 Research article
 Open Access
Timedependent structural transformation analysis to highlevel Petri net model with active state transition diagram
 Chen Li†^{1},
 Masao Nagasaki†^{1}Email author,
 Ayumu Saito^{1} and
 Satoru Miyano^{1}
https://doi.org/10.1186/17520509439
© Li et al; licensee BioMed Central Ltd. 2010
 Received: 14 September 2009
 Accepted: 1 April 2010
 Published: 1 April 2010
Abstract
Background
With an accumulation of in silico data obtained by simulating largescale biological networks, a new interest of research is emerging for elucidating how living organism functions over time in cells.
Investigating the dynamic features of current computational models promises a deeper understanding of complex cellular processes. This leads us to develop a method that utilizes structural properties of the model over all simulation time steps. Further, userfriendly overviews of dynamic behaviors can be considered to provide a great help in understanding the variations of system mechanisms.
Results
We propose a novel method for constructing and analyzing a socalled active state transition diagram (ASTD) by using timecourse simulation data of a highlevel Petri net. Our method includes two new algorithms. The first algorithm extracts a series of subnets (called temporal subnets) reflecting biological components contributing to the dynamics, while retaining positive mathematical qualities. The second one creates an ASTD composed of unique temporal subnets. ASTD provides users with concise information allowing them to grasp and trace how a key regulatory subnet and/or a network changes with time. The applicability of our method is demonstrated by the analysis of the underlying model for circadian rhythms in Drosophila.
Conclusions
Building ASTD is a useful means to convert a hybrid model dealing with discrete, continuous and more complicated events to finite timedependent states. Based on ASTD, various analytical approaches can be applied to obtain new insights into not only systematic mechanisms but also dynamics.
Keywords
 Discrete Transition
 Reachability Graph
 Input Place
 Connection Relationship
 Insert Edge
Background
A great deal of biological datasets have been measured in a lot of laboratories around the world in recent years. Petri nets have been applied successfully in modeling, simulating and analyzing biological networks [1, 2] (i.e., metabolic [3, 4], signal transduction [5, 6] and gene regulatory networks [7, 8]). In the meanwhile, a number of public and commercial databases have developed tools to automatically convert biological pathway information into various formats of models, e.g., the tool TRANSPATH2CSML [9] automatically converts data stored in TRANSPATH [10] to a simulationbased model encoded in a biological pathway format. These approaches make it possible to construct larger and more complex biological network models. However, the associated increase in complexity and output data result in the difficulty of grasping systematic characteristics of the models.
Several studies with respect to the topology of the interactions between biological compounds in cellular networks based on Petri net theory have been made in understanding biological networks [11–14]. These approaches use mathematical properties of Petri nets (e.g., reachability, liveness, boundedness and Tinvariant) to reveal some topological properties of biological networks on qualitative models. Other investigation regarding the dynamics of signal propagation in signaling pathway has been given by Hardy et al.[15]. This method gives temporal information about the flow of signal propagation. However, the analysis is limited to a single signal source. Nevertheless, it is expected to find a general methodology to analyze the dynamics with quantitative simulation information, i.e., timedependent dynamic behaviors among genes and their products which constitute biological networks [16].
This paper presents a novel method to build a framework for automatically constructing a socalled active state transition diagram (ASTD) for the dynamic analysis with respect to the structural changes over time in a hybrid functional Petri net with extension (HFPNe) model. Our method incorporates timecourse simulation data and temporal structural properties (connection relationship) of the HFPNe model. This method constructs an ASTD composed of unique temporal subnets which are exhaustively extracted from original HFPNe model, produces a simplified graphical representation about the temporal information of the dynamics. After the ASTD is built, various analysis can be applied to the ASTD to obtain new insights into the systematic dynamics. Note that HFPNe is an enhanced Petri net architecture which involves the functions of existing highlevel Petri nets [17].
The paper is organized as follows. In Methods, we first present a basic definition of HFPNe. We propose two new algorithms for constructing ASTD based on timecourse simulation data from an HFPNe model. In Results and Discussion, we present a case study describing how our method is employed to integrate and interpret the circadian rhythm model in Drosophila, and give three characteristic overviews of the ASTD for facilitating a systemlevel understanding. The final section concludes our paper and addresses the contribution.
Methods
Hybrid functional Petri net with extension (HFPNe)
A discrete place holds a positive integer number of content. A discrete transition is the same notion as used in the traditional discrete Petri net [18]. A continuous place holds a nonnegative real number as concentration of a substance such as mRNA and protein. A continuous transition is used to represent a biological reaction such as transcription and translation, at which the reaction speed is assigned as a parameter. A generic place can hold any kind of types including object, e.g., the string of nucleotide base sequence. A generic transition can deal with any kind of operations (e.g., alternative splicing and frameshifting) to all types of places. Generic place and transition have been practically applied for modeling and simulating more complicated biological processes [7, 19, 20], e.g., activities of enzymes for a multimodification protein. Arcs are classified into three types: normal arc, test arc, and inhibitory arc. Normal arc connects a place to a transition or vice versa. Test or inhibitory arc represents a condition and is only directed from a place to a transition. Each of normal arc from a place, test arc, and inhibitory arc has a threshold by which the parameter assigned to the transition at its head is controlled. A normal arc from a place or a test arc (an inhibitory arc) can participate in activating (repressing) a transition at its head, as far as the content of a place at its tail is over the threshold. For either of test or inhibitory arcs, no amount is consumed from a place at its tail.
