 Software
 Open Access
Parameter estimation of qualitative biological regulatory networks on high performance computing hardware
 Muhammad Tariq Saeed^{1},
 Jamil Ahmad^{1, 6}Email author,
 Jan Baumbach^{2},
 Josch Pauling^{3},
 Aamir Shafi^{4},
 Rehan Zafar Paracha^{1},
 Asad Hayat^{1} and
 Amjad Ali^{5}
 Received: 16 October 2017
 Accepted: 4 December 2018
 Published: 29 December 2018
Abstract
Background
Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware.
Results
The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speedup. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster.
Conclusions
Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speedup on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io.
Keywords
 Biological regulatory networks
 Qualitative modeling
 Parameter estimation
 René Thomas framework
 Hexosamine biosynthetic pathway (HBP)
 Model checking
 Parallel SMBioNet
 MPJ express
Introduction
A longstanding goal of biomedical research is to identify potential therapeutic targets of complex human diseases (e.g. Cancer, HIV etc.) by employing system level modeling and analysis approaches. The study of biomolecular interactions to understand genotypephenotype relationships constitutes the core of Systems Biology [1]. The dynamics of biological regulations can be represented using variety of modeling frameworks [2]. These modeling approaches can be broadly categorized into continuous [3], discrete [4] and hybrid methods [5]. Continuous modeling approaches that use ordinary differential equations require precise parameter information, which in many cases cannot be extracted from noisy data obtained through experimental methods, such as microarrays, spectroscopy and biochemical kinetics. Qualitative modeling, on the other hand, inspired mainly from the work of Kauffman [6], and René Thomas [7, 8] uses a qualitative abstraction that allows to focus on logical connections between variables of a network rather than precise expression levels. Due to finite levels of expression, the degree of difficulty for parameter computation is less in qualitative modeling. The qualitative modeling framework [7, 8] can be used to capture important properties in biological networks such as stable steady states [9], bifurcation points, and cycles (homeostasis) [10]. These properties provide key insights into identification of the therapeutic targets [11] and further validation in wet labs. In order to model a BRN using this framework, values of logical parameters have to be provided. These model parameters are usually unknown and can be inferred using formal method technique such as Model Checking [12].
Parameters estimation through model checking
Model Checking [13] is an automated technique for verification of complex hardware and software systems. Initially developed for concurrent program verification, model checking is now an industry standard methodology for proving correctness of digital circuits, security protocols and embedded systems. In many aspects, biological systems are similar to massively parallel software systems, characterized by nondeterministic behavior [14]. This analogy allows to use model checking for analysis of large number of possible outcomes of a biological model, similar to predicting behavior of a concurrent program.
Model checking approaches are differentiated on the basis of how they interpret the notion of time; Linear [15] or branching [12]. Due to branching nature of Computation Tree Logic (CTL), it is suitable to express properties of non deterministic dynamical systems such as BRNs, where a current state can have more than one successor states.
Among the sequential algorithms that solve the problem of parameter inference by employing model checking, Bernot et al. [16] introduced this approach by finding model parameters for mucus production network in Pseudomonas aeruginosa. They implemented this approach in SMBioNet, which has been used for analysis of several BRNs, including biosurfactants production in P. aeruginosa [20], tail resorption network controlling metamorphosis in tadpole [18] and immunity control in bacteriophage lamda [21].
Parallel parameter estimation methods
The use of parallel computing techniques to reduce complexity of biological systems has recently gained wide interest [22]. Barnat et al. [23] introduced an algorithm for partitioning of parameter estimation through Linear Temporal Logic (LTL) based parallel model checking. They defined the notion of Parametrized kripke Structure (PKS) to represent entire state space of model and parameters as single object, which is explored by multiple threads concurrently. On multicore platform with 8cores, their parallel implementation achieved up to 6x speedup on regulatory networks of G1/S Cell Cycle Transition [24] and Ammonium Transport in E. Coli [25] using parallel LTL model checker [26].
Klarner et al. [27] introduced a technique for parameter identification by using LTL based colored model checking from behavioral properties and time series data [28]. The distribution of parameter space lead to roughly linear speedup on 8core platform on models of bacteriophage λ [29] and mammalian cell cycle [30]. A parallel approach to accelerate sequential algorithm was presented in [31] with preliminary results obtained through a wrapper implementation in C on top of existing Java implementation of SMBioNet [18].
