Biblioteca Digital

One of the challenges of high-performance computing (HPC) is user accessibility. At the Cornell University Computational Biology Service Unit, which is also a Microsoft HPC institute, we have developed a computational biology application suite that allows researchers from biological laboratories to submit their jobs to the parallel cluster through an easy-to-use Web interface. Through this system, we are providing users with popular bioinformatics tools including BLAST, HMMER, InterproScan, and MrBayes. The system is flexible and can be easily customized to include other software. It is also scalable; the installation on our servers currently processes approximately 8500 job submissions per year, many of them requiring massively parallel computations. It also has a built-in user management system, which can limit software and/or database access to specified users. TAIR, the major database of the plant model organism Arabidopsis, and SGN, the international tomato genome database, are both using our system for storage and data analysis.

The advancement of the computational biology field hinges on progress in three fundamental directions – the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources–data, software tools and web-services. The iTools design, implementation and resource meta - data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search...

The molecular switch for nucleotide-regulated assembly and disassembly of the main prokaryotic cell division protein FtsZ is unknown despite the numerous crystal structures that are available. We have characterized the functional motions in FtsZ with a computational consensus of essential dynamics, structural comparisons, sequence conservation, and networks of co-evolving residues. Employing this information, we have constructed 17 mutants, which alter the FtsZ functional cycle at different stages, to modify FtsZ flexibility. The mutant phenotypes ranged from benign to total inactivation and included increased GTPase, reduced assembly, and stabilized assembly. Six mutations clustering at the long cleft between the C-terminal β-sheet and core helix H7 deviated FtsZ assembly into curved filaments with inhibited GTPase, which still polymerize cooperatively. These mutations may perturb the predicted closure of the C-terminal domain onto H7 required for switching between curved and straight association modes and for GTPase activation. By mapping the FtsZ assembly switch, this work also gives insight into FtsZ druggability because the curved mutations delineate the putative binding site of the promising antibacterial FtsZ inhibitor PC190723.

Computer science has become ubiquitous in many areas of biological research, yet most high school and even college students are unaware of this. As a result, many college biology majors graduate without adequate computational skills for contemporary fields of biology. The absence of a computational element in secondary school biology classrooms is of growing concern to the computational biology community and biology teachers who would like to acquaint their students with updated approaches in the discipline. We present a first attempt to correct this absence by introducing a computational biology element to teach genetic evolution into advanced biology classes in two local high schools. Our primary goal was to show students how computation is used in biology and why a basic understanding of computation is necessary for research in many fields of biology. This curriculum is intended to be taught by a computational biologist who has worked with a high school advanced biology teacher to adapt the unit for his/her classroom, but a motivated high school teacher comfortable with mathematics and computing may be able to teach this alone. In this paper, we present our curriculum, which takes into consideration the constraints of the required curriculum...

Computational biology and bioinformatics are gradually gaining grounds in Africa and other developing nations of the world. However, in these countries, some of the challenges of computational biology and bioinformatics education are inadequate infrastructures, and lack of readily-available complementary and motivational tools to support learning as well as research. This has lowered the morale of many promising undergraduates, postgraduates and researchers from aspiring to undertake future study in these fields. In this paper, we developed and described MACBenAbim (Multi-platform Mobile Application for Computational Biology and Bioinformatics), a flexible user-friendly tool to search for, define and describe the meanings of keyterms in computational biology and bioinformatics, thus expanding the frontiers of knowledge of the users. This tool also has the capability of achieving visualization of results on a mobile multi-platform context.

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology [ISMB] 2016, Orlando, Florida).

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology [ISMB] 2016, Orlando, Florida).

Biomolecular pathways are building blocks of cellular biochemical function. Computational biology is in rapid transition from diagrammatic representation of pathways to quantitative and predictive mathematical models, which span time-scales, knowledge domains and spatial-scales. This transition is being accelerated by high-throughput experimentation which isolates reactions and their corresponding rate constants. A grand challenge of systems biology is to model the whole cell by integrating these emerging quantitative biomolecular pathway models. Current integration approaches do not scale. A new parallel and distributed computational architecture, CytoSolve, directly addresses this scalability issue. Results are presented in the solution of a concrete biological model: the Epidermal Growth Factor Receptor (EGFR) pathway model published by Kholodenko. The EGFR pathway is selected since known solutions exist for this problem, enabling direct confirmation of the CytoSolve approach. Results from this effort demonstrate that CytoSolve provides a core platform for addressing a grand challenge of systems biology to model the whole cell by integrating multiple biomolecular pathway models.; Singapore-MIT Alliance (SMA)

Recently news technologies in molecular biology enormously improved the sequencing data production, making it possible to generate billions of short reads totalizing gibabases of data per experiment. Prices for sequencing are decreasing rapidly and experiments that were impossible in the past because of costs are now being executed. Computational methodologies that were successfully used to solve the genome assembler problem with data obtained by the shotgun strategy, are now inefficient. Efforts are under way to develop new programs. At this moment, a stabilized condition for producing quality assembles is to use paired-end reads to virtually increase the length of reads, but there is a lot of controversy in other points. The works described in literature basically use two strategies: one is based in a high coverage[1] and the other is based in an incremental assembly, using the made pairs with shorter inserts first[2]. Independently of the strategy used the computational resources demanded are actually very high. Basically the present computational solution for the de novo genome assembly involves the generation of a graph of some kind [3], and one because those graphs use as node whole reads or k-mers, and considering that the amount of reads is very expressive; it is possible to infer that the memory resource of the computational system will be very important. Works in literature corroborate this idea showing that multiprocessors computational systems with at least 512 Gb of principal memory were used in de novo projects of eukaryotes [1...

