CSIG-13. A DYNAMIC CAUSAL MODEL OF GLIOBLASTOMA STEM CELL SIGNALING PREDICTS EFFECTS OF KINASE INHIBITORS

Glioblastomas and glioblastoma stem cells are heterogeneous with respect to mutations, gene expression, and response to drugs. To make predictive responses of individual GBM stem cell lines to kinase inhibitors, we have constructed a causal model of glioblastoma stem cell signaling. The core model was built starting from pathways identified from TCGA mutation data with the addition of the Jak/STAT, Hedgehog, and Notch pathways. Elements and relations between them were validated and extended using the PCNet interaction database and the INDRA database which includes machine read extractions from the biomedical literature. The result is a high confidence executable model consisting of 209 elements (proteins, genes, RNAs) and 370 regulatory logic rules between the elements. Stochastic simulations of the model provide dynamic (quantile) changes in time and responses to perturbations. The output simulates activity of individual nodes as well as cell cycle progression, apoptosis, and differentiation. To simulate the responses of individual cell lines to kinase inhibitors, the model was initialized using DNA sequencing data, RNA-seq, and reverse phase protein array (RPPA) data from each cell line. Comparing the results of the simulations to the drug responses of 11 different kinase targets in 3 cell lines, the model was 88% accurate in predicting effects on growth and survival. The model was further tested by comparing the effects of Mek inhibition of each of the cell lines in the model to the results observed in the RPPA data which overlap by 127 elements. In this case, there was less than 65% concordance between the model and the data for individual nodes. Discrepancies between the model predictions and the data are being investigated to determine whether the model logic or extent needs to be revised to improve the model. This modeling approach is a step toward developing algorithms for personalized therapeutics for GBM.

Analysis of mathematical models of mobility of communication systems of special purpose radio communication systems

The object of research is the military radio communication system. One of the problems in improving the effectiveness of military radio communication systems is the correct description of the movement process in them. Efficient routing protocols are only possible if reliable information on network topology for network nodes is available. Thus, with this information, packets can be forwarded correctly between the sender and the recipient. Given that the mobility of individual nodes is insignificant in special wireless networks, nodes in the network show the mobility properties of a group of nodes. This observation is directly related to the very existence of military wireless networks with the ability to organize themselves, that is, to support group cooperation and group activities. In this work the problem of analysis (decomposition) of the mobility models of military radio communication networks with the possibility of self-organization is solved. The classification of mobility patterns, the description of individual mobility models and the analysis of various aspects currently available, as well as those properties lacking in the attempt to simulate the movement of individual nodes, have been carried out. During the research, the analysis of random, semi-deterministic and deterministic models was carried out. The advantages and disadvantages of the above models have been identified. In the course of the research, the authors of the work used the main principles of the theory of mass service, the theory of automation, the theory of complex technical systems, as well as general scientific methods of knowledge, namely analysis and synthesis. The research results will be useful in: ‒ synthesis of mathematical models of node mobility; ‒ evaluation of the effectiveness of the science-based tool for assessing the mobility of nodes; ‒ validation of recommendations to improve the efficiency of mobile radio networks; ‒ analysis of the radio-electronic situation during the conduct of military operations (operations); ‒ creating advanced technologies to improve the efficiency of mobile radio networks.

Step-wise target controllability identifies dysregulations of macrophage networks in multiple sclerosis

Identifying the nodes able to drive the state of a network is crucial to understand, and eventually control, biological systems. Despite recent advances, such identification remains difficult because of the huge number of equivalent controllable configurations, even in relatively simple networks. Based on the evidence that in many applications it is essential to test the ability of individual nodes to control a specific target subset, we develop a fast and principled method to identify controllable driver-target configurations in sparse and directed networks. We demonstrate our approach on simulated networks and experimental gene networks to characterize macrophage dysregulation in human subjects with multiple sclerosis.

