Macroscopic law of conservation revealed in the population dynamics of Toll-like receptor signaling
© Selvarajoo; licensee BioMed Central Ltd. 2011
Received: 11 March 2011
Accepted: 20 April 2011
Published: 20 April 2011
Stimulating the receptors of a single cell generates stochastic intracellular signaling. The fluctuating response has been attributed to the low abundance of signaling molecules and the spatio-temporal effects of diffusion and crowding. At population level, however, cells are able to execute well-defined deterministic biological processes such as growth, division, differentiation and immune response. These data reflect biology as a system possessing microscopic and macroscopic dynamics. This commentary discusses the average population response of the Toll-like receptor (TLR) 3 and 4 signaling. Without requiring detailed experimental data, linear response equations together with the fundamental law of information conservation have been used to decipher novel network features such as unknown intermediates, processes and cross-talk mechanisms. For single cell response, however, such simplicity seems far from reality. Thus, as observed in any other complex systems, biology can be considered to possess order and disorder, inheriting a mixture of predictable population level and unpredictable single cell outcomes.
Keywordscell signaling governing law systems biology microscopic and macroscopic dynamics immune response
The innate immune cell, e.g. macrophage, upon recognition of external stimuli, such as a pathogen, invokes a sequence of molecular events, from receptor activation to gene expressions in the nucleus. This results in the induction of various proinflammatory cytokines that subsequently eliminate the intruders, usually through the adaptive immunity . The well-orchestrated, self-organized and stable immune response, over a wide range and variety of perturbation, is observed at population level. However, recent reports at single cell resolution highlight the issue of cellular heterogeneity and stochasticity, switching attention to the variability and complexity of biological behaviors [2–4].
where F j X j and F k X i represent each formation (activation) and depletion (deactivation) term for the ith molecule, respectively, and they can be any linear or non-linear function. Note that the number of formation and depletion terms, p and q for each ith molecule, are variables obtained from the network topology of interest.
Given small perturbation to the generalized Eq.1, higher-order terms become negligible [15, 16]. Hence, the partial differentiation in Eq. 1 can be changed into ordinary differentiation, i.e. where X= (X1, X2,..., X n ). Our previous works have used the approximation and have shown that a linear superposition of propagation response waves, or first-order mass-action response equations, can sufficiently be used to model the reaction chains of the TLR signaling [18–21]. This is valid especially for average cell response investigated for time points with a restricted range, usually before 120 min, where post-translational regulations (e.g. feedback or feedforward mechanisms) are insignificant [18, 22].
Unlike typical kinetic models, which often use similar equations or sometimes with non-linear expressions to model the dynamics of biological networks, our perturbation-response approach considers the network as a sequence of events rather than molecules. As signaling networks are largely not fully understood, this difference is crucial as it prevents rigidly fixing the network topologies, and allows it to be modified according to experimental data so as to prevent overfitting problems and to identify novel features of signaling networks. In addition, as signaling process involves large number (thousands) of intracellular molecular activations, it is currently not plausible to model the dynamics of all possible reactions with the generally limited data. To overcome such difficulties, our approach permits the lumping of several molecules into a signaling specie in the model network. In this way, although the model does not become a comprehensive representation of an entire signaling process, however, it still allows the identification of overtly missing key features.
To successfully identify novel features of signaling networks, we set a target that the computational model should be able to simulate not just one experimental condition (like most models do), but in as many conditions as available. For the TLR4 signaling, we developed a wildtype model and compared the simulations with MyD88 and TRAF6 knock-outs (KOs), a total of three conditions. The initial wildtype model's parameter values for formation and depletion terms were determined directly by fitting simulations with quantified time-course activation experimental profiles of proinflammatory molecules (NF-κB, JNK, p38, Tnf, Il6, Ifit1 and Cxcl10) for LPS-stimulated murine macrophages [18, 19].
Like any other modeling approach, there are certain limitations that require mentioning. Firstly, the perturbation-response approach discussed does not comprehensively represent the details of each signaling reaction's kinetics. Secondly, the small perturbation assumption leading to the first-order mass-action equations represents an average cell response and this cannot be used to study single cell stochastic behavior. Thirdly, the model predictions will show relative, and not absolute, activation levels. However, the approach is not restricted to the TLR pathways and can be applied to model any pathways that experimentally display formation and depletion waves, e.g. the EGF receptor signaling . For information conservation to be observed, we need enough number of key output species to be monitored temporally.
In spite of the limitations, we predicted i) the presence of novel signaling intermediates along the TRAM-dependent pathways , ii) crosstalk mechanisms between the MyD88- and TRAM-dependent pathways , and iii) the concept of signaling flux redistribution or SFR. The prediction of novel terms were later confirmed experimentally to be the phosphorylation of TRAM by PKCε and the sequential events of TLR4 endocytosis leading to TRAM activation [23, 24]. The concept of SFR is based on the law of conservation where the removal of MyD88 resulted in the increased activation of the entire alternative TRAM-dependent pathway (Figure 3C, D). That is, the total signaling flux (information) propagation through the network from receptor activation through downstream gene activation is conserved. We experimentally validated SFR in two mutant conditions (MyD88 and TRAF6 KOs), where we observed increased activation of several alternative pathway molecules .
