Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

On the foundations of cancer modelling: selected topics, speculations, & perspectives

This paper presents a critical review of selected topics related to the modelling of cancer onset, evolution and growth, with the aim of illustrating, to a wide applied mathematical readership, some of the novel mathematical problems in the field. This review attempts to capture, from the appropriate literature, the main issues involved in the modelling of phenomena related to cancer dynamics at all scales which characterise this highly complex system: from the molecular scale up to that of tissue. The last part of the paper discusses the challenge of developing a mathematical biological theory of tumour onset and evolution

Drug repurposing using biological networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further

Network and multi-scale signal analysis for the integration of large omic datasets: applications in \u3ci\u3ePopulus trichocarpa\u3c/i\u3e

Poplar species are promising sources of cellulosic biomass for biofuels because of their fast growth rate, high cellulose content and moderate lignin content. There is an increasing movement on integrating multiple layers of ’omics data in a systems biology approach to understand gene-phenotype relationships and assist in plant breeding programs. This dissertation involves the use of network and signal processing techniques for the combined analysis of these various data types, for the goals of (1) increasing fundamental knowledge of P. trichocarpa and (2) facilitating the generation of hypotheses about target genes and phenotypes of interest. A data integration “Lines of Evidence” method is presented for the identification and prioritization of target genes involved in functions of interest. A new post-GWAS method, Pleiotropy Decomposition, is presented, which extracts pleiotropic relationships between genes and phenotypes from GWAS results, allowing for identification of genes with signatures favorable to genome editing. Continuous wavelet transform signal processing analysis is applied in the characterization of genome distributions of various features (including variant density, gene density, and methylation profiles) in order to identify chromosome structures such as the centromere. This resulted in the approximate centromere locations on all P. trichocarpa chromosomes, which had previously not been adequately reported in the scientific literature. Discrete wavelet transform signal processing followed by correlation analysis was applied to genomic features from various data types including transposable element density, methylation density, SNP density, gene density, centromere position and putative ancestral centromere position. Subsequent correlation analysis of the resulting wavelet coefficients identified scale-specific relationships between these genomic features, and provide insights into the evolution of the genome structure of P. trichocarpa. These methods have provided strategies to both increase fundamental knowledge about the P. trichocarpa system, as well as to identify new target genes related to biofuels targets. We intend that these approaches will ultimately be used in the designing of better plants for more efficient and sustainable production of bioenergy

Fast sequence evolution of Hox and Hox-derived genes in the genus Drosophila.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: It is expected that genes that are expressed early in development and have a complex expression pattern are under strong purifying selection and thus evolve slowly. Hox genes fulfill these criteria and thus, should have a low evolutionary rate. However, some observations point to a completely different scenario. Hox genes are usually highly conserved inside the homeobox, but very variable outside it. RESULTS: We have measured the rates of nucleotide divergence and indel fixation of three Hox genes, labial (lab), proboscipedia (pb) and abdominal-A (abd-A), and compared them with those of three genes derived by duplication from Hox3, bicoid (bcd), zerknüllt (zen) and zerknüllt-related (zen2), and 15 non-Hox genes in sets of orthologous sequences of three species of the genus Drosophila. These rates were compared to test the hypothesis that Hox genes evolve slowly. Our results show that the evolutionary rate of Hox genes is higher than that of non-Hox genes when both amino acid differences and indels are taken into account: 43.39% of the amino acid sequence is altered in Hox genes, versus 30.97% in non-Hox genes and 64.73% in Hox-derived genes. Microsatellites scattered along the coding sequence of Hox genes explain partially, but not fully, their fast sequence evolution. CONCLUSION: These results show that Hox genes have a higher evolutionary dynamics than other developmental genes, and emphasize the need to take into account indels in addition to nucleotide substitutions in order to accurately estimate evolutionary rates

Methods, tools, and computational environment for network-based analysis of biological data