Optimized Node Clustering based on Received Signal Strength with Particle Ordered-filter Routing Used in VANET

VANET is a critical and demanding mission. Numerous methods exist, but none profits in a distributed fashion from physical layer parameters. This paper describes a method that enables individual nodes to estimate node density, independent of beacon messages, and other infrastructure-based information, of their surrounding network. In this paper, a discrete simulator of events was proposed to estimate the average number of simultaneously transmitting nodes, a functional channel model for the VANETs system, and a method of estimating node density. Proposed based on some equations to allow individual nodes to estimate their surrounding node density in real-time Optimized Node Cluster Algorithm with Network Density in which the composition of a cluster is triggered adjacent, these traffic signals is the same and has been predicated mostly on the position a vehicle might well take after crossing. Additional Ordered Tracking with Particle -Filter Routing in which receives simultaneous signal intensity versus node transmission and node density transmission. Conduct multiple location-related analyzes to test the plausibility of the neighboring single-hop nodes on mobility data. The system is designed to operate in the most complex situations where nodes have little knowledge of network topology and the results, therefore, indicate that the system is fairly robust and accurate.

System Dynamics Simulation of a Supply Chain Intelligence Model

Supply chains today are, primarily, measured by Key Performance Indicators (KPIs) such as order-fulfillment, availability to the consumer (percent in-stock) and cost reduction, as well as financial KPIs such as return on investment (ROI), return on brand equity and inventory. These KPIs measure the performance of supply chain as a whole. A supply chain is a network of nodes. The performances of individual nodes are measured with KPIs such as production rate, shipment rate, inventory and the like. These metrics may indicate the performance but may not indicate the cause of such performance. For example, a node whose production rate is below the desired level may be because of poor supply of inputs of production by its supplier node.Thus mere identification of KPIs and their evaluation will not enable to identify the root cause of a problem in a supply chain. Therefore, we need a business intelligence framework that will satisfy the objectives, namely, identification of outcome of each node of the supply chain and its cause. The existing Supply Chain Intelligence (SCI) frameworks aims at identifying metrics that reflect the performance of individual nodes and the total supply chain, but fail to identify the cause of such outcomes. It implies that the linkages or association between the KPIs of individual nodes are required to be identified and defined. In this paper, contingency and systems approach has been used to identify the dimensions of the firm, its internal environment, the complement and the external environment. A system dynamics based approach has been used to identify the causality and resulting behavior of the supply chain. The paper proposes a SCI framework and a System dynamics Model that help in identifying the reasons for supply chin performance and lead to the actions required to be taken for improvement in performance of the supply chain.

Security and Trust Model Analysis for Banking System

: Security and trsut are the two major issues in current banking systems. There are mainly three types of banking which includes traditional banking, online banking, and mobile banking. Appropriate securities in the banking systems must be in place for reliability and trust of the customers. In this paper, we formulate a model that represents a means for detecting and describing the transmission of malicious objects through various individual nodes in banking systems. Proposed model can help in understanding the mechanism by which malicious object spread, to predict the future course of an outbreak and to evaluate the strategies to control. In addition, a trust model for the banking system in online or offline modes is also provided. This depicts how to maintain Trust in the banking system in different scenarios.

A study on the friendship paradox – quantitative analysis and relationship with assortative mixing

The friendship paradox is the observation that friends of individuals tend to have more friends or be more popular than the individuals themselves. In this work, we first study local metrics to capture the strength of the paradox and the direction of the paradox from the perspective of individual nodes, i.e., an indication of whether the individual is more or less popular than its friends. These local metrics are aggregated, and global metrics are proposed to express the phenomenon on a network-wide level. Theoretical results show that the defined metrics are well-behaved enough to capture the friendship paradox. We also theoretically analyze the behavior of the friendship paradox for popular network models in order to understand regimes where friendship paradox occurs. These theoretical findings are complemented by experimental results on both network models and real-world networks. By conducting a correlation study between the proposed metrics and degree assortativity, we experimentally demonstrate that the phenomenon of the friendship paradox is related to the well-known phenomenon of assortative mixing.

Escalabilidade de Aplicações Bag-of-Tasks em Plataformas Heterogêneas

Bag-of-Tasks (BoT) applications are parallel applications composed of independent (i.e., embarrassingly parallel) tasks, which do not communicate with each other, may depend upon one or more input files, and can be executed in any order. BoT applications are very frequent in several scientific areas, and it is the ideal application class for execution on large distributed computing systems composed of hundreds to many thousands of computational resources. This paper focusses on the scalability of BoT applications running on large heterogeneous distributed computing systems organized as a master-slave platform. The results demonstrate that heterogeneous master-slave platforms can achieve higher scalability than homogeneous platforms for the execution of BoT applications, when the computational power of individual nodes in the homogeneous platform is fixed. However, when individual nodes of the homogeneous platform can scale-up, experiments show that master-slave platforms can achieve near linear speedups.