Publications (57) View all
-
Article: Genome-wide system analysis reveals stable yet flexible network dynamics in yeast.
[show abstract] [hide abstract]
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.IET Systems Biology 08/2009; 3(4):219-28. · 1.35 Impact Factor -
Article: Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit.
[show abstract] [hide abstract]
ABSTRACT: Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein-protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a 'tearing-and-zooming' approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between state-variables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits. [Includes supplementary material].IET Systems Biology 04/2009; 3(2):113-29. · 1.35 Impact Factor -
Article: Correlation of serum IGF-I and IGFBP-1 and -3 to cardiovascular risk indicators and early carotid atherosclerosis in healthy middle-aged men.
[show abstract] [hide abstract]
ABSTRACT: IGF-I, IGFBP-1 and IGFBP-3 are putative mediators in cardiovascular disease. The present study examined (i) the correlations of circulating IGF-I, IGFBP-1 and IGFBP-3 to established cardiovascular risk factors and signs of early atherosclerosis as reflected by ultrasound measurement of common carotid intima-media thickness (IMT), and (ii) whether serum concentrations of these analytes are modulated during alimentary lipaemia. Cross-sectional clinical study. A biobank and clinical database based on 96 healthy Caucasian men, aged 50 years, with an apolipoprotein (apo) E3/E3 genotype, who had originally undergone investigations of postprandial lipoprotein metabolism was used for the study. Total IGF-I, IGFBP-1 and IGFBP-3 were determined in serum by radioimmunoassay (RIA). Free IGF-I was measured by a commercial two-site immunoradiometric assay (IRMA). In multivariate analyses, fasting serum free IGF-I correlated inversely with IMT and accounted for 5% of the variation in multiple R(2). When fasting serum IGFBP-1 was entered in the models instead of IGF-I, IGFBP-1 correlated positively with IMT and accounted for 6% of the variation in IMT. IGFBP-3 and total IGF-I were unrelated to IMT. There were no associations between free IGF-I and cardiovascular risk factors, whereas IGFBP-1 behaved like a component of the insulin resistance syndrome. Serum free IGF-I increased and IGFBP-1 decreased postprandially. The data indicate that serum free IGF-I and IGFBP-1 are implicated in early atherosclerosis.Clinical Endocrinology 02/2008; 68(1):51-8. · 3.17 Impact Factor -
Article: Growing Bayesian network models of gene networks from seed genes.
[show abstract] [hide abstract]
ABSTRACT: MOTIVATION: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. This means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. This is not a trivial decision. We propose an alternative approach to overcome this problem. RESULTS: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes; find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results.Bioinformatics 10/2005; 21 Suppl 2:ii224-9. · 5.47 Impact Factor -
Article: A deficiency of microsomal triglyceride transfer protein reduces apolipoprotein B secretion.
[show abstract] [hide abstract]
ABSTRACT: Microsomal triglyceride transfer protein (MTP) transfers lipids to apolipoprotein B (apoB) within the endoplasmic reticulum, a process that involves direct interactions between apoB and the large subunit of MTP. Recent studies with heterozygous MTP knockout mice have suggested that half-normal levels of MTP in the liver reduce apoB secretion. We hypothesized that reduced apoB secretion in the setting of half-normal MTP levels might be caused by a reduced MTP:apoB ratio in the endoplasmic reticulum, which would reduce the number of apoB-MTP interactions. If this hypothesis were true, half-normal levels of MTP might have little impact on lipoprotein secretion in the setting of half-normal levels of apoB synthesis (since the ratio of MTP to apoB would not be abnormally low) and might cause an exaggerated reduction in lipoprotein secretion in the setting of apoB overexpression (since the MTP:apoB ratio would be even lower). To test this hypothesis, we examined the effects of heterozygous MTP deficiency on apoB metabolism in the setting of normal levels of apoB synthesis, half-normal levels of apoB synthesis (heterozygous Apob deficiency), and increased levels of apoB synthesis (transgenic overexpression of human apoB). Contrary to our expectations, half-normal levels of MTP reduced the plasma apoB100 levels to the same extent ( approximately 25-35%) at each level of apoB synthesis. In addition, apoB secretion from primary hepatocytes was reduced to a comparable extent at each level of apoB synthesis. Thus, these results indicate that the concentration of MTP within the endoplasmic reticulum rather than the MTP:apoB ratio is the critical determinant of lipoprotein secretion. Finally, we found that heterozygosity for an apoB knockout mutation lowered plasma apoB100 levels more than heterozygosity for an MTP knockout allele. Consistent with that result, hepatic triglyceride accumulation was greater in heterozygous apoB knockout mice than in heterozygous MTP knockout mice.Journal of Biological Chemistry 04/2000; 275(11):7515-20. · 4.77 Impact Factor
