Application of Petri net based analysis techniques to signal transduction pathways
 Andrea Sackmann^{1, 2},
 Monika Heiner^{3} and
 Ina Koch^{1, 4}Email author
DOI: 10.1186/147121057482
© Sackmann et al; licensee BioMed Central Ltd. 2006
Received: 05 May 2006
Accepted: 02 November 2006
Published: 02 November 2006
Abstract
Background
Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal) flows within the network. Moreover, a huge amount of qualitative data is available due to highthroughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed.
Methods
We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study.
Results
We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible tinvariants, which represent minimal selfcontained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCTsets), which can be used for tinvariant examination and net decomposition into smallest biologically meaningful functional units.
Conclusion
The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible tinvariants and MCTsets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCTsets provide a decomposition of the net into disjunctive subnets, feasible tinvariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules.
Background
Signal transduction pathways are of special interest in biological and medical sciences. Many diseases are related to disturbances in signalling pathways. For example proteintyrosine kinases (PTKs) are important regulators of intracellular signal transduction pathways, mediating development and multicellular communication in metazoans. Their activity is tightly controlled and regulated, but perturbation of the normal autoinhibitory constraints on kinase activity can result in oncogenic PTK signalling [1]. Another example are human Gproteincoupled receptors, which mediate response to light, odour, taste, hormones, and neurotransmitters. Widely prescribed drugs, such as βadrenergic receptor blockers, antihistamines, and serotoninreuptake inhibitors bind to specific Gproteincoupled receptors [2]. There are signalling modules, e.g., this Gprotein and the mitogenactivated proteinkinase (MAPK) cascade, which occur in eukaryotic organisms from yeast to human. Budding yeast (Saccharomyces cerevisiae) has proven to be indispensable in understanding the mechanisms, interrelationships, and regulation of these components. On account of this crucial role of budding yeast we discuss our approach on the best understood signalling pathway in S. cerevisiae, the one of the mating pheromone response [3, 4].
Signal transduction pathways are usually modelled by a set of ordinary differential equations to describe the dynamic changes in the concentrations of the involved biochemical species [5–8]. One of the main problems in using these quantitative methods is the incomplete knowledge of kinetic parameters. Often only 30–50% of them are known, such that existing methods for parameter estimation fail. In addition, the numerical values of concentrations of species may not be considered, if they are very small, because of rounding effects in the solver algorithms. No insight into possible signal flows and the connections between species, e.g., feedback loops, are given explicitly. Moreover, more qualitative than quantitative data became available in the last years. This all has initiated the development of qualitative methods, i.e., discrete models and related evaluation techniques, which are used as a complementary intermediate step for construction and understanding of larger reliable models.
In order to model and analyse biochemical pathways on a qualitative level, dedicated methods have been developed. An example for a well established concept are the elementary modes [9], which are based on the incidence (stoichiometric) matrix of the underlying directed graph. This approach is applied for analysing metabolic networks. They are considered to be in steady state in order to use convex cone analysis techniques.
Recently, interaction graphs and Boolean networks are used to model and analyse signalling and regulatory networks [10]. [11] shows the limits of logical modelling techniques for gene regulatory networks. The authors propose an integrative systematic approach of logical and Petri net modelling discussing the tryptophan biosynthesis in E. coli. However, analyses are mentioned only in the outlook. Opposite to [12], who hold the view that the concept of elementary modes or minimal tinvariants, respectively, cannot be applied to signalling networks, [10] uses this concept, as we had already proposed in [13–15] to analyse an apoptosis model.
We have based our modelling approach of signalling pathways on Petri net theory, because it comprises several analysis techniques including those, which are based on the incidence matrix. In contrast to the above mentioned techniques, Petri nets provide useful unique visualisation techniques with possibilities for hierarchical modelling and for animation. Especially this aspect is of crucial importance for experimentalists and for the dialog between experimentalists and theoreticians.
Carl Adam Petri introduced the basic concepts of Petri nets in his dissertation in the context of technical systems [16]. He developed an approach for describing and studying models, which consist of concurrent, i.e., independent, and/or causally dependent components. Since this time, many theorems and algorithms have been developed and implemented to analyse Petri net models, see, e.g., [17, 18]. First applications of Petri net theory to biological systems were published in [19–21]. Meanwhile, metabolic pathways [22, 23], signal transduction pathways [14, 24], and generegulatory networks [25, 26] have been modelled and analysed successfully using various classes of Petri nets, qualitative as well as quantitative ones. An overview is given, e.g., in [27, 28]. For an earlier bibliography of related application papers see [29].
Signal transduction pathways exhibit some special properties. In contrast to metabolic networks, in signalling pathways there is no substance flow, but a signal flow, which is performed, for example, by phosphorylated and dephosphorylated protein forms. There are no stoichiometric reaction equations given, which dictate the net structure and define the arc weights. Modelling signalling pathways we have to work on another abstraction level than in modelling metabolic networks. Petri nets provide a generic description principle, applicable on any level of abstraction.
