Inho Park

Fuzzy continuous petri net-based approach for modeling immune systems

The immune system has unique defense mechanisms such as innate, humoral and cellular immunity. These mechanisms are closely related to prevent pathogens from spreading in the host and to clear them effectively. To get a comprehensive understanding of the immune system, it is necessary to integrate the knowledge through modeling. Many immune models have been developed based on differential equations and cellular automata. One of the most difficult problem in modeling the immune system is to find or estimate appropriate kinetic parameters. However, it is relatively easy to get qualitative or linguistic knowledge. To incorporate such knowledge, we present a novel approach, fuzzy continuous Petri nets. A fuzzy continuous Petri net has capability of fuzzy inference by adding new types of places and transitions to continuous Petri nets. The new types of places and transitions are called fuzzy places and fuzzy transitions, which act as kinetic parameters and fuzzy inference systems between input places and output places. The approach is applied to model helper T cell differentiation, which is a critical event in determining the direction of the immune response.

Integration of immune models using Petri nets

Immune system has unique defense mechanisms such as innate, humoral and cellular immunity. These immunities are closely related to prevent pathogens from spreading in host and to clear them effectively. To achieve those mechanisms, particular processes, such as clonal expansion, positive and negative selection, and somatic hypermutation and so on, have been evolved. These properties inspired people to open a new field, called artificial immune systems that mimics and modifies immune behaviors to invent new technologies in other fields. To explain immune mechanisms, many mathematical models focusing on one particular phenomenon were developed. We developed an integrated immune model that enables to understand immune responses as a whole and to find new emergent properties of the immune system that could not be seen in separate models. We used a continuous Petri net as modeling language, because of its easiness of modeling and analysis.

Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets

MOTIVATION Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. RESULTS We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. AVAILABILITY Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Comparative analysis of the JAK/STAT signaling through erythropoietin receptor and thrombopoietin receptor using a systems approach

BackgroundThe Janus kinase-signal transducer and activator of transcription (JAK/STAT) pathway is one of the most important targets for myeloproliferative disorder (MPD). Although several efforts toward modeling the pathway using systems biology have been successful, the pathway was not fully investigated in regard to understanding pathological context and to model receptor kinetics and mutation effects.ResultsWe have performed modeling and simulation studies of the JAK/STAT pathway, including the kinetics of two associated receptors (the erythropoietin receptor and thrombopoietin receptor) with the wild type and a recently reported mutation (JAK2V617F) of the JAK2 protein.ConclusionWe found that the different kinetics of those two receptors might be important factors that affect the sensitivity of JAK/STAT signaling to the mutation effect. In addition, our simulation results support clinically observed pathological differences between the two subtypes of MPD with respect to the JAK2V617F mutation.

Fuzzy continuous petri net-based approach for modeling helper t cell differentiation

Helper T(Th) cells regulate immune response by producing various kinds of cytokines in response to antigen stimulation. The regulatory functions of Th cells are promoted by their differentiation into two distinct subsets, Th1 and Th2 cells. Th1 cells are involved in inducing cellular immune response by activating cytotoxic T cells. Th2 cells trigger B cells to produce antibodies, protective proteins used by the immune system to identify and neutralize foreign substances. Because cellular and humoral immune responses have quite different roles in protecting the host from foreign substances, Th cell differentiation is a crucial event in the immune response. The destiny of a naive Th cell is mainly controlled by cytokines such as IL-4, IL-12, and IFN-γ. To understand the mechanism of Th cell differentiation, many mathematical models have been proposed. One of the most difficult problems in mathematical modeling is to find appropriate kinetic parameters needed to complete a model. However, it is relatively easy to get qualitative or linguistic knowledge of a model dynamics. To incorporate such knowledge into a model, we propose a novel approach, fuzzy continuous Petri nets extending traditional continuous Petri net by adding new types of places and transitions called fuzzy places and fuzzy transitions. This extension makes it possible to perform fuzzy inference with fuzzy places and fuzzy transitions acting as kinetic parameters and fuzzy inference systems between input and output places, respectively.

Mining cancer genes with running-sum statistics

In this paper, we propose a new method to detect candidate cancer genes for developing molecular biomarkers or therapeutic targets from cancer microarray datasets. To resolve problems resulted in the molecular heterogeneity of cancers on gene prioritizing, our proposed method is intended to identify genes that are over- or down- expressed not in the whole cancer samples but also in a subgroup of cancer samples. To this end, we propose the RS score for gene ranking calculated with a weighted running sum statistic on the ordered list of expression values of each gene. We apply the proposed method to publically available prostate cancer microarray datasets, showing that it can identify previously well known prostate cancer associated genes such as ERG, HPN, and AMACR at the top of the list of candidate genes. Embedding samples, represented as vectors of the expression values of the top 20 genes, into a two dimensional space using the commute time embedding shows the distinction between normal samples and cancer samples in the independent test datasets as well as in the training datasets. We further evaluate the proposed method by estimating classification performance on the independent test datasets, and it shows the better classification performance compared to the other cancer outlier profile approaches.

Breast cancer diagnosis from fluorescence spectroscopy using support vector machine

A novel support vector machine (SVM) classifier incorporating the complexity of fluorescent spectral data is designed to reliably differentiate normal and malignant human breast cancer tissues. Analysis has been carried out with parallel and perpendicularly polarized fluorescence data using 36 normal and 36 cancerous tissue samples. In order to incorporate the complexity of fluorescence spectral profile into a SVM design, the curvature of phase space trajectory is extracted as a useful complexity feature. We found that the fluorescence intensity peaks at 541nm-620nm as well as the complexity features at 621nm-700nm are important discriminating features. By incorporating both features in SVM design, we can improve both sensitivity and specificity of the classifier.

DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data

BackgroundTargeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests.ResultsWe developed a new computational method, DeviCNV, intended for the detection of exon-level copy number variants (CNVs) in targeted NGS data. DeviCNV builds linear regression models with bootstrapping for every probe to capture the relationship between read depth of an individual probe and the median of read depth values of all probes in the sample. From the regression models, it estimates the read depth ratio of the observed and predicted read depth with confidence interval for each probe which is applied to a circular binary segmentation (CBS) algorithm to obtain CNV candidates. Then, it assigns confidence scores to those candidates based on the reliability and strength of the CNV signals inferred from the read depth ratios of the probes within them. Finally, it also provides gene-centric plots with confidence levels of CNV candidates for visual inspection. We applied DeviCNV to targeted NGS data generated for newborn screening and demonstrated its ability to detect novel pathogenic CNVs from clinical samples.ConclusionsWe propose a new pragmatic method for detecting CNVs in targeted NGS data with an intuitive visualization and a systematic method to assign confidence scores for candidate CNVs. Since DeviCNV was developed for use in clinical diagnosis, sensitivity is increased by the detection of exon-level CNVs.