The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

James C. Liao - One of the best experts on this subject based on the ideXlab platform.

  • moonlighting function of glycerol kinase causes systems level changes in rat hepatoma cells
    Metabolic Engineering, 2010
    Co-Authors: Ganesh Sriram, James C. Liao, Lola Rahib, Lilly S Parr, Katrina M Dipple
    Abstract:

    Abstract Glycerol kinase (GK) is an enzyme with diverse (moonlighting) cellular functions. GK overexpression affects central metabolic fluxes substantially; therefore, to elucidate the mechanism underlying these changes, we employed a systems-level evaluation of GK overexpression in H4IIE rat hepatoma cells. Microarray analysis revealed altered expression of genes in metabolism (central carbon and lipid), which correlated with previous flux analysis, and of genes regulated by the glucocorticoid receptor (GR). Oil Red O staining showed that GK overexpression leads to increased fat storage in H4IIE cells. Network Component analysis revealed that activities of peroxisome proliferator-activated receptor α, GR, and seven other transcription factors were altered by GK overexpression. The increased activity of GR was experimentally verified by quantitative RT-PCR of GR-responsive genes in the presence and absence of the glucocorticoid agonist, dexamethasone. This systems biology approach further emphasizes GK's essential role in central and lipid metabolism and experimentally verifies GK's alternative (moonlighting) function of affecting GR transcription factor activity.

  • transcriptomic and Network Component analysis of glycerol kinase in skeletal muscle using a mouse model of glycerol kinase deficiency
    Molecular Genetics and Metabolism, 2009
    Co-Authors: Lola Rahib, James C. Liao, Ganesh Sriram, Melissa K Harada, Katrina M Dipple
    Abstract:

    Abstract Glycerol kinase (GK) is at the interface of fat and carbohydrate metabolism and has been linked to obesity and type 2 diabetes mellitus (T2DM). The purpose of this study was to investigate the role of GK in fat metabolism and insulin signaling in skeletal muscle (an important end organ tissue in T2DM). Microarray analysis determined that there were 525 genes that were differentially expressed (1.2-fold, p value Gyk ), phosphatidylinositol 3-kinase regulatory subunit, polypeptide 1 (p85 alpha) ( Pik3r1 ), insulin-like growth factor 1 ( Igf1 ), and growth factor receptor bound protein 2-associated protein 1 ( Gab1 ). Network Component analysis demonstrated that transcription factor activities of myogenic differentiation 1 (MYOD), myogenic regulatory factor 5 (MYF5), myogenin (MYOG), nuclear receptor subfamily 4, group A, member 1 (NUR77) are decreased in the Gyk KO whereas the activity of paired box 3 (PAX3) is increased. The activity of MYOD was confirmed using a DNA binding assay. In addition, myoblasts from Gyk KO had less ability to differentiate into myotubes compared to WT myoblasts. These findings support our previous studies in brown adipose tissue and demonstrate that the role of Gyk in muscle is due in part to its non-metabolic (moonlighting) activities.

  • integrated Network analysis identifies nitric oxide response Networks and dihydroxyacid dehydratase as a crucial target in escherichia coli
    Proceedings of the National Academy of Sciences of the United States of America, 2007
    Co-Authors: Daniel R Hyduke, Linh M. Tran, Laura R Jarboe, Katherine J Chou, James C. Liao
    Abstract:

    Nitric oxide (NO) is used by mammalian immune systems to counter microbial invasions and is produced by bacteria during denitrification. As a defense, microorganisms possess a complex Network to cope with NO. Here we report a combined transcriptomic, chemical, and phenotypic approach to identify direct NO targets and construct the biochemical response Network. In particular, Network Component analysis was used to identify transcription factors that are perturbed by NO. Such information was screened with potential NO reaction mechanisms and phenotypic data from genetic knockouts to identify active chemistry and direct NO targets in Escherichia coli. This approach identified the comprehensive E. coli NO response Network and evinced that NO halts bacterial growth via inhibition of the branched-chain amino acid biosynthesis enzyme dihydroxyacid dehydratase. Because mammals do not synthesize branched-chain amino acids, inhibition of dihydroxyacid dehydratase may have served to foster the role of NO in the immune arsenal.

  • Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using Network Component analysis
    Proceedings of the National Academy of Sciences of the United States of America, 2003
    Co-Authors: Young-lyeol Yang, Riccardo Boscolo, Chiara Sabatti, Vwani P. Roychowdhury, James C. Liao
    Abstract:

    Cells adjust gene expression profiles in response to environmental and physiological changes through a series of signal transduction pathways. Upon activation or deactivation, the terminal regulators bind to or dissociate from DNA, respectively, and modulate transcriptional activities on particular promoters. Traditionally, individual reporter genes have been used to detect the activity of the transcription factors. This approach works well for simple, non-overlapping transcription pathways. For complex transcriptional Networks, more sophisticated tools are required to deconvolute the contribution of each regulator. Here, we demonstrate the utility of Network Component analysis in determining multiple transcription factor activities based on transcriptome profiles and available connectivity information regarding Network connectivity. We used Escherichia coli carbon source transition from glucose to acetate as a model system. Key results from this analysis were either consistent with physiology or verified by using independent measurements.

  • Network Component analysis: Reconstruction of regulatory signals in biological systems
    Proceedings of the National Academy of Sciences of the United States of America, 2003
    Co-Authors: James C. Liao, Riccardo Boscolo, Young-lyeol Yang, Linh M. Tran, Chiara Sabatti, Vwani P. Roychowdhury
    Abstract:

    High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex Networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal Component analysis and independent Component analysis, ignore the underlying Network structures and provide decompositions based purely on a priori statistical constraints on the computed Component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called Network Component analysis, for uncovering hidden regulatory signals from outputs of Networked systems, when only a partial knowledge of the underlying Network topology is available. The a priori Network structure information is first tested for compliance with a set of identifiability criteria. For Networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a Network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory Networks.

Kitazoe Masato - One of the best experts on this subject based on the ideXlab platform.

Kash Barker - One of the best experts on this subject based on the ideXlab platform.

  • Social vulnerability and equity perspectives on interdependent infrastructure Network Component importance
    Sustainable Cities and Society, 2020
    Co-Authors: Deniz Berfin Karakoc, Kash Barker, Christopher W. Zobel, Yasser Almoghathawi
    Abstract:

    Abstract Critical infrastructure Networks are often described as (i) interdependent in nature for operability, (ii) vulnerable against multiple natural or human-made hazards, and (iii) vital for providing the essential needs and ensuring the functionality of societies. Developing a plan for infrastructure Network resilience is enabled by the identification of the most critical Components that have the largest impact on the performance interdependent Networks, as well as on society in terms of serving its needs. In this work, we propose a Component importance measure that is driven by the social aspects of resilience, which quantifies the impact of equitable restoration activities on Components of interdependent infrastructure Networks. To integrate the social expectations from various perspectives in the restoration scheduling of interdependent infrastructure Networks, we combine this Component importance measure with multiple social vulnerability measures that define different socio-economic characteristics in a society. Finally, we implement a multi-criteria decision analysis technique to determine the final importance ranking of the Components and illustrate our approach with two critical infrastructure Networks in Shelby County, TN. To our knowledge, our proposed methodology is the first to incorporate both social equity and social vulnerability concepts with the Component importance measures of critical interdependent infrastructure Network restoration scheduling.

  • a multi industry economic impact perspective on adaptive capacity planning in a freight transportation Network
    International Journal of Production Economics, 2019
    Co-Authors: Mohamad Darayi, Kash Barker, Charles Nicholson
    Abstract:

    Abstract The multi-modal freight transportation Network plays a vital role in maintaining commodity flows across multiple industries and multiple regions. As such, the effects of large-scale disruptive events could result in the closure of key transportation nodes and links, causing disruptions in commodity flows and larger disruptions to industries requiring those commodities for economic productivity. This work integrates a multi-commodity Network flow formulation with an economic interdependency model to quantify the multi-industry impacts of a disrupted transportation Network to devise contingent rerouting plans to strengthen the Network's adaptive capacity. The formulation proposed here is illustrated with a freight transportation planning case study in the state of Oklahoma, considering disruptive scenarios in which a Network Component is lost and how the proposed approach improves total economic productivity following a disruption.

