| 1. |
- 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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| 2. |
- 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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| 4. |
- 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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| 6. |
- Gustafsson, Mika, et al.
(författare)
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Integrating various data sources for improved quality in reverse engineering of gene regulatory networks
- 2009. - 1
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Ingår i: Handbook of Research on Computational Methodologies in Gene Regulatory Networks. - : IGI Global. - 9781605666853 - 9781605666860 ; , s. 476-496
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we outline a methodology to reverse engineer GRNs from various data sources within an ODE framework. The methodology is generally applicable and is suitable to handle the broad error distribution present in microarrays. The main effort of this chapter is the exploration of a fully data driven approach to the integration problem in a “soft evidence” based way. Integration is here seen as the process of incorporation of uncertain a priori knowledge and is therefore only relied upon if it lowers the prediction error. An efficient implementation is carried out by a linear programming formulation. This LP problem is solved repeatedly with small modifications, from which we can benefit by restarting the primal simplex method from nearby solutions, which enables a computational efficient execution. We perform a case study for data from the yeast cell cycle, where all verified genes are putative regulators and the a priori knowledge consists of several types of binding data, text-mining and annotation knowledge.
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| 7. |
- Gustafsson, Mika, 1977-
(författare)
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Large-scale topology, stability and biology of gene networks
- 2006
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Experimental innovations in cell biology have provided a huge amount of genomescale data sets, settling the stage for understanding organisms on a system level. Recently, complex networks have been widely adopted and serve as a unifying language for widely different systems, including social, technological and biological systems. Still- in most biological cases-the number of interacting units vastly exceeds the number of measurements, hence large-scale models must still be very simple or non-specific. In this thesis we explore the limits of a linear (Lasso) network model on a genomic-scale for the Saccharomyces cerevisae organism and the limits of some analysis tools from the research field of complex networks. The former study (Paper I and Paper III) mainly regards validation issues, but also stipulate novel statistical system hypotheses, e.g., the system is significantly more stable than random, but still flexible to target stimuli. The latter study (Paper II) explores different heuristics in the search for communities (i.e., dense sub-graphs) within large complex networks and how the concept relates to functional modules.
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| 8. |
- Gustafsson, Mika, et al.
(författare)
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Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions
- 2009
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Ingår i: CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY. - : Wiley. - 0077-8923 .- 1749-6632. ; 1158, s. 265-275
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Tidskriftsartikel (refereegranskat)abstract
- The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series kind steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed net-work, in which each edge has been assigned a score front it bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSillico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.
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