Volume 6 Supplement 1
Selected articles from The 5th IEEE International Conference on Systems Biology (ISB 2011)
On optimal control policy for probabilistic Boolean network: a state reduction approach
- Xi Chen^{1},
- Hao Jiang^{1}Email author,
- Yushan Qiu^{1} and
- Wai-Ki Ching^{1}Email author
DOI: 10.1186/1752-0509-6-S1-S8
© Chen et al.; licensee BioMed Central Ltd. 2012
Published: 16 July 2012
Abstract
Background
Probabilistic Boolean Network (PBN) is a popular model for studying genetic regulatory networks. An important and practical problem is to find the optimal control policy for a PBN so as to avoid the network from entering into undesirable states. A number of research works have been done by using dynamic programming-based (DP) method. However, due to the high computational complexity of PBNs, DP method is computationally inefficient for a large size network. Therefore it is natural to seek for approximation methods.
Results
Inspired by the state reduction strategies, we consider using dynamic programming in conjunction with state reduction approach to reduce the computational cost of the DP method. Numerical examples are given to demonstrate both the effectiveness and the efficiency of our proposed method.
Conclusions
Finding the optimal control policy for PBNs is meaningful. The proposed problem has been shown to be ${\sum}_{2}^{p}\mathsf{\text{-hard}}$. By taking state reduction approach into consideration, the proposed method can speed up the computational time in applying dynamic programming-based algorithm. In particular, the proposed method is effective for larger size networks.
Background
An important goal for studying genetic regulatory network is to understand the gene behavior and to develop optimal control policy for potential applications to medical therapy. While many models have been proposed for modeling gene regulatory networks, Boolean Networks (BNs) [1–3] and thier extension Probabilistic Boolean Networks (PBNs) [4] have received much attention. Because they form a class of models which can capture the logical interactions of genes and they are also effective in modeling pathways for drug discovery [5]. Recently applications in medical treatment for Parkinson's disease can also be found in [6]. In fact, a PBN can be considered as a collection of BNs driven by a Markov chain and therefore its dynamics and behavior can be studied by using Markov chain theory. For reviews on BNs and PBNs, we refer interested readers to [7–9] and the references therein.
Many methods in control theory are available for the intervention of PBNs. A gene control model has been proposed in [10]. The control model is formulated as a mixed integer programming problem and it aims at driving the PBN from the undesirable states to the desirable ones. A class of PBN control problems with hard constraints has been proposed in [11, 12]. The motivation of the control model is to reduce the side-effects of medical treatment. In [11], hard constraints are included in the optimal control problem and an approximation method is then proposed in [12] to obtain the optimal controls efficiently.
Datta et al. [13] proposed an external intervention method based on optimal control theory. In their work, genes are classified as internal nodes and external nodes (control nodes). One can intervene the values of internal nodes in some desirable manner by controlling the values of certain external nodes. By defining the control cost for each control input and terminal cost for each state, the problem is to find a sequence of control inputs that leads the network into desirable states at the terminal step with minimum average cost. The classical technique of dynamic programming is then employed to solve the optimal control problem.
Chen et al. [14] then consider an external intervention problem based on optimal control theory and dynamic programming. Given the terminal cost of each state, the objective is to drive the network into the state with the maximum cost being minimized by applying external controls. The problem is important in the view of medical therapy because patients/organisms would like to minimize the damage even for the worst case. They proved that both minimizing the maximum cost and minimizing the average cost are ${\sum}_{2}^{p}\mathsf{\text{-hard}}$. A dynamic programming-based algorithm is then proposed for finding a control sequence that minimizes the maximum cost in control of PBN. The above dynamic programming-based methods still have high computational complexity. The size of the underlying transition probability matrix increases exponentially with the number of nodes in the PBN. To tackle this problem a possible remedy is to consider network reduction approach.
Several reduction methods have been proposed recently. In [15], a CoD-based reduction algorithm is introduced. Coefficient of Determination (CoD) helps to evaluate the influence of a candidate node for deletion on the target node and find the optimal candidate node for deletion. The proposed algorithm can well preserve the attractor structure and long-run dynamics of the original network.
Qian et al. [16] proposed a state reduction method by considering deleting states directly. Instead of deleting the nodes in a network, they delete the out-most states having less influence to the network. Here we consider a transition probability-based reduction strategy. This strategy is easy to implement as we do not need to compute the stationary distribution of the PBN beforehand.