Basic definitions for HFPNe
We briefly give the necessary definitions for HFPNe used in this paper. The formal definition of HFPNe is given as additional material [see Additional file 1]. For further definition and application of HFPNe the reader is suggested to refer to Nagasaki et al.[17]. The following is the mathematic definitions used in this paper:
 1.
P is a set of places and T is a set of transitions. Place is labeled with either discrete, continuous, or generic. Transition is also labeled with discrete, continuous, or generic. The place and transition are called discrete, continuous, or generic according to its label.
For each transition t in T, it has two sets Input_{ t }and Output_{ t }of arcs. Arc a∈Input_{ t }is an edge from input place p_{ a }to the transition t called input arc. Arc a'∈Output_{ t }is an edge from the transition t to output place p_{ a' }called output arc. Each arc is labeled with either normal, test, or inhibitory, and arc labeled with normal (resp., test, inhibitory) is called normal arc (resp., test arc, inhibitory arc). We also say that arcs (a and a') are discrete (resp., continuous, generic) if transition t is discrete (resp., continuous, generic).
We denote by PT and TP the set of input arcs and the set of output arcs of all transitions, respectively. We also denote arc a in PT as a(p, t). In a similar way, arc a' in TP is denoted as a'(t, p). The set A of arcs is given by PT∪TP.
 2.
The types of places are given by a type function τ.
 3.
For each input arc a∈PT, its activity w(a) is given by an activity function w. Activity function w(a) is used as a function giving the threshold in discrete and continuous cases and the condition in generic case, which is required for enabling the transition t.
 4.
For each arc c (c = a(p, t)∈PT or c = a'(t, p)∈TP), the update u(c) is given by an update function u.
 5.
For each discrete or generic transition t, the delay of t is given by a delay function d.
We use the parameter x ≥ 0 for the time in HFPNe. Do not confuse t for transition with x for time.
A marking of P is defined as a mapping M that assigns a mark (the type of contents) to each place. M [p] is called the mark of p. The initial marking I is a marking at time x = 0 and we denote the marking at time x by M(x). The reserved marking M_{ r }(x) at time x represents the amount of "tokens" reserved for firing when firing conditions are satisfied. By convention, let M(p, x) be M [p](x), and M_{ r }(p, x) be M_{ r }[p](x) for p∈P. We define (x) by [p](x) = M [p](x)M_{ r }[p](x) if p is discrete or continuous and [p](x) = M [p](x) if p is generic. Given the initial marking of HFPNe, the marking M(x) and the reserved marking M_{ r }(x) at time x are defined in the following way:
 1.If t is discrete or continuous, then for all input arcs c = a(p, t)∈PT the following conditions hold:
 (a)
[p](x)>w(c) [M(x)] if a is not labeled with inhibitory;
 (b)
[p](x)<w(c) [M(x)] if a is labeled with inhibitory,
where w(c) [M(x)] is the threshold value of c on marking M at time x.
 (a)
 2.If t is generic, then for all input arcs a(p, t)∈PT the following conditions hold:
 (a)
w(a) [ (x)] = true if a is not labeled with inhibitory;
 (b)
w(a) [ (x)] = false if a is labeled with inhibitory. □
 (a)
 1.
If c is discrete or continuous, then [p](x)>w(c) [M(x)] holds;
 2.
If c is generic, then w(c) [ (x)] = true holds. □
Definition 3. If disabled transition t turns enabled at time x, we say that t is triggered at time x and x is called the trigger time. If enabled transition t turns disabled at time x, we say that t is switched off at time x and x is called the switchoff time. □
Definition 4. We define firing of discrete transition t. Assume that discrete transition t is triggered at time x. For each normal input arc a(p, t), the place p must be discrete or continuous by definition. Then M_{ r }[p] reserves a · u(a) [M (x)], i.e., α·u(a) [M (x)] is added to M_{ r }[p], for the time y > x until x + d(t) [M(x)], where α = {0, 1}, if α = 0, reserve is disabled; otherwise, token is reserved. If t is still enabled at x + d(t) [M(x)], then at the same time x + d(t) [M(x)], M [p] is decreased by u(a) [M(x)] and M_{ r }[p] releases u(a) [M (x)], i.e., u(a) [M (x)] is decreased from M_{ r }[p]. Simultaneously, for each output normal arc a'(t, p'), M [p'] is increased by u(a') [M(x)] at time x + d(t) [M(x)] by arc a'(t, p'). The time d(t) [M(x)] is called the delay that is determined by the function d(t) of the mark M(x) at time x.
As we will describe in Definitions 5 and 6 below, the reservation is not performed by generic or continuous transition. However, for the place p, there may be another discrete transitions t_{1},..., t_{ℓ} with normal input arcs a_{1}(p, t_{1}),..., a_{ m }(p, t_{ℓ}) which are triggered at time x. Then each discrete transition t_{ i }tries to reserve u(a_{ i }) [M (x)] from the same M [p] at time x for i = 0,..., ℓ, where a_{0} = a(p, t) and t_{0} = t. We say that there is a conflict with p at time x if . When a conflict occurs, some conflict resolution should be applied, e.g., random selection of transitions, priorities on transitions, etc.
