McGenus: A Monte Carlo algorithm to predict RNA secondary structures with pseudoknots

We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and performs an advanced stochastic search of their minimum free energy structure allowing for non trivial pseudoknot topologies. Specifically, McGenus employs a multiple Markov chain scheme for minimizing a general scoring function which includes not only free energy contributions for pair stacking, loop penalties, etc. but also a phenomenological penalty for the genus of the pairing graph. The good performance of the stochastic search strategy was successfully validated against TT2NE which uses the same free energy parametrization and performs exhaustive or partially exhaustive structure search, albeit for much shorter sequences (up to 200 bases). Next, the method was applied to other RNA sets, including an extensive tmRNA database, yielding results that are competitive with existing algorithms. Finally, it is shown that McGenus highlights possible limitations in the free energy scoring function. The algorithm is available as a web-server at http://ipht.cea.fr/rna/mcgenus.php .Comment: 6 pages, 1 figur

Rapid, modular and reliable construction of complex mammalian gene circuits

We developed a framework for quick and reliable construction of complex gene circuits for genetically engineering mammalian cells. Our hierarchical framework is based on a novel nucleotide addressing system for defining the position of each part in an overall circuit. With this framework, we demonstrate construction of synthetic gene circuits of up to 64 kb in size comprising 11 transcription units and 33 basic parts. We show robust gene expression control of multiple transcription units by small molecule inducers in human cells with transient transfection and stable chromosomal integration of these circuits. This framework enables development of complex gene circuits for engineering mammalian cells with unprecedented speed, reliability and scalability and should have broad applicability in a variety of areas including mammalian cell fermentation, cell fate reprogramming and cell-based assays.Synthetic Biology Engineering Research Center (SA5284-11210)United States. Defense Advanced Research Projects Agency (HR0011-12-C-0067)United States. Defense Advanced Research Projects Agency (DARPA-BAA-11-23)National Science Foundation (U.S.) (CBET-0939511)National Institutes of Health (U.S.). (5-R01-CA155320-02

Target prediction and a statistical sampling algorithm for RNA-RNA interaction

It has been proven that the accessibility of the target sites has a critical influence for miRNA and siRNA. In this paper, we present a program, rip2.0, not only the energetically most favorable targets site based on the hybrid-probability, but also a statistical sampling structure to illustrate the statistical characterization and representation of the Boltzmann ensemble of RNA-RNA interaction structures. The outputs are retrieved via backtracing an improved dynamic programming solution for the partition function based on the approach of Huang et al. (Bioinformatics). The $O(N^6)$ time and $O(N^4)$ space algorithm is implemented in C (available from \url{http://www.combinatorics.cn/cbpc/rip2.html})Comment: 7 pages, 10 figure

RNA secondary structure prediction from multi-aligned sequences

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.Comment: A preprint of an invited review manuscript that will be published in a chapter of the book `Methods in Molecular Biology'. Note that this version of the manuscript may differ from the published versio

A global sampling approach to designing and reengineering RNA secondary structures

The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present RNA-ensign, a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. RNA-ensign is available at http://csb.cs.mcgill.ca/RNAensign.National Science Foundation (U.S.). Graduate Research Fellowship ProgramNatural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN ) (386596-10)Fonds québécois de la recherche sur la nature et les technologies (PR-146375)National Institutes of Health (U.S.) (Grant GM081871)Natural Sciences and Engineering Research Council of Canada (NSERC)National Institutes of Health (U.S.

Advances in Cancer Treatment: Role of Nanoparticles

This chapter is devoted to the advances in the field of nanoparticles-mediated cancer treatment. A special attention is devoted to the use of magnetite and silver nanoparticles. The synthesis and properties of Fe3O4 and Ag nanoparticles as contrast or antitumoral agents as monolith or component of more complex systems such as polymer matrix composite materials based on: polymers (chitosan, collagen, polyethylene glycol, polyacrylates, and polymethacrylates, polylactic acid, etc.) and various antitumoral agents (cytostatics, natural agents and even nanoparticles-magnetite, silver, or gold) are discussed. Special attention is paid for the benefits and risks of using silver and magnetite nanoparticles. In both cases, the discussion focuses on aspects related to diagnosis and treatment of cancer. The influence of size and shape [1-3] is important from the materials characteristics as well as from the biological points of view. The role of magnetite is also analyzed from the point of view of its influence on the delivery of different components of interests (antitumoral components, analgesics/anti-inflammatory agents, etc.). The potentiating effect of the nanoparticles over the cytostatics and natural components is highlighted

Der Weg zur Erinnerungin W. G. Sebalds Austerlitz

The following essay provides a look into the role of the unconscious in the protagonist’s process of remembering in W.G. Sebald’s novel, Austerlitz. It aims to show that objects and places can reveal more than the actual information to which they are a repository or the purpose they were designed to serve. Rather, they function as connectors to the hidden world of repressed experiences. Recurring with obstinacy in the protagonist’s path, such connectors both show the way and obscure the view toward that which makes the real end of the search, that which has been lost. Thus, they not only provide an Ariadne’s thread to the protagonist’s search for himself but also to the reader’s literary experience. Using some concepts drawn from psychoanalytical theory, this essay hopes to make a contribution toward enhancing that experience