Similarly to the TLR4 signaling, we also investigated the TLR3 innate immune response against viral attacks by polyinosinic-polycytidylic acid (poly (I:C)). This leads to the activation of MAP kinases and NF-κB, which results in the induction of type I interferons and proinflammatory cytokines to combat the viral infection. Here, again analyzing the activation dynamics of the NF-κB, p38 and JNK in wildtype, TRAF6 KO and TRADD KO using a linear response model obeying the law of information conservation, we inferred i) the existence of missing intermediary steps between extracellular poly (I:C) stimulation and intracellular TLR3 binding, and ii) the presence of a novel pathway which is essential for JNK and p38, but not NF-κB, activation .
Here I present, with examples from TLR 3 & 4 signaling in wildtype and several mutants, that the law of conservation and first order response equations are sufficient and important to reveal novel features of the complex immune process. This result is surprising as there is a general consensus that we need to fully understand all molecular interactions in the signaling network in order to make useful predictions [8, 13]. Furthermore, the results from recent single cell experiments showing stochastic responses ask for spatial-temporal details to understand cellular signaling correctly [3, 25].
So, instead of losing interest in the population level dynamics and move towards more single cell analyses, I propose biology is a system that possesses both microscopic and macroscopic dynamics, as observed in other physical sciences. For example, in the study of fluid dynamics, at microscopic level we observe the motion of each individual particle to be highly random and unpredictable and at macroscopic level, the velocity of airflow follows the fundamental law of fluid mechanics (the law of conservation of mass, energy and momentum). Thus, it is necessary to treat the two dynamics distinct.
It is also interesting to ponder the origins of averaging effect from stochastic response of a single cell when ensembles of them form a population. I believe that the emergence of average cell deterministic response from single cell stochastic response complement each other. For example, the stochastic fluctuations produced by a single cell are necessary to induce probabilistic differentiation from genetically identical cells [26–28]. This allows multi-cellular organisms to switch fates and states to yield diversity, such as for development or stress, which, otherwise, may be impossible from a purely deterministic system.
On the other hand, the well-coordinated response of cell populations, such as differentiation or growth, demonstrates that the single cell noise could cancel out when ensembles of cells are formed to generate a stable and robust response. Thus, the search for governing laws arising from single cell dynamics will enable us to better understand the coordinated response of cell populations. Most importantly, finding the connection between microscopic and macroscopic dynamics and the unifying laws are crucial for understanding the origins of evolutionary and developmental robustness of living systems to diverse environmental attacks.
myeloid differentiation primary response gene (88)
TRIF-related adaptor molecule
TIR-domain-containing adapter-inducing interferon-β
c-Jun N-terminal kinases
TNF receptor associated factor 6 protein
tumor necrosis factor
Tumor necrosis factor receptor type 1-associated DEATH domain protein
interferon regulatory factor-3
inhibitor of kappa B-α.
I am grateful to Masa Tsuchiya who inspired me to write this commentary on the power of simple rules, Masaru Tomita for research support, and the two anonymous reviewers for their critical suggestions. My heart and dedication goes to the victims of the March 11 earthquake in Japan. This work was supported by the research fund of Yamagata Prefecture and Tsuruoka City, Japan.
- Kawai T, Akira S: The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat Immunol. 2010, 11: 373-84. 10.1038/ni.1863.View ArticlePubMed
- Elowitz MB, Levine AJ, Siggia ED, Swain PS: Stochastic gene expression in a single cell. Science. 2002, 297: 1183-1186. 10.1126/science.1070919.View ArticlePubMed
- Tay S, Hughey JJ, Lee TK, Lipniacki T, Quake SR, Covert MW: Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature. 2010, 466: 267-71. 10.1038/nature09145.PubMed CentralView ArticlePubMed
- Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S: Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature. 2008, 453: 544-547. 10.1038/nature06965.View ArticlePubMed
- Bar-Even A, Paulsson J, Maheshri N, Carmi M, O'Shea E, Pilpel Y, Barkai N: Noise in protein expression scales with natural protein abundance. Nat Genet. 2006, 38: 636-643. 10.1038/ng1807.View ArticlePubMed
