On the relevance of cellular signaling pathways for immune-inspired algorithms

Transcription

1 On the relevance of cellular signaling pathways for immune-inspired algorithms T. S. Guzella 1,2 and T. A. Mota-Santos 2 1 Dept. of Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte (MG) , Brazil, tguzella@cpdee.ufmg.br 2 Dept. of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte (MG) , Brazil, tomaz@icb.ufmg.br Abstract. In this conceptual paper, we discuss the relevance of cellular signaling pathways for immune-inspired algorithms. With complex dynamics, the mapping of environment stimuli to cellular responses is highlighted as a decision making capability. When considering applications which could benefit from these dynamics, the possibility of incorporating these pathways can be an interesting way to combine more biologically-plausible algorithms and improved performance. The structure of the NF-κB (Nuclear Factor κb) and MAP (Mitogen-activated protein) kinases pathways, and the pathways involved in signaling by Toll-like receptors, are presented. As an example, we then consider how these pathways could be incorporated in the Dendritic Cell Algorithm. Key words: Artificial Immune Systems, Signaling pathways, NF-κB, MAP kinases, Toll-like receptor signaling 1 Introduction Nature has always been an interesting source of inspiration for engineers and computer scientists. In recent years, it has lead to the proposal of important computational tools, such as Artificial Neural Networks and Genetic Algorithms. Based on the powerful cognitive capabilities of the human immune system, a more recent development are Artificial Immune Systems [1] (AISs). AISs have been used in various application areas, inspired by several processes taking place in the immune system. The immune system is often cited as possessing several interesting features from a computational perspective, such as pattern recognition, memory, homeostatic stability, among others. However, in attempting to capture such features in an algorithm, it should be kept in mind that they are implemented, at the cellular level, by signaling pathways. This is further emphasized by the increasing application of mathematical formulations to signaling dynamics in the immune system [2], with the objective of unveiling their roles. Due to the fact that many of these features of interest can be described as emerging from such pathways, along with inter-cellular interactions taking place in the immune system, understanding how these pathways are organized can have important consequences.

2 2 Guzella, Mota-Santos Thus, considering some of these features in a specific application might require the understanding, at some level of detail (even if very simplistic), of how these pathways shape the response of cells. From a conceptual point of view, this requires looking at complexity at the cellular level, in addition to that at the population level. This constitutes the main point of this paper: to discuss the relevance of considering these pathways in immune-inspired algorithms, and to suggest how this could be done. In line with our argument of the importance of understanding the structure of these signaling pathways, we presented a reasonably detailed description of some of them. In particular, in the area of neural networks, the importance of the dynamics of neurons, due mainly to processes involving ion channels, has been receiving increasing attention. In contrast to the Multi-layer perceptron (MLP), which assumes that the information transmitted between two neurons is coded in the average spiking rate, so that the neuron s output is a smooth function of the input, several recent models include the dynamics involved in the generation of spikes. In addition to being more biologically realistic, such models have a wide applicability in problems where dynamical aspects are important, such as sound analysis [3] and robotics [4]. When it comes to more biologically-plausible models, a similar change is occurring in AISs, with recent works advocating algorithms more realistic from a biological point of view [5]. However, in doing so, the characteristics of the algorithm should be tailored to the target application [6]. This process involves several steps, such as the understanding of the biological processes of interest, the construction of models for analysis of these processes and, then, the formulation of an algorithm [5], based on characteristics of the target application, thereby reinforcing the interdisciplinary characteristic of AISs [7]. This paper is organized in the following way: section 2 discusses the dynamics of signaling pathways, and the emergence of decision making capabilities. In sequence, section 3 presents two relatively well-known signaling pathways, the NF-κB (Nuclear Factor κb) and MAPK (Mitogen-activated protein kinases), followed by section 4, where the pathways involved in signaling by the Toll-like receptors are considered. Section 5 then considers, as an example, how signaling dynamics could be incorporated in the Dendritic Cell Algorithm [8, 9], followed by the final conclusions and future research directions in section 6. 2 Dynamics of signaling pathways Signaling pathways are ubiquitous in several cell types, not limited to cells in the immune system. They allow a cell to adapt in response to certain environmental stimuli. A general description of the steps involved in the activation of such pathways is shown in figure 1. The initial event is the binding of a ligand, such as cytokines, hormones, an antigen or a peptide-mhc complex, to membrane receptors. This activates downstream events taking place in the cytoplasm, which, usually (but not only) through possibly multiple phosphorylation or dephosphorylation (addition/removal of a phosphate group, respectively) steps of