Even if some conflict resolution procedure selected the transition t to go further, the place p of a(p, t) may be input places or output places of another discrete/continuous/generic transitions. By this, M [p] and M_{ r }[p], and therefore [p], may be changed, the conditions of "enabled" are not be necessarily satisfied until the firing time x + d(t) [M(x)]. When t becomes disabled before x + d(t) [M(x)], we say that a system error occurs with t.
Thus triggered transition does not necessarily fire. If all of these actions succeed, we say that t fires at time x + d(t) [M(x)]. □
Note that α is set to zero in this paper so that the reserved marking M_{ r }(x) at any time x equals to zero. That is, the token amount will not be reserved during the delay time of the transition t when it becomes enabled.
Definition 5. We define firing of generic transition t. Assume that generic transition t is triggered at time x. For each normal input arc a(p, t), the place p can be discrete, continuous and generic. For each output normal arc a'(t, p'), p' can be also any kind of places. If t keeps enabled until time x + d(t) [M(x)], then M [p] at time x + d(t) [M(x)] is updated to u(a) [M(x)] and M [p'] is updated to u(a') [M(x)] at time x + d(t) [M(x)]. We say that t fires at time x + d(t) [M(x)] if this action succeeds. If p is generic, it is always that M_{ r }[p](x) = null. No change is added to M_{ r }[p] by arc a(p, t) if p is discrete or continuous. In a similar way to discrete transition, if p is discrete or continuous, Mp and M_{ r }[p] have a possibility to be changed before x + d(t) [M(x)] by another transitions. Therefore w(a)[ (y)] = true is not necessarily kept for y ∈ (x, x + d(t) [M(x)]). As in the case of discrete transition, it should be reported as system error. Since generic transition updates M [p] and M [p'] at time x + d(t) [M(x)], there is a possibility of conflict with another transitions which use p and p'. Thus some conflict resolution should be applied or it should be reported as system error. □
Definition 6. We define firing of continuous transition t. When continuous transition t is triggered, it starts firing and updates the marks of its connected places continuously with the speeds determined by the update function u and the marking M as long as it is enabled. Assume that continuous transition t is enabled at time x. For each normal input arc a(p, t), the place p must be continuous by definition. Then the mark M [p] will be decreased through the arc a(p, t) with the additional speed u(a) [M(x)] at time x. No change is added to M_{ r }[p] by arc a(p, t). For output normal arc a'(t, p'), the place p' must be continuous by definition. Then the mark M [p'] will be increased through the arc a'(t, p') with the additional speed u(a') [M(x)] at time x. No change is added to M_{ r }[p'] by arc a'(t, p'). □
HFPNe modeling

Places are used to model biological molecules, conditions, states and cellular organelles. In the case of chemical reactions, the compounds involved usually have specific quantities. In HFPNe, places can take any object that can be expressed in programming languages like an instance of a class in C++ or Java.

Transitions are used to model interactions among places, such as phosphorylation, translocation, and apoptosis. In HFPNe, each transition can define any event/function that can be performed by programming languages. In a simple model, the event/function can be the speed of a reaction or a discrete reaction.

Arcs connecting the places and the transitions represent the relations between corresponding substances and interactions.
As described above, HFPNe model allows modeling and simulation of biological networks combining both an intuitive graphical representation and wellfounded mathematic definition. Because of the versatility of HFPNe, it has been successfully employed to develop and analyze complex biological networks [7, 19, 20]. For example, in [7], Li et al. employed a series of generic places/transitions to realize 48 distinct genetic conditions that are the combination of four genes (lin12, lin15, vul and lst) and one anchor cell (AC) for determining the vulval precursor cell fate. AC, lin15, vul and lst can toggle between true and false. lin12 has three stringtype values, i.e., "wt", "ko", "gf", indicating three genetic conditions of wild, knockout and overexpression of lin12 (refer to Figures seven and eight in [7]). Saito et al.[19] applied HFPNe to model regulatory networks that involve new key regulator microRNA. They selected the cell fate determination model of two gustatory neurons of Caenorhabditis elegans  ASE left (ASEL) and ASE right (ASER) (see Figure three in [19]). These neurons are morphologically bilaterally symmetric but physically asymmetric in function. By the simulation, they have confirmed the hypothesis that the cell fate is determined by the doublenegative feedback loop involving lsy6 and mir273 microRNAs. Tasaki et al.[20] introduced timeseries proteomic data to the HFPNe model. The authors semiautomatically constructed a welltuned epidermal growth factor receptor signal transduction pathway model (EGFR model, see Figure two in [20]) coupled with their data assimilation (DA) framework.
Model Analysis
Algorithm to extract temporal subnet from timecourse simulation data
First we show Algorithm 1 for extracting temporal subnets from timecourse simulation data (simulation data for short). The simulation data is generated by using the simulator of HFPNe, which is saved in an expression data format (called EDF). In EDF, the concentrations of all places (i.e., the marking) are stored at every time point during the simulation (in this case, a constant time interval). Let = x_{0}x_{1}⋯x_{ sim }be a nonempty list of simulation time points, where x_{0} is a start time point of the simulation and x_{ sim }is an end time point. The size (the number of elements) of the list is denoted by  .
 (1)
enabled arcs;
 (2)
transitions connected by (1); and
 (3)
places connected by (1) and places connected from (2).
