Now showing 1 - 10 of 12
  • Publication
    Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction
    Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.
      371Scopus© Citations 9
  • Publication
    Navigating the Multilayered Organization of Eukaryotic Signaling: A New Trend in Data Integration
    The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks are poorly addressed. Here we review recent advances in the development of publically available databases that help us analyze the signal integration and processing by multilayered networks that specify biological responses in model organisms and human cells.
    Scopus© Citations 7  227
  • Publication
    Primary cilium-associated genes mediate bone marrow stromal cell response to hypoxia
    Currently there is intense interest in using mesenchymal stem cells (MSC) for therapeutic interventions in many diseases and conditions. To accelerate the therapeutic use of stem cells we must understand how they sense their environment. Primary cilia are an extracellular sensory organelle present on most growth arrested cells that transduce information about the cellular environment into cells, triggering signaling cascades that have profound effects on development, cell cycle, proliferation, differentiation and migration. Migrating cells likely encounter differing oxygen tensions, therefore we investigated the effect of oxygen tension on cilia. Using bone marrow stromal cells (BMSCs, also known as bone marrow-derived mesenchymal stem cells) we found that oxygen tension significantly affected the length of cilia in primary BMSCs. Chronic exposure to hypoxia specifically down-regulated genes involved in hedgehog signaling and re-localized the Smo and Gli2 proteins to cilia. Investigating the effects of chemotactic migration on cilia, we observed significantly longer cilia in migrating cells which was again, strongly influenced by oxygen tension. Finally, using computational modeling we identified links between migration and ciliation signaling pathways, characterizing the novel role of HSP90 and PI3K signaling in regulating BMSC cilia length. These findings enhance our current understanding of BMSC adaptions to hypoxia and advance our knowledge of BMSC biology and cilia regulation.
    Scopus© Citations 16  570
  • Publication
    Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology
    (Springer (Biomed Central Ltd.), 2013) ; ;
    Background: Recent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations. Results: We propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells. Conclusions: The proposed algorithm provides a robust, scalable and cost effective solution for inferring network topologies from biological data. It can potentially be applied to explore novel pathways which play important roles in life threatening disease like cancer.
    Scopus© Citations 27  385
  • Publication
    Integrating network reconstruction with mechanistic modeling to predict cancer therapies
    (American Association for the Advancement of Science, 2016-11-22) ; ; ;
    Signal transduction networks (STNs) are often rewired in cancerous cells. Effective cancer treatment requires identifying and repairing these harmful alterations. We developed a computational framework which can identify these aberrations and predict potential targets for intervention. It reconstructs network models of STNs from noisy and incomplete perturbation response data, and then uses the reconstructed networks to develop mechanistic models of STNs for predicting potential treatments. As a proof of principle, we analysed a perturbation dataset targeting Epidermal Growth Factor Receptor (EGFR) and Insulin like 2 Growth Factor 1 Receptor (IGF1R) pathways in a panel of colorectal cancer (CRC) cells, revealing cell line specific STN rewiring. Specifically, we found that the feedback inhibition of IRS1 by p70S6K is associated with resistance to EGF receptor (EGFR) inhibition, and disrupting this feedback may restore sensitivity to EGFR inhibitors in CRC cells. These findings were experimentally validated in vitro and in zebrafish (Danio rerio) xenografts.
    Scopus© Citations 49  686
  • Publication
    A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research
    (Springer Nature, 2016-07-22) ;
    Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.
      234Scopus© Citations 5
  • Publication
    An Integrative Computational Approach for a Prioritization of Key Transcription Regulators Associated With Nanomaterial-Induced Toxicity
    A rapid increase of new nanomaterial products poses new challenges for their risk assessment. Current traditional methods for estimating potential adverse health effect of nanomaterials (NMs) are complex, time consuming and expensive. In order to develop new prediction tests for nanotoxicity evaluation, a systems biology approach and data from high-throughput omics experiments can be used. We present a computational approach that combines reverse engineering techniques, network analysis and pathway enrichment analysis for inferring the transcriptional regulation landscape and its functional interpretation. To illustrate this approach, we used published transcriptomic data derived from mice lung tissue exposed to carbon nanotubes (NM-401 and NRCWE-26). Because fibrosis is the most common adverse effect of these NMs, we included in our analysis the data for bleomycin (BLM) treatment, which is a well-known fibrosis inducer. We inferred gene regulatory networks for each NM and BLM to capture functional hierarchical regulatory structures between genes and their regulators. Despite the different nature of the lung injury caused by nanoparticles and BLM, we identified several conserved core regulators for all agents. We reason that these regulators can be considered as early predictors of toxic responses after NMs exposure. This integrative approach, which refines traditional methods of transcriptomic analysis, can be useful for prioritization of potential core regulators and generation of new hypothesis about mechanisms of nanoparticles toxicity.
    Scopus© Citations 7  543
  • Publication
    A Bayesian framework that integrates heterogeneous data for inferring gene regulatory networks
    (Frontiers Media, 2014-05-20)
    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.
      258Scopus© Citations 12
  • Publication
    Vascular Endothelial Growth Factor (VEGF) Promotes Assembly of the p130Cas Interactome to Drive Endothelial Chemotactic Signaling and Angiogenesis
    (American Society for Biochemistry and Molecular Biology, 2016-12-22) ; ; ; ; ;
    p130Cas is a polyvalent adapter protein essential for cardiovascular development, and with a key role in cell movement. In order to identify the pathways by which p130Cas exerts its biological functions in endothelial cells we mapped the p130Cas interactome and its dynamic changes in response to VEGF using high-resolution mass spectrometry and reconstruction of protein interaction (PPI) networks with the aid of multiple PPI databases. VEGF enriched the p130Cas interactome in proteins involved in actin cytoskeletal dynamics and cell movement, including actin-binding proteins, small GTPases and regulators or binders of GTPases. Detailed studies showed that p130Cas association of the GTPase-binding scaffold protein, IQGAP1, plays a key role in VEGF chemotactic signaling, endothelial polarization, VEGF-induced cell migration, and endothelial tube formation. These findings indicate a cardinal role for assembly of the p130Cas interactome in mediating the cell migratory response to VEGF in angiogenesis, and provide a basis for further studies of p130Cas in cell movement.
      297Scopus© Citations 21
  • Publication
    An Integrated Global Analysis of Compartmentalized HRAS Signaling
    Modern omics technologies allow us to obtain global information on different types of biological networks. However, integrating these different types of analyses into a coherent framework for a comprehensive biological interpretation remains challenging. Here, we present a conceptual framework that integrates protein interaction, phosphoproteomics, and transcriptomics data. Applying this method to analyze HRAS signaling from different subcellular compartments shows that spatially defined networks contribute specific functions to HRAS signaling. Changes in HRAS protein interactions at different sites lead to different kinase activation patterns that differentially regulate gene transcription. HRAS-mediated signaling is the strongest from the cell membrane, but it regulates the largest number of genes from the endoplasmic reticulum. The integrated networks provide a topologically and functionally resolved view of HRAS signaling. They reveal distinct HRAS functions including the control of cell migration from the endoplasmic reticulum and TP53-dependent cell survival when signaling from the Golgi apparatus.
    Scopus© Citations 31  440