Jianmin Zou

Analysis of Emergent Behavior for GA-based Topology Control Mechanism for Self-Spreading Nodes in MANETs

We introduce a genetic algorithm based MANET topology control mechanism to be used in decision making process of adaptive and autonomic systems at run time. A mobile node adapts its speed and direction using limited information collected from local neighbors operating in an unknown geographical terrain. We represent the genetic operators (i.e., selection, crossover and mutation) as a dynamical system model to describe the behavior of a single node’s decision mechanism. In this dynamical system model each mobile node is viewed as a stochastic variable. We build a homogeneous Markov chain to study the convergent nature of multiple mobile nodes running our algorithm, called FGA. Each state in our chain represents a configuration of the nodes in a MANET for a given instant. The homogeneous Markov chain model of our FGA is shown to be ergodic; its convergence is demonstrated using Dobrushin’s contraction coefficients. We also observe that the nodes with longer communication ranges utilize more information about their neighborhood to make better decisions, require less movement and converge faster, whereas smaller communication ranges utilize limited information, take more time to escape local optima, and, hence, consume more energy.

Formal convergence analysis for bio-inspired topology control in MANETs

We present a convergence analysis of a genetic algorithm based topological control mechanism for the decision making process of evolutionary and autonomous systems that adaptively reconfigures spatial configuration in mobile ad hoc networks (MANETs). Mobile nodes adjust their speed and direction using information collected from the local neighborhood environment in unknown geography. We extend the stochastic model of the genetic operators (i.e., selection, crossover and mutation) called the dynamical system model that represents the behavior of a single nodes decision mechanism in the network viewed as a stochastic variable. We introduce an ergodic homogeneous Markov chain to analyze the convergent nature of multiple mobile nodes running our algorithm, called the Force-based Genetic Algorithm (FGA). Here, a state represents an instantaneous spatial configuration of nodes in a MANET. It is shown that the Markov chain model of our FGA is ergodic and its convergence is shown using Dobrushins contraction coefficients. It is observed that scenarios where nodes have small communication ranges compared to their movement range converge quicker than larger ones due the limited information they have of their neighborhood, making movement decisions simpler, thus conserving energy.

Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth

Our Genomic Relevance Parameterization (GReP) model aims to explore a possible relationship between gene expression values from breast cancer patients and mathematical tumor growth modeling parameters calculated using data from clinical and preclinical measurements. We introduce two methods to relate genomic information and the tumor growth measurements. One method explores the impact of exponentiation of gene expression values, whereas the other utilizes the correlation between co-regulated genes and the growth parameters. As inputs to our GReP model, we used patient tumor volume measurements and genomic information for 74 breast cancer related genes from the I-SPY 1 TRIAL. We performed a preliminary validation of GReP model using experimental data from literature including MDA-MB-231 cell line, MDA-MB-231 cell line with CXCL12 gene over-expressed, and the MDA MB-231 sub-cell lines 1834 and 4175. Tumor growth curves generated by GReP model, for the initial exponential phase of tumor growth, closely match the pre-clinical data reported in the literature. These promising results show that it may be possible to build tools combining clinical information and genomic data to model cancerous tumor growth.

Markov chain model for differential evolution based topology control in MANETs

Mobile Ad hoc Networks (MANETs) are used for many strategic commercial and military applications where it is not feasible to use a centralized controller or manually deploy assets. They have proved useful for many practical applications, such as search and rescue, clearing mine fields, and transportation systems. We introduce a differential evolution based topological control mechanism for the decision making process of evolutionary and autonomous systems that adaptively reconfigures spatial configuration in MANETs. We present a formal analysis of the effectiveness of our topology control mechanism and introduce an inhomogeneous Markov chain model to prove its convergence. The experiment results from our simulation software show that our biologically-inspired algorithm produces encouraging results for uniform distribution of mobile nodes over unknown terrains.

Genomic based personalized chemotherapy analysis to support decision systems for breast cancer

Personalized approach to anti-cancer therapy necessitates the adaptation of standardized guidelines for chemotherapy schedules to individual cancer patients. We introduce a methodology, namely Personalized Relevance Parameterization (PReP-G), based on the genomic data of breast cancer patients to compute time course of drug efficacy on tumor progression. The pharmacodynamic (PD) parameters of transit compartmental systems are computed to quantify the drug efficacy and kinetics of cell death. We integrate the genetic information of 74 breast cancer related genes for 78 patients with clinical t-stage of 3 from the I-SPY 1 TRIAL with the tumor volume measurements from NBIA database into our PReP-G model to compute tumor growth and shrinkage parameters. The performance of the method is evaluated for the breast cancer cell lines of BT-474, MDA-MB-435 and MDA-MB-231 for a given chemotherapy, where the anti-cancer agents Doxorubicin and Cyclophosphamide are administered to animal models and the change of tumor size is measured in time. We compare our results from PReP-G model with the experimental measurements. The consistency between computed results and the volume measurements is encouraging to develop personalized tumor growth models and decision support systems based on genetic data.

