An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics

Reinforcement learning with probabilistic boolean network models of smart grid devices

The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics (using data analysis and asset condition to plan and perform activities). In this paper, we showcase the application of a complex-adaptive, self-organizing modeling method, and Probabilistic Boolean Networks (PBNs), as a way towards the understanding of the dynamics of smart grid devices, and to model and characterize their behavior. This work demonstrates that PBNs are equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an interaction with its environment and receives feedback from it in the form of a reward signal. Different reward structures were created to characterize preferred behavior. This information can be used to guide the PBN to avoid fault conditions and failures.Peer ReviewedPostprint (published version

Fault Detection and Diagnosis in Gene Regulatory Networks and Optimal Bayesian Classification of Metagenomic Data

It is well known that the molecular basis of many diseases, particularly cancer, resides in the loss of regulatory power in critical genomic pathways due to DNA mutations. We propose a methodology for model-based fault detection and diagnosis for stochastic Boolean dynamical systems indirectly observed through a single time series of transcriptomic measurements using Next Generation Sequencing (NGS) data. The fault detection consists of an innovations filter followed by a fault certification step, and requires no knowledge about the system faults. The innovations filter uses the optimal Boolean state estimator, called the Boolean Kalman Filter (BKF). We propose an additional step of fault diagnosis based on a multiple model adaptive estimation (MMAE) method consisting of a bank of BKFs running in parallel. The efficacy of the proposed methodology is demonstrated via numerical experiments using a p53-MDM2 negative feedback loop Boolean network. The results indicate the proposed method is promising in monitoring biological changes at the transcriptomic level. Genomic applications in the life sciences experimented an explosive growth with the advent of high-throughput measurement technologies, which are capable of delivering fast and relatively inexpensive profiles of gene and protein activity on a genome-wide or proteome-wide scale. For the study of microbial classification, we propose a Bayesian method for the classification of r16S sequencing pro- files of bacterial abundancies, by using a Dirichlet-Multinomial-Poisson model for microbial community samples. The proposed approach is compared to the kernel SVM, Random Forest and MetaPhyl classification rules as a function of varying sample size, classification difficulty, using synthetic data and real data sets. The proposed Bayesian classifier clearly displays the best performance over different values of between and within class variances that defines the difficulty of the classification

Model Evaluation of the Stochastic Boolean Control Networks

National Key Research and Development Program of China under Grant 2018AAA0100202; National Natural Science Foundation of China under Grants 62003083, 61873059, 61873148, 61922024 and 61933007; Shanghai Science and Technology Program of China under Grant 20JC1414500; Shanghai Sailing Program of China under Grant 19YF1402400; Program of Shanghai Academic/Technology Research Leader of China under Grant 20XD1420100; Royal Society of the UK; Alexander von Humboldt Foundation of Germany

Biological Pathways Based Approaches to Model and Control Gene Regulatory Networks

The aim of effective cancer treatment is to prolong the patients’ life while offering a reasonable quality of life during and after treatment. The treatments must carry their actions/effects in a manner such that a very large percentage of tumor cells die or shift into a state where they stop proliferating. The fundamental issue in systems biology is to model gene interaction via gene regulatory networks (GRN) and hence provide an informatics environment to study the effects of gene mutation as well as derive newer and effective intervention (via drugs) strategies to alter the cancerous state of the network, thereby eradicating the tumor. In this dissertation, we present two approaches to model gene regulatory networks. These approach are different, albeit having a common structure to them. We develop the GRN under a Boolean formalism with deterministic and stochastic framework. The knowledge used to model these networks are derived from biological pathways, which are partial and incomplete. This work is an attempt towards understanding the dynamics of a proliferating cell and to control this system. Initial part (deterministic) of this work focuses on formulating a deterministic model by assuming the pathway regulations to be complete and accurate. Using these models algorithms were developed to pin-point faults (mutations) in the network and design personalized combination therapy depending on the expression signature of specific output genes. To introduce stochastic nature onto the model due to incompleteness in the prior biological knowledge, an uncertainty class of models was defined over the biological network. Two such uncertainty class of models are modeled- one over the state transitions and the other over the node transitions in the system. This knowledge is transferred to priors, and the existing Bayesian theory is used to update and converge to a good model. The Bayesian control theory for Markovian processes is applied to the problem of intervention in Markovian gene regulatory networks, while simultaneously updating the model. Via a toy example, it is shown that effective prior knowledge quantification can significantly help in converging on to the actual model with limited information from the system and take advantage of the optimality promised by Bayesian intervention. These control methods however, suffer from computational and memory complexity issues- Curse of Dimensionality, to be useful for any network size of biological relevance. To counter these issues associated with Dynamic Programming, suboptimal approximate algorithm known as Q-learning and its Bayesian variation are used to save on computational and memory complexities. These sub-optimal approximate algorithms perform very close (but inferior) to optimal policy, but the computational saving, both in terms of time and memory are significant to extend them to networks of larger size

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements