The nucleolar detention pathway

Molecular dynamics ensures that proteins and other factors reach their site of action in a timely and efficient manner. This is essential to the formation of molecular complexes, as they require an ever-changing framework of specific interactions to facilitate a model of self-assembly. Therefore, the absence or reduced availability of any key component would significantly impair complex formation and disrupt all downstream molecular networks. Recently, we identified a regulatory mechanism that modulates protein mobility through the inducible expression of a novel family of long noncoding RNA. In response to diverse environmental stimuli, the nucleolar detention pathway (NoDP) captures and immobilizes essential cellular factors within the nucleolus away from their effector molecules. The vast array of putative NoDP targets, including DNA (cytosine-5)-methyltransferase 1 (DNMT1) and the delta catalytic subunit of DNA polymerase (POLD1), suggests that this may be a common and significant regulatory mechanism. Here, we discuss the implications of this new posttranslational strategy for regulating molecular networks.     

The nucleolar detention pathway: A cellular strategy for regulating molecular networks

Molecular dynamics ensures that proteins and other factors reach their site of action in a timely and efficient manner. This is essential to the formation of molecular complexes, as they require an ever-changing framework of specific interactions to facilitate a model of self-assembly. Therefore, the absence or reduced availability of any key component would significantly impair complex formation and disrupt all downstream molecular networks. Recently, we identified a regulatory mechanism that modulates protein mobility through the inducible expression of a novel family of long noncoding RNA. In response to diverse environmental stimuli, the nucleolar detention pathway (NoDP) captures and immobilizes essential cellular factors within the nucleolus away from their effector molecules. The vast array of putative NoDP targets, including DNA (cytosine-5)-methyltransferase 1 (DNMT1) and the delta catalytic subunit of DNA polymerase (POLD1), suggests that this may be a common and significant regulatory mechanism. Here, we discuss the implications of this new posttranslational strategy for regulating molecular networks.     

Gene regulatory network inference: Data integration in dynamic models—A review

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein–DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.     

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

In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.     

Receptor binding by influenza virus: using computational techniques to extend structural data

Influenza attaches to host cells via hemagglutinin binding of cell-surface glycans. These relatively low-affinity interactions involving flexible ligands are critical in determining tissue and host specificity, but their dynamic nature complicates structural characterization of hemagglutinin-receptor complexes. Molecular simulation can assist in analyzing glycan and protein flexibility in crystallized complexes, assessing how binding might change under mutation or altered glycosylation patterns, and evaluating how soluble ligands may relate to physiological presentation on the plasma membrane. Molecular dynamics simulation also has the potential to help integrate structural and dynamic data sources. Here we review recent progress from analysis of molecular dynamics simulation and outline challenges for the future.     

Translated Chemical Reaction Networks

Many biochemical and industrial applications involve complicated networks of simultaneously occurring chemical reactions. Under the assumption of mass action kinetics, the dynamics of these chemical reaction networks are governed by systems of polynomial ordinary differential equations. The steady states of these mass action systems have been analyzed via a variety of techniques, including stoichiometric network analysis, deficiency theory, and algebraic techniques (e.g., Gröbner bases). In this paper, we present a novel method for characterizing the steady states of mass action systems. Our method explicitly links a network’s capacity to permit a particular class of steady states, called toric steady states, to topological properties of a generalized network called a translated chemical reaction network. These networks share their reaction vectors with their source network but are permitted to have different complex stoichiometries and different network topologies. We apply the results to examples drawn from the biochemical literature.     

Modeling of Platinum Clusters in H-Mordenite

The size, location and structure of Pt clusters in H-mordenite have been investigated by molecular mechanics energy minimization and molecular dynamics simulation techniques using the Catalysis software of Molecular Simulations (MSI). Lattice energy minimizations are performed to study the effects of the specific framework aluminum positions on the location and stability of monoatomic Pt sites in H-mordenite. The lattice energies relative to the siliceous platinum-aluminosilicate structure reveal that the stability of a single Pt atom in H-mordenite is remarkably influenced by the specific location of the Al atoms in the lattice. At the studied Si/Al ratio of two Al ions per unit cell, a stabilization of the H-mordenite lattice upon Pt deposition is obtained. Moreover, lattice energy calculations on Pt/aluminosilicate mordenites of different metal contents per unit cell have been performed. An optimum size for the aggregate confined to the 12-ring main channel that is almost independent of the Pt content per mordenite unit cell has been found. The structural features of the resulting clusters at the end of molecular dynamics simulations on Pt/alumina-mordenites reflect a strong metal-zeolite interaction. The present results are consistent with a previous molecular dynamics simulation study on the structure of platinum deposited on SiO₂ surfaces.     

KnowledgeEditor: a new tool for interactive modeling and analyzing biological pathways based on microarray data

KnowledgeEditor is a graphical workbench for biological experts to model biomolecular network graphs. The modeled network data are represented by SRML, and can be published via the internet with the help of plug-in module 'GSCope'. KnowledgeEditor helps us to model and analyze biological pathways based on microarray data. It is possible to analyze the drawn networks by simulating up-down regulatory cascade in molecular interactions. AVAILABILITY: KnowledgeEditor is available at http://gscope.gsc.riken.go.jp/.     

