Ciliate Gene Unscrambling with Fewer Templates

One of the theoretical models proposed for the mechanism of gene unscrambling in some species of ciliates is the template-guided recombination (TGR) system by Prescott, Ehrenfeucht and Rozenberg which has been generalized by Daley and McQuillan from a formal language theory perspective. In this paper, we propose a refinement of this model that generates regular languages using the iterated TGR system with a finite initial language and a finite set of templates, using fewer templates and a smaller alphabet compared to that of the Daley-McQuillan model. To achieve Turing completeness using only finite components, i.e., a finite initial language and a finite set of templates, we also propose an extension of the contextual template-guided recombination system (CTGR system) by Daley and McQuillan, by adding an extra control called permitting contexts on the usage of templates.Comment: In Proceedings DCFS 2010, arXiv:1008.127

Combining Insertion and Deletion in RNA-editing Preserves Regularity

Inspired by RNA-editing as occurs in transcriptional processes in the living cell, we introduce an abstract notion of string adjustment, called guided rewriting. This formalism allows simultaneously inserting and deleting elements. We prove that guided rewriting preserves regularity: for every regular language its closure under guided rewriting is regular too. This contrasts an earlier abstraction of RNA-editing separating insertion and deletion for which it was proved that regularity is not preserved. The particular automaton construction here relies on an auxiliary notion of slice sequence which enables to sweep from left to right through a completed rewrite sequence.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347

Compatibility of Breeding Techniques in Organic Systems

Introduction The rapid development of genetic engineering techniques is leading to a level of genetic disruption never experienced before. In order to safeguard organic integrity and to ensure organic food will continue to meet the highest consumer expectations in this challenging situation, IFOAM - Organics International is proposing a number of measures to be put in place to further fortify and enhance the organic sector’s available genetic resources. This position paper provides clarity and transparency on the criteria used by the organic sector as to what breeding techniques are compatible with organic systems, which techniques to exclude, and definitions on what should be considered as genetic engineering and genetically modified organisms (GMOs). We further differentiate between the criteria relevant for organic breeding as defined in the IFOAM – Organics International norms, versus the criteria for cultivars and breeds derived from nonorganic breeding programs regarding their compatibility for the use in commercial organic production and processing. The following experts are members of the IFOAM Working Group on New Plant Breeding Techniques: Michael Glos, Monika Messmer, Gebhard Rossmanith, Gunter Backes, Michael Sligh, Adrian Rodriguez-Burruezo, Heli Matilainen, Andre Leu, Louise Luttikholt, Helen Jensen, Eric Gall, Chito Medina, Krishna Prasad, Kirsten Arp

Semi-blind Eigen-analyses of Recombination Histories Using CMB Data

Cosmological parameter measurements from CMB experiments such as Planck, ACTpol, SPTpol and other high resolution follow-ons fundamentally rely on the accuracy of the assumed recombination model, or one with well prescribed uncertainties. Deviations from the standard recombination history might suggest new particle physics or modified atomic physics. Here we treat possible perturbative fluctuations in the free electron fraction, \Xe(z), by a semi-blind expansion in densely-packed modes in redshift. From these we construct parameter eigenmodes, which we rank order so that the lowest modes provide the most power to probe the \Xe(z) with CMB measurements. Since the eigenmodes are effectively weighed by the fiducial \Xe history, they are localized around the differential visibility peak, allowing for an excellent probe of hydrogen recombination, but a weaker probe of the higher redshift helium recombination and the lower redshift highly neutral freeze-out tail. We use an information-based criterion to truncate the mode hierarchy, and show that with even a few modes the method goes a long way towards morphing a fiducial older {\sc Recfast} $X_{\rm e,i} (z)$ into the new and improved {\sc CosmoRec} and {\sc HyRec} $X_{\rm e,f} (z)$ in the hydrogen recombination regime, though not well in the helium regime. Without such a correction, the derived cosmic parameters are biased. We discuss an iterative approach for updating the eigenmodes to further hone in on $X_{\rm e,f} (z)$ if large deviations are indeed found. We also introduce control parameters that downweight the attention on the visibility peak structure, e.g., focusing the eigenmode probes more strongly on the \Xe (z) freeze-out tail, as would be appropriate when looking for the \Xe signature of annihilating or decaying elementary particles.Comment: 28 pages, 26 Fig

Engineering the Drosophila Genome for Developmental Biology.

The recent development of transposon and CRISPR-Cas9-based tools for manipulating the fly genome in vivo promises tremendous progress in our ability to study developmental processes. Tools for introducing tags into genes at their endogenous genomic loci facilitate imaging or biochemistry approaches at the cellular or subcellular levels. Similarly, the ability to make specific alterations to the genome sequence allows much more precise genetic control to address questions of gene function.BBSRC BB/L002817/1 and BB/N007069/

An evolutionary approach to constraint-regularized learning

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to em- ploy fuzzy set-based modeling techniques in order to express such knowl- edge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn- ing process within the regularization framework of inductive inference. After a brief review of this idea, the paper offers an operationalization of constraint- regularized learning. The corresponding framework is based on evolutionary methods for model optimization and employs fuzzy rule bases of the Takagi- Sugeno type as flexible function approximators