3 Signaling pathways 3 one or more proteins, mediated by kinases and phosphatases, respectively, leads to the generation of one or more multi-protein complexes. As indicated in figure 1, some of the steps involved in a certain pathway may be shared with another pathway, for example when an enzyme needed for the activation of one pathway also mediates activation (or even inhibition steps) in another pathway. At some point, the nuclear transport of certain species formed (or activated) during the activation of the pathway takes place, where they influence the expression of certain genes. These genes can, in turn, lead to the expression of proteins involved in one or more pathways or the secretion of soluble factors. In the former case, the resultant proteins might up- or down-regulate the activated pathway, through interaction with the receptors or by regulating some of the steps during the activation of the pathway. It is also possible that the affected genes induce the up- or down-regulation of the membrane receptors. In turn, the secreted factors can stimulate another or even the same type of receptor initially activated, resulting in the activation of other (or the same) pathway. Fig. 1. Illustration of some of the general events involved in signaling pathways. The dotted arrows indicate events that occur in response to activation of a pathway (see text) While the previous discussion might give an impression that the activation of such pathways is a linear, sequential, event, the realization that this is not the case is a growing theme in the literature. It is being increasingly acknowledged that the functioning of these pathways is extremely complex, due to the interconnection between components taking place in several pathways, challenging reasonably simple chain of events following activation. One characteristic of many of these pathways is combinatorial complexity, which results from the combinatorial number of complexes that can be formed as a result of ligand stimulation (e.g. [10]). In particular, this is one of the major obstacles for studying the signaling networks in a cell. Nevertheless, the large number of connections and the modularity [11] typical of such networks might allow them to perform complex input-output mappings [12], in terms of the cellular responses (proliferation, apoptosis, among others) to stimulation, highlighting the emerging information processing and decision-making capabilities of these networks [13]. Despite the difficulties involved in elucidating the molecular events taking place in signaling pathways, the growing availability of technologies allowing the study of such systems has allowed an increased understanding of such events.

4 4 Guzella, Mota-Santos In this context, an important aspect studied is the structure of these signaling pathways, i.e. what are the components involved and how they are organized during the response. This information is extremely important, due to the fact that the structure of a pathway, represented by the interactions between components involved in such pathway, shape the cellular response to stimulation. An increasingly used tool to analyze this structure is mathematical modeling, which allows a characterization of the emerging features of these pathways. Among the approaches that have been used to model signaling pathways, the following can be cited (although this list is far from complete): differential equations, either ordinary (where the spatial distribution of components is assumed to be homogeneous) or partial (which consider spatial aspects). Sometimes, stochastic effects are also considered rule-based models [13], reviewed in [14] algebra-based models [15] P-Systems, also known as membrane computing [16] 3 The NF-κB and MAPK signaling pathways: In this section, we briefly present two pathways involved, among other functions, in several aspects of the immune system: the NF-κB (Nuclear Factor κb) and the MAPK (Mitogen-activated protein kinases) pathways. This discussion serves two purposes: illustrating the complex dynamics of these two signaling pathways, while paving the road for the next section, as these two pathways are involved in signaling mediated by the family of Toll-like receptors (TLRs). The NF-κB pathway, depicted in figure 2a, is believed to the original signaling pathway (see the final words in [17]), given the widespread expression in invertebrates of genes coding proteins involved in this pathway. In a stimulationfree scenario, the majority of NF-κB dimers (complexes formed by two molecules of NF-κB), indicated in figure 2a with the subscript d, are located in the cytoplasm, associated with one of three IκB proteins (IκBα/β/ε), which precludes their nuclear translocation. The IκB proteins, on the other hand, constitutively translocate between the cytoplasm and the nucleus. Stimulation of the NF-κB pathway leads to the activation of IKK (IκB kinase), which triggers the phosphorylation and degradation of the IκB proteins, allowing the NF-κB dimers to reach the nucleus, where they regulate the activation of hundreds of genes (such as inflammatory genes), including the one coding IκBα. Once in the nucleus, the dimers require association with IκB proteins in order to be transported back to the cytoplasm. The dynamics of this pathway can be analyzed using, for example, the three-dimensional nonlinear model proposed by Krishna et al. [18]. These authors have argued that the emergence of periodic spikes in the nuclear concentration of NF-κB is associated with an increased sensitivity of the pathway, which could allow the differential regulation of certain genes. The MAPK pathway is another evolutionarily conserved signaling pathway, featuring three pathways, mediated by JNK (JUN N-terminal kinase), p38 and ERK (extracellular-signal-regulated kinase), although a further distinction is