The molecular and mathematical basis of Waddington's epigenetic landscape: A framework for post‐Darwinian biology?

The Neo-Darwinian concept of natural selection is plausible when one assumes a straightforward causation of phenotype by genotype. However, such simple 1:1 mapping must now give place to the modern concepts of gene regulatory networks and gene expression noise. Both can, in the absence of genetic mutations, jointly generate a diversity of inheritable randomly occupied phenotypic states that could also serve as a substrate for natural selection. This form of epigenetic dynamics challenges Neo-Darwinism. It needs to incorporate the non-linear, stochastic dynamics of gene networks. A first step is to consider the mathematical correspondence between gene regulatory networks and Waddington's metaphoric 'epigenetic landscape', which actually represents the quasi-potential function of global network dynamics. It explains the coexistence of multiple stable phenotypes within one genotype. The landscape's topography with its attractors is shaped by evolution through mutational re-wiring of regulatory interactions - offering a link between genetic mutation and sudden, broad evolutionary changes.     

Generalized lattice graphs for 2D-visualization of biological information

Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: protein sequence; mass spectra (MS) of protein peptide mass fingerprints (PMF); molecular dynamic trajectory (MDTs) from structural studies; mRNA microarray data; single nucleotide polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein polymorphisms and protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of Graph theory in theoretical biology and other areas of biomedical sciences.     

Modeling DNA sequence-based cis-regulatory gene networks

Gene network analysis requires computationally based models which represent the functional architecture of regulatory interactions, and which provide directly testable predictions. The type of model that is useful is constrained by the particular features of developmentally active cis-regulatory systems. These systems function by processing diverse regulatory inputs, generating novel regulatory outputs. A computational model which explicitly accommodates this basic concept was developed earlier for the cis-regulatory system of the endo16 gene of the sea urchin. This model represents the genetically mandated logic functions that the system executes, but also shows how time-varying kinetic inputs are processed in different circumstances into particular kinetic outputs. The same basic design features can be utilized to construct models that connect the large number of cis-regulatory elements constituting developmental gene networks. The ultimate aim of the network models discussed here is to represent the regulatory relationships among the genomic control systems of the genes in the network, and to state their functional meaning. The target site sequences of the cis-regulatory elements of these genes constitute the physical basis of the network architecture. Useful models for developmental regulatory networks must represent the genetic logic by which the system operates, but must also be capable of explaining the real time dynamics of cis-regulatory response as kinetic input and output data become available. Most importantly, however, such models must display in a direct and transparent manner fundamental network design features such as intra- and intercellular feedback circuitry; the sources of parallel inputs into each cis-regulatory element; gene battery organization; and use of repressive spatial inputs in specification and boundary formation. Successful network models lead to direct tests of key architectural features by targeted cis-regulatory analysis.     

Regulated multicistronic expression technology for mammalian metabolic engineering

Contemporary basic research is rapidly revealing increasingly complex molecular regulatory networks which are often interconnected via key signal integrators. These connections among regulatory and catalytic networks often frustrate bioengineers as promising metabolic engineering strategies are bypassed by compensatory metabolic responses or cause unexpected, undesired outcomes such as apoptosis, product protein degradation or inappropriate post- translational modification. Therefore, for metabolic engineering to achieve greater success in mammalian cell culture processes and to become important for future applications such as gene therapy and tissue engineering, this technology must be enhanced to allow simultaneous, in cases conditional, reshaping of metabolic pathways to access difficult-to-attain cell states. Recent advances in this new territory of multigene metabolic engineering are intimately linked to the development of multicistronic expression technology which allows the simultaneous, and in some cases, regulated expression of several genes in mammalian cells. Here we review recent achievements in multicistronic expression technology in view of multigene metabolic engineering. This revised version was published online in August 2006 with corrections to the Cover Date.     

New inhibitors of VEGFR-2 targeting the extracellular domain dimerization process

We are reporting the discovery of small molecule inhibitors for vascular endothelial growth factor receptor type 2 (VEGFR-2) extracellular domain. The VEGFR-2 extracellular domain is responsible for the homo-dimerization process, which has been recently reported as a main step in VEGFR signal transduction cascade. This cascade is essential for the vascularization and survival of most types of cancers. Two main design strategies were used; Molecular docking-based Virtual Screening and Fragment Based Design (FBD). A virtual library of drug like compounds was screened using a cascade of docking techniques in order to discover an inhibitor that binds to this new binding site. Rapid docking methodology was used first to filter the large number of compounds followed by more accurate and slow ones. Fragment based molecular design was adopted afterwards due to unsatisfactory results of screening process. Screening and design process resulted in a group of inhibitors with superior binding energies exceeding that of the natural substrate. Molecular dynamics simulation was used to test the stability of binding of these inhibitors and finally the drug ability of these compounds was assisted using Lipinski rule of five. By this way the designed compounds have shown to possess high pharmacologic potential as novel anticancer agents.