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Sökning: WFRF:(Nelander Sven 1974)

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1.
  • Nelander, Sven, 1974, et al. (författare)
  • Predictive screening for regulators of conserved functional gene modules (gene batteries) in mammals
  • 2005
  • Ingår i: BMC genomics. - : BioMed Central. - 1471-2164. ; 6:1
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND: The expression of gene batteries, genomic units of functionally linked genes which are activated by similar sets of cis- and trans-acting regulators, has been proposed as a major determinant of cell specialization in metazoans. We developed a predictive procedure to screen the mouse and human genomes and transcriptomes for cases of gene-battery-like regulation. RESULTS: In a screen that covered approximately 40 percent of all annotated protein-coding genes, we identified 21 co-expressed gene clusters with statistically supported sharing of cis-regulatory sequence elements. 66 predicted cases of over-represented transcription factor binding motifs were validated against the literature and fell into three categories: (i) previously described cases of gene battery-like regulation, (ii) previously unreported cases of gene battery-like regulation with some support in a limited number of genes, and (iii) predicted cases that currently lack experimental support. The novel predictions include for example Sox 17 and RFX transcription factor binding sites that were detected in approximately 10% of all testis specific genes, and HNF-1 and 4 binding sites that were detected in approximately 30% of all kidney specific genes respectively. The results are publicly available at http://www.wlab.gu.se/lindahl/genebatteries. CONCLUSION: 21 co-expressed gene clusters were enriched for a total of 66 shared cis-regulatory sequence elements. A majority of these predictions represent novel cases of potential co-regulation of functionally coupled proteins. Critical technical parameters were evaluated, and the results and the methods provide a valuable resource for future experimental design.
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2.
  • Cvijovic, Marija, 1977, et al. (författare)
  • Bridging the gaps in systems biology
  • 2014
  • Ingår i: Molecular Genetics and Genomics. - 1617-4615 .- 1617-4623. ; 289:5, s. 727-734
  • Tidskriftsartikel (refereegranskat)abstract
    • Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding-the elucidation of the basic and presumably conserved "design" and "engineering" principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.
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3.
  • Jörnsten, Rebecka, 1971, et al. (författare)
  • Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
  • 2011
  • Ingår i: Molecular Systems Biology. - : Nature Publishing Group. - 1744-4292. ; 7
  • Tidskriftsartikel (refereegranskat)abstract
    • DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
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5.
  • Petit, Marleen MR, et al. (författare)
  • Smooth muscle expression of lipoma preferred partner is mediated by an alternative intronic promoter that is regulated by serum response factor/myocardin.
  • 2008
  • Ingår i: Circulation research. - 1524-4571. ; 103:1, s. 61-9
  • Tidskriftsartikel (refereegranskat)abstract
    • Lipoma preferred partner (LPP) was recently recognized as a smooth muscle marker that plays a role in smooth muscle cell migration. In this report, we focus on the transcriptional regulation of the LPP gene. In particular, we investigate whether LPP is directly regulated by serum response factor (SRF). We show that the LPP gene contains 3 evolutionarily conserved CArG boxes and that 1 of these is part of an alternative promoter in intron 2. Quantitative RT-PCR shows that this alternative promoter directs transcription specifically to smooth muscle containing tissues in vivo. By using chromatin immunoprecipitation, we demonstrate that 2 of the CArG boxes, including the promoter-associated CArG box, bind to endogenous SRF in cultured aortic smooth muscle cells. Electrophoretic mobility-shift assays show that the conserved CArG boxes bind SRF in vitro. In reporter experiments, we show that the alternative promoter has transcriptional capacity that is dependent on SRF/myocardin and that the promoter associated CArG box is required for that activity. Finally, we show by quantitative RT-PCR that the alternative promoter is strongly downregulated in SRF-deficient embryonic stem cells and in smooth muscle tissues derived from conditional SRF knockout mice. Collectively, our data demonstrate that expression of LPP in smooth muscle is mediated by an alternative promoter that is regulated by SRF/myocardin.
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6.
  • Abenius, Tobias, 1979, et al. (författare)
  • System-scale network modeling of cancer using EPoC
  • 2012
  • Ingår i: Advances in Experimental Medicine and Biology. - 0065-2598. ; 736:5, s. 617-643
  • Tidskriftsartikel (refereegranskat)abstract
    • One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.
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7.
  • Barretina, Jordi, et al. (författare)
  • Subtype-specific genomic alterations define new targets for soft-tissue sarcoma therapy.