We consider the problem of minimizing the maximum cost in control of PBN and we employ transition probability-based reduction strategy to reduce the network complexity of a PBN. We show that under some condition and in many of our numerical examples, the optimal control sequence obtained from the reduced network is the same as the one in the original network. Then we apply the dynamic programming-based algorithm to the reduced network. The computational complexity of dynamic programming-based algorithm when applied to the original network is O(2^{ n }) (depending on the number of network states) when the number of control nodes m and the number of steps M are fixed. When our state reduction method is applied, the computational complexity is reduced to O(|R|), where R is the set of states after reduction.
The remainder of the paper is structured ae follows. We first give a brief review on PBNs and the dynamic programming method. We then introduce our state reduction approach together with some theoretical results to support our proposed approach. Numerical examples are given to demonstrate both the effectiveness and the efficiency of our proposed method. Finally some discussion will be given to conclude the paper.
A brief review on BNs and PBNs
A BN consists of a set of n nodes (genes) as follows: {v_{1},v_{2},..., v_{ n }}, v_{ i }∈ {0,1} and a set of Boolean functions denoted by {f_{1}, f_{2},..., f_{ n }}. Each v_{ i }(t) is defined as the state of node i at time t. The rules of regulatory interactions among nodes are then represented by the Boolean functions: v_{ i }(t + 1) = f_{ i }(v_{ i }_{1}, v_{ i }_{2},..., v_{ ik }) where {v_{ i }_{1},v_{ i }_{2},...,v_{ ik }} are input nodes of f_{ i }, and they are called parent nodes of node v_{ i }. We define IN(vi) = {v_{ i }_{1}, v_{ i }_{2},..., v_{ ik }}. The number of parent nodes to v_{ i }is called the in-degree of v_{ i }. The largest in-degree of {v_{1}, v_{2},..., v_{ n }} is called the maximum in-degree of BN and is denoted by K.
Since v_{ i }(t) ∈ {0,1}, w(t) can take any integral value in [1, 2^{ n }].
Here i = w(t + 1) and j = w(t) and is set of BNs that the network can enter state i from state j. We remark that A is a column stochastic matrix, i.e., ${\sum}_{j=1}^{{2}^{n}}{\mathsf{\text{A}}}_{ij}=1$.
A review on dynamic programming
Here we are interested in the following problem: Minimizing the maximum cost in control of PBN.
Given the terminal cost C(z_{ M }) for each state z_{ M }∈ {1,2,...,2^{ n }} at terminal time step M, find a sequence of control input u_{0},u_{1},...,u_{ M }such that starting from the given initial state the network will enter into the state with minimized maximum cost at time step M. In [14], a dynamic programming-based method is proposed for the above problem:
Step 0: Set t = M;
J(z_{ M }, h_{ M }) = C(z_{ M }) for all h_{ M } = {0,..., M}.
Step 1: t : = t -1.
Step 3: If t > 0, go back to Step 1; Otherwise, stop.
The state reduction approach
In this section we propose our state reduction method.
Transition probability-based state reduction strategy
Which means that the network will never enter state i from other states. Hence, deleting state i will not influence the steady-state distribution of the network.
The dynamic programming-based algorithm on the reduced network
Since the computational complexity of the dynamic programming is O(2^{ n }) when the number of control nodes m and the number of steps M are fixed, using state reduction may reduce the computation complexity to O(|R|), where R is the set of states after reduction. It is straightforward to see that we have the following proposition.
Proposition 1 The result of dynamic programming-based algorithm on the reduced network will be the same as the one on the original network.
It is straightforward to see that, starting from the initial state, the network will never enter into transient states to be deleted. Therefore the network will never stop at those states at the terminal time step. This means that the deleted states will not be included in the optimal route, and the cost of deleted states will not be counted. Hence deleting these transient states will not influence the result obtained from the DP method when applied to the reduced network.
Based on transition probability-based strategy, one can iteratively delete those transient states until all the remaining states are critical states. In each step, we need to update the transition matrix for the reduced network by deleting the corresponding row and column from the transition matrix. After making the reduction, one can get a reduced network with a set of states R and a |R|-by-|R| transition probability matrix B. Then we can apply dynamic programming-based algorithm on the reduced network. In the following, we give a theoretical result on the reduction method when the indegree of the network is one.