The previous work has made significant contributions for development of LTL based efficient parameter estimation techniques [32–34]. On the other hand, it is established that for biological systems, which are nondeterministic, CTL is more suitable due to its branching nature [16, 35] and structured patterns for querying biological pathways [36]. Due to inherently sequential nature of CTL algorithm [37], it can not scale on modern multicore and Cluster platforms. In this study, we present results of our parallel implementation that exploits application level parallelism in order to accelerate parameter estimation in qualitative modeling of biological networks. We employ a data parallel decomposition scheme that provides almost linear speedup on multicore and cluster platforms. We extend the existing implementation of SMBioNet using the MPJ Express software, which is capable of executing on shared and distributed memory HPC hardware [1]. The details of the parallel implementation are accessible online at https://psmbionet.github.io.
Methods
Qualitative modeling framework
In this section, we briefly revisit the formal framework introduced by René Thomas as originally found in literature [38, 39].
Definition 1
(Biological Regulatory Network) “A Biological Regulatory Network (BRN) is a labeled directed graph G=(V,E), where V is a finite set of vertices, also called biological entities and E⊆V×V is the set of interactions.
The successors and predecessors of a biological entity are represented as \(G_{\nu _{i}}^{+}\) and \(G_{\nu _{i}}^{}\), respectively. Each vertex is provided with a limit \(\ell _{\nu _{i}} = \left G_{\nu _{i}}^{+}\right \) when \(\left G_{\nu _{i}}^{+}\right  \geq 1\), and \(\ell _{\nu _{i}} = 1\) when \(\left G_{\nu _{i}}^{+}\right  = 0\). The edges are labeled by a pair (τ,σ), where \(\tau \leq \ell _{\nu _{x}}\) is the threshold of influence, and σ={+,−} is called sign of interaction (+ for activation and  for inhibition). Each entity ν_{i}∈V has its abstract expression level in the set \( E_{\nu _{i}} \,=\, \left \{0,1,....,r_{\nu _{i}}\right \}\) where \( r_{\nu _{i}} \!\leq \!l_{\nu _{i}}\). The state of a BRN is a configuration of expression levels of all biological entities at a particular time instant.
Definition 2
(State) A State of BRN is ntuple \(S = \left \{s_{\nu _{1}},.., s_{\nu _{n}}\right \}\), \(\forall s_{\nu _{i}} \in E_{\nu _{i}}\), where \(s_{\nu _{i}}\) is the abstract expression level of ν_{i}.
In a given state, each biological entity ν_{i} is regulated by its predecessors \(G_{\nu }^{}\), formally denoted as set of resources, \(W_{\nu _{i}}\), defined as follows;
Definition 3
(Resources) Let G=(V,E) be a BRN. The set of resources \(W_{\nu _{y}}\) of a variable ν_{y}∈V, at level \(s_{\nu _{y}}\), is defined as; \(W_{\nu _{y}} = \left \{ \nu _{x} \in G^{}_{\nu _{y}}  \left (s_{\nu _{x}} \geq \tau _{\nu _{x},\nu _{y}}\ and\ \alpha _{\nu _{x},\nu _{y}}=+\right) or \left (s_{\nu _{x}}< \tau _{\nu _{x},\nu _{y}}\ and \ \alpha _{\nu _{x},\nu _{y}}=\right) \right \}\)
When variable ν_{i} has a certain expression level \(s_{\nu _{i}}\), its evolution has three possibilities: (1) When \( s_{\nu _{i}} < K_{\nu _{i}}\left (W_{\nu _{i}}\right)\), the value of \(s_{\nu _{i}}\) is incremented by one unit. Conversely, if \(s_{\nu _{i}} > K_{\nu _{i}}\left (W_{\nu _{i}}\right)\), \(s_{\nu _{i}}\) is decremented by one unit. However, \(s_{\nu _{i}}\) does not evolve and remains constant, if \(s_{\nu _{i}} = K_{\nu _{i}}\left (W_{\nu _{i}}\right)\).
Definition 4
Different updating schemes have been proposed for qualitative modeling of biological regulatory networks. These updating methods follow synchronous or asynchronous schemes [40]. In a synchronous qualitative model, all variables in the network evolve simultaneously with time. The synchronous mechanism is considered as computationally less expensive [40]. However, it is also less accurate because biological systems are considered asynchronous where changes in expression level of genes or proteins are not concurrent and take place at different time points [16, 21]. In this work, we use asynchronous updating scheme for construction of state graph. The asynchronous scheme is computationally expensive and therefore, we use parallel computing to reduce processing time [40].