In this paper we present a method, which enables the systematic deduction of the net structure from the known signalling processes in the cell, which are translated into logical terms as implication, conjunction, inclusive or exclusive disjunction, and negation. These terms can be translated unambiguously to Petri net components. The resulting model is a qualitative, discrete Petri net, which can be analysed for certain system properties to validate the model.
In the following, we give a brief introduction into the mating pheromone response pathway in S. cerevisiae and recall the necessary Petri net definitions. Then, we propose and discuss in detail a systematic modelling method with respect to signalling pathways. We focus on the analysis of signal flows and introduce for this purpose the new concepts of feasible tinvariants and of maximal common transition sets (MCTsets). Feasible tinvariants stand for minimal selfcontained subnets being active under a given input situation, while MCTsets can be interpreted as smallest biologically meaningful functional units. Both concepts define a fully automatic net decomposition into biologically meaningful modules. Whereas MCTsets provide a decomposition of the net into disjunctive subnets, feasible tinvariants describe subnets, which generally overlap. We apply our modelling technique to the analysis of the pheromone response pathway in S. cerevisiae and discuss the results. Finally, we give conclusions indicating also further projects.
The signal transduction pathway of mating pheromone response
The signalling pathway of the mating pheromone response in S. cerevisiae is reviewed, e.g., in [3, 4, 30]. In S. cerevisiae there are two different mating types of haploid cells, MATα and MATa. Cells of opposite mating type can mate, i.e., fuse to a diploid cell. This process is stimulated through small peptide pheromones, the αfactor and the afactor, respectively. A haploid cell secretes the own factor and carries receptors for detecting a cell of opposite mating type via the other factor. When a yeast cell is stimulated by pheromone, secreted by a nearby cell of the opposite mating type, it starts a complex signalling pathway to undergo a series of physiological changes in preparation for mating. These changes are controlled and regulated through the pheromone signalling pathway, which is nearly the same in MATα and MATa cells.
In the following, we refer to this pathway in MATa cells, so the considered cells have an αfactorspecific cell surface receptor STE2, which is coupled to a heterotrimeric Gprotein. This Gprotein consists of the three subunits Gα, Gβ, and Gγ, whereby the latter two can act as heterodimer Gβγ. A conformation change of the receptor catalyses an exchange of GDP to GTP in the Gα subunit leading to a dissociation of this monomer. If GTP is hydrolysed to GDP, the monomer reassociates with the dimer to the trimeric form of the Gprotein [30]. The Gprotein Gβγ subunits mediate a signal on the MAP kinase cascade by interacting with Cdc24 (Far1 transmitted), protein kinase Ste20, and scaffold protein Ste5. The Ste20, Ste11, and Ste7 kinases, which are activated serially, phosphorylate and activate itself the two MAP kinases Fus3 and Kss1. But, Fus3 attenuates the activation of Kss1 [31]. Fus3PP releases the transcription factor Ste12 of its suppression through Dig1 and Dig2 proteins. (Re)Forming such a repression complex necessitates inactive Fus3 or inactive Kss1 [32]. Pheromone induced genes prepare the mating of the two cells and the fusion of their nuclei. They are responsible for cell cycle arrest in the phase G1, and the synthesis of some signalling regulators, e.g., the protease Bar1, the regulator of GProtein Sst2, and the phosphatase Msg5. Other regulations of the signalling pathway are realised through receptor endocytosis [33] or degradation of involved kinases [34].
Petri nets
Let us first recall some basic definitions of Petri nets. More formal definitions are given, e.g., in [17, 18, 35]. Petri nets are bipartite directed multigraphs, i.e., they consist of two types of nodes, called places P = {p_{1},..., p_{ n }} and transitions T = {t_{1},..., t_{ m }}, and directed arcs, which are weighted by natural numbers and connect only nodes of different type. Places model typically passive system elements as conditions, states, or biological species, i.e., chemical compounds as proteins. Transitions stand generally for active system elements as events, or chemical reactions as de/activation. In graphical representations, places are depicted as circles and transitions as rectangles. Transitions without preplaces (postplaces) are called input (output) transitions and are drawn as flat rectangles. The arcs in the net describe the causal relation between active and passive elements. They are illustrated as arrows and labelled with their weight, if it is larger than one. Arcs connect an event with its preconditions, which must be fulfilled to trigger this event, and with its postconditions, which will be fulfilled, when the event takes place.
The fulfilment of a condition is realised via tokens residing in places. Principally, a place in a discrete net may carry any integer number of tokens, indicating different degrees of fulfilment. If all preplaces of a transition are marked sufficiently (corresponding to the arc weights) with tokens, this transition may fire. If a transition fires, tokens are removed from all its preplaces and added to all its postplaces, each corresponding to the given arc weights. Thus, the tokens are the dynamic elements of the system. If a condition must be fulfilled, but the firing of an adjacent transition does not remove any tokens from it, these nodes are connected via two converse arcs. In the following, these arcs are represented by bidirectional arrows as a shorthand notation and are called read arcs.