The temporal subnet H'(x_{ i }) has following positive mathematical qualities: (i) the places involved in H'(x_{ i }) is able to be simulated with corresponding concentration distribution M(x_{ i }), and the simulation result of these places from x_{ i }to x_{i+1}is exactly the same to the one of the original HFPNe model; and (ii) all elements in H'(x_{ i }) take part in the firing at x_{ i }, i.e., corresponding biological components do participate in regulatory activity. We formalize this notion in the following definitions:
 (1)
Let p be a place in P, °p (or p°) is a set of the input (or output) transitions of p.
 (2)
A ^{ T }is a set of the test arcs; A ^{ I }is a set of the inhibitory arcs; and A ^{ N }is a set of the normal input arcs.
 (3)
For each arc c, let °c denote the source of c; let c° denote the target of c. □
 (1)
°t is the set of the input places {pλ _{1}, pλ _{2},⋯, pλ _{°t}} of t; t° is the set of the output places {PO _{1}, PO _{2}, ⋯, PO _{t°}} of t.
 (2)
PT ^{ t }is the set of the arcs from the places in °t to t; TP ^{ t }is the set of the arcs from t to the places in t°.
 (3)
The set of input places connected by the inhibitory arcs to the transition t is denoted as . A ^{I, t}is the set of the inhibitory arcs . Similarly, the set of the test (or normal) arcs from the input places of t to t is denoted as A ^{T, t}(or A ^{N, t}).
 (4)
is the set of disabled normal and test arcs {c(c∈(A ^{T, t}∪A ^{N, t})) ∧ ((w(c)≥M [°c])∨(w(c) = false))}, where w(c) is the activity function. is the set of enabled inhibitory arcs {c(c∈A ^{I, t}) ∧ ((w(c) < M [°c])∨(w(c) = true))}. □
Algorithm 1. EXTRACTING TEMPORAL SUB NET
For a given HFPNe H = (P, T, A, τ, w, u, d) at time x, calculate TSN(H, x) and return H'(x).
 1.
H'←H
 2.
RMVA(H') /* delete disabled arcs */
 3.
RMVT(H') /* delete isolated transitions */
 4.
RMVP(H') /* delete isolated places*/
 5.
return H'
RMVA(H'):
For ∀t∈T of H',
1. if A ^{I, t}  = 0 /* if there exists no inhibitory arc */
/* if there exists such a normal/test arc whose evaluated value of activity function is greater than or equal to the concentration of the connected place */
2. if delete PT^{ t }∪TP^{ t }
3. else /* more than one inhibitory arc existing */
4. if
/* if there exists an inhibitory arc whose evaluated value of activity function is less than the concentration of the connected place */
5.if delete ∪TP^{ t }
6. else delete PT^{ t }∪TP^{ t }
7. else
8. if delete ∪TP^{ t }
9. else delete A^{I, t}
RMVT(H'):
For ∀t∈T of H,
if ((PT^{ t }= ϕ) ∧ (TP^{ t }= ϕ)) delete t /* delete isolated transition */
RMVP(H'):
For ∀p∈P of H,
if ((°p = ϕ) ∧ (p° = ϕ)) delete p /* delete isolated place */
Building Active State Transition Diagram (ASTD)
We here show the other algorithm based on the outputs (i.e., temporal subnets) obtained in Algorithm 1. For a given time points list and a set of temporal subnets obtained from Algorithm 1, by applying following Algorithm 2, we derive a directed graph, called active state transition diagram (ASTD), which is denoted by G = (N, E). N represents a set of distinct nodes {z_{1},⋯, z_{N}}. Each node z∈N is called active subnet (ASN) (or state for short), extracted from the set of temporal subsets without repetition. E denotes a set of directed edges e from z_{ i }to z_{ j }, represented by e = (z_{ i }, z_{ j }).
Algorithm 2. CONSTRUCTING ACTIVE STATE TRANSITION DIAGRAM
x: current time point.
sid : state ID.
StateMap: a state map storing H'(x) as key and sid as value.
Gen(H'(x)): a function to output sid by referring to the state map.
curState: current state.
prevState: previous state.
sim: simulation time.
1. x←x _{0}, sid←1, z _{1}←H' (0), prevState←H'(0), N←{z _{1}}
2. insert (prevState, 1) to StateMap, push sid to FIFO queue Q
3. for i = 1 to sim
4. x←x_{ i }, curState←H'(x)
5. if (curState≠prevState)
6. if ({curState}∩N = ϕ)
7. sid++, z_{ sid }← H'(x), N←N∪{z_{ sid }}
insert (curState, sid) to StateMap and push sid to Q
insert edge e = (z_{Gen(prevState)}, z_{ sid }) to E
8. else push Gen(curState) to Q and
insert edge e = (z_{Gen(prevState)}, z_{Gen(curState)}) to E
9. prevState←curState
10 return G = (N, E)
In steps 1 and 2 of the above algorithm, we mainly build a state map storing temporal subnet H' (x) as key and state ID sid as value that is used as the suffix of node z∈N. The state map is employed to find the state ID sid by referring to the state map when using the function Gen(H'(x)). We also construct a FIFO queue Q to store the state ID in turn when reading the temporal subnet H(x') along the list of (see Figure 2).