An analysis of the reduction method when indegree K = 1
All the possible BNs for 2 genes when K = 1
States | f_{1} | f_{2} | ||||||
---|---|---|---|---|---|---|---|---|
f _{11} | f _{12} | f _{13} | f _{14} | f _{21} | f _{22} | f _{23} | f _{24} | |
00 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 |
01 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 |
10 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
11 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
In general, we can also compute the number of all the possible BNs for n genes: (2n)^{ n }. For example, from Table 1, one can calculate there are totally (2 × 2)^{2} = 16 networks with 2^{2} × 2^{2} sizes. When every row contains 1, it means the number of nonzero rows is 2^{2}. To satisfy this condition, we have to choose 2 genes as parent genes and consider every gene has two possible states. Thus, we can deduce that the number of such networks is ${A}_{\mathsf{\text{2}}}^{\mathsf{\text{2}}}\times 2\times 2=8$ where ${A}_{r}^{n}=n\mathsf{\text{!/}}r\mathsf{\text{!}}$. But when the number of nonzero rows is 2^{1}, we just select only one gene as the parent gene and the corresponding selected possibilities are ${C}_{1}^{2}$ where ${C}_{r}^{n}=n\mathsf{\text{!}}/\left(r\mathsf{\text{!}}\left(n-r\right)\mathsf{\text{!}}\right)$. Since for each gene, there exist two states to be selected. Therefore, the total number of such networks is ${C}_{1}^{2}\times 2\times 2=8$. In determination of the linear combination of BNs for construction of PBN, the intrinsic structure of BNs plays an utmost role. Here we study the distribution of nonzero rows in BNs and we give the following distribution theory.
Distribution of number of nonzero rows in BNs when K = 1
Number of nonzero rows in BN | Number of BNs in all the (2n)^{ n }BNs |
---|---|
2^{ n } | n!2^{ n } |
2^{(n-k)}k = 1,2,...,(n - 1) | $n!{C}_{n}^{n-k}{2}^{n}{\displaystyle {\sum}_{j=1}^{k}{C}_{n-k}^{j}}{\displaystyle {\sum}_{\underset{{A}_{i>2,i=1,2,...,j}}{{\displaystyle {\sum}_{i=1}^{j}{A}_{i}=k+i}}}\frac{1}{{A}_{1}!...{A}_{i}!}}$ |
Therefore to compute the number of BNs when the number of non-zero rows is 2^{ n-k }, one should select n-k out of n genes as parent nodes. And that is the reason why we have ${C}_{n}^{n-k}$. Since the n-k parent nodes will fill in n positions, we should take all possible selection pattern into account. Then we have the double summation part for calculating the number of BNs when the number of nonzero rows is 2^{ n-k }, k = 1,2,..., (n - 1).
Hence this guarantees the efficiency of state reduction.
State reduction for PBN with random perturbation
is satisfied, then we can delete the state.
Reduction Rates for PBN with Gene Perturbations
n= 6 | n= 8 | n= 10 | n= 12 | |
---|---|---|---|---|
p = 0.001 | 12.5% | 37.5% | 36.0% | 35.4% |
p = 0.002 | 42.2% | 60.5% | 42.8% | 48.4% |
p = 0.005 | 77.5% | 67.8% | 63.1% | 63.8% |
p = 0.010 | 84.9% | 73.1% | 77.0% | 72.4% |
Results and discussions
In this section, we give some numerical examples to compare the result of dynamic programming-based algorithm on the reduced network with the one on the original network.