Fault tolerant bio-inspired topology control mechanism for autonomous mobile node distribution in MANETs

We introduce a fault tolerant bio-inspired topolog-ical control mechanism (TCM-Y) for the evolutionary decision making process of autonomous mobile nodes that adaptively adjust their spatial configuration in MANETs. TCM-Y is based on differential evolution and maintains a user-defined minimum connectivity for each node with its near neighbors. TCM-Y, therefore, provides a topology control mechanism which is fault tolerant with regards to network connectivity that each mobile node is required to maintain. In its fitness calculations, TCM-Y uses the Yao graph structure to enforce a user-defined minimum number of neighbors while obtaining uniform network topology. The effectiveness of TCM-Y is evaluated by comparing it with our differential evolution based topology mechanism (TCM-DE) that uses virtual forces from neighbors in its fitness function. Experimental results obtained from simulation software show that TCM-Y performs well with respect to normalized area coverage, the average connectivity, and the minimum connectivity achieved by mobile nodes. Simulation experiments demonstrate that TCM-Y generates encouraging results for uniform distribution of mobile nodes over unknown terrains while maintaining a user-defined minimum connectivity between neighboring nodes.

Performance evaluation of differential evolution based topology control method for autonomous MANET nodes

We present a differential evolution based topology control mechanism, called TCM-DE, for the decision making process of evolutionary and autonomous systems that adaptively reconfigures spatial configuration in MANETs. We introduce quantitative metrics to evaluate performance of our TCM-DE with respect to uniform distribution, total terrain covered by communication areas of all nodes, and distance traveled by each node until a desired network topology is reached. Voronoi tessellation for configurations of mobile nodes is used to create two uniformity metrics. Physical relocation of mobile nodes is a power consuming task. Therefore, minimizing the average distance each node travels (ADT) until the network reaches a desired distribution is an important indicator for the performance of MANET nodes. Another important performance metric is the network area coverage (NAC) achieved by all nodes. NAC is used to measure the speed of network convergence and the efficiency of node deployment. Experimental results from our simulation software shows that TCM-DE performs well with respect to NAC, ADT, and Voronoi-based uniformity evaluation techniques.

Differential Evolution Based Fault Tolerant Topology Control in MANETs

We study a fault tolerant differential evolution based topology control mechanism, called TCM-Y, to direct the movements of autonomous vehicles that dynamically adjust their speed and directions in MANETs. TCM-Y uses a Yao graph inspired fitness function to preserve a nodes minimum desired number of connections with its neighbors while uniformly dispersing mobile nodes in an unknown terrain. We present a formal analysis of TCM-Y to show that it provides a fault tolerant node spreading mechanism since any node will have at least k neighbors at all times. The effectiveness of TCM-Y is evaluated by comparing it with a popular deterministic node spreading mechanism called Constrained Coverage for Mobile Sensor Nodes (CC-MSN) that has similar objectives as TCM-Y. Experimental results obtained from our simulation software show that TCM-Y performs significantly better than CC-MSN with respect to normalized area coverage, average distance traveled, average connectivity, and the minimum connectivity achieved by mobile nodes.

A gene expression-based mathematical modeling approach for breast cancer tumor growth and shrinkage

In this paper, we introduce a personalized parameterization approach, namely prep-g, to explore impact of gene expression values from breast cancer patients on tumor growth and shrinkage characteristics using xenograft models. In construction of prep-g parameterization, in addition to individual effects of the breast cancer-related gene expressions, the impact of the correlation among them and the contribution of their multiple orders are considered. Tumor growth behavior, and delay and shrinkage effects of anti-cancer agents are examined in six case studies using xenograft models implanted with breast cancer cell lines. Tumor growth parameters for er+ cell lines bt-474 and mcf-7, and drug-related shrinkage parameters for cell lines mda-mb-231, mda-mb-468 and bt-474 under the monotherapy of drugs paclitaxel and doxorubicin are computed. Consistency of the experimental data reported in several studies in literature for multiple breast cancer cell lines in mice models and the computed results from prep-g are encouraging, which indicates that construction of mathematical models for tumor growth and shrinkage by combining gene expressions and clinical information may be feasible.

Evaluation of anti-cancer therapy using in silico analysis of treatments for HER2+ breast cancer

Cancer treatment has continually evolved towards the personalized selection and delivery of anticancer therapies. In this paper we evaluate the effectiveness of three treatments recommended by the NCCN guidelines for HER2-positive breast cancer using our clinical decision support tool called ChemoDSS. For our in silico analysis, we used pre-clinical data from the literature for HER2 transfected MCF7 human breast cancer xenografts in athymic mice. In particular, we analyzed the expected effects for the multi-drug treatments of AC-TH (i.e., Doxorubicin and Cyclophosphamide followed by Paclitaxel and Trastuzumab), TCH (i.e., Docetaxel, Carboplatin, and Trastuzumab), and TH (i.e., Docetaxel and Trastuzumab). Our results show that, using the pharmacokinetic (PK) and pharmacodynamic (PD) characteristics of the reported pre-clinical data, AC-TH appears to be the most effective regimen for treating this occurrence of breast cancer compared to TCH and TH. This result is consistent with literature findings for HER2 transfected MCF7 breast cancer xenografts, and demonstrates the effectiveness of various treatments recommended by the NCCN guidelines for HER2-positive breast cancer. We plan to incorporate various genetic markers into ChemoDSS, and verify existing and novel treatment regimens for different types of cancers.