Young Hwan Chang

Exact reconstruction of gene regulatory networks using compressive sensing

BackgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network’s sparseness.ResultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented.ConclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

Sparse network identifiability via Compressed Sensing

The problem of identifying sparse solutions for the link structure and dynamics of an unknown linear, time-invariant network is posed as finding sparse solutions x to A x = b . If the matrix A satisfies a rank condition, this problem has a unique, sparse solution. Here each row of A comprises one experiment consisting of input/output measurements and cannot be freely chosen. We show that if experiments are poorly designed, the rank condition may never be satisfied, resulting in multiple solutions. We discuss strategies for designing experiments such that A has the desired properties and the problem is therefore well posed. This formulation allows prior knowledge to be taken into account in the form of known nonzero entries of x , requiring fewer experiments to be performed. Simulated examples are given to illustrate the approach, which provides a useful strategy commensurate with the type of experiments and measurements available to biologists. We also confirm suggested limitations on the use of convex relaxations for the efficient solution of this problem.

Data-driven graph reconstruction using compressive sensing

Modeling of biological signal pathways forms the basis of systems biology. Also, network models have been important representations of biological signal pathways. In many biological signal pathways, the underlying networks over which the propagations spread are unobserved so inferring network structures from observed data is an important procedure to study the biological systems. In this paper, we focus on protein regulatory networks which are sparse and where the time series measurements of protein dynamics are available. We propose a method based on compressive sensing (CS) for reconstructing a sparse network structure based on limited time-series gene expression data without any a priori information. We present a set of numerical examples to demonstrate the method. We discuss issues of coherence in the data set, and we demonstrate that incoherence in the sensing matrix can be used as a performance metric and a guideline for designing effective experiments.

Optimization-based inference for temporally evolving Boolean networks with applications in biology

The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.

Secure estimation based Kalman Filter for cyber–physical systems against sensor attacks

Cyber–physical systems are found in many applications such as power networks, manufacturing processes, and air and ground transportation systems. Maintaining security of these systems under cyber attacks is an important and challenging task, since these attacks can be erratic and thus difficult to model. Secure estimation problems study how to estimate the true system states when measurements are corrupted and/or control inputs are compromised by attackers. The authors in Fawzi et al. (2014) proposed a secure estimation method when the set of attacked nodes (sensors, controllers) is fixed. In this paper, we extend these results to scenarios in which the set of attacked nodes can change over time. We formulate this secure estimation problem into the classical error correction problem (Candes and Tao, 2005) and we show that accurate decoding can be guaranteed. Furthermore, we propose a combined secure estimation method with our proposed secure estimator and the Kalman Filter for improved practical performance. Finally, we demonstrate the performance of our method through simulations of two scenarios where an unmanned aerial vehicle is under attack.

Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics

The cellular heterogeneity and complex tissue architecture of most tumor samples is a major obstacle in image analysis on standard hematoxylin and eosin-stained (H&E) tissue sections. A mixture of cancer and normal cells complicates the interpretation of their cytological profiles. Furthermore, spatial arrangement and architectural organization of cells are generally not reflected in cellular characteristics analysis. To address these challenges, first we describe an automatic nuclei segmentation of H&E tissue sections. In the task of deconvoluting cellular heterogeneity, we adopt Landmark based Spectral Clustering (LSC) to group individual nuclei in such a way that nuclei in the same group are more similar. We next devise spatial statistics for analyzing spatial arrangement and organization, which are not detectable by individual cellular characteristics. Our quantitative, spatial statistics analysis could benefit H&E section analysis by refining and complementing cellular characteristics analysis.

Biologically-inspired coordination of multiple UAVs using sliding mode control

We consider the problem of prey (evader) hunting for single or multiple Unmanned Aircraft Vehicles (UAVs) based on biologically-inspired predator-prey behavior. First, we apply sliding mode control (SMC) to a single predator/single prey model. Next, we propose motion synchronization of multiple UAVs to hunt prey effectively. The proposed motion control scheme is formulated and synchronization is proved. Also, numerical examples demonstrate the performance of the proposed SMC controller and synchronization of multiple UAVs. Therefore, a biologically-inspired strategy of multiple UAVs with synchronization might be a possible approach to effectively hunt other UAVs.