In steps 39, the procedures are executed until x_{ sim }with a constant time interval to find out the ASNs among the temporal subnets . In step 5, we compare current state with previous state, where current state indicates the temporal subnet H'(x_{ i }) and previous state indicates the temporal subnet H' (x_{i1}) at the previous time point.
If H'(x_{ i }) is different from H'(x_{i1}), and a temporal subnet equivalent to H'(x_{ i }) does not exist in N, the current state H'(x_{ i }) will be treated as a new node z_{ sid }and the following procedures will be processed in step 7: (i) N←N∪{z_{ sid }}, (ii) insert (current state, state ID) to the state map StateMap and push sid to Q, and (iii) an edge e(H'(x_{i1}), H'(x_{ i })) from previous state to current state is inserted to E. Otherwise if current state H'(x) already exists in N, we (i) push sid to Q, where sid is derived by referring to the state map via the function Gen(H' (x_{ i })), and (ii) insert an edge from the previous state to the current state to E. Time complexity of Algorithm 2 is .
In Figure 2, we can finally derive the ASTD G after performing Algorithm 2, where N = {z_{1}, z_{2}, z_{3}} and E = {e_{1}, e_{2}}. Three distinct states (i.e., ASNs) z_{1}, z_{2}, z_{3} are derived from   temporal subnets. It can be noticed that constructing ASTD can avoid redundancies in HFPNe structure, while retaining expressiveness of dynamic behaviors.
In the next section, we demonstrate how to integrate and interpret the simulation data from circadian rhythm model in Drosophila to obtain a deeper understanding of structural and dynamic behaviors with ASTD.
Results
A case study: model of circadian clock
Biological background and modeling
Results and Discussion
[ASTD analysis of normal model]
The constructed ASTD G = (N, E) of the normal model is composed of 24 unique nodes N={z_{1}, z_{2},⋯, z_{24}} connected by the black and green edges in E on the right side of Figure 5. Each node with a blue circle represents a state. The result demonstrates that our method successfully extracts 24 unique temporal subnets (i.e., ASNs) out of 15,000. This result suggests that (i) the number of all the temporal subnets of Drosophila circadian clock model can be reduced from 15,000 to only 24 when considering structural transformation due to the concentration variation, and further (ii) these 24 ASNs are all the states regulating the stable oscillations in the normal model. Each edge connected from previous state to current state denotes a direct structural transformation from the previous net structure to the current one. In Figure 5, dashedline block is shown as an example to depict the state transitions of "z_{19}→z_{20}→z_{10}↔z_{11}" connected with the edges (z_{19}, z_{20}), (z_{20}, z_{10}), (z_{10}, z_{11}) and (z_{11}, z_{10}). Detailed direct structural transformations of HFPNe elements are illustrated in the left dashedline block.
As an example we discuss the structural transformation from z_{19} to z_{20}. Figures of the structural transformations of all the states along with corresponding ASTD are given as additional materials [see Additional files 2 and 3]. Due to an increase in PER/TIM concentration, two inhibitory arcs a(p_{13}, t_{10}) and a(p_{13}, t_{14}) are enabled (highlighted in pink), which results in the deletion of four arcs a(p_{5}, t_{10}), a(p_{5}, t_{14}), a'(t_{10}, p_{6}) and a'(t_{14}, p_{8}) (highlighted in blue). Meanwhile, a test arc from PER/TIM is also enabled, which results in the adding of arc a(t_{0}, p_{1}). It thus leads to a new temporal subnet structure regarded as z_{20} connected from the previous state z_{19}. This captures the fact that in the Drosophila circadian clock model, the net structure in z_{20} can only transform from z_{19} on account of the rising of PER/TIM level. This restricts the reasonable transitions into state z_{20} to be z_{19}, which simplifies analysis for both biologists and computational biologists.
The sparseness of this network will be of great value to computational biologists who need to rapidly investigating a range of regulatory interactions and dynamic behaviors from simulation data of their models. More detailedly, ASTD (i) gives researchers a concise impression of the connection relationship between the nodes. The nodes that researchers are interested in can be comprehensively focused and traced according to its connection in the ASTD; and (ii) such nodes can be further explored to explicitly elucidate the mechanism how the occurrence of structural transformation triggers oscillations along the time axis.
We also observe that state transitions from z_{1} to z_{9} are used only once, and are likely the period before the circadian rhythm systems reach a stable cycle from the initial marking. Excluding such nodes, the ASTD becomes only a cycle composed of the outerring nodes (i.e., all the nodes excluding {z_{1}, z_{2},⋯, z_{9}}). The state transfers following the cycle of these outerring nodes in the ASTD on the rhythms of mRNAs and proteins in this oscillation systems. In the following, we investigate how the ASTD changes when modifying the gene dbt.
[Mutant analysis]
Price et al. discussed the property of dbt^{ L }("L" for long) that is a mutation of dbt. They showed that the transcription of the gene per is affected by this mutant, i.e., the period of per mRNA in dbt^{ L }mutant is longer than the one in the normal model [23]. The behavior of per mRNA in dbt^{ L }mutant and normal model is validated afterwards by Matsuno et al.[22] (see Additional file 4 illustrating the simulation results). It is obtained by changing the formula at the transition (t_{23} = m_{7}*m_{12}/1000 in our case) denoting the complex forming rate of PER and DBT. Roughly speaking, when the forming rate of PER/DBT is slowed, it leaves more PER to bind to TIM, which leads to a faster increase of PER/TIM to a higher concentration. It thus will take longer time to inhibit the transcription of per mRNA and tim mRNA until the concentration of PER/TIM decreases to the respective threshold values of the inhibitor arcs (see Figure 4(a)). Therefore, the next increases of per mRNA and tim mRNA are accordingly postponed because of the longer inhibition effect resulting from the slow forming rate of PER/DBT. The results gave the suggestion that the circadian rhythm is controlled by this forming rate, which is affected by the mutant dbt^{ L }.