5 Signaling pathways 5 usually made between some pathways involving ERK [19 21]. In the following discussion, we focus only on the first two pathways, as those have been implicated in signaling by Toll-like receptors. Each one of the MAP kinase pathways can be described in a general way as a three level cascade, as shown in figure 2b. The first level is formed by the MAPK kinase kinases (MAPKKK), which are activated by phosphorylation. The phosphorylated MAPKKKs mediate, in turn, the double phosphorylation of MAPKKs (MAPK kinases, also known as MKKs), which in a similar way, mediate the activation of MAPKs. In figure 2b, single- and double-phosphorylation are indicated by the p and pp subscripts, respectively. In these two levels, only the double-phosphorylated forms are capable of mediating the activation of downstream substrates. In addition, phosphatases mediate the de-phosphorylation of the activated species, shown in figure 2b as dotted arrows, with the expression of some of those phosphatases influenced by the activation of the MAPKs. Once activated, MAPKs mediate signaling in the cytoplasm or in the nucleus, leading to various responses, such as the production of pro-inflammatory cytokines, the induction of cellular differentiation or apoptosis. After nuclear translocation, they affect the activity of transcription factors. In the cytoplasm, they mediate the activation of downstream signaling pathways, through the activation of kinases such as MAPKAPK-2 (MAPK-activated protein kinases). The dynamics of a mathematical model [19] of the MAPK cascade indicate an ultra-sensitive (step-like) response and bistability. In addition, when considering a negative feedback loop induced by activated MAPK that de-activates the MAPKKK, sustained oscillations can arise. a) NF-κB pathway b) MAPK pathway Fig. 2. Structure of the NF-κB and MAPK pathways 4 The Toll-like receptor signaling pathway The Toll-like receptors (TLRs) are one of the front-line mechanisms for the identification of pathogens by the innate immune system. These receptors, expressed by cells such as macrophages and dendritic cells (DCs), recognize specific molecular patterns, and are crucial in the early response to pathogenic microorganisms. Recent results point out the existence of at least 12 mammalian TLRs [22], some of which are expressed on the surface of cells, while others are present in intracellular compartments. The former (TLRs 1, 2, 4, 5 and 6) recognize mainly

6 6 Guzella, Mota-Santos bacterial products (such as the recognition of LPS, produced by Gram-negative bacteria, by TLR4) which are not made by the host, while the latter (TLRs 3, 7, 8 and 9) recognize nucleic-acid structures, which are not unique to pathogens, but are not accessible to TLRs under normal conditions [23, 24]. In addition, each receptor is capable of recognizing several distinct ligands (e.g. in the case of TLR4, LPS, heat shock proteins, fibrinogen and others [24]). TLRs occur as either homo or heterodimers (complexes formed by two either equal or different species, respectively), whose formation is ligand-independent. In the case of TLR2, it associates with either TLR1 or TLR6, while the remaining TLRs mainly occur as homodimers. Following ligand stimulation, the triggered TLRs recruit molecular adaptors, leading to the activation of downstream signaling cascades. Currently, five adaptors are known: MyD88 (Myeloid Differentiation Factor 88), TRIF (TIR-domain-containing adaptor protein inducing IFN-β), MAL (MyD88-adaptor-like protein), also known as TIRAP (TIR-containing adaptor protein), TRAM (TRIF-domain-containing adaptor molecule) SARM (Sterile α- and armadillo-motif-containing protein) The currently held model of TLR signaling, shown in figure 3, features two main pathways, referred to as MyD88-dependent and -independent pathways, where the former is shared with IL-1 (a pro-inflammatory cytokine), and the latter is mediated by TRIF. The activation of these pathways depends on the stimulated receptors. TLR3 uses only the MyD88-independent pathway, while TLR4 uses both, with the remaining receptors activating the MyD88-dependent pathway. Fig. 3. Depiction of the TLR signaling pathway (see text for the description of the steps involved) An important characteristic of the TLR pathway is that several steps involved in signal transduction are shared between several receptors. Therefore, a major question that arises is how response specificity is achieved, which can be described as the activation of specific genes as a result of the stimulation of certain receptors. Currently, it is believed that the specificity results from the extracellular and intracellular interactions between TLRs. The former is related to the dimerization of receptors, and the latter is due to the differential recruitment