Four distinct immune microenvironment subtypes in gastric adenocarcinoma with special reference to microsatellite instability

Introduction Programmed death-ligand 1 (PD-L1) can be overexpressed in tumours other than Epstein-Barr virus (EBV)-positive (EBV+) or microsatellite instability-high (MSI-H) gastric cancer (GC) subtypes. We aimed to determine the tumour immune microenvironment (TME) classification of GC to better understand tumour–immune interactions and help patient selection for future immunotherapy with special reference to MSI-H. Methods Immunohistochemistry (IHC) for PD-L1 and CD8+ T cells in three distinct subtypes of GC (43 EBV+, 79 MSI-H and 125 EBV−/MSS) were performed and analysed. In 66 MSI-H GC, mutation counts were compared with PD-L1 expression and survival of the patients. Results GC TME divided by PD-L1 IHC and tumour-infiltrating lymphocytes (TIL) measured by intratumoural CD8 density showed: (1) about 40% of GC are type I (PD-L1+/TIL+) consisting ~70% of MSI-H or EBV+ GC, and ~15% of EBV−/microsatellite stable (MSS) GC patients show the best survival in both disease-free (HR 2.044) and overall survival (HR 1.993); this type would respond to a checkpoint blockade therapy; (2) almost 30% of GC are type II (PD-L1−/TIL−) with the worst survival; (3) approximately 10% of GC are type III (PD-L1+/TIL−); and (4) up to 20% are type IV (PD-L1−/TIL+) and, unexpectedly, ~25% of EBV+ or MSI-H GC are within this subtype. In MSI-H GC, frequent frameshift mutations were observed in ARID1A, RNF43, NF1, MSH6, BRD3, NCOA3, BCORL1, TNKS2 and NPM1 and the numbers of frameshift mutation correlated significantly with PD-L1 expression (P<0.05). Discussion GC can be classified into four TME types based on PD-L1 and TIL, and numbers of frameshift mutation correlate well with PD-L1 expression in MSI-H GC.

Accelerating Submovement Decomposition With Search-Space Reduction Heuristics

Objective: Movements made by healthy individuals can be characterized as superpositions of smooth bell-shaped velocity curves. Decomposing complex movements into these simpler “submovement” building blocks is useful for studying the neural control of movement as well as measuring motor impairment due to neurological injury. Approach: One prevalent strategy to submovement decomposition is to formulate it as an optimization problem. This optimization problem is nonconvex and finding an exact solution is computationally burdensome. We build on previous literature that generated approximate solutions to the submovement optimization problem. Results: First, we demonstrate broad conditions on the submovement building block functions that enable the optimization variables to be partitioned into disjoint subsets, allowing for a faster alternating minimization solution. Specifically, the amplitude parameters of a submovement can typically be fit independently of its shape parameters. Second, we develop a method to concentrate the search in regions of high error to make more efficient use of optimization routine iterations. Conclusion: Both innovations result in substantial reductions in computation time across multiple nonhuman primate subjects and diverse task conditions. Significance: These innovations may accelerate analysis of submovements for basic neuroscience and enable real-time applications of submovement decomposition.

Secure state estimation for nonlinear power systems under cyber attacks

This paper focuses on securely estimating the state of a nonlinear dynamical system from a set of corrupted measurements. In particular, we consider a wide class of nonlinear systems, and propose a technique which enables us to perform secure state estimation for such nonlinear systems. We then provide guarantees on the achievable state estimation error against arbitrary corruptions, and analytically characterize the number of errors that can be perfectly corrected by a decoder. To illustrate how the proposed nonlinear estimation approach can be applied to practical systems, we focus on secure estimation for the wide area control of an interconnected power system under cyber-physical attacks and communication failures, and propose a secure estimator for the power system. Finally, we numerically show that the proposed secure estimation algorithm enables us to reconstruct the attack signals accurately.