- Open Access
Systems biology approaches in cell signaling research
© BioMed Central Ltd 2005
Published: 29 September 2005
The use of methods for global and quantitative analysis of cells is providing new systems-level insights into signal transduction processes. Recent studies reveal important information about the rates of signal transmission and propagation, help establish some general regulatory characteristics of multi-tiered signaling cascades, and illuminate the combinatorial nature of signaling specificity in cell differentiation.
The most useful road maps are those that provide an overview of the major highways, as well as displaying street-by-street detail for specific locations to reveal the connections at points of interest. In the same sense, a major goal of current research is to collect information and devise tools to help us understand biological phenomena at multiple levels of abstraction. Traditional biochemistry and molecular biology focus on the properties of individual molecules, including, for proteins and enzymes, their immediate sub-parts (domains), their substrates and ligands, and the company they keep (interacting partners and complexes). This approach has been remarkably successful at elucidating the structures and functions of many cellular constituents and will continue to be so for years to come. In contrast, our understanding of biological processes at larger scales of resolution, such as entire intracellular signal transduction networks, is much less developed.
Over the past few years, powerful methodological advances have enabled high-throughput data acquisition in biology, including sequencing of entire genomes, microarray analysis of global patterns of gene expression, evaluation by mass spectrometry of the nature and modification state of cellular proteomes, and genetic and biochemical methods for identifying protein-protein complexes and entire gene and protein interaction networks. Fortunately, this progress has occurred contemporaneusly with other technological advances that have increased the power, versatility, and accessibility of computers. Hence, we now have the capacity to extract a plethora of new insights from what would otherwise be an overwhelming amount of primary information. Of course, deducing the biological relevance of the observations made on such a large scale depends crucially on the understanding and annotation of cellular molecules and processes gleaned from the knowledge base accumulated from decades of small-scale studies. But teasing the meaning out of genome-wide data also depends on conceptual and quantitative frameworks imported from other scientific disciplines, such as electrical and chemical engineering, mathematics, statistics, and computer science. As a result, large-scale approaches combined with computational methods are now facilitating the expansion of biochemistry and molecular biology to the whole-systems level. The new perspectives that such approaches provide are illustrated by three recent studies focused on cell signaling - two investigations of the properties of complex multi-step pathways that include in silico simulations [1, 2], and a large-scale proteomic analysis of the difference in cellular responses to epidermal growth factor (EGF) and platelet-derived growth factor (PDGF) .
Properties of signal transduction cascades
Quantitative analysis is increasingly being used to discover the general principles relating the functional properties of a signaling pathway to its basic topological characteristics. Among the various signaling modules employed by eukaryotic cells, some involve the activation of only one component downstream of the receptor. One example is the transforming growth factor-beta (TGFβ) receptor-catalyzed phosphorylation of Smad transcription factors, which permits their nuclear entry (which is crucial for pattern formation and cell-fate determination in metazoan embryonic development) . Similarly, after binding of cyclic 3',5'-AMP to the regulatory subunit of protein kinase A (PKA), the dissociated catalytic subunit can enter the nucleus and phosphorylate the CREB transcription factor . We refer to such pathways as 'single-step'. Other signaling systems, including mitogen-activated protein (MAP) kinase cascades, involve the sequential activation of multiple intermediaries and we refer to these as 'multi-step' pathways .
Properties of single- and multi-step signaling pathways
Optimized for high input strengths
Optimized for low input strengths
A recent paper by Nakabayashi and Sasaki  suggests that signaling cascades exhibit another important emergent property: optimization of the speed at which information is transmitted through the system. The authors analyzed in silico a simplified linear kinase-phosphatase cascade that is frequently employed as a model of the core MAP kinase module [2, 8, 12]. For any particular input signal strength, the time required for the pathway output to reach a desired level depends on the number of steps in the cascade. Nakabayashi and Sasaki  sought to determine the number of steps that would minimize this signal transmission time. Interestingly, they observed that the shortest (single-step) pathways were not always the fastest. Specifically, for a given output level, the optimal number of steps increased as the input strength decreased, consistent with earlier analyses performed on models with decaying inputs and weakly activated kinases . Furthermore, for pathways of sufficient length, the optimal step number is proportional to the order of magnitude of the response amplification .
Hornberg et al.  further found that although the activating and inactivating processes together are indeed balanced with regard to response amplitude, individual kinase-phosphatase pairs generally are not: equivalent increases in the activities of a kinase and a phosphatase that act on the same target will lead to a net increase in signal strength. This asymmetry is counterbalanced at the level of the upstream receptor, where inactivation exerts stronger control than activation. Thus, even within a single level of a cascade, counteracting signaling components cannot be treated as mere opposites, and they can be differentially controlled to regulate distinct response characteristics.