7 Signaling pathways 7 of adaptors following stimulation. In addition to the activation of the MyD88- dependent and TRIF-dependent pathways, TLRs 2 and 4 require the recruitment of MAL before MyD88 is recruited (see figure 3), while the recruitment of TRAM to TLR4 is necessary before the activation of the TRIF-dependent pathway can take place. Finally, SARM, whose expression is increased following TLR3/4 stimulation, inhibits downstream activation of the TRIF-dependent pathway. Following MyD88 recruitment, IRAK-4 (IL-1R-associated kinase) is recruited and binds to MyD88. It then recruits and phosphorylates IRAK-1, associating with TRAF6 (TNF-receptor-associated factor 6). Following this, intermediate steps omitted in figure 3 involve the TRAF6-mediated activation of TAK1 (TGF-β-activated kinase), leading to the activation of the NF-κB and MAPK pathways, which induce the expression of genes encoding pro-inflammatory cytokines (such as TNF-α, IL-1β, IL-6 and IL-12). In case of the MAPK pathway, this is mediated by the activation of the JNK and p38 cascades by TAK1, which functions as a MAPKKK. In addition, following association with phosphorylated IRAK-1, TRAF-6 mediates the activation of IRF5 (Interferon regulatory factor 5), which also mediates the activation of pro-inflammatory genes. Another pathway mediated by MyD88 is the activation of IRF1, which requires the nuclear translocation of a MyD88-IRF1 complex, resulting in the temporary sequestration of MyD88, and the up-regulation of type I IFNs (IFN-α/β). On the other hand, the TRIF-dependent pathway leads to the activation of TRAF6, and the recruitment of TRAF3, which results in the activation of TBK1 (TRAF-family-member-associated NF-κB-activator-binding kinase). TRAF6 activates the NF-κB pathway, up-regulating pro-inflammatory cytokines, while TBK1 activates IRF3 and IRF7, the latter only in plasmacytoid DCs, inducing the production of type I IFNs. As a consequence of the intricate pathways involved in the signaling by TLRs, interesting emerging features in the TLR pathway are cooperation, synergism and antagonism, resulting from signaling from different receptors [22]. These features include the non-additive production of TNF following simultaneous stimulation of TLR2 and TLR4, the differential induction of genes resulting from the combined TLR3/TLR9 signaling [25], and the secretion of anti-inflammatory cytokines (such as IL-10) following TLR2 stimulation, which inhibit effects mediated by the subsequent stimulation with TLR3 or TLR4. Therefore, the combination of signals and their particular timing can have a profound influence on the cellular responses induced and the immune response. 5 Conceptualization of signaling pathways in immune-inspired algorithms As listed in the previous sections, the organization and dynamics of signaling pathways can have an important consequence in the functioning of the immune system. In this section, we look at the biological information presented and discuss how it may be incorporated into immune-inspired algorithms, taking the

