1 General introduction
2.5 Conclusions
Oncogenes such as Ras are frequently overexpressed in tumours, e.g., due to genomic amplification or due to promoter deregulation [109] . However, in many cases, including Ras, the relevance of such overexpression remains unclear (e.g., [7,8,9] ). This chapter was focussed on the steady state basal activation level in an enzymatic futile cycle. Intuitively, one would expect that the basal activation level is proportional to the total concentration of the cycling substrate. Thus, strong overexpression might be expected to result in strongly enhanced basal signalling, and possibly in tumourigenesis. The modelling analyses revealed that the kinetic properties of enzymatic futile cycles can prevent deregulation of signal transduction by substrate overexpression, and thus identified a possible mechanism for tumour suppression.
The differential equations of the model are based on the assumption that spontaneous Ras (de)activation occurs with first-order kinetics, while GEF- and GAP-catalysed reactions were assumed to proceed with Michaelis-Menten kinetics. Therefore, the results presented above also apply for covalent modification cycles (e.g., phosphorylation / dephosphorylation cycles) which might explain why mutational activation of oncogenic kinases is typically required to transform cells, and/or to activate downstream signalling, while even strong overexpression of wildtype proteins is insufficient [110,111,112,113,114,115] .
Experimental studies support that the proposed tumour suppression mechanism is physiologically relevant for protein kinase cascades: (i) Experiments with kinase or phosphatase inhibitors in starved cells [116,117] suggest that the basal state activation level of cascade intermediates is mainly determined by the ratio of kinase and phosphatase activities. In other words, the basal state seems to be controlled by nonlinear Michaelis-Menten kinetics, and not simply by linear auto(de)phosphorylation. (ii) Protein kinase cascade intermediates are typically expressed at concentrations close to (or even above) the KM-values of the upstream kinase. This implies that the kinetic requirements for insensitivity (Section 2.3) are often fulfilled in kinase cascades. (iii) Quantitative measurements revealed that more than 5% of total molecules of cascade intermediates are frequently activated even in serum-depleted medium [57,81,117,118,119,120] . Accordingly, even strong stimulation often induces only about 10-fold increases in kinase activation [19,25,57,117,121] , and biological responses can be induced by a 10-fold kinase activation in cell culture [57,122]
and in vivo [123] . Thus, 10 – 20 fold overexpression of kinase cascade intermediates should be sufficient to induce biological responses unless the suppression effects such as those discussed in this chapter avoid deregulation of signalling by protein overexpression.
Few theoretical studies thoroughly analysed basal state signalling so far [81,124] , even though it is the background activity (and not the dynamics) of signalling pathways is deregulated in many diseases. It is becoming increasingly clear that basal signal transduction serves to control important functions such as cell survival [125] , gene expression [126] , and cell adhesion [127] . The simulations in Sections 2.2 and 2.3 reveal how cells robustly maintain such background activity of signalling pathways, independent of fluctuations in protein expression. Other mechanisms that have been reported for robust signal transduction include assembly into multisubunit protein complexes [128] , negative feedback [129] , and co-expression of antagonistic enzymes [130] . Moreover, it is known that transcription factors often act as repressors in the inactive state, while being transcriptional activators in the active state (e.g., [131] ). Competition of active and inactive states thus gives rise to robustness towards fluctuations in total transcription factor protein expression.
Figure 2.3: Implications for oncogene cooperation.
(A) Effects of Ras overexpression on signal transduction for varying upstream stimulus levels in the “sequestration model” that takes Ras sequestration in enzyme-substrate complexes into account. The GEF- and GAP-catalysed reactions are modelled using an elementary step description of the irreversible Michaelis-Menten mechanism (E + S ↔ ES → E + P; Appendix B), and the intrinsic reaction steps (i.e., the grey arrows in Fig. 2.1 A) were neglected. The RasGTP concentration is plotted as a function of total Ras expression (i.e., the sum of RasGTP, RasGDP, the Ras-GEF complex and the Ras-GAP complex), and the total GEF concentration is varied. The total GEF concentrations are 3 ⋅ 10-8, 3 ⋅ 10-7 and 3 ⋅ 10-6 mol/l (this corresponds to [GEF]
/ [GAP] = 0.04, 0.4 and 4). Note that the curve for [GEF] / [GAP] = 4 also applies for higher stimulus levels ([GEF] / [GAP] > 4), and thus represents the maximal effect induced by strong stimulation. The Michaelis-Menten constants of the GEF reactions were set to KM,GDP = 3.86 ⋅ 10-6 mol/l and KM,GTP = 3 ⋅ 10-6 mol/l, and the kcat,GEF, the KM,GAP, and the kcat,GAP were as in [10] . The undetermined off-rates of the GEF- and GAP-Ras complexes were estimated to be 10 s-1 (Appendix B). The experimentally measured Ras expression level in living cells is indicated by the vertical dashed line.. Points A – D are described in B. (B) Schematic representation of the logical AND-gate for oncogene cooperation. Normal cells (point A in panel A) remain completely untransformed by both, GEF activation (point B in panel A) and Ras overexpression (point C in panel A), but they become primed for strong transformation by a secondary mutational event (point D in panel A).
The robustness mechanism (“kinetic tumour suppression”) presented here is similar to that discussed for bacterial two-component systems [132,133] . However, two component systems are topologically different from mammalian signalling cascades, as a single phosphate group is transferred from upstream proteins to downstream effectors, while no such mass transfer is observed in mammalian systems. Using an experimentally validated model of Ras signalling, it is shown in this chapter that robustness is observed for parameter values that are physiologically relevant for Ras signalling in mammalian cells. Moreover, the simulations reveal that the robustness in mammalian cells is restricted to weak stimulation, while it is observed for the full dose-response in two-component systems [133] . Figures 2.2 and 2.3 indicate that loss of robustness upon strong stimulation might give rise to priming and oncogene cooperation effects in mammalian cancer development. The physiological relevance of priming and cooperation is further supported by a study focussing on cellular transformation by Ras and its downstream effector Raf: Transfection with either wildtype protein did not induce any significant phenotypic response, while strong transformation upon co-transfection of Ras and Raf [115] . The easiest way to test the model predictions regarding tumour suppression and oncogene cooperation is to mix recombinant Ras, GAP and GEF proteins in vitro, and to measure the steady state concentration of RasGTP as a readout.
This chapter was focussed on the pathological deregulation of basal state signal transduction. More specifically, it was analysed how long-term alterations (e.g., at the transcriptional or the genomic level) affect information transfer via the basic motif of intracellular signalling networks, the activation-deactivation cycle. The following two chapters
(Section 3 and 4) deal with the question of how transient physiological signals are converted into persistent cell-fate decisions by the intracellular signalling network in the absence of slow transcriptional regulation. More specifically, the analysis is focussed on two signal transduction pathways (MAPK signalling and apoptosis signalling) that have been shown experimentally to remain irreversibly activated even after input stimuli were removed. In Sections 5 and 6, it is then investigated how slow transcriptional feedback regulation affects the signals generated by the signalling network. Therefore, Sections 5 and 6 extend the analyses presented here, as slow transcriptional regulation is no longer assumed to be an
‘external’ event, but is rather a part of the signalling network.