From a biological viewpoint, the tokens residing in places indicate whether the corresponding chemical species is present, i.e., its concentration is above a certain concentration level (threshold) in the cell. This presence enables the chemical reactions modelled by the place's posttransitions to take place.
A current distribution of the tokens over all places, usually given as m ∈ ${\mathbb{N}}_{0}^{n}$, describes a certain system state and is called a marking of the net. Accordingly, the initial marking m_{0} of a net describes the system state before any transition has fired.
The incidence matrix C of a given Petri net is an (n × m)matrix (where n denotes the number of places and m the number of transitions, compare above). Every matrix entry c_{ ij }gives the token change on the place p_{ i }by the firing of the transition t_{ j }. Thus, the incidence matrix does not reflect read arcs. A tinvariant is defined as a nonzero vector x ∈ ${\mathbb{N}}_{0}^{m}$, which holds the equation
C·x = 0. (1)
A tinvariant represents a multiset of transitions, which have altogether a zero effect on the marking, i.e., if all of them have fired the required number of times, a given marking is reproduced. The invariant property holds for an arbitrary initial marking. A tinvariant is called realisable, if a marking is reachable, such that all transitions of the tinvariant are able to fire in a suitable partial order. Analogously, a pinvariant is defined as a nonzero vector y ∈ ${\mathbb{N}}_{0}^{n}$, which holds the equation
y·C = 0. (2)
A pinvariant characterises a token conservation rule for a set of places, over which the weighted sum of tokens is constant independently from any firing, i.e., for a pinvariant y and any markings m_{ i }, m_{ j }∈ ${\mathbb{N}}_{0}^{n}$, which are reachable from m_{0} by the firing of transitions, it holds
y·m_{ i }= y·m_{ j }. (3)
The nodes corresponding to the nonzero entries of an invariant x are called the support of x, written as supp(x). Considering Equations (1) and (2), it is obvious that a sum of tinvariants (pinvariants) gives again a tinvariant (pinvariant). An invariant x is called minimal, if its support does not contain the support of any other invariant z, i.e.,
∄ invariant z : supp(z) ⊂ supp(x), (4)
and the greatest common divisor of all nonzero entries of x is one.
A net is covered by tinvariants (pinvariants), if every transition (place) participates in a tinvariant (pinvariant). A tinvariant (pinvariant) defines a connected subnet, consisting of its support, the support's pre and postplaces (pre and posttransitions), and all arcs in between.
Methods
Modelling
In all implications, presented in Figure 1 and Figure 2, the tokens in the preplaces are removed by the firing of their posttransitions. Thus, the precondition is no longer fulfilled when the connected event took place. The situation, where the state of a precondition fulfilment is preserved, is discussed below in a general form, see case 1.
Altogether, there are three cases in signal transduction models, which can be distinguished by their different use of read arcs:

Case 1: A substance A does not lose its activity by interacting with a second substance B, see Figure 4.

Case 2: A substance C triggers several events represented by transitions, which are independent of each other, see Figure 5.

Case 3: The above mentioned context of a pinvariant, see Figure 3.
The subnet presented in Figure 5 differs from the subnet on the left hand side of Figure 2 by the use of read arcs. In Figure 2 the two posttransitions of place G are in conflict. Thus this subnet represents an exclusive disjunction G ⇒ (H ∨ I). By applying read arcs in such a substructure, the posttransitions of the corresponding place are concurrent, i.e., independent from each other. Thus, the subnet in Figure 5 represents an inclusive disjunction C ⇒ (D ∨ E).
 1.
compilation of biological knowledge about the signalling pathway of interest, e.g., from the literature or by database search,
 2.
translation of the interactions of relevant biological species into basic logical terms,
 3.
transferring these logical terms into net components,
 4.
assembling these net components to one Petri net,
 5.
if necessary, include input and output transitions ensuring that there are no places without pre or posttransitions in the net.
The last but one point ensures that the developed Petri net is connected. The input and output transitions introduced in the last point make the net to a transitionbordered one. They represent the interface of the biological system to its surroundings.
Model validation
The input transitions of a transitionbordered Petri net cause unboundedness, i.e., there is no upper bound for the number of tokens in such a net. Therefore, the state space of the net, i.e., all reachable markings, is infinite, and the dynamic net properties cannot be decided by the computation of the reachability graph, which contains all reachable markings as nodes. But, we do get information about the model dynamics via the analysis of the net structure, represented by the incidence matrix. Thus, our approach to validate the model is mainly based on the net invariants. The definition of invariants, see Equations (1) and (2), was introduced in [36]. The application of a system's minimal tinvariants to analyse metabolic networks in the steady state was proposed in [9] by the definition of elementary modes. In the context of metabolic networks pinvariants are used to model substrate conservations, see, e.g., [23, 37]. However, in order to apply these techniques, which have been proven to be useful in the context of metabolic networks, to signal transduction networks, we have to adjust them first to our special needs.
