Optimal State Estimation for Boolean Dynamical Systems using a Boolean Kalman Smoother

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1 Optimal State Estimation for Boolean Dynamical Systems using a Boolean Kalman Smoother Mahdi Imani and Ulisses Braga-Neto Department of Electrical and Computer Engineering Texas A&M University College Station, TX, USA m.imani88@tamu.edu, ulisses@ece.tamu.edu Abstract This paper is concerned with state estimation at a fixed time point in a given time series of observations of a Boolean dynamical system. Towards this end, we introduce the Boolean Kalman Smoother, which provides an efficient algorithm to compute the optimal MMSE state estimator for this problem. Performance is investigated using a Boolean network model of the p53-mdm2 negative feedback loop gene regulatory network observed through time series of Next-Generation Sequencing (NGS) data. Index Terms Boolean Dynamical System, Boolean Kalman Smoother, Gene Regulatory Network I. INTRODUCTION Gene regulatory networks govern the functioning of key cellular processes, such as the cell cycle, stress response, and DNA repair. Boolean networks [1], [2] have emerged as an effective model of the dynamical behavior of regulatory network consisting of genes in activated/inactivated states, the relationship among which is governed by networks of logical gates updated at discrete time intervals. The Boolean Kalman Filter (BKF) [3] [8] is an online, recursive algorithm to compute the optimal state estimator for a signal model consisting of a Boolean state process measured through a noisy observation process. The state process corresponds to a Boolean network with noisy state transitions, while the observation process is quite general. In this paper, we present an extension of the BKF to the smoothing problem, when a fixed interval of data is acquired and available for processing offline. This algorithm, called the Boolean Kalman Smoother (BKS), bears similarities to the forward-backward algorithm used in the inference of hidden markov models [9]. Performance of the BKS in the inference of Boolean networks is investigated using a model of the p53- MDM2 negative feedback loop network observed through next-generation sequencing data. The results indicate that on average, the BKS has lower MSE and lower error rates than the BKF, as expected. II. STOCHASTIC SIGNAL MODEL Deterministic Boolean network models are unable to cope with (1) uncertainty in state transition due to system noise and the effect of unmodeled variables; the fact that (2) the Boolean states of a system are never observed directly, but only indirectly through expression-based technologies, such as RNA-seq. This calls for a stochastic approach. We describe below the stochastic signal model for Boolean dynamical systems, first proposed in [3], which will be employed here. A. State Model Assume that the system is described by a state process {X k ; k = 0, 1,...}, where X k {0, 1} d is a Boolean vector of size d in the case of a gene regulatory network, the components of X k represent the activation/inactivation state, at discrete time k, of the genes comprising the network. The state is assumed to be updated at each discrete time through the following nonlinear signal model: X k = f (X k 1, u k 1 ) n k (state model) (1) for k = 1, 2,...; where u k 1 {0, 1} p is an input vector of dimension p at time k 1, {n k ; k = 1, 2,...} is a white noise process with n k = (N k1,..., N kd ) {0, 1} d, f {0, 1} d+p {0, 1} d is a network function, and indicates component-wise modulo-2 addition. In this paper, we employ a specific model for the network function that is motivated by gene pathway diagrams commonly encountered in biomedical research. We assume that the state vector is updated according to