8 8 Guzella, Mota-Santos dendritic cell algorithm (DCA) [8, 9] as an example. In doing so, we formulate a general agent-based representation of a cell, incorporating the dynamics of signaling pathways. As agent-based representations are widely used (e.g. [26, 8]), this formulation should facilitate the incorporation of signaling pathways into existing algorithms, in addition to providing a starting point for the development of new algorithms. However, in considering the DCA, we do not present a concrete approach to this incorporation, due to the fact that, as this work is still ongoing, there are some theoretical aspects requiring investigation before incorporating this information into the algorithm. One of these aspects is understanding to which degree the differential use of adaptors explains the emergence of specificity in the response to different TLR ligands, which can suggest the importance of additional mechanisms operating in these cells. The need for more biologically-plausible algorithms is highlighted by Stepney et al. [5], which proposed a conceptual framework for the development of such algorithms. This framework encompasses three main steps: probing the biological system, formulating a model incorporating some of the features of the biological system, and, after validation, developing an algorithm. In turn, this is an iterative process, because each step is amended to refinements. In particular, the intermediate step involving the development of models is particularly important, as it can support the development of algorithms involving simplified models of signaling pathways (e.g. where certain molecular species are neglected) while, at the same time, allowing for a reasonable reproduction of the properties of a given pathway. In addition, when dealing with complex systems (such as signaling pathways), whose emergent behavior cannot be easily predicted from simply looking at the biological system, the importance of formulating models is further highlighted. In fact, the development of biologically-plausible algorithms is a growing theme in AISs. Twycross and Aickelin [27] discuss the possibility of using models inspired on the immune systems of plants and invertebrates, which are relatively simpler than those of vertebrates, and the need to consider systemic models, as most real-world applications require systems based on a holistic view of the immune system. The latter is, in fact, receiving an increasing focus, especially by researchers working with homeostasis-inspired systems [28]. Finally, Guzella et al. [29] point out some signal processing capabilities of T cells, and discuss the incorporating of some of these mechanisms involved in a more biologicallyplausible model of T cells. In particular, this is an interesting candidate for the incorporation of signaling pathways, which implement these signal processing capabilities. Additional discussions on recent developments on new immuneinspired algorithms and inspirations can be found in recent reviews and position papers [30, 7, 31]. In the following, we consider the DCA in greater details, although most of the discussion applies also to the TLR algorithm [26]. In the DCA, signal processing by DCs is incorporated in a simplified way (see chapter 4 in [8]), which can be described by the following equation: [ Ψcs (t) Ψ mt (t) Ψ sm (t) ] T = W (1 + If (t)) [ I d (t) I p (t) I s (t) ] T (1)

9 Signaling pathways 9 where Ψ cs, Ψ mt and Ψ sm are the co-stimulation, mature and semi-mature output signals, I f, I d, I p, I s are the inflammatory, danger, PAMP and safe input signals, and the W matrix is constant. In particular, one of the PAMP (and also danger) signals in the natural immune system is the ligation of TLRs, a view which is incorporated in the DCA. The output signals define the state of a DC, which, through the application of a threshold function, determine if it will migrate, and its phenotype (mature or semi-mature) upon migration. The inflammatory signal is generally held constant, so that system 1 becomes a linear time-invariant dynamical system with dynamics faster than that of the input signals (so that the transient response can be neglected). However, the dynamics of cellular responses, which are mediated by signaling pathways, are neither linear or time-invariant. As a consequence of the use of this simplified model of DCs in the DCA, it follows that DCs with different previous experiences process input signals in the same way, which may be undesirable in some applications. In particular, as discussed in section 4, the responses resulting from TLR ligation (which can be interpreted in the DCA as I p and I d ) display features such as synergism (e.g. the non-additive secretion of cytokines following the combined stimulation of different TLRs) and antagonism, which can be attributed to nonlinearity and the temporal sequence of receptor signaling, respectively. For example, consider the application of the DCA in a simplified intrusion detection scenario, where danger and PAMP signals are mapped to suspicious activities, while safe signals indicate the normal operation of the network. In this case, a DC which has only received danger/pamp signals up to a certain instant, which has accumulated evidence of suspicious activies, and should be more likely to acquire a mature phenotype upon migration, and a DC which hasn t received any signals yet, will process an incoming input signal in the same way, in terms of the output signals derived. By incorporating the dynamics of signaling pathways and the expression of related genes, it may be possible to improve the integration of input signals, so that if a danger-experienced DC receives another danger/pamp signal it is more likely to migrate due to synergistic effect of two of time-correlated danger signals, even it its co-stimulation value is lower than the migration threshold. To understand how some modifications could be incorporated, we follow the general, but simplified, representation of an agent presented in figure 4. In this representation, an agent is seen as an input-output mapping with an internal state. The inputs are stimuli received from the environment or other cells, while the output is the secretion of soluble factors. The internal state represents, collectively, the states of signaling pathways (e.g. the concentrations of certain molecular species), and the expression of genes, and is under continuous update. It also influences how an input signal is received (by modulating certain signaling pathways). Upon stimulation, the internal state is modified by a response mediated by the signaling pathways, changing the state of the cell (e.g. inducing proliferation, apoptosis or other responses), in addition to the secretion of soluble factors (such as cytokines or chemokines). Therefore, the two most immediate modifications in the DCA, which would result in more biologically-