In order to measure parameters, experimental observations are encoded into temporal logic formulas. In case of pseudomonas aeruginosa, Fig. 2b shows two CTL formulas ψ_{1} and ψ_{2} that represent normal homeostasis and a pathogenic response receptively. In normal response, the expression level of gene x, starting from (x=0), does not reach (x=2). Whereas, when a pathogenic condition arises, the biological system reaches to a state where gene x is overexpressed finally leading to mucus production.
Figure 2c shows model construction for one parameter combination that satisfies experimental observations. For each state in biological system, its successor states are generated using Definitions 3 and 4. The model construction results in a dynamic model that provides useful insights such as stable steady states and deadlocks. Figure 2d shows the dynamic model as a State Graph(See Definition 4).
Model checking
Model Checking is used to evaluate the dynamic model M against experimental observations expressed as formula CTL ϕ. The verification process determines correctness of ϕ in M, by employing a graphtheoretic procedure that exhaustively explores the entire state space of the system. Finally, model checker confirms correctness of ϕ, if formula is satisfied, or it produces a counter example to provide a trace of the execution path that violates ϕ. The counter example generation is a useful feature for diagnostic purposes.
In a CTL formula, We denote boolean true as ⊤ and boolean false as ⊥. The formula \((s_{\nu _{i}}=n)\) is true iff expression level of variable ν_{i}, in current state, is equal to n. The CTL formula combines a set of connectives: ¬(negation), ∧ (logical AND), ∨ (logical OR) and ⇒ (implication) with temporal operators. The temporal operators are pairs of symbols; the first element of which is A (all paths) or E (at least one path), followed by X (next state), F (any future state) or G (all future states).
Definition 5

atomic formulas are ⊤, ⊥ or any atomic proposition of the form (ν_{i}=n), where ν_{i} is a variable in state graph and \(n \in \left [0, \ell _{\nu _{i}}\right ]\).

If ϕ and ψ are atomic formulas, then so are (¬ϕ), (ϕ∧ψ), (ϕ∨ψ), (ϕ⇒ψ), Xϕ, EXϕ, AGϕ, EGϕ, EFϕ, AFϕ, \((A\phi \bigcup \psi)\) and \((E\phi \bigcup \psi)\)
Parallel implementation in MPJ express
In general, parallel computations are divided into two categories based on communication requirements during the computation phase. The applications that do not need any communication during different computation phases are known as embarrassingly parallel computations. On the other hand, the applications that require frequent communication in between different computation phases are generally known as synchronous computations. One programming approach to implement the embarrassingly parallel computations is to use the “master/slave” model. Since the parameter estimation problem that we are tackling in this study is embarrassingly parallel in nature, we employ the master/slave model to produce the parallel code [1].
Embarrassingly parallel applications are parallelized using master/slave model typically involving three stages. In the first stage, the master process reads the input data, performs domain decomposition, and communicates the relevant chunk to each slave process. The second stage is the computation stage, where all worker processes perform parameter estimation on their own data. During the third and the final stage, all slave processes communicate results back to the master process that generates output for the end user. Out of all three stages, the second stage i.e. the computation phase typically requires the most processing time. In embarrassingly parallel applications, there is no communication required during computation phase, leading to almost linear speedup [1].
Problem decomposition

The first approach exploits the data parallel nature of parameter estimation problem [31]. The parameter state space is partitioned among available processors. We refer to this as coarsegrained parallelism that employs high level data parallelism.

The second approach harnesses finegrained parallelism available in parallel model checkers [26, 41–43] where the underlying algorithms partitions a state graph for verification of biological behaviors encoded in temporal logic.
Coarse grained parallelism
The first partitioning scheme we use in our study divides parameter space into mutually exclusive regions—explored by different worker processes. Since a new model needs to be constructed for each parameter combination, these regions can be explored in parallel by a collection of processes referred to as processing elements (PE). Each PE inspects only a subset of parameter space; and for each combination in that space, a state graph/model is generated. The verification is performed by invoking model checker as an external process to determine whether CTL observations are true. Finally, a reduction operation involves a communication step for receiving accepted sets of parameters. Treating each valuation of parameter separately allows to formulate task of parameter estimation as high level data parallel problem and the decomposition is embarrassingly parallel without any significant communication.
In this study, we use master/worker model of computation to implement high level data parallelism. An important step in parallelizing the code is to perform domain decomposition or partitioning of the input data at the master process. We employ primitive block domain decomposition to produce equallysized independent chunks of input data for each worker process.