Due to the space limitation, we show the ASTD of dbt^{ L }mutant model (ASTD^{ L }for short) in the figure together with the ASTD of the normal model (denoted as ASTD^{ W }for short). Note that we employ the same mutant model in [22] for comparison. In Figure 5, the ASTD^{ L }(N, E) is composed of 23 distinct ASNs, where N = {z_{1}, z_{2},⋯, z_{21}, z_{24}, z_{25}} and E is the set of edges drawn in black and red. The resulting ASTD^{ W }and ASTD^{ L }give information that the temporal structure of ASTD^{ L }is simpler than the one of ASTD^{ W }from the viewpoints of node number and connection relationship. Figure 5 also provides the information to generate following views: (i) shared 22 nodes {z_{1}, z_{2},⋯, z_{21}, z_{24}} in ASTD^{ W }and ASTD^{ L }contribute to produce oscillations regardless of the period length; and (ii) the temporal structures of disappeared z_{22} and z_{23} in ASTD^{ L }do not contribute to the regulation of the forming rate of complex PER/DBT. In Additional files 2 and 3, it can be confirmed that the transitions t_{23} in z_{22} and z_{23} are both disabled (in grey), which reflects that no complex forming action occurs by the formula alteration in the mutant model.
[Graphicalbased analyses of ASTD]
As described above, ASTD can give the user concise impression regarding the timedependent structural changes in the pathway, which provides a great help in investigating a range of regulatory interactions and dynamic system behaviors. Additionally, it would be helpful to consider some more intuitive graphical representations to express the characteristic information from ASTD. With the help of such characteristics, one can obtain an intuitive understanding to the dynamic behaviors such as "Which state is maintained longer?", "How does the frequency of entering a certain state vary with the parameters?", and "How does the concentration of a substance vary in each state?"
We incorporate graphical representation applying to the states in the ASTD showing three characteristics: (i) duration, (ii) outdegree, and (iii) total concentration difference of substance in each state.
 (i)
When duration is "B" (B for the big size of the node) and the outdegree is "S" (S for the small size of the node), the state of such node (e.g., z _{18} and z _{19},) is maintained at a relative stable condition;
 (ii)
When duration and outdegree are both "S", the state of such node (e.g., z _{3} and z _{13}) is seldomused during the simulation and is negligible. z _{3} with both "S" duration and outdegree in ASTD^{ W }and ASTD^{ L }, is considered negligible. On the other hand, z _{13} displays "B" duration and outdegree in ASTD^{ L }unlike in ASTD^{ W }. As shown in Additional file 4, per mRNA has a longer periodic oscillation in the mutant model than in the normal one. In ASTD^{ L }, the state z _{15} is transformed from z _{13} and z _{14}. Not only duration but also outdegree of z _{14} in ASTD^{ W }and ASTD^{ L }are almost in the same size. But those of z _{13} changed from "S" in the normal model to "L" in the mutant one, and the node size is closed to z _{14}. It can be considered that the temporal structure of z _{13} contributes to the regulatory mechanism to generate longer periodic oscillations of per mRNA in the mutant model;
 (iii)
The case of that duration is "S" and outdegree is "B" is not observed in this ASTD. Such nodes are highly unstable and trigger frequent structural transformations. This instability is likely the reason such nodes are not found in the stable oscillations of the circadian rhythm;
 (iv)
When duration and outdegree are both "B", the state of such node (e.g., z _{12} and z _{14}) is unstable, but it is likely such node is important to the regulation of the oscillations.
Note that the ASTD allows multiple edges between two nodes. Several bidirectional arcs are observed in the resulting ASTD, e.g., the arcs between z_{14} and z_{15}, z_{21} and z_{23}, which represent two states oscillates from one another. We examined there occurs a series of shortterm tiny concentration vibrations of PER and dCLK around respective threshold values, which lead to the bidirectional arcs coming into being. For example, when the concentration of dCLK (p_{2}) changes up and down around evaluated threshold value 1.0 of the arc (p_{2}, t_{4}), the states z_{14} and z_{15} will oscillates from one another. The results of outdegree in Figure 6(a) shows that the nodes connected with bidirectional arcs are relatively larger than others. These larger nodes reflecting unstable shortterm tiny concentration vibrations are considered to contribute to the formation of the steady oscillation period (i.e., the cycle composed of the outerring nodes).
