314,838 research outputs found
Protein search for multiple targets on DNA
Protein-DNA interactions are crucial for all biological processes. One of the
most important fundamental aspects of these interactions is the process of
protein searching and recognizing specific binding sites on DNA. A large number
of experimental and theoretical investigations have been devoted to uncovering
the molecular description of these phenomena, but many aspects of the
mechanisms of protein search for the targets on DNA remain not well understood.
One of the most intriguing problems is the role of multiple targets in protein
search dynamics. Using a recently developed theoretical framework we analyze
this question in detail. Our method is based on a discrete-state stochastic
approach that takes into account most relevant physical-chemical processes and
leads to fully analytical description of all dynamic properties. Specifically,
systems with two and three targets have been explicitly investigated. It is
found that multiple targets in most cases accelerate the search in comparison
with a single target situation. However, the acceleration is not always
proportional to the number of targets. Surprisingly, there are even situations
when it takes longer to find one of the multiple targets in comparison with the
single target. It depends on the spatial position of the targets, distances
between them, average scanning lengths of protein molecules on DNA, and the
total DNA lengths. Physical-chemical explanations of observed results are
presented. Our predictions are compared with experimental observations as well
as with results from a continuum theory for the protein search. Extensive Monte
Carlo computer simulations fully support our theoretical calculations
Classes of fast and specific search mechanisms for proteins on DNA
Problems of search and recognition appear over different scales in biological
systems. In this review we focus on the challenges posed by interactions
between proteins, in particular transcription factors, and DNA and possible
mechanisms which allow for a fast and selective target location. Initially we
argue that DNA-binding proteins can be classified, broadly, into three distinct
classes which we illustrate using experimental data. Each class calls for a
different search process and we discuss the possible application of different
search mechanisms proposed over the years to each class. The main thrust of
this review is a new mechanism which is based on barrier discrimination. We
introduce the model and analyze in detail its consequences. It is shown that
this mechanism applies to all classes of transcription factors and can lead to
a fast and specific search. Moreover, it is shown that the mechanism has
interesting transient features which allow for stability at the target despite
rapid binding and unbinding of the transcription factor from the target.Comment: 65 pages, 23 figure
A Supervised Learning Approach to Acronym Identification
This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a
supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features
used by our supervised learning system, and our experiments with various learning schemes
The riddle of togelby
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.At the 2017 Artificial and Computational Intelligence in Games meeting at Dagstuhl, Julian Togelius asked how to make spaces where every way of filling in the details yielded a good game. This study examines the possibility of enriching search spaces so that they contain very high rates of interesting objects, specifically game elements. While we do not answer the full challenge of finding good games throughout the space, this study highlights a number of potential avenues. These include naturally rich spaces, a simple technique for modifying a representation to search only rich parts of a larger search space, and representations that are highly expressive and so exhibit highly restricted and consequently enriched search spaces. We treat the creation of plausible road systems, useful graphics, highly expressive room placement for maps, generation of cavern-like maps, and combinatorial puzzle spaces.Final Accepted Versio
VirusPKT: A Search Tool For Assimilating Assorted Acquaintance For Viruses
Viruses utilize various means to circumvent the immune detection in the
biological systems. Several mathematical models have been investigated for the
description of viral dynamics in the biological system of human and various
other species. One common strategy for evasion and recognition of viruses is,
through acquaintance in the systems by means of search engines. In this
perspective a search tool have been developed to provide a wider comprehension
about the structure and other details on viruses which have been narrated in
this paper. This provides an adequate knowledge in evolution and building of
viruses, its functions through information extraction from various websites.
Apart from this, tool aim to automate the activities associated with it in a
self-maintainable, self-sustainable, proactive one which has been evaluated
through analysis made and have been discussed in this paper
On the Informativeness of the DNA Promoter Sequences Domain Theory
The DNA promoter sequences domain theory and database have become popular for
testing systems that integrate empirical and analytical learning. This note
reports a simple change and reinterpretation of the domain theory in terms of
M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on
the 106 items of the database. Moreover, an exhaustive search of the space of
M-of-N domain theory interpretations indicates that the expected accuracy of a
randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2%
is achieved in 12 cases. This demonstrates the informativeness of the domain
theory, without the complications of understanding the interactions between
various learning algorithms and the theory. In addition, our results help
characterize the difficulty of learning using the DNA promoters theory.Comment: See http://www.jair.org/ for any accompanying file
Contrasting Views of Complexity and Their Implications For Network-Centric Infrastructures
There exists a widely recognized need to better understand
and manage complex âsystems of systems,â ranging from
biology, ecology, and medicine to network-centric technologies.
This is motivating the search for universal laws of highly evolved
systems and driving demand for new mathematics and methods
that are consistent, integrative, and predictive. However, the theoretical
frameworks available today are not merely fragmented
but sometimes contradictory and incompatible. We argue that
complexity arises in highly evolved biological and technological
systems primarily to provide mechanisms to create robustness.
However, this complexity itself can be a source of new fragility,
leading to ârobust yet fragileâ tradeoffs in system design. We
focus on the role of robustness and architecture in networked
infrastructures, and we highlight recent advances in the theory
of distributed control driven by network technologies. This view
of complexity in highly organized technological and biological systems
is fundamentally different from the dominant perspective in
the mainstream sciences, which downplays function, constraints,
and tradeoffs, and tends to minimize the role of organization and
design
- âŠ