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3 Publications visible to you, out of a total of 3
GraphAlignment: Bayesian pairwise alignment of biological networks

Abstract (Expand)

ABSTRACT: BACKGROUND: With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies … is the alignment of networks. RESULTS: We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Graemlin 2.0.On simulated data, GraphAlignment outperforms Graemlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Graemlin 2.0. It is faster than Graemlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N^2.6). On empirical bacterial protein-protein interaction networks (PIN) and gene co-expression networks, GraphAlignment outperforms Graemlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Graemlin 2.0 outperforms GraphAlignment. CONCLUSIONS: The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity.

Authors: Michal Kolar, Jörn Meier, Ville Mustonen, Michael Lässig,

Date Published: 21st Nov 2012

Publication Type: Not specified

Nonlinear fitness landscape of a molecular pathway

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Genes are regulated because their expression involves a fitness cost to the organism. The production of proteins by transcription and translation is a well-known cost factor, but the enzymatic activity … of the proteins produced can also reduce fitness, depending on the internal state and the environment of the cell. Here, we map the fitness costs of a key metabolic network, the lactose utilization pathway in Escherichia coli. We measure the growth of several regulatory lac operon mutants in different environments inducing expression of the lac genes. We find a strikingly nonlinear fitness landscape, which depends on the production rate and on the activity rate of the lac proteins. A simple fitness model of the lac pathway, based on elementary biophysical processes, predicts the growth rate of all observed strains. The nonlinearity of fitness is explained by a feedback loop: production and activity of the lac proteins reduce growth, but growth also affects the density of these molecules. This nonlinearity has important consequences for molecular function and evolution. It generates a cliff in the fitness landscape, beyond which populations cannot maintain growth. In viable populations, there is an expression barrier of the lac genes, which cannot be exceeded in any stationary growth process. Furthermore, the nonlinearity determines how the fitness of operon mutants depends on the inducer environment. We argue that fitness nonlinearities, expression barriers, and gene-environment interactions are generic features of fitness landscapes for metabolic pathways, and we discuss their implications for the evolution of regulation.

Authors: Lilia Perfeito, Stéphane Ghozzi, , Karin Schnetz, Michael Lässig

Date Published: 21st Jul 2011

Publication Type: Not specified

Significance analysis and statistical mechanics: an application to clustering

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This Letter addresses the statistical significance of structures in random data: given a set of vectors and a measure of mutual similarity, how likely is it that a subset of these vectors forms a cluster … with enhanced similarity among its elements? The computation of this cluster p value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple-testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes.

Authors: Marta Łuksza, Michael Lässig,

Date Published: 27th Nov 2009

Publication Type: Not specified

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