Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

Carbon sequestration in Synechococcus Sp.: from molecular machines to hierarchical modeling

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to "achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life." While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling." This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.     

The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems

Multiscale computational modeling of drug delivery systems (DDS) is poised to provide predictive capabilities for the rational design of targeted drug delivery systems, including multi-functional nanoparticles. Realistic, mechanistic models can provide a framework for understanding the fundamental physico-chemical interactions between drug, delivery system, and patient. Multiscale computational modeling, however, is in its infancy even for conventional drug delivery. The wide range of emerging nanotechnology systems for targeted delivery further increases the need for reliable in silico predictions. This review will present existing computational approaches at different scales in the design of traditional oral drug delivery systems. Subsequently, a multiscale framework for integrating continuum, stochastic, and computational chemistry models will be proposed and a case study will be presented for conventional DDS. The extension of this framework to emerging nanotechnology delivery systems will be discussed along with future directions. While oral delivery is the focus of the review, the outlined computational approaches can be applied to other drug delivery systems as well.     

Computational exploration of metal–organic frameworks: examples of advances in crystal structure predictions and electronic structure tuning

The purpose of this article is to consider some recent developments in the area of the computational chemistry of metal–organic frameworks (MOFs), and more specifically on their crystal structure prediction and electronic structures. We intend here to illustrate how computational approaches might be powerful tool for the discovery of new families of hybrid frameworks, helping to understand their often complex energy landscapes. Also, MOFs have attracted a lot of attention due to their potential use for photocatalysis and optoelectronic, making it necessary to develop strategies to control their electronic structures. We will show how recent computational studies in this area have allowed a better understanding of their electronic properties and their potential tunability, highlighting when they have given successful guidelines for the discovery of novel MOFs with targeted properties.     

Crossing over...Markov meets Mendel

Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the mathematically more elaborate parts. I kept those as separate sections in the exposition.     

The roots of bioinformatics in theoretical biology

From the late 1980s onward, the term "bioinformatics" mostly has been used to refer to computational methods for comparative analysis of genome data. However, the term was originally more widely defined as the study of informatic processes in biotic systems. In this essay, I will trace this early history (from a personal point of view) and I will argue that the original meaning of the term is re-emerging.     

Computational protein design

The field of computational protein design is reaching its adolescence. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. Further developments of scoring functions, sampling strategies, and optimization methods will expand the range of applicability of computational protein design to larger and more varied systems, with greater incidence of success. Developments in this field are beginning to have significant impact on biotechnology and chemical biology.     

Result verification, code verification and computation of support values in phylogenetics

Verification in phylogenetics represents an extremely difficult subject. Phylogenetic analysis deals with the reconstruction of evolutionary histories of species, and as long as mankind is not able to travel in time, it will not be possible to verify deep evolutionary histories reconstructed with modern computational methods. Here, we focus on two more tangible issues that are related to verification in phylogenetics (i) the inference of support values on trees that provide some notion about the 'correctness' of the tree within narrow limits and, more importantly; (ii) issues pertaining to program verification, especially with respect to codes that rely heavily on floating-point arithmetics. Program verification represents a largely underestimated problem in computational science that can have fatal effects on scientific conclusions.     

Vector NTI Suite在分子生物学领域的应用

有许多软件可以帮助分子生物学工作者解决一些他们日常实验室工作中遇到的问题.Vector NTI Suite是一种高度集成、功能齐全的分子生物学应用软件,它可以对核酸、蛋白质等分子进行大量的操作和分析,以及建立和管理生物数据库.重点对Vector NTI Suite软件的概况、应用现状及其在分子生物学领域的应用进行综述与探讨.     

Structural and regulatory evolution of cellular electrophysiological systems

SUMMARY Cellular electrophysiological systems, like developmental systems, appear to evolve primarily by means of regulatory evolution. It is suggested that electrophysiological systems share two key features with developmental systems that account for this dependence on regulatory evolution. For both systems, structural evolution has the potential to create significant problems of pleiotropy and both systems are predominantly computational in nature. It is concluded that the relative balance of physical and computational tasks that a biological system has to perform, combined with the probability that these tasks may have to change significantly during the course of evolution, will be major factors in determining the relative mix of regulatory and structural evolution that is observed for a given system. Physiological systems that directly interface with the environment will almost always perform some low-level physical task. In the majority of cases this will require evolution of protein function in order for the tasks themselves to evolve. For complex physiological systems a large fraction of their function will be devoted to high-level control functions that are predominantly computational in nature. In most cases regulatory evolution will be sufficient in order for these computational tasks to evolve.     

Quantitative analysis of signaling networks

The response of biological cells to environmental change is coordinated by protein-based signaling networks. These networks are to be found in both prokaryotes and eukaryotes. In eukaryotes, the signaling networks can be highly complex, some networks comprising of 60 or more proteins. The fundamental motif that has been found in all signaling networks is the protein phosphorylation/dephosphorylation cycle--the cascade cycle. At this time, the computational function of many of the signaling networks is poorly understood. However, it is clear that it is possible to construct a huge variety of control and computational circuits, both analog and digital from combinations of the cascade cycle. In this review, we will summarize the great versatility of the simple cascade cycle as a computational unit and towards the end give two examples, one prokaryotic chemotaxis circuit and the other, the eukaryotic MAPK cascade.     