A few years ago, Bhalla et al.  showed that the concentration of MAP kinase phosphatase is crucial for determining whether MAP kinase signaling in NIH-3T3 fibroblasts displayed bistability or not. What about other cell types? It is well known that treatment of cultured neuroendocrine (PC12) cells with EGF induces only transient ERK activation and results in cell proliferation, whereas treatment of the same cells with nerve growth factor (NGF) causes sustained ERK activation and results in cell differentiation, including extension of dendritic and axonal projections . A recent analysis by Sasagawa et al.  found that this difference depends on differences in the regulation of the GTPase-activating proteins (GAPs) that inactivate the small GTPases, Ras and Rap1 - the activators of the respective MAP kinase kinase kinases in the proliferation and differentiation pathways. In these systems, therefore, inactivating enzymes in MAP kinase cascades have a key role not only in suppressing the level of pathway activity in unstimulated cells and during recovery from stimuli, but also in regulating the specific dynamics of signaling in ways that are biologically meaningful. The studies by Hornberg et al.  and Heinrich et al.  suggest that this may be a general feature of MAP kinase signaling systems.
Quantitative and large-scale approaches are also proving useful in elucidating the underlying molecular basis of differential cellular responses to similar extracellular cues. Given that many growth-factor receptors are ligand-activated protein-tyrosine kinases, a prominent feature of the behavior induced by such growth factors is the phosphorylation of numerous downstream effector proteins on tyrosine, including autophosphorylation of the growth-factor receptors themselves . Many phosphorylation targets appear to be regulated similarly upon exposure to different growth factors, even when the growth factors induce different biological behavior. For example, while exposure of human mesenchymal stem cells (hMSCs) to either EGF or PDGF leads to equivalent levels of MAP kinase enrichment in phosphotyrosine-containing complexes, EGF induces MAP kinase-dependent differentiation to bone cells, whereas PDGF does not [3, 17].
In order to identify differences in the signaling networks activated by EGF and PDGF in hMSCs, Kratchmarova et al.  compared the entire set of tyrosine-phosphorylated proteins and their interacting partners in EGF- versus PDGF-stimulated cells. Equivalent populations of hMSCs were metabolically labeled with isotopically different (but biochemically identical) variants of arginine and exposed to EGF, PDGF, or no growth factor. Equal amounts of cell lysates from these populations were pooled and subjected to anti-phosphotyrosine immunopurification, tryptic digestion, and mass spectrometry. For each identified protein, the isotopically distinguished mass spectrum of the arginine-containing peptides indicated the relative cellular level of that species in tyrosine-phosphorylated complexes across the growth-factor treatment conditions.
Using this method, the researchers discovered that among the few proteins (less than 10%) that were uniquely regulated by the two different stimuli, phosphatidylinositol (PI) 3-kinase was preferentially enriched in phosphotyrosine-containing complexes in cells exposed to PDGF relative to those exposed to EGF (or no growth factor) . The SH2 domain-containing subunit (p85) of PI 3-kinase binds to a specific phosphotyrosine-containing motif on the PDGF receptor, thereby recruiting the enzyme to the plasma membrane (owing to the resulting proximity, the receptor also phosphorylates the enzyme at tyrosine) . Tethering PI 3-kinase at the plasma membrane permits generation of PI 3,4,5-P3, which stimulates activation of additional downstream signaling components, such as the protein kinases PDK1  and c-Akt , that promote cell survival and cell migration [21, 22]. Hence, the fact that PDGF, but not EGF, leads to PI 3-kinase recruitment suggested a possible and novel negative regulatory role for this lipid kinase in blocking differentiation. Indeed, hMSCs treated with PDGF in the presence of wortmannin, a specific inhibitor of PI 3-kinase, exhibited osteoblast differentiation comparable to that of EGF-stimulated cells both in culture, as assayed by acquisition of a cell-type-specific enzymatic activity and mineralization, and in vivo, as assayed by bone formation following implantation in mice . The ability to pinpoint PI 3-kinase as one of the very few major molecular differences between the EGF- and PDGF-stimulated signaling networks, and subsequently to demonstrate that PI 3-kinase is a critical regulatory node for hMSC differentiation, is remarkable and clearly validates the authors' global proteomic approach . This study also highlights the importance of a feature inherent in large-scale analyses in which many components are directly assessed in parallel, namely the ability to rule out the 'uninteresting' players (in this work  proteins that were equivalently affected by both ligands).
The fact that an individual protein can elicit distinctly different biological effects depending on the nature of the interacting partners that are present in the same cell or compartment is a frequently encountered paradigm in transcription factor function and the regulation of gene expression [23, 24]. The conclusion of Kratchmarova et al.  - that EGF induces hMSC osteoblast differentiation by activating the MAP kinase pathway, whereas PDGF stimulation avoids the differentiation response by stimulating PI 3-kinase in addition to MAP kinase - suggests that cells also achieve appropriate signaling outputs through simple combinations of entire signaling pathways. In other words, even among extracellular stimuli that evoke responses leading to essentially identical modification of a substantially similar set of proximal targets, the additional input of differential regulation at one or a few selected upstream nodes can yield dramatically different biological consequences by uniquely triggering the activity of an entire downstream module.
