and -ε isoforms results in a dampened activation of NF-κB. The model also revealed discrete functional roles for the mammalian IκB isoforms in Special Issue: Future of Biochemistry Received: November 8, 2017 Published: December 8, 2017 9
DOI: 10.1021/acs.biochem.7b01133 Biochemistry 2018, 57, 9−10
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Biochemistry
The effect of many variables and parameters can be checked in silico, providing a fast and economical method for designing the most insightful experiments. Built in specific health and disease contexts, these high-quality multiscale computational models will enable predictions of dynamic organ-level physiology, with the potential to serve as powerful and flexible tools for clinical diagnostics and therapy.
NF-κB regulation. The authors identified that IκBα is responsible for the prolonged oscillatory nuclear translocation of NF-κB that is observed experimentally in a variety of cell lines stimulated with tumor necrosis factor (TNF).2 The work also showed that unlike IκBα, the effect of IκBβ and IκBε on NF-κB is one-way (they inhibit NF-κB, but NF-κB does not control their production). The realization of the potential of NF-κB as a drug target in a specific disease is dependent on understanding the mechanisms that specifically and temporally govern NF-κB-responsive gene expression. Hoffmann et al. used their model as a predictive tool to design new experiments and showed how NF-κB activation dynamics can be manipulated to switch on different target genes. Cellular signaling pathways can also exhibit switchlike properties in response to transient or spatial-gradient stimuli. Shinohara, Behar, Hoffmann, and colleagues identified such a switch mechanism for NF-κB activation.3 The authors combined system-level genomic data with a detailed computational model of NF-κB activation in B-cell receptor (BCR) signaling to study receptor behavior over different stimulation time scales. This model of NF-κB activity exhibited two characteristic behaviors: oscillations regulated by negative feedback and switchlike activation regulated by positive feedback. Quantitative model simulations further demonstrated that different signaling thresholds of NF-κβ are regulated by different modifications of caspase recruitment domain-containing intracellular protein 11 (CARMA1). The results showed that IKK activity is regulated by positive feedback from IKKβ to TGF-β activated kinase 1 (TAK1), generating an activation threshold (a steep dose response) to B-cell receptor stimulation.3 These thresholds are challenging to probe experimentally because of limitations in the resolution of experimental assays. Models also allow studies of signaling in the context of cells or cell lineage in response to environmental cues. A model by Inoue et al. showed that oscillations and switchlike activation of NF-κB are two separate behaviors caused by antigen receptor activation.4 Their model integrated the earlier model of Shinohara et al. with new experimental data on multiple pathway proteins and transcriptional regulation of NF-κB. The new comprehensive framework robustly predicted an oscillatory response in both TNF and BCR signaling networks and showed that NF-κB abundance causes oscillations in the absence of a switchlike behavior. Their computational analysis further provides a rationale for the tight control of specific cell types over protein abundances and interaction kinetics, offering a path forward for inhibiting dysregulation of key proteins in specific disease.4 These insights could not have been obtained from the heavily simplified experimental context alone. Biochemically detailed computational models will further be critical in creating personalized multiscale models of human physiology and pathophysiology. Shokhirev et al. developed a multiscale model of B-cell population dynamics based on the intracellular NF-κB network to characterize the underlying cellular mechanisms regulating cell decisions.5 Their biochemically detailed model included processes involved in transducing receptor-mediated signaling, cell growth, cell cycling, and apoptosis, predicting that the cRel subunit of NF-κB enforces the execution of a cellular decision between race and death fates by promoting survival in growing cells. Thus, carefully combining models and measurements will yield exciting mechanistic contributions to the future of biochemistry, physiology, and the design of novel therapeutic strategies.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Sarvenaz Sarabipour: 0000-0001-5097-5509 Feilim Mac Gabhann: 0000-0003-3481-7740 Funding
This work was supported by National Institutes of Health Grant R01-HL101200 to F.M.G. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank Martin Rietveld and Allan Doyle at Johns Hopkins Institute for NanoBio Technology for help with the illustration.
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REFERENCES
(1) Mitchell, S., Vargas, J., and Hoffmann, A. (2016) Signaling via the NF kappa B system. Wiley Interdisciplinary Reviews-Systems Biology and Medicine 8, 227−241. (2) Hoffmann, A., Levchenko, A., Scott, M. L., and Baltimore, D. (2002) The I kappa B-NF-kappa B signaling module: Temporal control and selective gene activation. Science 298, 1241−1245. (3) Shinohara, H., Behar, M., Inoue, K., Hiroshima, M., Yasuda, T., Nagashima, T., Kimura, S., Sanjo, H., Maeda, S., Yumoto, N., Ki, S., Akira, S., Sako, Y., Hoffmann, A., Kurosaki, T., and OkadaHatakeyama, M. (2014) Positive Feedback Within a Kinase Signaling Complex Functions as a Switch Mechanism for NF-kappa B Activation. Science 344, 760−764. (4) Inoue, K., Shinohara, H., Behar, M., Yumoto, N., Tanaka, G., Hoffmann, A., Aihara, K., and Okada-Hatakeyama, M. (2016) Oscillation dynamics underlie functional switching of NF-[kappa] B for B-cell activation. npj Systems Biology and Applications 2, 10. (5) Shokhirev, M. N., Almaden, J., Davis-Turak, J., Birnbaum, H. A., Russell, T. M., Vargas, J. A. D., and Hoffmann, A. (2015) A multi-scale approach reveals that NF-kappa B cRel enforces a B-cell decision to divide. Mol. Syst. Biol. 11, 783.
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DOI: 10.1021/acs.biochem.7b01133 Biochemistry 2018, 57, 9−10