2 the following equation (where the input u k 1 is omitted): 1, d j=1 a ij (X k 1 ) j + b i > 0, (X k ) i = f i (X k 1 ) = 0, d j=1 a ij (X k 1 ) j + b i < 0, (2) for i = 1, 2,..., d, where the parameter a ij can take three values: +1 if there is positive regulation (activation) from gene j to gene i; 1 if there is negative regulation (inhibition) from gene j to gene i; and 0 if gene i is not an input to gene j. The second set of parameters, biases (b i ), can take two values: +1/2 if gene i is positively biased, in the sense that an equal number of activation and inhibition inputs activate the gene; 1/2 if the reverse is true. Each element of state at time step k can be obtained by adding noise to the output of equation 2. The proposed model is depicted in figure 1 (where the input u k 1 is omitted). f where λ ki is the mean read count of transcript i at time k. Recall that, according to the Boolean state model, there are two possible states for the abundance of transcript i at time k: high (X ki = 1, active gene) and low (X ki = 0, inactive gene). Accordingly, the parameter λ ki is modeled as follows: log(λ ki ) = log(s) + µ b + θ i, if X ki = 0, log(λ ki ) = log(s) + µ b + δ i + θ i, if X ki = 1. where the parameter s is the sequencing depth, µ b > 0 is the baseline level of expression in the inactivated transcriptional state, δ i > 0 expresses the effect on the observed RNA-seq read count as gene i goes from the inactivated to the activated state, and θ i is a noise parameter that models unknown and unwanted technical effects that may occur during the experiment. We assume δ i to be Gaussian with mean µ δ > 0 and variance σδ 2, common to all transcripts, where σ δ is assumed to be small enough to keep δ i positive. Furthermore, we assume that θ i is zero-mean Gaussian noise with small variance σθ 2, common to all transcripts. Typical values for all these parameters are given in section IV. (5) III. BOOLEAN KALMAN SMOOTHER Fig. 1: Proposed network model (without inputs). B. Observation Model In most real-world applications, the system state is only partially observable, and distortion is introduced in the observations by sensor noise this is certainly the case with RNA-seq transcriptomic data. Let Y k be the observation corresponding to the state X k at time k. The observation Y k is formed from the state X k through the equation: Y k = h (X k 1, v k ) (observation model) (3) for k = 1, 2,...; where v k is observation noise. Assume Y k = (Y k1,..., Y kd ) is a vector containing the RNAseq data at time k, for k = 1, 2,... A single-lane NGS platform is considered here, in which Y ki is the read count corresponding to transcript i in the single lane, for i = 1, 2,... In this study, we choose to use a Poisson model for the number of reads for each transcript: P (Y ki = m λ ki ) = e λ λm ki ki, m = 0, 1,... (4) m! The optimal smoothing problem consists of, given a time point 1 < S < T and data in the interval {Y 1,..., Y T }, finding an estimator ˆX S = g(y 1,..., Y T ) of the Boolean state X S at time S that minimizes the mean-square error (MSE): MSE(Y 1,..., Y T ) = E [ ˆX S X S 2 Y 1,..., Y T ] (6) at each value of {Y 1,..., Y T }. A recursive algorithm to solve this problem, called the (fixed-interval) Boolean Kalman Smoother (BKS), is described next. Let (x 1,..., x 2d ) be an arbitrary enumeration of the possible state vectors. Define the following distribution vectors of length 2 d : Π k k (i) = P (X k = x i Y k,..., Y 1 ), Π k k 1 (i) = P (X k = x i Y k 1,..., Y 1 ), k k (i) = P (Y k+1,..., Y T X k = x i ), k k 1 (i) = P (Y k,..., Y T X k = x i ), (7) for i = 1,..., 2 d and k = 1,..., T, where T T = 1 d 1, by definition. Also define Π 0 0 to be the initial (prior) distribution of the states at time zero. Let the prediction matrix M k of size 2 d 2 d be the transition matrix of the Markov chain defined by the state

3 model: (M k ) ij = P (X k = x i X k 1 = x j ) = P (n k = x i f(x j, u k 1 )), (8) for i, j = 1,..., 2 d. Additionally, given a value of the observation vector Y k at time k, the update matrix T k, also of size 2 d 2 d, is a diagonal matrix defined by: (T k ) jj = p (Y k X k = x j ) (9) for j = 1,..., 2 d. In the specific case of RNA-seq data, considered in the previous section, we have: (T k ) jj = e ( d i=1 λji) d λ Y ki ji i=1 Y ki! d = s( i=1 Y ki) d d i=1 Y ki! exp s exp(µ b + θ i + δ i (x j ) i ) i=1 + (µ b + θ i + δ i (x j ) i )Y ki ), (10) for j = 1,..., 2 d. Finally, define the matrix A of size d 2 d via A = [x 1 x 2d ]. The following result, given here without proof, provides a recursive algorithm to compute the optimal MMSE state estimator. Theorem 1. (Boolean Kalman Smoother.) The optimal minimum MSE estimator ˆX S of the state X S given the observations Y 1,..., Y T, where 1 < S < T, is given by: ˆX S = E [X S Y 1,..., Y T ], (11) where v(i) = I v(i)>1/2 for i = 1,..., d. This estimator and its optimal MSE can be computed by the following procedure. Forward Estimator: 1) Initialization Step: Π 1 0 = M 1 Π 0 0. For k = 1, 2,..., S 1, do: 2) Update Step: β k = T k (y k ) Π k k 1. 3) Normalization Step: Π k k = β k / β k 1. 4) Prediction Step: Π k+1 k = M k+1 Π k k. Backward Estimator: 1) Initialization Step: T T 1 = T T (y T )1 d 1. For k = T 1, T 2,..., S, do: 2) Prediction Step: k k = M k+1 T k+1 k. 3) Update Step: k k 1 = T k (y k ) k k. Smoothed Distribution Vector: Π S S 1 S S 1 Π S T =, Π S S 1 S S 1 1 where denotes componentwise vector multiplication. MMSE Estimator: The MMSE estimator is given by: with optimal conditional MSE ˆX S = AΠ S T (12) MSE(Y 1,..., Y T ) = min{aπ S T, (AΠ S T ) c } 1, (13) where the minimum is applied component-wise, and v c (i) = 1 v(i), for i = 1,..., d. Estimation of the state at time k = T requires only the forward estimation step, in which case the Boolean Kalman Smoother reduces to the Boolean Kalman Filter (BKF), introduced in [3]. The normalization step in the forward estimator is not strictly necessary and can be skipped, by letting Π k k = β k (though in this case the meaning of the vectors Π k k and Π k+1 k change, of course). In addition, the matrix T k can be scaled at will. In particular, the constant s ( d i=1 Y ki) / d i=1 Y ki! in (10) can be dropped, which results in significant computational savings. IV. NUMERICAL EXPERIMENT In this section, we conduct a numerical experiment using a Boolean network based on the well-known pathway for the p53 MDM2 negative feedback system, shown in figure 2. We consider the input to be either no stress, dna dsb = 0 or DNA damage, dna dsb = 1, separately. The process noise is assumed to have independent Wip1 dna dsb ATM p53 Mdm2 Fig. 2: Activation/inactivation diagram for the p53 MDM2 negative feedback loop. components distributed as Bernoulli(p), where the noise parameter p gives the amount of perturbation to the Boolean state process; the process noise is categorized into two levels, p = 0.01 (small noise) and p = 0.1 (large noise). On the other hand, σθ 2 (see Section II-B) is the technical effect variance, which specifies the uncertainty of the observations. This value is likewise categorized into two levels: σθ 2 = 0.01 (clean observations) and