Simulation of the kinetics and behavioral characteristics of various arrangements of signal transduction circuitry, and the ability to interrogate simultaneously all cellular components, provides an unprecedented view of the cell that is unavailable at smaller scales of analysis. When informed by and combined with traditional methods, these system-wide approaches enhance our understanding of complex biological phenomena. Although the application of quantitative systems-level techniques to signal transduction research is still relatively new, compared with the established use of such techniques in investigations of metabolic and neuronal networks [25, 26], signs are promising that, in this area too, such methods can help delineate testable hypotheses and generate useful conceptualizations about the biological processes involved [27–30]. The studies by Nakabayashi and Sasaki , Hornberg et al. , and Kratchmarova et al.  illuminate the functions and relationships among components and pathways in MAP kinase and growth factor signaling and provide insights into properties that may be generalizable to other signal transduction mechanisms and networks. As proteomic and other large-scale methods are continually and rapidly improving , in lock-step with new computational methods , we are likely to see rapid progress on this front.
- Nakabayashi J, Sasaki A: Optimal phosphorylation step number of intracellular signal-transduction pathway. J Theor Biol. 2005, 233: 413-421. 10.1016/j.jtbi.2004.10.022.PubMedView ArticleGoogle Scholar
- Hornberg JJ, Bruggeman FJ, Binder B, Geest CR, de Vaate AJ, Lankelma J, Heinrich R, Westerhoff HV: Principles behind the multifarious control of signal transduction. ERK phosphorylation and kinase/phosphatase control. FEBS J. 2005, 272: 244-258. 10.1111/j.1432-1033.2004.04404.x.PubMedView ArticleGoogle Scholar
- Kratchmarova I, Blagoev B, Haack-Sorensen M, Kassem M, Mann M: Mechanism of divergent growth factor effects in mesenchymal stem cell differentiation. Science. 2005, 308: 1472-1477. 10.1126/science.1107627.PubMedView ArticleGoogle Scholar
- Attisano L, Wrana JL: Signal transduction by the TGF-beta superfamily. Science. 2002, 296: 1646-1647. 10.1126/science.1071809.PubMedView ArticleGoogle Scholar
- Mayr B, Montminy M: Transcriptional regulation by the phosphorylation-dependent factor CREB. Nat Rev Mol Cell Biol . 2001, 2: 599-609. 10.1038/35085068.PubMedView ArticleGoogle Scholar
- Widmann C, Gibson S, Jarpe MB, Johnson GL: Mitogen-activated protein kinase: conservation of a three-kinase module from yeast to human. Physiol Rev. 1999, 79: 143-180.PubMedGoogle Scholar
- Ferrell JE: Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr Opin Cell Biol. 2002, 14: 140-148. 10.1016/S0955-0674(02)00314-9.PubMedView ArticleGoogle Scholar
- Huang CY, Ferrell JE: Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc Natl Acad Sci USA . 1996, 93: 10078-10083. 10.1073/pnas.93.19.10078.PubMedPubMed CentralView ArticleGoogle Scholar
- Ferrell JE: Tripping the switch fantastic: how a protein kinase cascade can convert graded inputs into switch-like outputs. Trends Biochem Sci. 1996, 21: 460-466. 10.1016/S0968-0004(96)20026-X.PubMedView ArticleGoogle Scholar
- Brown GC, Hoek JB, Kholodenko BN: Why do protein kinase cascades have more than one level?. Trends Biochem Sci. 1997, 22: 288-10.1016/S0968-0004(97)82216-5.PubMedView ArticleGoogle Scholar
- Ferrell JE: How responses get more switch-like as you move down a protein kinase cascade. Trends Biochem Sci. 1997, 22: 288-289. 10.1016/S0968-0004(97)82217-7.PubMedView ArticleGoogle Scholar
- Heinrich R, Neel BG, Rapoport TA: Mathematical models of protein kinase signal transduction. Mol Cell. 2002, 9: 957-970. 10.1016/S1097-2765(02)00528-2.PubMedView ArticleGoogle Scholar
- Bhalla US, Ram PT, Iyengar R: MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science. 2002, 297: 1018-1023. 10.1126/science.1068873.PubMedView ArticleGoogle Scholar