  • 2010
  • Ingår i: Nature genetics. - 1546-1718. ; 42:8, s. 715-21
  • Tidskriftsartikel (refereegranskat)abstract
    • Soft-tissue sarcomas, which result in approximately 10,700 diagnoses and 3,800 deaths per year in the United States, show remarkable histologic diversity, with more than 50 recognized subtypes. However, knowledge of their genomic alterations is limited. We describe an integrative analysis of DNA sequence, copy number and mRNA expression in 207 samples encompassing seven major subtypes. Frequently mutated genes included TP53 (17% of pleomorphic liposarcomas), NF1 (10.5% of myxofibrosarcomas and 8% of pleomorphic liposarcomas) and PIK3CA (18% of myxoid/round-cell liposarcomas, or MRCs). PIK3CA mutations in MRCs were associated with Akt activation and poor clinical outcomes. In myxofibrosarcomas and pleomorphic liposarcomas, we found both point mutations and genomic deletions affecting the tumor suppressor NF1. Finally, we found that short hairpin RNA (shRNA)-based knockdown of several genes amplified in dedifferentiated liposarcoma, including CDK4 and YEATS4, decreased cell proliferation. Our study yields a detailed map of molecular alterations across diverse sarcoma subtypes and suggests potential subtype-specific targets for therapy.
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8.
  • Gerlee, Philip, 1980, et al. (författare)
  • Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening
  • 2013
  • Ingår i: PLoS ONE. - 1932-6203. ; 8:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Functionally interacting perturbations, such as synergistic drugs pairs or synthetic lethal gene pairs, are of key interest in both pharmacology and functional genomics. However, to find such pairs by traditional screening methods is both time consuming and costly. We present a novel computational-experimental framework for efficient identification of synergistic target pairs, applicable for screening of systems with sizes on the order of current drug, small RNA or SGA (Synthetic Genetic Array) libraries (>1000 targets). This framework exploits the fact that the response of a drug pair in a given system, or a pair of genes' propensity to interact functionally, can be partly predicted by computational means from (i) a small set of experimentally determined target pairs, and (ii) pre-existing data (e.g. gene ontology, PPI) on the similarities between targets. Predictions are obtained by a novel matrix algebraic technique, based on cyclical projections onto convex sets. We demonstrate the efficiency of the proposed method using drug-drug interaction data from seven cancer cell lines and gene-gene interaction data from yeast SGA screens. Our protocol increases the rate of synergism discovery significantly over traditional screening, by up to 7-fold. Our method is easy to implement and could be applied to accelerate pair screening for both animal and microbial systems.
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9.
  • Gerlee, Philip, 1980, et al. (författare)
  • The Impact of Phenotypic Switching on Glioblastoma Growth and Invasion
  • 2012
  • Ingår i: PLoS Computational Biology. - 1553-7358 .- 1553-734X. ; 8:6
  • Tidskriftsartikel (refereegranskat)abstract
    • The brain tumour glioblastoma is characterised by diffuse and infiltrative growth into surrounding brain tissue. At the macroscopic level, the progression speed of a glioblastoma tumour is determined by two key factors: the cell proliferation rate and the cell migration speed. At the microscopic level, however, proliferation and migration appear to be mutually exclusive phenotypes, as indicated by recent in vivo imaging data. Here, we develop a mathematical model to analyse how the phenotypic switching between proliferative and migratory states of individual cells affects the macroscopic growth of the tumour. For this, we propose an individual-based stochastic model in which glioblastoma cells are either in a proliferative state, where they are stationary and divide, or in motile state in which they are subject to random motion. From the model we derive a continuum approximation in the form of two coupled reaction-diffusion equations, which exhibit travelling wave solutions whose speed of invasion depends on the model parameters. We propose a simple analytical method to predict progression rate from the cell-specific parameters and demonstrate that optimal glioblastoma growth depends on a non-trivial trade-off between the phenotypic switching rates. By linking cellular properties to an in vivo outcome, the model should be applicable to designing relevant cell screens for glioblastoma and cytometry-based patient prognostics.
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10.
  • Gerlee, Philip, 1980, et al. (författare)
  • Travelling wave analysis of a mathematical model of glioblastoma growth
  • 2016
  • Ingår i: Mathematical Biosciences. - 0025-5564 .- 1879-3134. ; 276, s. 75-81
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper we analyse a previously proposed cell-based model of glioblastoma (brain tumour) growth, which is based on the assumption that the cancer cells switch phenotypes between a proliferative and motile state (Gerlee and Nelander, PLoS Comp. Bio., 8(6) 2012). The dynamics of this model can be described by a system of partial differential equations, which exhibits travelling wave solutions whose wave speed depends crucially on the rates of phenotypic switching. We show that under certain conditions on the model parameters, a closed form expression of the wave speed can be obtained, and using singular perturbation methods we also derive an approximate expression of the wave front shape. These new analytical results agree with simulations of the cell-based model, and importantly show that the inverse relationship between wave front steepness and speed observed for the Fisher equation no longer holds when phenotypic switching is considered.
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  • Resultat 1-10 av 19
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