A 6-gene example
A 6-Node Example for PBN Without Perturbation
Size | Cost | CPU Time (sec.) | ||||
---|---|---|---|---|---|---|
Original | Reduced | Original | Reduced | Original | Reduced | |
m= 1 | ||||||
N = 2 | 32 | 24 | 17 | 17 | 0.1276 | 0.0943 |
K = 2 | ||||||
m= 1 | ||||||
N = 2 | 32 | 26 | 5 | 5 | 0.1255 | 0.0997 |
K = 3 | ||||||
m = 1 | ||||||
N = 4 | 32 | 26 | 21 | 21 | 0.1286 | 0.1062 |
K = 2 | ||||||
m = 1 | ||||||
N = 4 | 32 | 27 | 19 | 19 | 0.1291 | 0.1085 |
K = 3 | ||||||
m = 1 | ||||||
N = 8 | 32 | 30 | 25 | 25 | 0.1355 | 0.1274 |
K = 2 | ||||||
m = 1 | ||||||
N = 8 | 32 | 32 | 29 | 29 | 0.1355 | 0.1355 |
K = 2 | ||||||
m = 2 | ||||||
N = 2 | 16 | 11 | 9 | 9 | 0.1061 | 0.0692 |
K = 2 | ||||||
m = 2 | ||||||
N = 2 | 16 | 10 | 3 | 3 | 0.0996 | 0.0585 |
K = 3 | ||||||
m = 2 | ||||||
N = 4 | 16 | 14 | 9 | 9 | 0.1051 | 0.0996 |
K = 2 | ||||||
m = 2 | ||||||
N = 4 | 16 | 16 | 6 | 6 | 0.1040 | 0.0997 |
K = 3 | ||||||
m = 2 | ||||||
N = 8 | 16 | 16 | 14 | 14 | 0.1063 | 0.1050 |
K = 2 | ||||||
m = 2 | ||||||
N = 8 | 16 | 16 | 12 | 12 | 0.1049 | 0.1053 |
K = 3 |
A 6-Node Example for PBN With Random Perturbation
Size | Cost | CPU Time (sec.) | ||||
---|---|---|---|---|---|---|
Original | Reduced | Original | Reduced | Original | Reduced | |
m = 1 | ||||||
N = 2 | 32 | 12 | 31 | 31 | 0.0402 | 0.0162 |
K = 2 | ||||||
m= 1 | ||||||
N = 2 | 32 | 23 | 10 | 10 | 0.0375 | 0.0257 |
K = 3 | ||||||
m = 1 | ||||||
N = 4 | 32 | 28 | 24 | 24 | 0.0396 | 0.0329 |
K = 2 | ||||||
m = 1 | ||||||
N = 4 | 32 | 30 | 26 | 26 | 0.0386 | 0.0363 |
K = 3 | ||||||
m = 1 | ||||||
N = 8 | 32 | 32 | 30 | 30 | 0.0402 | 0.0399 |
K = 2 | ||||||
m = 1 | ||||||
N = 8 | 32 | 32 | 30 | 30 | 0.0419 | 0.0418 |
K = 3 | ||||||
m = 2 | ||||||
N = 2 | 16 | 12 | 13 | 13 | 0.0315 | 0.0213 |
K = 2 | ||||||
m = 2 | ||||||
N = 2 | 16 | 15 | 9 | 9 | 0.0277 | 0.0262 |
K = 3 | ||||||
m = 2 | ||||||
N = 4 | 16 | 14 | 9 | 9 | 0.0300 | 0.0250 |
K = 2 | ||||||
m = 2 | ||||||
N = 4 | 16 | 16 | 7 | 7 | 0.0293 | 0.0292 |
K = 3 | ||||||
m = 2 | ||||||
N = 8 | 16 | 16 | 15 | 15 | 0.0316 | 0.0314 |
K = 2 | ||||||
m = 2 | ||||||
N = 8 | 16 | 16 | 13 | 13 | 0.0316 | 0.0307 |
K = 3 |
A 12-gene example
A 12-Node Example for PBN Without Perturbation
Size | Cost | CPU Time (sec.) | ||||
---|---|---|---|---|---|---|
Original | Reduced | Original | Reduced | Original | Reduced | |
m = 1 | ||||||
N = 2 | 2048 | 426 | 757 | 757 | 63.84 | 4.07 |
K = 2 | ||||||
m = 1 | ||||||
N = 2 | 2048 | 700 | 1258 | 1258 | 256.68 | 36.89 |
K = 3 | ||||||
m = 1 | ||||||
N = 4 | 2048 | 502 | 1830 | 1830 | 272.27 | 23.27 |
K = 2 | ||||||
m = 1 | ||||||
N = 4 | 2048 | 1462 | 1591 | 1591 | 264.39 | 143.48 |
K = 3 | ||||||
m = 1 | ||||||
N = 8 | 2048 | 1103 | 2036 | 2036 | 279.58 | 88.80 |
K = 2 | ||||||
m = 1 | ||||||
N = 8 | 2048 | 1801 | 1987 | 1987 | 272.35 | 243.79 |
K = 3 | ||||||
m = 2 | ||||||
N = 2 | 1024 | 350 | 607 | 607 | 153.85 | 24.35 |
K = 2 | ||||||
m = 2 | ||||||
N = 2 | 1024 | 444 | 179 | 179 | 148.80 | 36.10 |
K = 3 | ||||||
m = 2 | ||||||
N = 4 | 1024 | 415 | 342 | 342 | 140.62 | 29.49 |
K = 2 | ||||||