- Traverse S, Gomez N, Paterson H, Marshall C, Cohen P: Sustained activation of the mitogen-activated protein (MAP) kinase cascade may be required for differentiation of PC12 cells. Comparison of the effects of nerve growth factor and epidermal growth factor. Biochem J. 1992, 288: 351-355.PubMedPubMed CentralView ArticleGoogle Scholar
- Sasagawa S, Ozaki Y, Fujita K, Kuroda S: Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat Cell Biol. 2005, 7: 365-373. 10.1038/ncb1233.PubMedView ArticleGoogle Scholar
- Schlessinger J: Cell signaling by receptor tyrosine kinases. Cell. 2000, 103: 211-225. 10.1016/S0092-8674(00)00114-8.PubMedView ArticleGoogle Scholar
- Jaiswal RK, Jaiswal N, Bruder SP, Mbalaviele G, Marshak DR, Pittenger MF: Adult human mesenchymal stem cell differentiation to the osteogenic or adipogenic lineage is regulated by mitogen-activated protein kinase. J Biol Chem. 2000, 275: 9645-9652. 10.1074/jbc.275.13.9645.PubMedView ArticleGoogle Scholar
- Tallquist M, Kazlauskas A: PDGF signaling in cells and mice. Cytokine Growth Factor Rev. 2004, 15: 205-213. 10.1016/j.cytogfr.2004.03.003.PubMedView ArticleGoogle Scholar
- Mora A, Komander D, va Aalten DM, Alessi DR: PDK1, the master regulator of AGC kinase signal transduction. Semin Cell Dev Biol . 2004, 15: 161-170. 10.1016/j.semcdb.2003.12.022.PubMedView ArticleGoogle Scholar
- Brazil DP, Yang ZZ, Hemmings BA: Advances in protein kinase B signalling: AKTion on multiple fronts. Trends Biochem Sci. 2004, 29: 233-242. 10.1016/j.tibs.2004.03.006.PubMedView ArticleGoogle Scholar
- Debiais F, Lefevre G, Lemonnier J, Le Mee S, Lasmoles F, Mascarelli F, Marie PJ: Fibroblast growth factor-2 induces osteoblast survival through a phosphatidylinositol 3-kinase-dependent, beta-catenin-independent signaling pathway. Exp Cell Res. 2004, 297: 235-246. 10.1016/j.yexcr.2004.03.032.PubMedView ArticleGoogle Scholar
- Fukuyama R, Fujita T, Azuma Y, Hirano A, Nakamuta H, Koida M, Komori T: Statins inhibit osteoblast migration by inhibiting Rac-Akt signaling. Biochem Biophys Res Commun. 2004, 315: 636-642. 10.1016/j.bbrc.2004.01.104.PubMedView ArticleGoogle Scholar
- Remenyi A, Scholer HR, Wilmanns M: Combinatorial control of gene expression. Nat Struct Mol Biol. 2004, 11: 812-815. 10.1038/nsmb820.PubMedView ArticleGoogle Scholar
- Istrail S, Davidson EH: Logic functions of the genomic cis-regulatory code. Proc Natl Acad Sci USA. 2005, 102: 4954-4959. 10.1073/pnas.0409624102.PubMedPubMed CentralView ArticleGoogle Scholar
- Fell D: Understanding the Control of Metabolism. 1997, London: Portland PressGoogle Scholar
- Laughlin SB, Sejnowski TJ: Communication in neuronal networks. Science. 2003, 301: 1870-1874. 10.1126/science.1089662.PubMedPubMed CentralView ArticleGoogle Scholar
- Bhalla US, Iyengar R: Emergent properties of networks of biological signaling pathways. Science. 1999, 283: 381-387. 10.1126/science.283.5400.381.PubMedView ArticleGoogle Scholar
- Lee E, Salic A, Kruger R, Heinrich R, Kirschner MW: The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol. 2003, 1: E10-10.1371/journal.pbio.0000010.PubMedPubMed CentralView ArticleGoogle Scholar
- Angeli D, Ferrell JE, Sontag ED: Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci USA. 2004, 101: 1822-1827. 10.1073/pnas.0308265100.PubMedPubMed CentralView ArticleGoogle Scholar
- Barrios-Rodiles M, Brown KR, Ozdamar B, Bose R, Liu Z, Donovan RS, Shinjo F, Liu Y, Dembowy J, Taylor IW, et al: High-throughput mapping of a dynamic signaling network in mammalian cells. Science. 2005, 307: 1621-1625. 10.1126/science.1105776.PubMedView ArticleGoogle Scholar
- Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP: Causal protein-signaling networks derived from multiparameter single-cell data [Erratum Science 309:1187]. Science. 2005, 308: 523-529. 10.1126/science.1105809.PubMedView ArticleGoogle Scholar
- Lok L, Brent R: Automatic generation of cellular reaction networks with Moleculizer 1.0. Nat Biotechnol. 2005, 23: 131-136. 10.1038/nbt1054.PubMedView ArticleGoogle Scholar