5,432 research outputs found
A Self-Organizing Algorithm for Modeling Protein Loops
Protein loops, the flexible short segments connecting two stable secondary
structural units in proteins, play a critical role in protein structure and
function. Constructing chemically sensible conformations of protein loops that
seamlessly bridge the gap between the anchor points without introducing any
steric collisions remains an open challenge. A variety of algorithms have been
developed to tackle the loop closure problem, ranging from inverse kinematics to
knowledge-based approaches that utilize pre-existing fragments extracted from
known protein structures. However, many of these approaches focus on the
generation of conformations that mainly satisfy the fixed end point condition,
leaving the steric constraints to be resolved in subsequent post-processing
steps. In the present work, we describe a simple solution that simultaneously
satisfies not only the end point and steric conditions, but also chirality and
planarity constraints. Starting from random initial atomic coordinates, each
individual conformation is generated independently by using a simple alternating
scheme of pairwise distance adjustments of randomly chosen atoms, followed by
fast geometric matching of the conformationally rigid components of the
constituent amino acids. The method is conceptually simple, numerically stable
and computationally efficient. Very importantly, additional constraints, such as
those derived from NMR experiments, hydrogen bonds or salt bridges, can be
incorporated into the algorithm in a straightforward and inexpensive way, making
the method ideal for solving more complex multi-loop problems. The remarkable
performance and robustness of the algorithm are demonstrated on a set of protein
loops of length 4, 8, and 12 that have been used in previous studies
CTCF-mediated transcriptional regulation through cell type-specific chromosome organization in the {\beta}-globin locus
The principles underlying the architectural landscape of chromatin beyond the
nucleosome level in living cells remains largely unknown despite its potential
to play a role in mammalian gene regulation. We investigated the 3-dimensional
folding of a 1 Mbp region of human chromosome 11 containing the {\beta}-globin
genes by integrating looping interactions of the insulator protein CTCF
determined comprehensively by chromosome conformation capture (3C) into a
polymer model of chromatin. We find that CTCF-mediated cell type specific
interactions in erythroid cells are organized to favor contacts known to occur
in vivo between the {\beta}-globin locus control region (LCR) and genes. In
these cells, the modeled {\beta}-globin domain folds into a globule with the
LCR and the active globin genes on the periphery. By contrast, in non-erythroid
cells, the globule is less compact with few but dominant CTCF interactions
driving the genes away from the LCR. This leads to a decrease in contact
frequencies that can exceed 1000-fold depending on the stiffness of the
chromatin and the exact positioning of the genes. Our findings show that an
ensemble of CTCF contacts functionally affects spatial distances between
control elements and target genes contributing to chromosomal organization
required for transcription.Comment: Full article, including Supp. Mat., is available at Nucleic Acids
Research, doi: 10.1093/nar/gks53
Noise control and utility: From regulatory network to spatial patterning
Stochasticity (or noise) at cellular and molecular levels has been observed
extensively as a universal feature for living systems. However, how living
systems deal with noise while performing desirable biological functions remains
a major mystery. Regulatory network configurations, such as their topology and
timescale, are shown to be critical in attenuating noise, and noise is also
found to facilitate cell fate decision. Here we review major recent findings on
noise attenuation through regulatory control, the benefit of noise via
noise-induced cellular plasticity during developmental patterning, and
summarize key principles underlying noise control
Seven properties of self-organization in the human brain
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward
Detecting rich-club ordering in complex networks
Uncovering the hidden regularities and organizational principles of networks
arising in physical systems ranging from the molecular level to the scale of
large communication infrastructures is the key issue for the understanding of
their fabric and dynamical properties [1-5]. The ``rich-club'' phenomenon
refers to the tendency of nodes with high centrality, the dominant elements of
the system, to form tightly interconnected communities and it is one of the
crucial properties accounting for the formation of dominant communities in both
computer and social sciences [4-8]. Here we provide the analytical expression
and the correct null models which allow for a quantitative discussion of the
rich-club phenomenon. The presented analysis enables the measurement of the
rich-club ordering and its relation with the function and dynamics of networks
in examples drawn from the biological, social and technological domains.Comment: 1 table, 3 figure
Graphs in machine learning: an introduction
Graphs are commonly used to characterise interactions between objects of
interest. Because they are based on a straightforward formalism, they are used
in many scientific fields from computer science to historical sciences. In this
paper, we give an introduction to some methods relying on graphs for learning.
This includes both unsupervised and supervised methods. Unsupervised learning
algorithms usually aim at visualising graphs in latent spaces and/or clustering
the nodes. Both focus on extracting knowledge from graph topologies. While most
existing techniques are only applicable to static graphs, where edges do not
evolve through time, recent developments have shown that they could be extended
to deal with evolving networks. In a supervised context, one generally aims at
inferring labels or numerical values attached to nodes using both the graph
and, when they are available, node characteristics. Balancing the two sources
of information can be challenging, especially as they can disagree locally or
globally. In both contexts, supervised and un-supervised, data can be
relational (augmented with one or several global graphs) as described above, or
graph valued. In this latter case, each object of interest is given as a full
graph (possibly completed by other characteristics). In this context, natural
tasks include graph clustering (as in producing clusters of graphs rather than
clusters of nodes in a single graph), graph classification, etc. 1 Real
networks One of the first practical studies on graphs can be dated back to the
original work of Moreno [51] in the 30s. Since then, there has been a growing
interest in graph analysis associated with strong developments in the modelling
and the processing of these data. Graphs are now used in many scientific
fields. In Biology [54, 2, 7], for instance, metabolic networks can describe
pathways of biochemical reactions [41], while in social sciences networks are
used to represent relation ties between actors [66, 56, 36, 34]. Other examples
include powergrids [71] and the web [75]. Recently, networks have also been
considered in other areas such as geography [22] and history [59, 39]. In
machine learning, networks are seen as powerful tools to model problems in
order to extract information from data and for prediction purposes. This is the
object of this paper. For more complete surveys, we refer to [28, 62, 49, 45].
In this section, we introduce notations and highlight properties shared by most
real networks. In Section 2, we then consider methods aiming at extracting
information from a unique network. We will particularly focus on clustering
methods where the goal is to find clusters of vertices. Finally, in Section 3,
techniques that take a series of networks into account, where each network i
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
- …