Once the computation phase is over, the reduction step is required to write selected model parameters in a single output file. At this point, each worker process sends its list of selected parameters to the master process that receives selected parameters and produces a single output file. The complexity of the communication involved in reduction step is linear in terms of number of models satisfying the CTL property.
Fine grained parallel decomposition
Although a highlevel decomposition scheme provides a good partitioning strategy for distributed memory architectures (HPC Clusters) due to low communication cost, a lower level decomposition of parameter estimation is motivated by increasing computational capabilities of shared memory multicore computers. Moreover, the maximum speedup achieved by highlevel data parallelism is bounded by serial factor to evaluate one set of parameters. In theory, the integration of a multithreaded model checker implementation such as Java Temporal Logic Framework (JTLV) [45] at multicore level can further reduce processing time. But the potential speedup depends on the granularity of tasks. In practice, the parameter estimation problem comprises of large number of small ’work units’ such that the complexity of each task is O(S.ψ). The symbolic model checking algorithm uses Binary Decision Diagrams (BDDs) as internal data structure for state representation. The multithreaded packages such as JTLV do not parallelize the core operations involved in BDD computation. In turn, a taskparallel simulation using JTLV can provide relatively better performance when several CTL formulas are to be verified independently. One such case is the use of temporal logic patterns to check all the potential qualitative states for a specific property [36]. The related work on parallelization of core BDD operations for multicore processors is notably absent [46]. van Dijk and van de Pol [47] introduce a BDD package Sylvan that demonstrates a nonlinear speedup up to 12X on large models.
Results and discussion
In order to validate results of our parallel implementation, we undertake case study of Hexosamine Biosynthetic Pathway (HBP) and its involvement in Cancer [1, 48].
Additionally, for performance evaluation we use three models of biological pathways selected from the literature. These include tail resorption network during tadpole metamorphosis [18], immunity control in lambda phage [29, 35], MALassociated pathway controlling Cerebral Malaria [39] and qualitative model of Fibroblast Growth Factor (FGF) Signalling in Drosophila melanogaster [49].
Case study 1: role of Olinked Nacetylglucosamine transferase (OGT) in cancer
Cancer caused by complex genetic alterations is a diverse group of diseases. The amplification of oncogenes such as MYC, PI3K and EGFR and down regulation of tumor suppressor proteins is well established. There is a growing evidence about glycolytic fueling of cancer cells that results in oncogenic activation, evasion of apoptosis and proliferation of cancer cells. Fardini et al. [50] proposed OGlcNAcylation as a new hallmark and approach for treatment of cancer. Increased expression of OGT has been reported in various types of cancer, including cancer of breasts, lungs, liver, bladder, endometrium, prostate, pancreas, and colon [51–57].
The main difference between the two sets of parameters: the ones having the deadlock state (1,0,1,1,1,1,1,0,1) and those shown in Fig. 6 is that in the later sets, OGTCMyc loop is downregulated. Since CMyc is the activator of OGT, it should also be kept at a low expression level along with OGT. Thus, the results suggest an integrative therapeutic strategy for the treatment of cancer. The qualitative trajectories in the modified model show that the sustained downregulation of these two genes results in restoration of P53MDM2 oscillations and recovery to normal homeostasis. One such trajectory is shown in Fig. 6b. As the biological system progresses from one qualitative state to another, the changes in gene expression at each step is highlighted in Fig. 6b. The simulation results show that under the influence of computed logical parameters, the biological system moves from the qualitative state (1,0,1,1,1,1,1,0,1) to a cycle comprising of four states: (0,0,0,0,0,1,0,0,0), (0,0,0,0,0,0,0,0,0), (0,0,0,0,1,0,0,0,0) and (0,0,0,0,1,1,0,0,0). This cycle serves as an important attractor for the normal homeostatic response to take over.
Case study 2: parameters scanning of the qualitative model of fibroblast growth factor (FGF) signalling in drosophila melanogaster
Performance evaluation
The running time (in seconds) and speedup achieved on four input models is shown Fig. 8. The observed speedup shows that scalability improves with increase in the size of input models. For smaller models, the maximum speedup is not linear when number of threads are increased to maximum. This is due to two reasons: (1) small granularity of operations carried out by threads and (2) overhead of invoking a model checker as an external process. The performance in the multicore mode is better on tadpole’s tail resorption network. The observed speedup is almost linear. Generally, the coarsegrained decomposition can scale linearly due to low communication overhead in shared memory model.