4 Average MSE σθ 2 = 0.1 (noisy observations). Two sequencing depths are considered for the observations in the simulations, s = and s = which correspond to 1K 50K and 50K 100K reads respectively. The parameters δ i are generated from a Gaussian distribution with mean (µ δ ) 3 and variance (σδ 2 ) 0.5. The baseline expression µ b is set to as the number of reads or the noise increases. In addition, when the input dna dsb is 0 (no DNA damage), the performance of both methods is better than when the input is 1 (DNA damage). The reason can be found by looking at the state transitions of p53 MDM2 network for both inputs. When the input is 0, the network has one singleton attractor ( 0000 ), while for input 1, there are two cyclic attractors, the first of which contains two states, while the other contains six states. In the presence of cyclic or multiple attractors, due to the changes of state trajectories, the estimation process is more difficult. TABLE I: Performance of the BKF and the BKS. Noise Parameters dna dsb = 0 dna dsb = 1 Reads BKF BKS BKF BKS p = K-50K σθ 2 = K-100K p = 0.1 1K-50K σθ 2 = K-100K p = K-50K σθ 2 = 0.1 5K-100K p = 0.1 1K-50K σθ 2 = 0.1 5K-100K V. CONCLUSION Time Fig. 3: Average MSE of BKF and BKS over 1000 runs. Figure 3 displays the average MSE achieved by the BKS at a fixed time point, as well as the BKF, for T = 100 observations, over 1000 independent runs. It is seen that BKS has smaller MSE on average in comparison to BKF as expected, since the BKS uses future observations, but the BKF uses only the observations up to the present time. Furthermore, we can see that the average MSE of both methods is higher in the presence of large noise. Next, the performance of the BKS and the BKF with different values of noise and different number of reads is examined. The average performance of the methods is defined here as the estimation error rate, i.e., the average number of correctly estimated states (over the length T = 100 of the signal and 1000 runs), which is presented in table I. The results show that the average performance of the BKS is higher than that of the BKF. Furthermore, the performance of both methods decreases This paper introduced a method for the inference of gene regulatory networks that is based on a novel algorithm, called the Boolean Kalman Smoother, which efficiently computes the optimal state estimator for discretetime Boolean dynamical systems given the entire history of observations. The smoothing process at each time step contains two estimators: the forward estimator, in which the previous observations are involved in the process, and the backward estimator, in which the future observations are employed in the process of estimation, in a process that bears similarities to the forward-backward algorithm commonly applied to the inference of hidden markov models [9]. The method was illustrated by application to the p53 MDM2 negative feedback network observed through next-generation sequencing data. The results indicate that on average, the BKS has lower MSE and lower error rates than the BKF. ACKNOWLEDGMENT The authors acknowledge the support of the National Science Foundation, through NSF award CCF

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