Finally, we investigate the dynamic behaviors of each substance's concentration level. We present this in a heatmaplike representation, where the total concentration difference is represented by colors as shown in Figure 6(b). The total difference in concentration level Diff_{ Total }(p, z) is the difference summation in place p' s concentration (M(p) [x_{ i }]M (p) [x_{i1}]) at adjacent time points in the given state z. Figure 6(b) compares concentration differences in per mRNA (p_{1}) and dClk mRNA (p_{6}). In tracking per mRNA, the nodes z_{17}, z_{18} and z_{19} are colored in red around z_{18}, while for dClk mRNA, the nodes z_{20}, z_{10} and z_{11} are colored in red around z_{11}. The shift states accurately reflect the difference in rise times for per and dClk mRNA level. Further, by investigating the temporal subnets in the ASTD, it is confirmed that the transition t_{10} denoting the transcription of per mRNA is enabled only in the red states z_{17}, z_{18} and z_{19}, while t_{10} is disabled in all the other states due to the inhibition of PER/TIM. Similarly, the transition t_{0} of dClk mRNA transcription is enabled in the red states z_{20}, z_{10} and z_{11}, while it is disabled in the others because of the inhibition by the dCLK/CYC complex. In this way, the information of each substance's relative expression can be easily visualized using ASTD with this characteristic.
Discussion
Investigating dynamic behaviors of biological networks is usually achieved by analyzing concentration plots of the simulation data. However, studying such concentration variations of a model generate more vital temporal structural information than considering dynamics as an ensemble. We thus proposed a novel techniques combines quantitative simulation data and topological analysis to deduce the dynamic behaviors of system mechanisms from the data. In this paper, we give a cycle ASTD of circadian rhythm of Drosophila from the simulation data as an example. ASTD can also be a linear succession of states, which is usually derived from the signaling pathway owing to its feature of propagating signals from transmembrane to the DNA nucleus. Such linear ASTD can also be employed as an analysis tool for quick interpretation. With the aid of graphicalbased analyses, ASTD can yield concise impression of the connection relationship among the states from various viewpoints, such as time period, concentration variation and so on.
ASTD and reachability graph
ASTD is different from the concept of reachability graph (i.e., graph of markings) [25]. ASTD is made up of the nodes that are the unique temporal subnets, and of directed edges corresponding to the structural transformation of temporal subnets resulting in the passing from one state to another. Each node in ASTD is the grouping of identical temporal subnets from the viewpoint of structure. That is, each node simply possesses the structural information of the entries in the minimal element set that is extracted by eliminating the disabled transitions/arcs and isolated places. No concentration information (i.e., marking) is recorded in the ASTD (this is the point different from the concept of reachability graph) although such information is used to determine the ASTD. In contrast, reachability graph consists of nodes corresponding to reachable markings and of arcs corresponding to firing of transitions [25]. From this case study with the given sampling interval, the state space of reachability graph is 15,000, while that of ASTD^{ L }is significantly reduced to 24. A series of nonnegative real numbers in the column of the EDF file is equivalent to current marking (state) in the reachability graph. Additionally, ASTD can deal with any general type, e.g., string and object. There are totally 15,000 unique markings obtained from EDF file. The state space of reachability graph will not be less than 15,000 when further decreasing the sampling interval time of simulation.
Conclusions
This paper describe a novel methodology to construct a socalled active state transition diagram (ASTD) by using the timecourse simulation data from a wellfounded formal framework of hybrid functional Petri net with extension (HFPNe). The main contributions are as follows: (i) Automatically constructed ASTD we have presented suggests that building an ASTD representation can eliminate redundant HFPNe structures, while maintaining equivalent expressiveness as the full model; (ii) Characterized ASTD gives the user concise impression and new insights to grasp and trace how a key regulatory subnet and/or a network changes with time; (iii) Due to the nature of the ASTD, any state belonging to the ASTD is able to be simulated and it enables us to simulate equivalent concentration distributions; and (iv) The applicability of the proposed method is investigated by the analysis of an HFPNe model of circadian rhythm in Drosophila.
Another approach to represent biochemical reactions as a system is to use a series of ordinary differential equations (ODEs). Since the HFPNe allows quantities to be continuous and generic, the biological processes with ODEbased kinetics can be realized [22], i.e., an ODEbased system that is convertible into an HFPNe model, can yield an ASTD and give simplified graphical representation of the timedependent structural transformation. There is, however, a special case at this point, for models without inhibitory arcs and with threshold value of normal and test arcs equal to zero, the resulting ASTDs will contain only one state  a full HFPNe structure  for all the time points. Moreover, for the ODE model of hybrid dynamical systems, an existing method has been developed providing a mathematical approach with applying reachability analysis by Halász et al.[16]. It serves as a promising theoretic basis and leads us to make further investigation on this special case as the future work.
In this paper, the circadian rhythm model of Drosophila is a deterministic one, in which all the parameters of transition speeds and arc thresholds have been determined in advance [22]. Since HFPNe model supports stochastic transitions as well, in the future, ASTD will be adapted to such probabilistic features of the system as well as the firing conflict problem of the discrete transition by means of particular graphicalbased representation. Additionally, building ASTD makes possible converting a hybrid model dealing with discrete, continuous and more complicated events to finite timedependent states. Various analysis techniques, e.g., network motif analysis, centrality analysis, clustering analysis and model checking technique, will also be imported to the ASTD to obtain better understanding of systematic dynamics from simulation data as the future research.
