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1. Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty

   https://doi.org/ddoi.org/10.1186/s12864-019-5720-3  

   Hajiramezanali, Ehsan; Imani, Mahdi; Braga-Neto, Ulisses M; Qian, Xiaoning; Dougherty, Edward R (June 2019, BMC genomics)

   Background: Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty. Results:The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution. Conclusion:The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression 

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2. Optimal Control of Gene Regulatory Networks with Unknown Cost Function

   https://doi.org/10.23919/ACC.2018.8431514  

   Imani, Mahdi; Braga-Neto, Ulisses (August 2018, 2018 Annual American Control Conference (ACC))

   Most of the existing methodologies for control of Gene Regulatory Networks (GRNs) assume that the immediate cost function at each state and time point is fully known. In this paper, we introduce an optimal control strategy for control of GRNs with unknown or partially-known immediate cost function. Toward this, we adopt a partially-observed Boolean dynamical system (POBDS) model for the GRN and propose an Inverse Reinforcement Learning (IRL) methodology for quantifying the imperfect behavior of experts, obtained via prior biological knowledge or experimental data. The constructed cost function then is used in finding the optimal infinite-horizon control strategy for the POBDS. The application of the proposed method using a single sequence of experimental data is investigated through numerical experiments using a melanoma gene regulatory network. 

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3. Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems

   https://doi.org/10.1109/TCBB.2018.2880234  

   Bahadorinejad, Arghavan; Imani, Mahdi; Braga-Neto, Ulisses (November 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   We propose a novel methodology for fault detection and diagnosis in partially-observed Boolean dynamical systems (POBDS). These are stochastic, highly nonlinear, and derivative- less systems, rendering difficult the application of classical fault detection and diagnosis methods. The methodology comprises two main approaches. The first addresses the case when the normal mode of operation is known but not the fault modes. It applies an innovations filter (IF) to detect deviations from the nominal normal mode of operation. The second approach is applicable when the set of possible fault models is finite and known, in which case we employ a multiple model adaptive estimation (MMAE) approach based on a likelihood-ratio (LR) statistic. Unknown system parameters are estimated by an adaptive expectation- maximization (EM) algorithm. Particle filtering techniques are used to reduce the computational complexity in the case of systems with large state-spaces. The efficacy of the proposed methodology is demonstrated by numerical experiments with a large gene regulatory network (GRN) with stuck-at faults observed through a single noisy time series of RNA-seq gene expression measurements. 

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4. INFERENCE OF GENE REGULATORY NETWORKS BY MAXIMUM-LIKELIHOOD ADAPTIVE FILTERING AND DISCRETE FISH SCHOOL SEARCH

   https://doi.org/10.1109/MLSP.2018.8516967  

   Tan, Yukun; Lima Neto, Fernando B.; Braga Neto, Ulisses (September 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP))

   We propose a new algorithm for inference of gene regulatory networks (GRN) from noisy gene expression data based on maximum-likelihood (ML) adaptive filtering and the discrete fish school search algorithm (DFSS). The approach is based on the general partially-observed Boolean dynamical system (POBDS) model, and as such can be used for simultaneous state and parameter estimation for any Boolean dynamical system observed in noise. The proposed DFSS-ML-BKF algorithm combines the ML adaptive Boolean Kalman Filter (ML-BKF) with DFSS, a version of the Fish School Search algorithm tailored for discrete parameter spaces. Results based on synthetic gene expression time-series data using the well-known p53-MDM2 negative-feedback loop GRN demonstrate that DFSS-ML-BKF can infer the network topology accurately and efficiently. 

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5. Control of Gene Regulatory Networks using Bayesian Inverse Reinforcement Learning

   https://doi.org/10.1109/TCBB.2018.2830357  

   Imani, Mahdi; Braga-Neto, Ulisses (April 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point, referred to as the immediate cost function, is fully known. In this paper, we employ the Partially-Observed Boolean Dynamical System (POBDS) signal model for a time sequence of noisy expression measurement from a Boolean GRN and develop a Bayesian Inverse Reinforcement Learning (BIRL) approach to address the realistic case in which the only available knowledge regarding the immediate cost function is provided by the sequence of measurements and interventions recorded in an experimental setting by an expert. The Boolean Kalman Smoother (BKS) algorithm is used for optimally mapping the available gene-expression data into a sequence of Boolean states, and then the BIRL method is efficiently combined with the Q-learning algorithm for quantification of the immediate cost function. The performance of the proposed methodology is investigated by applying a state-feedback controller to two GRN models: a melanoma WNT5A Boolean network and a p53-MDM2 negative feedback loop Boolean network, when the cost of the undesirable states, and thus the identity of the undesirable genes, is learned using the proposed methodology. 

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