Alberto de la Fuente

Bio: Alberto de la Fuente is an academic researcher from Leibniz Association. The author has contributed to research in topics: Gene regulatory network & Systems biology. The author has an hindex of 21, co-authored 37 publications receiving 3804 citations. Previous affiliations of Alberto de la Fuente include Virginia Bioinformatics Institute & Polaris Industries.

Wisdom of crowds for robust gene network inference

Abstract: Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ∼1,700 transcriptional interactions at a precision of ∼50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

Discovery of meaningful associations in genomic data using partial correlation coefficients

Abstract: Motivation: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. Results: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. Availability: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software Supplementary information: Supplementary information can be found at: URL to be decided

The origin of correlations in metabolomics data

Abstract: A phenomenon observed earlier in the development of metabolomics as a systems biology methodology, consists of a small but significant number of metabolites whose levels are highly correlated between biological replicates. Contrary to initial interpretations, these correlations are not necessarily only between neighboring metabolites in the metabolic network. Most metabolites that participate in common reactions are not correlated in this way, while some non-neighboring metabolites are highly correlated. Here we investigate the origin of such correlations using metabolic control analysis and computer simulation of biochemical networks. A series of cases is identified which lead to high correlation between metabolite pairs in replicate measurement. These are (1) chemical equilibrium, (2) mass conservation, (3) asymmetric control distribution, and (4) unusually high variance in the expression of a single gene. The importance of identifying metabolite correlations within a physiological state and changes of correlation between different states is discussed in the context of systems biology.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Network biology: understanding the cell's functional organization

Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

Modeling and simulation of genetic regulatory systems: a literature review.

Abstract: The spatiotemporal expression of genes in an organism is determined by regulatory systems that involve a large number of genes connected through a complex network of interactions. As an intuitive understanding of the behavior of these systems is hard to obtain, computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This report reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, ordinary and partial differential equations, stochastic equations, Boolean networks and their generalizations, qualitative differential equations, and rule-based formalisms. In addition, the report discusses how these formalisms have been used in the modeling and simulation of regulatory systems.