Cancer currently affects more than 18 million persons world-wide annually. It is a leading cause of death and so far, only 60% cure rate can be reached within the most developed health care systems. The nature of cancer has been a mystery for centuries, until discoveries during recent decades shed light on the underlying molecular events. This depended on the progress in understanding cell and tissue biology, developments of molecular technologies and of -omics technologies. Cancer has then emerged as a highly heterogeneous disease, however with some very basic mechanistic features common to all cancers. To deal with the complexity of causes and consequences of pathological changes in the molecular machinery, methods and tools of network analysis can be helpful. Complexity of this task requires easy-to-use tools, which allow researchers and clinicians with no background in computer science to perform network analysis. Paper I describes a web-based framework for network enrichment analysis (NEA), using previously developed algorithm and code. The developed platform introduces functionality for a researcher to use data pre-downloaded from various popular databases as well as own data, perform NEA and obtain statistical estimations, export results in different formats for publications or further use in research pipeline. Paper II presents development of another web server, which provided vast opportunities for exploration and integrated analysis of multiple public cancer datasets that describe in vitro and in vivo sample collections. The web server linked molecular data at the single gene level, phenotype and pharmacological response variables, as well as pathway level variables calculated with NEA and connected to the framework presented in Paper I. Researchers can use the platform for creating multivariate models based on raw or pre-processed data from various sources, visualize created models, estimate their performance and compare them, export models for further usage in own research environments. Paper III demonstrates NEAdriver, a practical application of NEA to probabilistic evaluation of driver roles of mutations reported in ten cancer cohorts. NEAdriver results are compared with cancer gene sets produced by other, both network analysis and network-free methods. The paper demonstrated ability of NEA to be used directly for discovering novel driver genes as well as being used in combination with other methods. In order to demonstrate benefits of using NEA, some rare cancer types and types with low mutation burden were used. Paper IV is a manuscript evaluating performance of most representative methods of network analysis across methods’ parameters, functional ontologies and network versions. This study emphasizes discovery of novel functional associations for known genes, as opposed to previous tests dominated by a few “gold standard” genes which were well characterized previously. We performed the analysis in the context of various topological properties of networks, pathways of interest, and genes. It employed both existing, widely used topological metrics and a number of novel ones developed for this analysis

White Paper 2: Origins, (Co)Evolution, Diversity & Synthesis Of Life

Publicado en Madrid, 185 p. ; 17 cm.How life appeared on Earth and how then it diversified into the different and currently existing forms of life are the unanswered questions that will be discussed this volume. These questions delve into the deep past of our planet, where biology intermingles with geology and chemistry, to explore the origin of life and understand its evolution, since “nothing makes sense in biology except in the light of evolution” (Dobzhansky, 1964). The eight challenges that compose this volume summarize our current knowledge and future research directions touching different aspects of the study of evolution, which can be considered a fundamental discipline of Life Science. The volume discusses recent theories on how the first molecules arouse, became organized and acquired their structure, enabling the first forms of life. It also attempts to explain how this life has changed over time, giving rise, from very similar molecular bases, to an immense biological diversity, and to understand what is the hylogenetic relationship among all the different life forms. The volume further analyzes human evolution, its relationship with the environment and its implications on human health and society. Closing the circle, the volume discusses the possibility of designing new biological machines, thus creating a cell prototype from its components and whether this knowledge can be applied to improve our ecosystem. With an effective coordination among its three main areas of knowledge, the CSIC can become an international benchmark for research in this field

Pathways, Networks and Systems Medicine Conferences

The 6th Pathways, Networks and Systems Medicine Conference was held at the Minoa Palace Conference Center, Chania, Crete, Greece (16-21 June 2008). The Organizing Committee was composed of Joe Nadeau (CWRU, Cleveland), Rudi Balling (German Research Centre, Brauschweig), David Galas (Institute for Systems Biology, Seattle), Lee Hood (Institute for Systems Biology, Seattle), Diane Isonaka (Seattle), Fotis Kafatos (Imperial College, London), John Lambris (Univ. Pennsylvania, Philadelphia),Harris Lewin (Univ. of Indiana, Urbana-Champaign), Edison Liu (Genome Institute of Singapore, Singapore), and Shankar Subramaniam (Univ. California, San Diego). A total of 101 individuals from 21 countries participated in the conference: USA (48), Canada (5), France (5), Austria (4), Germany (3), Italy (3), UK (3), Greece (2), New Zealand (2), Singapore (2), Argentina (1), Australia (1), Cuba (1), Denmark (1), Japan (1), Mexico (1), Netherlands (1), Spain (1), Sweden (1), Switzerland (1). With respect to speakers, 29 were established faculty members and 13 were graduate students or postdoctoral fellows. With respect to gender representation, among speakers, 13 were female and 28 were male, and among all participants 43 were female and 58 were male. Program these included the following topics: Cancer Pathways and Networks (Day 1), Metabolic Disease Networks (Day 2), Day 3 ? Organs, Pathways and Stem Cells (Day 3), and Day 4 ? Inflammation, Immunity, Microbes and the Environment (Day 4). Proceedings of the Conference were not published