m = 2 | ||||||
N = 4 | 1024 | 801 | 328 | 328 | 150.21 | 107.27 |
K = 3 | ||||||
m = 2 | ||||||
N = 8 | 1024 | 759 | 736 | 736 | 146.98 | 87.76 |
K = 2 | ||||||
m = 2 | ||||||
N = 8 | 1024 | 937 | 756 | 756 | 172.93 | 146.29 |
K = 3 |
A 12-Node Example for PBN With Random Perturbation
Size | Cost | CPU Time (sec.) | ||||
---|---|---|---|---|---|---|
Original | Reduced | Original | Reduced | Original | Reduced | |
m = 1 | ||||||
N = 2 | 2048 | 363 | 1906 | 1906 | 18.19 | 0.89 |
K = 2 | ||||||
m = 1 | ||||||
N = 2 | 2048 | 867 | 322 | 322 | 17.99 | 3.75 |
K = 3 | ||||||
m = 1 | ||||||
N = 4 | 2048 | 581 | 2005 | 2005 | 18.27 | 1.92 |
K = 2 | ||||||
m = 1 | ||||||
N = 4 | 2048 | 1340 | 1931 | 1931 | 18.27 | 8.35 |
K = 3 | ||||||
m = 1 | ||||||
N = 8 | 2048 | 830 | 1990 | 1990 | 18.42 | 3.59 |
K = 2 | ||||||
m = 1 | ||||||
N = 8 | 2048 | 1882 | 1847 | 1847 | 18.38 | 15.86 |
K = 3 | ||||||
m = 2 | ||||||
N = 2 | 1024 | 204 | 286 | 286 | 9.67 | 0.67 |
K = 2 | ||||||
m = 2 | ||||||
N = 2 | 1024 | 508 | 153 | 153 | 9.60 | 2.81 |
K = 3 | ||||||
m = 2 | ||||||
N = 4 | 1024 | 384 | 242 | 242 | 9.63 | 1.78 |
K = 2 | ||||||
m = 2 | ||||||
N = 4 | 1024 | 785 | 486 | 486 | 9.69 | 6.09 |
K = 3 | ||||||
m = 2 | ||||||
N = 8 | 1024 | 747 | 839 | 839 | 9.91 | 5.68 |
K = 2 | ||||||
m = 2 | ||||||
N = 8 | 1024 | 992 | 611 | 611 | 9.87 | 9.26 |
K = 3 |
Conclusions
From the experiment results, one can see that applying dynamic programming-based algorithm on the reduced network can reduce the computational complexity. The performance of the algorithm on the reduced network depends on the parameters of n, m, N and K. For n = 6, from Table 3 and Table 4, one can see that in general, there are some improvements in computational time when reduction method is applied. However, for n = 12, Table 6 and Table 7 indicate that when the number of nodes is large and K = 2, the algorithm on the reduced network performs much better than the one on the original network. Therefore, our proposed method is effective for larger size networks. Future research issues will pay attention to statistical analysis of the distribution of zero rows in transition matrix in terms of n. Moreover, we will keep exploring ways of reducing computational complexity of intervention strategies.
Declarations
Acknowledgements
The authors would like to thank the three anonymous referees for their helpful and encouraging comments and suggestions. The preliminary version has been presented in IEEE Conference on Systems Biology (ISB), Zhuhai, China and published in the conference proceedings [19]. Research support in part by GRF grant and HKU CERG Grants, National Natural Science Foundation of China Grant No. 10971075 and Guangdong Provincial Natural Science Grant No. 9151063101000021.
This article has been published as part of BMC Systems Biology Volume 6 Supplement 1, 2012: Selected articles from The 5th IEEE International Conference on Systems Biology (ISB 2011). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/6/S1.
Authors’ Affiliations
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