- Costa MN, Radhakrishnan K, Wilson BS, Vlachos DG, Edwards JS: Coupled stochastic spatial and non-spatial simulations of ErbB1 signaling pathways demonstrate the importance of spatial organization in signal transduction. PLoS One. 2009, 4: e6316-10.1371/journal.pone.0006316.PubMed CentralView ArticlePubMed
- Kholodenko BN, Hancock JF, Kolch W: Signalling ballet in space and time. Nat Rev Mol Cell Biol. 2010, 11: 414-426. 10.1038/nrm2901.PubMed CentralView ArticlePubMed
- Dehmelt L, Bastiaens PIH: Spatial organization of intracellular communication: insights from imaging. Nat Rev Mol Cell Biol. 2010, 11: 440-452. 10.1038/nrm2903.View ArticlePubMed
- Ando T, Skolnick J: Crowding and hydrodynamic interactions likely dominate in vivo macromolecular motion. Proc Natl Acad Sci USA. 2010, 107: 18457-462. 10.1073/pnas.1011354107.PubMed CentralView ArticlePubMed
- Wilkinson Darren: Stochastic modelling for quantitative description of heterogeneous biological systems. Nature Reviews Genetics. 2009, 10 (2): 122-133. 10.1038/nrg2509.View ArticlePubMed
- Heylighen F: Complexity and Self-organization. Encyclopedia of Library and Information Sciences. Edited by: Bates MJ, Maack MN. 2009, CRC Press, Taylor & Francis, [http://pespmc1.vub.ac.be/Papers/ELIS-complexity.pdf]3
- Haken H: Information and Self-organization: A Macroscopic Approach to Complex Systems. Springer Series in Synergetics. 2006, Springer Berlin Heidelberg, 3
- Hood L, Rowen L, Galas DJ, Aitchison JD: Systems biology at the Institute for Systems Biology. Brief Funct Genomic Proteomic. 2008, 7: 239-48. 10.1093/bfgp/eln027.View ArticlePubMed
- Vance W, Arkin A, Ross J: Determination of causal connectivities of species in reaction networks. Proc Natl Acad Sci USA. 2002, 99: 5816-5821. 10.1073/pnas.022049699.PubMed CentralView ArticlePubMed
- Ross J: From the determination of complex reaction mechanisms to systems biology. Annu Rev Biochem. 2008, 77: 479-494. 10.1146/annurev.biochem.77.102507.115132.View ArticlePubMed
- Selvarajoo K, Tomita M, Tsuchiya M: Can complex cellular processes be governed by simple linear rules?. J Bioinform Comput Biol. 2009, 7 (1): 243-268. 10.1142/S0219720009003947.View ArticlePubMed
- Blagoev B, Ong SE, Kratchmarova I, Mann M: Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nat Biotechnol. 2004, 22: 1139-1145. 10.1038/nbt1005.View ArticlePubMed
- Selvarajoo K, Takada Y, Gohda J, Helmy M, Akira S, Tomita M, Tsuchiya M, Inoue J, Matsuo K: Signaling Flux Redistribution at Toll-Like Receptor Pathway Junctions. PLoS One. 2008, 3: e3430-10.1371/journal.pone.0003430.PubMed CentralView ArticlePubMed
- Selvarajoo K: Discovering differential activation machinery of the Toll-like receptor 4 signaling pathways in MyD88 knockouts. FEBS Lett. 2006, 580: 1457-64. 10.1016/j.febslet.2006.01.046.View ArticlePubMed
- Selvarajoo K: Decoding the Signaling Mechanism of Toll-Like Receptor 4 Pathways in Wild Type and Knockouts. E-Cell System--Basic Concepts and Applications. Edited by: Ghosh S, Arjunan S, Dhar P, Tomita M. 2007, New York: Landes Bioscience and Springer Science, [http://www.landesbioscience.com/curie/chapter/3584/]
- Helmy M, Gohda J, Inoue J, Tomita M, Tsuchiya M, Selvarajoo K: Predicting novel features of toll-like receptor 3 signaling in macrophages. PLoS One. 2009, 4: e4661-10.1371/journal.pone.0004661.PubMed CentralView ArticlePubMed
- Werner SL, Barken D, Hoffmann A: Stimulus specificity of gene expression programs determined by temporal control of IKK activity. Science. 2005, 309: 1857-61. 10.1126/science.1113319.View ArticlePubMed
- Kagan JC, Su T, Horng T, Chow A, Akira S, Medzhitov R: TRAM couples endocytosis of Toll-like receptor 4 to the induction of interferon-beta. Nat Immunol. 2008, 9: 361-8. 10.1038/ni1569.PubMed CentralView ArticlePubMed
- McGettrick AF, Brint EK, Palsson-McDermott EM, Rowe DC, Golenbock DT, Gay NJ, Fitzgerald KA, O'Neill LA: Trif-related adapter molecule is phosphorylated by PKCepsilon during Toll-like receptor 4 signaling. Proc Natl Acad Sci USA. 2006, 103: 9196-9201. 10.1073/pnas.0600462103.PubMed CentralView ArticlePubMed
- Hsieh MY, Yang S, Raymond-Stinz MA, Edwards JS, Wilson BS: Spatio-temporal modeling of signaling protein recruitment to EGFR. BMC Syst Biol. 2010, 4: 57-10.1186/1752-0509-4-57.PubMed CentralView ArticlePubMed
- Losick R, Desplan C: Stochasticity and cell fate. Science. 2008, 320: 65-68. 10.1126/science.1147888.PubMed CentralView ArticlePubMed
- Eldar A, Elowitz MB: Functional roles for noise in genetic circuits. Nature. 2010, 467: 167-73. 10.1038/nature09326.PubMed CentralView ArticlePubMed
- Raj A, Rifkin SA, Andersen E, van Oudenaarden A: Variability in gene expression underlies incomplete penetrance. Nature. 2010, 463: 913-8. 10.1038/nature08781.PubMed CentralView ArticlePubMed
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