Pinvariants in signal transduction models represent also some kind of conservation relationship, however in the sense of the several modifications of a given species as introduced in Figure 3. A species cannot be consumed or produced, it can change its current state only, and it has to be always in exactly one state. The first step of model validation aims at checking the minimal pinvariants for their biological plausibility. Since the weighted sum of tokens over a pinvariant is constant (see Equation 3), there have to be always some tokens residing in at least one place of each minimal pinvariant ensuring that this part of the net may contribute to the system behaviour. Moreover, because a given species can always be in one state only, each pinvariant gets just one token, here. These tokens are typically placed in the pinvariants in such a way that the initial marking of the net represents an inactive state of the biological system.
The next step is to consider all possible signal flows through the network represented by realisable tinvariants, and to check their biological meaning. All possible tinvariants can be computed as nonnegative linear combination of the minimal ones, see Equation (1). In former case studies, which do not contain the special pinvariant construct, see Figure 3, it was sufficient to discuss the minimal tinvariants, which are always realisable by construction [14]. Including pinvariants, we have to process the minimal tinvariants to get realisable ones, defining minimal selfcontained subnets, which are active under a given input situation. For this purpose, we introduce the concept of feasible tinvariants.
Feasible tinvariants
The calculation of tinvariants does not take into account the initial marking. Moreover, read arcs are not reflected in the incidence matrix. Therefore, the question arises, whether a minimal tinvariant is really realisable. We assume that read arcs are the only source for nonrealisability, i.e., there is a sufficient token number in each of the minimal pinvariants in the initial marking.
The use of read arcs corresponding to the first case described above is discussed in [15]. For an analysis, these read arcs are replaced by unidirectional ones in the direction of the main token (signal) flow. The same rationale holds true for the replacement of the read arcs corresponding to the second case. But those ones, which are connected with (see the third case), have to remain in the net in order to keep the tokenpreserving structure of the pinvariant. As mentioned above, read arcs are not reflected in the incidence matrix. Therefore, depending on a given marking, some of the minimal tinvariants may not be realisable considering them in a net with read arcs. For example, the pinvariantadjacent transitions t 3 and t 4 in Figure 3 are treated as apparent input transitions, because their connection with the places p 1 and p 2, respectively, is not reflected in the incidence matrix. If this kind of transitions is part of a tinvariant, whereby their preplaces (connected via read arcs) carry no tokens, they are actually not able to fire. Therefore, the minimal tinvariants have to be processed to get realisable tinvariants for a suitable marking.
In accordance with our objective of model validation, we are looking for minimal selfcontained tinvariants, which are realisable in the initial marking, i.e., minimal multisets of transitions, which can fire in an appropriate order without the firing of the other transitions, not belonging to that tinvariant. Those tinvariants are called feasible tinvariants. To make a tinvariant feasible, all of its involved transitions have to be able to fire, i.e., for each of them it is true either that it is an input transition or that its preplaces enable it to fire. The latter case means that either the considered preplaces carry already sufficient tokens in the initial marking or these preplaces get sufficient tokens via the firing of other transitions involved in this tinvariant. We distinguish between realisable and feasible tinvariants, because the notion of realisability of a tinvariant is a general term of Petri net theory, referring to the reachability of a marking, where the tinvariant's transitions can fire. Feasible tinvariants are special realisable tinvariants, which are minimal ones concerning their realisability in the initial marking. They are introduced because of a special net structure, which causes the interruption of the tinvariant, when it leads for example to a pinvariant, which carries a special marking.
The tinvariants of the example in Figure 3.
No.  Involved transitions  Minimal?  Feasible?  Composed of 

1.  1, 2  √  √   
2.  3, 5  √  √   
3.  4, 6  √     
4.  1, 2, 4, 6    √  3+1 
z = x + y ∀x ∃ l ∈ {1,...,k} : x_{ l }≠ 0
with x_{ j }= 0
and y_{ j }≠ 0 ∧ ∀ l ∈ {l,...,k} : y_{ l }= 0. (5)
Note that for each feasible minimal tinvariant x providing a token on the place p_{ i }one linear combination z is constructed. Appropriate combinations have to be performed iteratively, if there occur several read arcs in the net, which have to be bridged. The combined tinvariants do not hold the criterion to be minimal, see Equation (4). But, since only inevitable combinations of minimal tinvariants are constructed, see the restrictions in Equation (5), the resulting combined tinvariants are minimal with respect to their realisability in the initial marking. Moreover, they only have nonnegative entries, because they are build as a sum of two tinvariants, which are defined as vectors in ${\mathbb{N}}_{0}^{m}$, see Equation (1).
Feasible tinvariants define, similarly to the tinvariants, connected subnets, characterised by its support. Generally, these subnets overlap. They represent minimal selfcontained subnets being active under a given input situation. So, each of these subnets stands for a possible signal flow in the net. The examination of the feasible (minimal or combined) tinvariants for their biological plausibility is a central step of model validation. It has to be checked, whether each feasible tinvariant has a biological meaning and whether every modelled part of the considered signalling pathway is reflected in a corresponding feasible tinvariant [14].
Finally, it has to be checked, whether the net is covered by tinvariants. This property ensures that every transition participates in a tinvariant, i.e., every biological atomic action in the model may take place as part of the basic behaviour of the net.