In the cluster mode, we evaluated performance of our parallel approach on 32 node HPC Cluster, hosted at RCMS National University of Sciences and Technology, Pakistan. Each compute node was equipped with dual quadcore Intel Xeon E5520 processors with 24GB of RAM. The nodes are interconnected via Gigabit Ethernet and QDR InfiniBand (40 Gbps). The software environment consisted of MPJ Express 0.40, Oracle JDK 1.7.0 25 version and GNU GCC 4.8.1. In the cluster mode, parallel applications execute in a typical cluster environment where processing elements are connected to one another using a fast interconnect like Infiniband and Myrinet. The MPJ Express software provides various communication devices for various interconnects.
One limitation of this approach is that individual processes are likely to suffer from state space explosion—a major limitation of the underlying exhaustive model checking algorithm. The state space of a BRN is a cartesian product obtained on the range of expression levels of all entities and given as a commaseparated string of ones and zeros. When the state graph is too large for single system’s memory, a high level data parallel approach will suffer from state space explosion. Each qualitative state in a BRN has a maximum of n outgoing transitions. The total number of state graphs for a boolean network with n genes is \(2\left (n^{2^{n}}\right)\). By comparing the worst case complexities the two aforementioned approaches, we argue that parameter synthesis has more computational complexity than memory requirements, which paves the way for using high level data parallelism (HLDP) on a distributed memory architecture. The distributed memory systems are exemplified by commodity compute clusters—group of computers connected using a fast and private network— where multiple processing elements communicate to one another using some form of messaging to solve a single problem. High level data parallelism for distributed memory systems is achieved by the Message Passing Interface MPI standard, which is considered as the de facto API for programming parallel applications. The most popular implementations of MPI include MPICH and Open MPI for C, and MPJExpress for Java language. The main idea of our parallel implementation is based on partitioning of parameter space. Instead of using a parameterized Kripke structure, treating each valuation of parameter separately allows to formulate task of parameter estimation as data parallel problem and therefore achieves a more linear speedup. The running time for tail resorption network, for 2,4,8,16,32,64,128 processes, is plotted in Fig. 8.
Conclusion
Parameter inference is a key challenge in qualitative modeling of biological regulatory networks. Model checking techniques are used to decipher values of parameters from known biological observations expressed as temporal logic formulas. However, with increase in the size of network, complexity of parameter estimation algorithm increases exponentially. Therefore, efficient computational techniques are required to reduce the processing time of parameters computation. In this study, we investigated the use of parallel computing to accelerate parameter inference procedure by using a Java based library MPJ Express. We extended the sequential implementation by partitioning the parameter space and evaluated our parallel implementation on multicore and cluster platforms. We undertook a case study of the Hexosamine Biosynthetic Pathway (HBP) and its involvement in cancer progression. Through parameter computation with our parallel implementation, we were able to suggest a therapeutic intervention that can lead the system from a deadlock state to normal homeostasis. The experimental results obtained on a 23 genes network of Fibroblast Growth Factor in Drosophila melanogaster indicate that our approach is scalable and reduces execution time. Furthermore, our parallel implementation can be used through a webbased interface that can be accessed online. The reduction in execution time shows that this approach can be used in parameter inference applications on multicore desktop computers and laptops, and on special distributed architectures such as clusters. In future, we aim to provide a graphical editor for creating of qualitative models and construction of CTL properties. Moreover, we also aim to provide support for synchronous computation of the state graphs.
Availability and requirements
Project name: Parallel SMBioNetProject home page:https://psmbionet.github.ioOperating system(s): Linux, Tested with CentOS 6.5Programming language: JavaOther requirements: SMBioNet, MPJExpress 0.41, NuSMV 2.4.3License: GPL
Declarations
Acknowledgements
We would like to sincerely thank Dr. Adrien Richard for providing source code of SMBioNet software. We also acknowledge the computational support provided by the Supercomputing Research and Education Centre (ScREC) at National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Funding
The publication charges for this article have been funded by National University of Sciences and Technology (NUST).
Availability of data and materials
All the necessary input and output files for the tools/software used in this study have been provided in Additional files to replicate the results of the study. Softwares and Tools: All the tools and softwares used in this study are open source/freely available and have been cited sufficiently in the study.
Authors’ contributions
MTS and JA contributed in conception and design of study, conducting of experiments, analysing the results and writing/proofreading of paper. JB, JP and AA contributed in design of study, analysing the results and proofreading of paper. AS, RZP and AH contributed to analysing the results and proofreading of paper. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not Applicable.
Consent for publication
Not Applicable.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
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