Notes
Declarations
Acknowledgements
We are grateful to the anonymous reviewers for their valuable hints and suggestions. This work was supported by KAKENHI (GrantinAid for Scientific Research) on Priority Areas "Systems Genomics" from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
Authors’ Affiliations
References
 Chaouiya C: Petri net modelling of biological networks. Brief Bioinform. 2007, 8 (4): 210219.View ArticlePubMedGoogle Scholar
 Koch I, Heiner M: Petri nets. Analysis of Biological Networks. Edited by: Junker BH, Schreiber F. 2008, 139180. A Wiley Interscience Publication,View ArticleGoogle Scholar
 Chen M, Hofestädt R: Quantitative Petri net model of gene regulated metabolic networks in the cell. In Silico Biol. 2003, 3 (3): 347365.PubMedGoogle Scholar
 Koch I, Junker BH, Heiner M: Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber. Bioinformatics. 2005, 21 (7): 12191226.View ArticlePubMedGoogle Scholar
 Ruths D, Muller M, Tseng JT, Nakhleh L, Ram PT: The signaling petri netbased simulator: a nonparametric strategy for characterizing the dynamics of cellspecific signaling networks. PLoS Comput Biol. 2008, 4 (2):Google Scholar
 Sackmann A, Formanowicz D, Formanowicz P, Blazewicz J: New insights into the human body iron metabolism analyzed by a Petri net based approach. Biosystems. 2009, 96 (1): 104113.View ArticlePubMedGoogle Scholar
 Li C, Nagasaki M, Ueno K, Miyano S: Simulationbased model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension. BMC Syst Biol. 2009, 3 (42):Google Scholar
 Steggles LJ, Banks R, Shaw O, Wipat A: Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach. Bioinformatics. 2007, 23 (3): 336343.View ArticlePubMedGoogle Scholar
 Nagasaki M, Saito A, Li C, Jeong E, Miyano S: Systematic reconstruction of TRANSPATH data into Cell System Markup Language. BMC Syst Biol. 2008, 2 (53):Google Scholar
 Krull M, Voss N, Choi C, Pistor S, Potapov A, Wingender E: TRANSPATH: an integrated database on signal transduction and a tool for array analysis. Nucleic Acids Res. 2003, 31 (1): 97100.PubMed CentralView ArticlePubMedGoogle Scholar
 ZevedeiOancea I, Schuster S: Topological analysis of metabolic networks based on Petri net theory. In Silico Biol. 2003, 3 (3): 323345.PubMedGoogle Scholar
 GrafahrendBelau E, Schreiber F, Heiner M, Sackmann A, Junker BH, Grunwald S, Speer A, Winder K, Koch I: Modularization of biochemical networks based on classification of Petri net tinvariants. BMC Bioinformatics. 2008, 9 (90):Google Scholar
 Heiner M, Koch I, Will J: Model validation of biological pathways using Petri nets, demonstrated for apoptosis. Biosystems. 2004, 75 (13): 1528.View ArticlePubMedGoogle Scholar
 Li C, Suzuki S, Ge QW, Nakata M, Matsuno H, S M: Structural modeling and analysis of signaling pathways based on Petri nets. J Bioinform Comput Biol. 2006, 4 (5): 11191140.View ArticlePubMedGoogle Scholar
 Hardy S, Robillard PN: Petri netbased method for the analysis of the dynamics of signal propagation in signaling pathways. Bioinformatics. 2008, 24 (2): 209217.View ArticlePubMedGoogle Scholar
 Halász A, Kumar V, Imieliński M, Belta C, Sokolsky O, Pathak S, Rubin H: Analysis of lactose metabolism in E. Coli using reachability analysis of hybrid systems. IET Syst Biol. 2007, 1 (2): 130148.View ArticlePubMedGoogle Scholar
 Nagasaki M, Doi A, Matsuno H, Miyano S: A versatile Petri net based architecture for modeling and simulation of complex biological processes. Genome Inform. 2004, 15 (1): 180197.PubMedGoogle Scholar
 Peterson JL: Petri Net Theory and the Modeling of Systems. 1981, Prentice Hall,Google Scholar
 Saito A, Nagasaki M, Doi A, Ueno K, Miyano S: Cell fate simulation model of gustatory neurons with MicroRNAs doublenegative feedback loop by hybrid functional Petri net with extension. Genome Inform. 2006, 17 (1): 100111.PubMedGoogle Scholar
 Tasaki S, Nagasaki M, Oyama M, Hata H, Ueno K, Yoshida R, Higuchi T, Sugano S, Miyano S: Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data. Genome Inform. 2006, 17 (2): 226238.PubMedGoogle Scholar
 Kloss B, Price JL, Saez L, Blau J, Rothenfluh A, Wesley CS, Young MW: The Drosophila clock gene doubletime encodes a protein closely related to human casein Kinase Iepsilon. Cell. 1998, 94: 97107.View ArticlePubMedGoogle Scholar
 Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S: Biopathways representation and simulation on hybrid functional Petri net. In Silico Biol. 2003, 3 (3): 389404.PubMedGoogle Scholar
 Price JL, Blau J, Rothenfluh A, Abodeely M, Kloss B, Young MW: Doubletime is a novel Drosophila clock gene that regulates PERIOD protein accumulation. Cell. 1998, 94 (1): 8395.View ArticlePubMedGoogle Scholar
 ASTD of Drosophila Circadian rhythm model.http://www.csml.org/models/csmlmodels/circadianrhythmsindrosophila/ASTD/
 David R, Alla H: Discrete, Continuous, and Hybrid Petri Nets. 2004, Springer,Google Scholar
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