While minimal tinvariants are computed based on the net structure only, feasible tinvariants are constructed with respect to the marking of the net. Summarising the discussion above, the following situations are possible: the minimal tinvariants can reach (1) from input to output transitions, (2) from input transitions to minimal pinvariants, (3) from minimal pinvariants to minimal pinvariants, or (4) from minimal to output transitions. The transitions of a tinvariant influence a minimal pinvariant in such a way that altogether its initial state is reproduced. Not every possible combination is considered to bridge a read arc and to connect minimal tinvariants. Such a procedure would lead to tinvariants reaching from input transitions to output transitions, only. As described above, those minimal tinvariants are combined, which meet at a minimal pinvariant with a corresponding place not carrying a token.
Maximal common transition sets (MCTsets)
In order to support the check of feasible tinvariants for their biological meaning, the transitions are grouped into socalled maximal common transition sets (MCTsets) by their occurrence in the minimal tinvariants: ∀i, j ∈ {1,...,m} the transitions t_{ i }and t_{ j }are grouped into the same MCTset, if and only if they participate in exactly the same minimal tinvariants, i.e., all tinvariants x hold
χ_{{0}}(x_{ i }) = χ_{{0}}(x_{ j }), (6)
whereas χ_{{0}} denotes the characteristic function, binary indicating if an argument is equal to zero. This supportoriented grouping leads to maximal sets of transitions, where each set of transitions ϑ holds
∀x ∈ X : ϑ ⊆ supp(x) ∨ ϑ ∩ supp(x) = ∅, (7)
whereas X denotes the set of all minimal tinvariants x.
The grouping according to Equation (6) represents an equivalence relation in T, the set of transitions, which leads to a partition of T. The equivalence classes ϑ are the MCTsets. Transitions, not contained in any tinvariant, form their own MCTset. MCTsets define subnets, but not necessarily connected ones. An example of a disconnected MCTset will be given below in the presented model.
The resulting MCTsets are disjunctive and represent a possible decomposition of large biochemical networks into rather small subnets, which can be read as functional units. Because of the way, in which they are generated, each of the MCTsets may represent a signalling unit or a building block with its own biological meaning. In the following, only those MCTsets are considered, which contain more than one transition.
Results and discussion
Derivation of the Petri net model
The places of the model.
ID  Place name  Biological species 

1  alphafactor  pheromone released by an MATα cell in the surroundings 
2  Ste2 _receptor  mating pheromone receptor of the modelled MATa cell 
3  receptor_ factor_complex  complex consisting of the αfactor and the Ste2 receptor 
4  receptor_complex  the above named complex is activated by a conformation change 
5  trimer_bound_to_receptor  heterotrimeric G protein, which is coupled to the Ste2 receptor 
6  G_alpha_GTP  dissociated Gα subunit (exchange of GDP to GTP in this monomer) 
7  G_beta_gamma_dimer  Gprotein Gβγ subunits in a dimeric form 
8  Cdc24  Cdc24, i.e., guanine nucleotide exchange factor of Cdc42 
9  Cdc42(at_pm)  Cdc42 located at the plasma membrane 
10  Ste20  protein kinase Ste20 
11  Ste5 (scaffold)  Ste5, acting as a scaffold protein 
12  Ste5/Ste11  protein complex consisting of Ste5 and Ste11 
13  Fus3  MAP kinase Fus3 
14  Ste7/Fus3  protein complex consisting of Ste7 and Fus3 
15  MAPK_complex1  MAPK complex consisting of Ste5, Ste11, Ste7 and Fus3 
16  Ste20_at_pm  Ste20 located at the plasma membrane, i.e., near the MAPK complex 
17  complex2  as complex1, but Ste11 is activated additionally 
18  complex3  as complex2, but Ste7 is activated additionally 
19  complex4  as complex3, but Fus3 is activated additionally 
20  Fus3PP  dissociated Fus3 in the activated form 
21  compl_without_Fus3  as complex4, but without Fus3 
22  repr_complex  complex containing Ste12 repressed by Fus3 or Kss1 and Dig1/Dig2 
23  Dig1/Dig2  Ste12 inhibitors, i.e., cofactors for the repression 
24  free_Ste12  Ste12 released out of the repression complex 
25  Ste12  activated transcription factor Ste12 
26  Msg5  phosphatase Msg5 being able to deactivate Fus3 or Kss1 
27  Fus3_dephos  deactivated Fus3 
28  other_genes  pheromone regulated genes encoding mating related cell responses 
29  Bar1_in_nucleus  synthesised protease Bar1 located in the nucleus 
30  Bar1  Bar1 secreted in the cell environment 
31  inact_Far1  synthesised Far1 located in the nucleus in an inactive form 
32  Far1  Far1 activated by phosphorylation 
33  Far1_in_cytosol  active Far1 located in the cytosol 
34  Sst2_in_nucleus  synthesised Sst2 located in the nucleus in an inactive form 
35  phos_Sst2  Sst2 activated by phosphorylation 
36  Sst2  active Sst2 located in the cytosol 
37  inact_component  complex labelled for degradation by phosphorylation 
38  phos_Kss1  MAP kinase Kss1 activated by phosphorylation 
39  unphos_Kss1  inactive Kss1 
40  Akr1  protein Akr1 located at plasma membrane 
41  Yck1/Yck2_at_pm  kinases Yck1/Yck2 being able to label the Ste2 for degradation 
42  inact_receptor  receptor labelled for ubiquitination and endocytosis 
The transitions of the model.
