Biomedical subjects

Dimitris Anastassiou

Publications and source records attributed to Dimitris Anastassiou.

5 recordsLinked to original sources

PubMed2006

Computational inference of the molecular logic for synaptic connectivity in C. elegans.

MOTIVATION: The nematode C. elegans is an ideal model organism in which to investigate the biomolecular mechanisms underlying the connectivity of neurons, because synaptic connections are described in a comprehensive wiring diagram and methods for defining gene expression profiles of individual neurons are now available. RESULTS: Here we present computational techniques linking these two types of information. A systems-based approach (EMBP: Entropy Minimization and Boolean Parsimony) identifies sets of synergistically interacting genes whose joint expression predicts neural connectivity. We introduce an information theoretic measure of the multivariate synergy, a fundamental concept in systems biology, connecting the members of these gene sets. We present and validate our preliminary results based on publicly available information, and demonstrate that their synergy is exceptionally high indicating joint involvement in pathways. Our strategy provides a robust methodology that will yield increasingly more accurate results as more neuron-specific gene expression data emerge. Ultimately, we expect our approach to provide important clues for universal mechanisms of neural interconnectivity.

Animals↗

PubMed2006

Inference of disease-related molecular logic from systems-based microarray analysis.

Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage.

Algorithms↗

PubMed2006

Variable window binding for mutually exclusive alternative splicing.

BACKGROUND: Genes of advanced organisms undergo alternative splicing, which can be mutually exclusive, in the sense that only one exon is included in the mature mRNA out of a cluster of alternative choices, often arranged in a tandem array. In many cases, however, the details of the underlying biologic mechanisms are unknown. RESULTS: We describe 'variable window binding'--a mechanism used for mutually exclusive alternative splicing by which a segment ('window') of a conserved nucleotide 'anchor' sequence upstream of the exon 6 cluster in the pre-mRNA of the fruitfly Dscam gene binds to one of the introns, thereby activating selection of the exon directly downstream from the binding site. This mechanism is supported by the fact that the anchor sequence can be inferred solely from a comparison of the intron sequences using a genetic algorithm. Because the window location varies for each exon choice, regulation can be achieved by obstructing part of that sequence. We also describe a related mechanism based on competing pre-mRNA stem-loop structures that could explain the mutually exclusive choice of exon 17 of the Dscam gene. CONCLUSION: On the basis of comparative sequence analysis, we propose efficient biologic mechanisms of alternative splicing of the Drosophila Dscam gene that rely on the inherent structure of the pre-mRNA. Related mechanisms employing 'locus control regions' could be involved on other occasions of mutually exclusive choices of exons or genes.

Algorithms↗

PubMed2005

Phenotype analysis using network motifs derived from changes in regulatory network dynamics.

The intrinsic dynamic response of a transcriptional regulatory network depends directly on molecular interactions in the cellular transcription, translation, and degradation machineries. These interactions can be incorporated into dynamic mathematical models of the biochemical system using the biophysical relationship with the model parameters. Modifications of such interactions bring changes to the biological behavior of the cells, and therefore, many normal and pathological cellular states depend on them. It is important for analysis, prediction, diagnosis, and treatment of cellular function to have an experimentally derived model with parameters that adequately represent the molecular interactions of interest. Finding the model and parameters of a transcriptional regulatory network is a difficult task that has been approached at different levels and with different techniques. We develop here a new analysis method (based on previous work on network inference, modeling, and parameter identification) that finds the most changed parameters from yeast oligonucleotide microarray expression patterns in cases where a phenotype difference exists between two samples. We then relate and examine the changed parameters with their associated genes, corresponding genetic functional categories, and particular subnetworks and connectivities. The biophysical bases for these changes are also identified by studying the relationship of the changed parameters with the transcription, translation, and degradation mechanisms. The method is improved to cases where there are two or more transcription factors influencing transcription, and a statistical analysis is performed to give a measurement of the uniqueness and robustness of the parameter fit.

Algorithms↗

PubMed2004

Data-based model and parameter evaluation in dynamic transcriptional regulatory networks.

Finding the causality and strength of connectivity in transcriptional regulatory networks from time-series data will provide a powerful tool for the analysis of cellular states. Presented here is the design of tools for the evaluation of the network's model structure and parameters. The most effective tools are found to be based on evolution strategies. We evaluate models of increasing complexity, from lumped, algebraic phenomenological models to Hill functions and thermodynamically derived functions. These last functions provide the free energies of binding of transcription factors to their operators, as well as cooperativity energies. Optimization results based on published experimental data from a synthetic network in Escherichia coli are presented. The free energies of binding and cooperativity found by our tools are in the same physiological ranges as those experimentally derived in the bacteriophage lambda system. We also use time-series data from high-density oligonucleotide microarrays of yeast meiotic expression patterns. The algorithm appropriately finds the parameters of pairs of regulated regulatory yeast genes, showing that for related genes an overall reasonable computation effort is sufficient to find the strength and causality of the connectivity of large numbers of them.

Algorithms↗