  • flow based vulnerability measures for Network Component importance experimentation with preparedness planning
    Reliability Engineering & System Safety, 2016
    Co-Authors: Charles Nicholson, Kash Barker, Jose Emmanuel Ramirezmarquez
    Abstract:

    Abstract This work develops and compares several flow-based vulnerability measures to prioritize important Network edges for the implementation of preparedness options. These Network vulnerability measures quantify different characteristics and perspectives on enabling maximum flow, creating bottlenecks, and partitioning into cutsets, among others. The efficacy of these vulnerability measures to motivate preparedness options against experimental geographically located disruption simulations is measured. Results suggest that a weighted flow capacity rate, which accounts for both (i) the contribution of an edge to maximum Network flow and (ii) the extent to which the edge is a bottleneck in the Network, shows most promise across four instances of varying Network sizes and densities.

  • Resilience-based Network Component importance measures
    Reliability Engineering & System Safety, 2013
    Co-Authors: Kash Barker, Jose Emmanuel Ramirez-marquez, Claudio M. Rocco
    Abstract:

    Disruptive events, whether malevolent attacks, natural disasters, manmade accidents, or common failures, can have significant widespread impacts when they lead to the failure of Network Components and ultimately the larger Network itself. An important consideration in the behavior of a Network following disruptive events is its resilience, or the ability of the Network to “bounce back†to a desired performance state. Building on the extensive reliability engineering literature on measuring Component importance, or the extent to which individual Network Components contribute to Network reliability, this paper provides two resilience-based Component importance measures. The two measures quantify the (i) potential adverse impact on system resilience from a disruption affecting link i, and (ii) potential positive impact on system resilience when link i cannot be disrupted, respectively. The resilience-based Component importance measures, and an algorithm to perform stochastic ordering of Network Components due to the uncertain nature of Network disruptions, are illustrated with a 20 node, 30 link Network example.

Charles Nicholson - One of the best experts on this subject based on the ideXlab platform.

  • a multi industry economic impact perspective on adaptive capacity planning in a freight transportation Network
    International Journal of Production Economics, 2019
    Co-Authors: Mohamad Darayi, Kash Barker, Charles Nicholson
    Abstract:

    Abstract The multi-modal freight transportation Network plays a vital role in maintaining commodity flows across multiple industries and multiple regions. As such, the effects of large-scale disruptive events could result in the closure of key transportation nodes and links, causing disruptions in commodity flows and larger disruptions to industries requiring those commodities for economic productivity. This work integrates a multi-commodity Network flow formulation with an economic interdependency model to quantify the multi-industry impacts of a disrupted transportation Network to devise contingent rerouting plans to strengthen the Network's adaptive capacity. The formulation proposed here is illustrated with a freight transportation planning case study in the state of Oklahoma, considering disruptive scenarios in which a Network Component is lost and how the proposed approach improves total economic productivity following a disruption.

  • flow based vulnerability measures for Network Component importance experimentation with preparedness planning
    Reliability Engineering & System Safety, 2016
    Co-Authors: Charles Nicholson, Kash Barker, Jose Emmanuel Ramirezmarquez
    Abstract:

    Abstract This work develops and compares several flow-based vulnerability measures to prioritize important Network edges for the implementation of preparedness options. These Network vulnerability measures quantify different characteristics and perspectives on enabling maximum flow, creating bottlenecks, and partitioning into cutsets, among others. The efficacy of these vulnerability measures to motivate preparedness options against experimental geographically located disruption simulations is measured. Results suggest that a weighted flow capacity rate, which accounts for both (i) the contribution of an edge to maximum Network flow and (ii) the extent to which the edge is a bottleneck in the Network, shows most promise across four instances of varying Network sizes and densities.

Phuyal Umesh - One of the best experts on this subject based on the ideXlab platform.