ID  Transition name  Biological event 

1  MATalpha_cell(surroundings)  a near MATα cell secretes its mating pheromone 
2  binding_factor _to_receptor  the αfactor binds to the Ste2 receptor 
3  receptor_synthesis  synthesis of the cell surface receptor Sst2 
4  receptor _conformation_change  conformation change of the receptor 
5  division(in_alpha_subunit:GDP>GTP)  dissociation of the Gα subunit of the Gprotein 
6  hydrolysis_GTP>GDP  hydrolysis reassociates Gα with Gβγ 
7  interact_through_Far1  Gβγ interacts Far1 transmitted with Cdc24 
8  Cdc42:GDP>GTP  Cdc24 supported activation of Cdc42 
9  active_Cdc42_constitutive_at_pm  constitutive active Cdc42 attending the processes 
10  binding_to_Ste20  Gβγ binds Ste20 
11  Ste20_activated  Cdc42 at plasma membrane activates Ste20 
12  binding_to_Ste5  Gβγ binds Ste5 
13  Ste5_binds_Ste11  Ste5 binds Ste11 
14  Fus3_synth  synthesis of kinase Fus3 
15  Fus3_binds_Ste7  Ste7 binds Fus3 
16  complexformation  Ste5/Ste11 binds Ste7/Fus3 
17  Ste20_phos_Ste11  phosphorylation of Ste11 by Ste20 
18  Ste11_phos_Ste7  phosphorylation of Ste7 by Ste11 
19  Ste7_phos_Fus3  phosphorylation of Fus3 by Ste7 
20  Fus3PPrelease  release of activated Fus3 out of the MAPK complex 
21  binding_free_Fus3  remaining MAPK complex binds Fus3 
22  Ste12_inhibit _phos  phosphorylation of Ste12 inhibitors Dig1/Dig2 by Fus3PP 
23  Ste12release  release of Ste12 out of the repression complex 
24  Ste12_phos  phosphorylation of Ste12 by Fus3PP 
25  transcr_activation  transcription activation of pheromone regulated genes 
26  Fus3PP_dephos  dephosphorylation of Fus3PP by Msg5 
27  repression_through_Fus3  Ste12 repression through inactive Fus3 and Dig1/Dig2 
28  cell_fusion  processes leading to the fusion of the two haploid cells 
29  transport_out_of_cell  Bar1 transport into the cell environment 
30  factor _destruction  Bar1 transmitted destruction of the αfactor 
31  Far1_phos  phosphorylation of Far1 by Fus3PP 
32  cell_cycle_arrest in_G1  Far1 caused arrest in the cell cycle phase G1 
33  transport_out_of_nucleus  Far1 transport out of the nucleus 
34  Sst2_phos  phosphorylation of Sst2 by Fus3PP 
35  transport_out_of_nucleus  Sst2 transport out of the nucleus 
36  accelerated_hydr_GTP>GDP  accelerated hydrolysis reassociates the Gprotein 
37  Ste11_neg_phos  Fus3PP labels the MAPK complex at Ste11 for degradation 
38  degradation  degradation of the MAPK complex 
39  Ste7_neg_phos  Fus3PP labels the MAPK complex at Ste7 for degradation 
40  Ste7_phos_Kss1  phosphorylation of Kss1 by Ste7 
41  accelerateddephos_Kss1  deactivation of phosphorylated Kss1 by Fus3PP 
42  Kss1_dephos  dephosphorylation of phosphorylated Kss1 by Msg5 
43  repression_through_Kss1  Ste12 repression through inactive Kss1 and Dig1/Dig2 
44  techn_input  technical: the repressed Ste12 complex assumed to be present 
45  Akr1_synthesis  synthesis of Akr1 
46  Akr1_binds_Yck1/Yck2  Akr1 binds Yck1/Yck2 
47  receptor_phos  labelling of Ste2 for degradation 
48  ubiquit_endocytosis  ubiquitination and endocytosis of the receptor 
Proteins, protein complexes, and other chemical compounds are represented as places, and complex formation/cleavage, protein de/phosphorylation, and other reactions by transitions. Tables 3 and 4 show in their right column, which chemical compounds and which biological events are considered in the model. Generally formulated, the model contains a complete pheromone response pathway, i.e., activation and composition of the pheromone receptor complex, composition of the MAP kinase cascade complex and MAP kinase cascade, transcription factor activation, and transcription of genes, which response to the mating pheromone. Moreover, there are several positive and negative feedback regulations included in the model, i.e., regulation of the ligand αfactor (via Bar1), of the receptor Ste2 (endocytosis induced by Yck1 and Yck2), of Gprotein subunits (via Sst2), of the MAPK cascade (via labelling for degradation), and of the regulation of transcription (via repression of the transcription factor Ste12).