Machine learning has become an essential tool for analyzing, predicting, and understanding biological properties and processes. Machine learning models can substantially support the work of biologists by reducing the number of expensive and time-consuming experiments. They are able to uncover novel properties of biological systems and can be used to guide experiments. Machine learning models have been successfully applied to various tasks ranging from gene prediction to three-dimensional structure prediction of proteins. However, due to their lack of interpretability, many biologists put only little trust in the predictions made by computational models. In this thesis, we show how to overcome the typical "black box" character of machine learning algorithms by presenting two novel interpretable approaches for classification and regression. In the first part, we introduce YLoc, an interpretable classification approach for predicting the subcellular localization of proteins. YLoc is able to explain why a prediction was made by identifying the biological properties with the strongest influence on the prediction. We show that interpretable predictions made by YLoc help to understand a protein's localization and, moreover, can assist biologists in engineering the location of proteins. Furthermore...

Background: Computer-based modeling is part of a new approach to predictive toxicology. Objectives: We investigated the usefulness of an integrated computational systems biology approach in a case study involving the isomers and metabolites of the pesticide dichlorodiphenyltrichloroethane (DDT) to ascertain their possible links to relevant adverse effects. Methods: We extracted chemical–protein association networks for each DDT isomer and its metabolites using ChemProt, a disease chemical biology database that includes both binding and gene expression data, and we explored protein–protein interactions using a human interactome network. To identify associated dysfunctions and diseases, we integrated protein–disease annotations into the protein complexes using the Online Mendelian Inheritance in Man database and the Comparative Toxicogenomics Database. Results: We found 175 human proteins linked to p,p'-DDT, and 187 to o,p'-DDT. Dichlorodiphenyldichloroethylene (p,p'-DDE) was the metabolite with the highest number of links, with 52. We grouped proteins for each compound based on their disease annotations. Although the two data sources differed in linkage to diseases, integrated results predicted that most diseases were linked to the two DDT isomers. Asthma was uniquely linked with p...

Past anthrax attacks in the United States have highlighted the need for improved measures against bioweapons. The virulence of anthrax stems from the shielding properties of the Bacillus anthracis poly-γ-d-glutamic acid capsule. In the presence of excess CapD, a B. anthracis γ-glutamyl transpeptidase, the protective capsule is degraded, and the immune system can successfully combat infection. Although CapD shows promise as a next generation protein therapeutic against anthrax, improvements in production, stability, and therapeutic formulation are needed. In this study, we addressed several of these problems through computational protein engineering techniques. We show that circular permutation of CapD improved production properties and dramatically increased kinetic thermostability. At 45 °C, CapD was completely inactive after 5 min, but circularly permuted CapD remained almost entirely active after 30 min. In addition, we identify an amino acid substitution that dramatically decreased transpeptidation activity but not hydrolysis. Subsequently, we show that this mutant had a diminished capsule degradation activity, suggesting that CapD catalyzes capsule degradation through a transpeptidation reaction with endogenous amino acids and peptides in serum rather than hydrolysis.

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future.

We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled {\em in silico} cancers and natural {\em in vivo} cancers. The theory lays the foundation for a new research paradigm for understanding and investigating cancer. The theory of cancer networks implies that new diagnostic methods and new treatments to cure cancer will become possible.; Comment: Key words: Cancer networks...

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.; Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Our work is motivated by and illustrated with application of association networks in computational biology, specifically in the context of gene/protein regulatory networks. Association networks represent systems of interacting elements, where a link between two different elements indicates a sufficient level of similarity between element attributes. While in reality relational ties between elements can be expected to be based on similarity across multiple attributes, the vast majority of work to date on association networks involves ties defined with respect to only a single attribute. We propose an approach for the inference of multi-attribute association networks from measurements on continuous attribute variables, using canonical correlation and a hypothesis-testing strategy. Within this context, we then study the impact of partial information on multi-attribute network inference and characterization, when only a subset of attributes is available. We consider in detail the case of two attributes, wherein we examine through a combination of analytical and numerical techniques the implications of the choice and number of node attributes on the ability to detect network links and, more generally, to estimate higher-level network summary statistics...

For decades, researchers have been applying computer simulation to address problems in biology. However, many of these "grand challenges" in computational biology, such as simulating how proteins fold, remained unsolved due to their great complexity. Indeed, even to simulate the fastest folding protein would require decades on the fastest modern CPUs. Here, we review novel methods to fundamentally speed such previously intractable problems using a new computational paradigm: distributed computing. By efficiently harnessing tens of thousands of computers throughout the world, we have been able to break previous computational barriers. However, distributed computing brings new challenges, such as how to efficiently divide a complex calculation of many PCs that are connected by relatively slow networking. Moreover, even if the challenge of accurately reproducing reality can be conquered, a new challenge emerges: how can we take the results of these simulations (typically tens to hundreds of gigabytes of raw data) and gain some insight into the questions at hand. This challenge of the analysis of the sea of data resulting from large-scale simulation will likely remain for decades to come.

Biochemical networks are used in computational biology, to model the static and dynamical details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multi-scaleness is another property of these networks, that can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler networks, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state and quasi-equilibrium approximations, and provide practical recipes for model reduction of linear and nonlinear networks. We also discuss the application of model reduction to backward pruning machine learning techniques.

Karlin and Altschul in their statistical analysis for multiple high-scoring segments in molecular sequences introduced a distribution function which gives the probability there are at least r distinct and consistently ordered segment pairs all with score at least x. For long sequences this distribution can be expressed in terms of the distribution of the length of the longest increasing subsequence in a random permutation. Within the past few years, this last quantity has been extensively studied in the mathematics literature. The purpose of these notes is to summarize these new mathematical developments in a form suitable for use in computational biology.; Comment: 9 pages, no figures. Revised version makes minor changes