| 1. |
- Eriksen, K.A., et al.
(författare)
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Scale-free growing networks imply linear preferential attachment
- 2002
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Ingår i: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. - 1539-3755. ; 65:1
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Tidskriftsartikel (refereegranskat)abstract
- It has been recognized for some time that a network grown by the addition of nodes with linear preferential attachment will possess a scale-free distribution of connectivities. Here we prove by some analytical arguments that the linearity is a necessary component to obtain this kind of distribution. However, the preferential linking rate does not necessarily apply to single nodes, but to groups of nodes of the same connectivity. We also point out that for a time-varying mean connectivity the linking rate will deviate from a linear expression by an extra asymptotically logarithmic term. © 2001 The American Physical Society.
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| 2. |
- Eriksen, K. A., et al.
(författare)
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Visualization of large-scale correlations in gene expressions
- 2004
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Ingår i: Functional & Integrative Genomics. - : Springer Science and Business Media LLC. - 1438-793X .- 1438-7948. ; 4:4, s. 241-245
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale expression data are today measured for several thousands of genes simultaneously. Furthermore, most genes are being categorized according to their properties. This development has been followed by an exploration of theoretical tools to integrate these diverse data types. A key problem is the large noise-level in the data. Here, we investigate ways to extract the remaining signals within these noisy data sets. We find large-scale correlations within data from Saccharomyces cerevisiae with respect to properties of the encoded proteins. These correlations are visualized in a way that is robust to the underlying noise in the measurement of the individual gene expressions. In particular, for S. cerevisiae we observe that the proteins corresponding to the 400 highest expressed genes typically are localized to the cytoplasm. These most expressed genes are not essential for cell survival.
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| 5. |
- Gustafsson, Mika, 1977-, et al.
(författare)
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Comparison and validation of community structures in complex networks
- 2006
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Ingår i: Physica A. - : Elsevier BV. - 0378-4371 .- 1873-2119. ; 367, s. 559-576
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Tidskriftsartikel (refereegranskat)abstract
- The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information.Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.
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| 6. |
- Gustafsson, Mika, 1977-, et al.
(författare)
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Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
- 2005
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Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - 1545-5963 .- 1557-9964. ; 2:3, s. 254-261
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Tidskriftsartikel (refereegranskat)abstract
- We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.
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| 7. |
- Gustafsson, Mika, 1977-, et al.
(författare)
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Gene Expression Prediction by Soft Integration and the Elastic Net : Best Performance of the DREAM3 Gene Expression Challenge
- 2010
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 5:2, s. e9134-
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Tidskriftsartikel (refereegranskat)abstract
- Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance.Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance.Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.
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| 9. |
- Gustafsson, Mika, 1977-
(författare)
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Gene networks from high-throughput data : Reverse engineering and analysis
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Experimental innovations starting in the 1990’s leading to the advent of high-throughput experiments in cellular biology have made it possible to measure thousands of genes simultaneously at a modest cost. This enables the discovery of new unexpected relationships between genes in addition to the possibility of falsify existing. To benefit as much as possible from these experiments the new inter disciplinary research field of systems biology have materialized. Systems biology goes beyond the conventional reductionist approach and aims at learning the whole system under the assumption that the system is greater than the sum of its parts. One emerging enterprise in systems biology is to use the high-throughput data to reverse engineer the web of gene regulatory interactions governing the cellular dynamics. This relatively new endeavor goes further than clustering genes with similar expression patterns and requires the separation of cause of gene expression from the effect. Despite the rapid data increase we then face the problem of having too few experiments to determine which regulations are active as the number of putative interactions has increased dramatic as the number of units in the system has increased. One possibility to overcome this problem is to impose more biologically motivated constraints. However, what is a biological fact or not is often not obvious and may be condition dependent. Moreover, investigations have suggested several statistical facts about gene regulatory networks, which motivate the development of new reverse engineering algorithms, relying on different model assumptions. As a result numerous new reverse engineering algorithms for gene regulatory networks has been proposed. As a consequent, there has grown an interest in the community to assess the performance of different attempts in fair trials on “real” biological problems. This resulted in the annually held DREAM conference which contains computational challenges that can be solved by the prosing researchers directly, and are evaluated by the chairs of the conference after the submission deadline.This thesis contains the evolution of regularization schemes to reverse engineer gene networks from high-throughput data within the framework of ordinary differential equations. Furthermore, to understand gene networks a substantial part of it also concerns statistical analysis of gene networks. First, we reverse engineer a genome-wide regulatory network based solely on microarray data utilizing an extremely simple strategy assuming sparseness (LASSO). To validate and analyze this network we also develop some statistical tools. Then we present a refinement of the initial strategy which is the algorithm for which we achieved best performer at the DREAM2 conference. This strategy is further refined into a reverse engineering scheme which also can include external high-throughput data, which we confirm to be of relevance as we achieved best performer in the DREAM3 conference as well. Finally, the tools we developed to analyze stability and flexibility in linearized ordinary differential equations representing gene regulatory networks is further discussed.
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| 10. |
- Gustafsson, Mika, et al.
(författare)
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Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
- 2009
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Ingår i: IET SYSTEMS BIOLOGY. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 3:4, s. 219-228
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Tidskriftsartikel (refereegranskat)abstract
- Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.
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