We take into account the above mentioned substructures, e.g., the place complex2 (p 17) does not lose its activity by firing of transition Ste11_phos_Ste7 (t 18), i.e., in the MAPKcomplex the protein Ste11 remains in its active form after activating the protein Ste7, see case 1. The place Fus3PP (p 20) triggers several transitions Ste12_inhibit_phos t 22), Ste12_phos (t 24), Far1phos (t 31), and Sst2_phos (t 34), which are independent of each other, see case 2. The place pair Kss1_phos (p 38) and unphos_Kss1 (p 39) forms a pinvariant as discussed in case 3.
With one exception, there exist only arcs weighted by 1 because no substance flow with stoichiometric relations takes place, but signal flow is described by the model. This special arc is weighted by 2 to reduce the token number in the structural cycle, which is formed by the places complex3 (p 18), complex4 (p 19), compl_without_Fus3 (p 21), and the transitions Ste7_phos_Fus3 (t 19), Fus3PPrelease (t 20), and binding_free_Fus3 (t 21). By using this arc weight, it is ensured that the activation of the transition Ste7_phos_Fus3 (t 19) depends on firing of transition Ste11_phos_Ste7 (t 18), see Figure 7. In the biological context, this means that the crucial phosphorylation of the last kinases in the MAPK cascade depends on the activation of the last but one.
In the initial marking there is one token residing in the place inact_Far1 (p 31) and Sst2_in_nucleus (p 34), respectively, which symbolise that these proteins are already present in the cell in low concentration independently of a signal.
Application of Petri net concepts
Pinvariant analysis
The minimal pinvariants and their biological meaning.
No.  Places  Biological meaning 

1.  5, 7  Gβγ subunits: dimer and bound in G protein trimer 
2.  5, 6  Gα subunit: monomer and bound in G protein trimer 
3.  38, 39  Kss1: active and inactive form 
In the initial marking of the net there are some tokens already residing in special places. According to the elucidation in section Model validation, the places trimer_bound_to_receptor (p 5) and unphos_Kss1 (p 39) carry tokens ensuring that the minimal pinvariants contribute to the system behaviour.
Tinvariant analysis
The maximal common transition sets.
MCTset  Transitions  Biological meaning 

1  1, 2, 3, 4  Receptor activation 
2  7, 8, 33  Interaction GβγCdc24 via Far1 
3  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21  MAP kinase cascade 
4  22, 23, 24  Ste12 activation 
5  25, 28, 29, 30, 31, 34, 35, 36, 44  Transcription of the pheromoneresponding genes 
6  26, 27  Ste12 repression through inactive Fus3 
7  45, 46, 47, 48  Receptor endocytosis 
The tinvariants.
No.  Involved transitions  Minimal?  Feasible?  Composed of  

MCTsets  single transitions  
1.  1  5, 6  √  √   
2.  1, 7    √  √   
3.  3  37, 38  √     
4.  3  38, 39  √     
5.  3, 4  43  √     
6.  3  40, 41  √     
7.  1, 3, 4, 5  5, 32, 40, 42  √  √   
8.  1, 2, 3, 4, 5  5, 40, 42  √  √   
9.  1, 3, 4, 5, 6  5, 32  √  √   
10.  1, 2, 3, 4, 5, 6  5  √  √   
11.  1, 3  5, 6, 37, 38    √  3 + 1 
12.  1, 3  5, 6, 38, 39    √  4 + 1 
13.  1, 3, 4  5, 6, 43    √  5 + 1 
14.  1, 3  5, 6, 40, 41    √  6 + 1 
The biological meaning of the feasible tinvariants.
No.  Biological meaning 

1.  Dissociation and reassociation of the G protein subunits 
2.  Endocytosis of the activated receptor 
7.  Changed gene transcription, cell cycle arrest, de/phosphorylation Kss1 
8.  Changed gene transcription, Far1 transport out of the nucleus, de/phosphorylation Kss1 
9.  Changed gene transcription, cell cycle arrest, repression of Ste12 through inactive Fus3 
10.  Changed gene transcription, Far1 transport out of the nucleus, repression of Ste12 through inactive Fus3 
11.  Signalling via the cascade, feedback degradation of Ste11 
12.  Signalling via the cascade feedback degradation of Ste7 
13.  Signalling via the cascade, repression of Ste12 through inactive Kss1 
14.  Signalling via the cascade, phosphorylation and dephosphorylation of Kss1 
Recapitulating, tinvariants 7 to 10 lead to a mating of the cell, while the other tinvariants enable the cell to regulate, and modulate a pheromone induced signal by attenuated response or response desensitisation.
Altogether it can be summarised that the known signalling processes are represented in the feasible tinvariants.
