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2.  P-glycoprotein mediated efflux limits substrate and drug uptake in a preclinical brain metastases of breast cancer model 
The blood–brain barrier (BBB) is a specialized vascular interface that restricts the entry of many compounds into brain. This is accomplished through the sealing of vascular endothelial cells together with tight junction proteins to prevent paracellular diffusion. In addition, the BBB has a high degree of expression of numerous efflux transporters which actively extrude compounds back into blood. However, when a metastatic lesion develops in brain the vasculature is typically compromised with increases in passive permeability (blood-tumor barrier; BTB). What is not well documented is to what degree active efflux retains function at the BTB despite the changes observed in passive permeability. In addition, there have been previous reports documenting both increased and decreased expression of P-glycoprotein (P-gp) in lesion vasculature. Herein, we simultaneously administer a passive diffusion marker (14C-AIB) and a tracer subject to P-gp efflux (rhodamine 123) into a murine preclinical model of brain metastases of breast cancer. We observed that the metastatic lesions had similar expression (p > 0.05; n = 756–1214 vessels evaluated) at the BBB and the BTB. Moreover, tissue distribution of R123 was not significantly (p > 0.05) different between normal brain and the metastatic lesion. It is possible that the similar expression of P-gp on the BBB and the BTB contribute to this phenomenon. Additionally we observed P-gp expression at the metastatic cancer cells adjacent to the vasculature which may also contribute to reduced R123 uptake into the lesion. The data suggest that despite the disrupted integrity of the BTB, efflux mechanisms appear to be intact, and may be functionally comparable to the normal BBB. The BTB is a significant hurdle to delivering drugs to brain metastasis.
PMCID: PMC3816283  PMID: 24312053
drug resistance; chemotherapy; autoradiography; fluorescence microscopy; tumor
4.  Reverse Engineering Sparse Gene Regulatory Networks Using Cubature Kalman Filter and Compressed Sensing 
Advances in Bioinformatics  2013;2013:205763.
This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.
PMCID: PMC3664478  PMID: 23737768
5.  An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks 
Advances in Bioinformatics  2013;2013:953814.
The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.
PMCID: PMC3594945  PMID: 23509452
6.  Optimal reference sequence selection for genome assembly using minimum description length principle 
Reference assisted assembly requires the use of a reference sequence, as a model, to assist in the assembly of the novel genome. The standard method for identifying the best reference sequence for the assembly of a novel genome aims at counting the number of reads that align to the reference sequence, and then choosing the reference sequence which has the highest number of reads aligning to it. This article explores the use of minimum description length (MDL) principle and its two variants, the two-part MDL and Sophisticated MDL, in identifying the optimal reference sequence for genome assembly. The article compares the MDL based proposed scheme with the standard method coming to the conclusion that “counting the number of reads of the novel genome present in the reference sequence” is not a sufficient condition. Therefore, the proposed MDL scheme includes within itself the standard method of “counting the number of reads that align to the reference sequence” and also moves forward towards looking at the model, the reference sequence, as well, in identifying the optimal reference sequence. The proposed MDL based scheme not only becomes the sufficient criterion for identifying the optimal reference sequence for genome assembly but also improves the reference sequence so that it becomes more suitable for the assembly of the novel genome.
PMCID: PMC3608252  PMID: 23186305
7.  Intervention in Biological Phenomena via Feedback Linearization 
Advances in Bioinformatics  2012;2012:534810.
The problems of modeling and intervention of biological phenomena have captured the interest of many researchers in the past few decades. The aim of the therapeutic intervention strategies is to move an undesirable state of a diseased network towards a more desirable one. Such an objective can be achieved by the application of drugs to act on some genes/metabolites that experience the undesirable behavior. For the purpose of design and analysis of intervention strategies, mathematical models that can capture the complex dynamics of the biological systems are needed. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. Due to the complex nonlinear dynamics of the biological phenomena represented by S-systems, nonlinear intervention schemes are needed to cope with the complexity of the nonlinear S-system models. Here, we present an intervention technique based on feedback linearization for biological phenomena modeled by S-systems. This technique is based on perfect knowledge of the S-system model. The proposed intervention technique is applied to the glycolytic-glycogenolytic pathway, and simulation results presented demonstrate the effectiveness of the proposed technique.
PMCID: PMC3502753  PMID: 23209459

Results 1-7 (7)