10 10 Guzella, Mota-Santos plausible models of DCs, are the consideration of the transient dynamics (i.e. through the internal states), and accounting for how the internal states affect these dynamics, by defining how the internal states affect the transduction of a given signal. Fig. 4. A generic representation of an agent where the dynamics of signaling pathways are considered This section would not be complete if the argument that, due to the inherent complexity of signaling pathways, their consideration in algorithms is not feasible at this moment, is not discussed. Although biological systems (such as the immune or nervous systems) are immensely complex, this has not precluded their use as inspiration for developing algorithms. In addition, due to growing technological advances, the biological understanding of many pathways is increasing in a fast pace, such that sufficient information on several pathways (such as those discussed in this paper) is starting to become available. Another aspect noteworthy of discussion is an important difference between spiking neurons and signaling pathway models, which can pose some difficulties in the incorporation of the latter in computational algorithms. In contrast to models of ion channels, which can usually be described by low dimensional nonlinear systems (in the case of the FitzHugh-Nagumo [32] model, two dimensions), making their analysis and implementation relatively easy, models of signal transduction pathways may be very large, due to the usually large number of molecular species involved. Nevertheless, through an appropriate study of the key steps involved in the activation of a given pathway, it may be possible to obtain simplified models. 6 Conclusions This paper discussed the potential of incorporating cellular signaling pathways in immune-inspired algorithms. In contrary to a simple linear, sequential, cascade, most of these pathways have a complex behavior, being capable of decision making in the face of a constantly changing environment. In the case of the immune system, these pathways lead to a response to a signal (such as receptor ligation), initiating a quick and appropriate cellular response. We have discussed in relative detail the dynamics of Toll-like receptor signaling, which could be applied in incorporating some new aspects into the Dendritic Cell Algorithm [8, 9]. While this is definitely not a simple task, it is believed that incorporating, even in a simplified way, how these pathways shape cellular responses can

11 Signaling pathways 11 have an interesting impact on AISs, especially on applications that can benefit from the dynamics of these pathways. However, this incorporation should be conducted with two important aspects in mind: the need for considering the characteristics of the target problem (i.e. how these dynamics could be beneficial for some application) and the need for formulating models for studying them before applying in an algorithm. The latter is particularly important, because the emergent dynamics of signaling pathways are hard to be predicted from simply looking at the biological system and, understanding them is required, especially as emergent behavior is recently pointed out as being an evaluation criterion of immune-inspired algorithms [31]. References 1. de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. 1 edn. Springer (2002) 2. Kholodenko, B.N.: Cell-signalling dynamics in time and space. Nat Rev Mol Cell Biol 7 (2006) Voutsas, K., Adamy, J.: A biologically inspired spiking neural network for sound source lateralization. IEEE Trans. Neural Networks 18(6) (2007) Wang, X., Hou, Z.G., Zou, A., Tana, M., Cheng, L.: A behavior controller based on spiking neural networks for mobile robots. Neurocomputing 71 (2008) Stepney, S., Smith, R.E., Timmis, J., Tyrrell, A.M., Neal, M.J., Hone, A.N.W.: Conceptual frameworks for artificial immune systems. Int J of Unconv Comp 1(3) (2005) Freitas, A.A., Timmis, J.: Revisiting the foundations of artificial immune systems for data mining. IEEE Trans. Evol. Comput. 11(4) (2007) Timmis, J., Andrews, P., Owens, N., Clark, E.: An interdisciplinary perspective on artificial immune systems. Evolut Intel 1(1) (2008) Greensmith, J.: The Dendritic Cell Algorithm. PhD thesis, School of Computer Science, University of Nottingham (2007) 9. Greensmith, J., Aickelin, U., Tedesco, G.: Information fusion for anomaly detection with the dendritic cell algorithm. Inform Fusion; In Press - (2008) 10. Hlavacek, W.S., Faeder, J.R., Blinov, M.L., Perelson, A.S., Goldstein, B.: The complexity of complexes in signal transduction. Biotechnol and Bioeng 84(7) (2003) Hartwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W.: From molecular to modular cell biology. Nature 402(6761) (1999) C47 C52, Suppl. S 12. Janes, K.A., Albeck, J.G., Gaudet, S., Sorger, P.K., Lauffenburger, D.A., Yaffe, M.B.: A systems model of signaling identifies a molecular basis set for cytokineinduced apoptosis. Science 310(5754) (2005) Helikar, T., Konvalina, J., Heidel, J., Rogers, J.A.: Emergent decision-making in biological signal transduction networks. PNAS 105(6) (2008) Hlavacek, W.S., Faeder, J.R., Blinov, M.L., Posner, R.G., Hucka, M., Fontana, W.: Rules for modeling signal-transduction systems. Sci. STKE 344 (2006) re6 15. Ciocchetta, F., Hillston, J.: Bio-PEPA: An extension of the process algebra PEPA for biochemical networks. Electron Notes in Theor Comput Sci 194(3) (2008)