Theoretical knockout experiments
The Petri net model can serve as basis of theoretical knockout experiments. They can be constructed by deleting the corresponding place and adjacent transitions, modelling the absence of this compound in a null mutant. Animating the resulting net shows already where some token accumulations arise under the new situation. These affected compounds can characterise a null mutant. Theoretical knockout experiments by the Petri net model could point out, which compounds indicate a mutant and nominate possible indicators for biological exploration.
Furthermore, a deletion of transitions leads to a deletion of feasible tinvariants, at least in a net, which is covered by tinvariants. The remaining feasible tinvariants show, which events still take place in this new cell type. For example, it is known [30] that Ste5 null mutant cells are unable to mate or to arrest the cell cycle. This experiment can be reconstructed in our Petri net model by deleting the place Ste5(scaffold) (p 11) and its adjacent transitions binding_to_Ste5 (t 12) and Ste5_binds_Ste11 (t 13). This results in the deletion of the MCTset 3, see Table 5. The feasible tinvariants 7 to 14 do not exist in the new mutant net because they include MCTset 3, compare Table 6. The transitions cell_fusion (t 28) (i.e., MCTset 5), and celI_cycle_arrest_in_G1 (t 32) occur only in the feasible tinvariants 7 to 10, which were deleted. Thus, the Ste5 mutant cells modelled by that mutant net are neither able to arrest the cell cycle nor to undergo a cell fusion, because the corresponding transitions do not occur in the feasible tinvariants of the net.
Conclusion
In this paper we describe a systematic approach to model and analyse signal transduction pathways using qualitative Petri nets. We propose a stepwise model development via the translation of the biological interactions into logical terms, which then in turn are transferred into net components.
We explain the model validation step with a strong focus on the invariant analysis. In our modelling approach, we use minimal pinvariants, e.g., for the representation of the switching behaviour representing a compound in its activated and deactivated form. These pinvariants are embedded into the whole network by read arcs. Therefore, minimal tinvariants have generally to be processed to get feasible tinvariants. According to the construction principle, feasible tinvariants correspond to minimal selfcontained subnets active under a given input situation. So, feasible tinvariants have to be checked for their biological meaning.
Furthermore, we define maximal common transition sets, which provide a decomposition of the net into not necessarily connected subnets. These structures can be interpreted as the smallest biologically meaningful functional units or building blocks of a given network.
The new concepts of feasible tinvariants and MCTsets have been proven to be useful for model validation and the interpretation of the biological system behaviour by their fully automatic approach to decompose a given network into biologically meaningful modules. Please note that it is possible to build the MCTsets based on the feasible tinvariants instead of the minimal ones. That makes sense in Petri nets, which contain more substructures of minimal pinvariants. MCTsets provide a decomposition of the net into disjunctive subnets, and feasible tinvariants describe subnets, which can overlap.
We illustrate this approach using the mating pheromone pathway in S. cerevisiae. The validated model denotes the known signal flows through the net, which are obtained by the invariant analysis. We are able to assign a reasonable biological meaning to all feasible tinvariants. These reflect correctly the signal transduction in such a system, i.e., the signal response behaviour. Thus, Petri net theory is also useful to model and analyse signalling pathways considering not a substance flow, but a flow of information (signals) through the net.
The Petri net model may serve as basis for theoretical knockout experiments. By deleting appointed places and adjacent transitions, null mutants can be modelled. The analysis results of the original net can be easily modified to get some information about the derived mutant cell.
Having once validated the structure of the qualitative discrete Petri net, several applications and extensions are possible. The net may be refined and extended to a quantitative one by including known or estimated kinetic parameters (e.g., as concentrations, reaction rates, equilibrium constants), using continuous or hybrid Petri nets, respectively [38, 39].
In this way, the resulting quantitative Petri net preserves the structure of the underlying net topology. Continuous Petri nets may be considered as a structured ODEs description. They can be evaluated by standard differential equation solvers. For a related case study see [40]. Hybrid Petri net comprise generally discrete as well as continuous parts. Their evaluation requires dedicated simulation approaches. For a related case study see [24]. In both cases, the quantitative simulation will allow the observation of signal flows, already revealed by the underlying discrete model. However, contrary to our qualitative approach, where signal flows are represented explicitly by the computed feasible tinvariants, signal flows in the quantitative model are given only implicitly by the simulation results.
In this paper, we have chosen deliberately a rather small running example to be able to present all steps of the analysis procedure in full detail, which should help the reader to mimic the approach using his/her own case studies. It would be worth applying our approach to larger networks as presented in [41, 42].
Software tools and supplementary material
The development of the net has been done using the graphical Petri net editor and animator Snoopy [43] described in [44]. The analyses have been performed using the Integrated Net Analyzer INA [45]. Both tools are freely available. They are running under Windows as well as under Unix/Linux. We provide the Petri net model and the INA analysis result file in the supplementary material [46].
Declarations
Acknowledgements
This work has been partly supported by the Federal German Ministry of Education and Research (BMBF), BCB project 0312705D. We would like to thank Bente Kofahl, Edda Klipp, and Günter Czichowski for the fruitful discussions.
Authors’ Affiliations
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Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.