Genomics reaches the clinic: from basic discoveries to clinical impact

Today, more than ever, basic science research provides significant opportunities to advance our understanding about the genetic basis of human disease. Close interactions among laboratory, computational, and clinical research communities will be crucial to ensure that genomic discoveries advance medical science and, ultimately, improve human health.     

Molecular dynamics simulations of proteins in lipid bilayers

With recent advances in X-ray crystallography of membrane proteins promising many new high-resolution structures, molecular dynamics simulations will become increasingly valuable for understanding membrane protein function, as they can reveal the dynamic behavior concealed in the static structures. Dramatic increases in computational power, in synergy with more efficient computational methodologies, now allow us to carry out molecular dynamics simulations of any structurally known membrane protein in its native environment, covering timescales of up to 0.1 micros. At the frontiers of membrane protein simulations are ion channels, aquaporins, passive and active transporters, and bioenergetic proteins.     

Central nervous system and computation

Computational systems are useful in neuroscience in many ways. For instance, they may be used to construct maps of brain structure and activation, or to describe brain processes mathematically. Furthermore, they inspired a powerful theory of brain function, in which the brain is viewed as a system characterized by intrinsic computational activities or as a "computational information processor. "Although many neuroscientists believe that neural systems really perform computations, some are more cautious about computationalism or reject it. Thus, does the brain really compute? Answering this question requires getting clear on a definition of computation that is able to draw a line between physical systems that compute and systems that do not, so that we can discern on which side of the line the brain (or parts of it) could fall. In order to shed some light on the role of computational processes in brain function, available neurobiological data will be summarized from the standpoint of a recently proposed taxonomy of notions of computation, with the aim of identifying which brain processes can be considered computational. The emerging picture shows the brain as a very peculiar system, in which genuine computational features act in concert with noncomputational dynamical processes, leading to continuous self-organization and remodeling under the action of external stimuli from the environment and from the rest of the organism.     

H. influenzae Consortium: integrative study of H. influenzae-human interactions

Developments in high-throughput analysis tools coupled with integrative computational techniques have enabled biological studies to reach new levels. The ability to correlate large volumes of diverse data types into cohesive models of organism function has spawned a new systematic approach to biological investigation. The creation of a new consortium has been proposed to investigate a single organism utilizing these comprehensive approaches. The Haemophilus influenzae Consortium (HIC) would be comprised of five laboratories, each providing separate and complementary areas of expertise in the study of Haemophilus influenzae (HI). The 5-year study proposes to develop coherent models of HI, both as a stand-alone organism, and more importantly, as a human pathogen. Studies in growth condition specificity followed by genomic, metabolic, and proteomic experimentation will be combined and integrated through computational and experimental analyses to form dynamic and predictive models of HI and its responses. Data from the HIC will allow greater understanding of cellular behavior, pathogen-host interactions, bacterial infection, and provide future scientific endeavors with a template for studies of other pathogens.     

Multi-species sequence comparison: the next frontier in genome annotation

Multi-species comparisons of DNA sequences are more powerful for discovering functional sequences than pairwise DNA sequence comparisons. Most current computational tools have been designed for pairwise comparisons, and efficient extension of these tools to multiple species will require knowledge of the ideal evolutionary distance to choose and the development of new algorithms for alignment, analysis of conservation, and visualization of results.     

LIPID arrays: new tools in the understanding of membrane dynamics and lipid signaling

Phospholipids are the structural building blocks of the membrane bilayer, which retains and regulates intra-cellular content. In addition to creating a protective barrier around the cell, lipids modulate membrane trafficking and are themselves precursors of important intracellular signaling molecules. Identification and quantification of these molecular species is essential for a more complete understanding of cell signaling pathways, and more reliable and sensitive methods are needed for determining membrane phospholipid content. Recent improvements in electrospray ionization mass spectrometry have made possible the direct identification of more than 400 phospholipid species from biological extracts of a single cell type. Changes in the cellular concentration of diverse lipids can be determined by analysis of the mass spectra by statistical algorithms. In the future, lipid arrays will be integrated with other high-throughput profiling technologies, and computational lipidomics will expand our understanding of the molecular basis of cellular processes and diseases.     

Optimal parameter settings for information processing in gene regulatory networks

Gene networks can often be interpreted as computational circuits. This article investigates the computational properties of gene regulatory networks defined in terms of the speed and the accuracy of the output of a gene network. It will be shown that there is no single optimal set of parameters, but instead, there is a trade-off between speed and accuracy. Using the trade-off it will also be shown how systems with various parameters can be ranked with respect to their computational efficiency. Numerical analysis suggests that the trade-off can be improved when the output gene is repressing itself, even though the accuracy or the speed of the auto-regulated system may be worse than the unregulated system.