12 12 Guzella, Mota-Santos 16. Spicher, A., Michel, O., Cieslak, M., Giavitto, J.L., Prusinkiewicz, P.: Stochastic P systems and the simulation of biochemical processes with dynamic compartments. BioSystems 91(3) (2008) Janeway, C.A., Travers, P., Walport, M., Shlonmchik, M.: Immunobiology: the immune system in health and disease. 5 edn. Garland Publishing, Inc, New York, USA (2002) 18. Krishna, S., Jensen, M.H., Sneppen, K.: Minimal model of spiky oscillations in NF-κB signaling. PNAS 103(29) (2006) Kholodenko, B.N.: Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur J Biochem 267(6) (2000) Chang, L., Karin, M.: Mammalian MAP kinase signalling cascades. Nature 410 (2001) Ashwell, J.D.: The many paths to p38 mitogen-activated protein kinase activation in the immune system. Nat Rev Immunol 6 (2006) Trinchieri, G., Sher, A.: Cooperation of toll-like receptor signals in innate immune defence. Nat Rev Immunol 7 (2007) Iwasaki, A., Medzhitov, R.: Toll-like receptor control of the adaptive immune responses. Nat Immunol 5(10) (2004) Akira, S., Takeda, K.: Toll-like receptor signalling. Nat Rev Immunol 4 (2004) Whitmore, M.M., Iparraguirre, A., Kubelka, L., Weninger, W., Hai, T., Williams, B.R.G.: Negative regulation of TLR-signaling pathways by activating transcription factor-3. J Immunol 179 (2007) Twycross, J.P.: Integrated Innate and Adaptive Artificial Immune Systems Applied to Process Anomaly Detection. PhD thesis, School of Computer Science, University of Nottingham (2007) 27. Twycross, J., Aickelin, U.: Biological inspiration for artificial immune systems. Lect Notes Comput Sc 4628 (2007) Owens, N.D., Timmis, J., Greensted, A.J., Tyrell, A.M.: On immune inspired homeostasis for electronic systems. Lect Notes Comput Sc 4628 (2007) Guzella, T.S., Mota-Santos, T.A., Caminhas, W.M.: Towards a novel immune inspired approach to temporal anomaly detection. Lect Notes Comput Sc 4628 (2007) Guzella, T.S., Mota-Santos, T.A., Caminhas, W.M.: Regulatory T cells: Inspiration for artificial immune systems. Lect Notes Comput Sc 4628 (2007) Hart, E., Timmis, J.: Application areas of AIS: The past, the present and the future. Appl Soft Comput 8 (2008) Fall, C., Marland, E., Wagner, J., Tyson, J., eds.: Computational Cell Biology. 1 edn. Springer (2002)