132 research outputs found
Maximum entropy models for antibody diversity
Recognition of pathogens relies on families of proteins showing great
diversity. Here we construct maximum entropy models of the sequence repertoire,
building on recent experiments that provide a nearly exhaustive sampling of the
IgM sequences in zebrafish. These models are based solely on pairwise
correlations between residue positions, but correctly capture the higher order
statistical properties of the repertoire. Exploiting the interpretation of
these models as statistical physics problems, we make several predictions for
the collective properties of the sequence ensemble: the distribution of
sequences obeys Zipf's law, the repertoire decomposes into several clusters,
and there is a massive restriction of diversity due to the correlations. These
predictions are completely inconsistent with models in which amino acid
substitutions are made independently at each site, and are in good agreement
with the data. Our results suggest that antibody diversity is not limited by
the sequences encoded in the genome, and may reflect rapid adaptation to
antigenic challenges. This approach should be applicable to the study of the
global properties of other protein families
El marketing relacional y la lealtad de compra de los clientes del segmento b2b de una empresa de telecomunicaciones, Arequipa, 2020
La lealtad de compra de los clientes es actualmente una de las principales prioridades de toda empresa, debido a que son estos quienes mantienen o no un producto o marca en el mercado. El segmento B2B nace de la expresión en inglés “business to business” donde en este modelo los clientes son empresas que representan sectores específicos del mercado. Este tipo de cliente compra solo lo que necesita para crecer, actuar o ahorrar costos, siendo una decisión apoyada en la razón y con un impacto a largo plazo. Debido a ello, en el modelo B2B se pretende generar un vínculo duradero con cada cliente y con el marketing relacional las marcas pueden impulsar dicho vínculo con sus clientes existentes y, al mismo tiempo, optimar la lealtad; desde este punto de vista la investigación tuvo como objetivo: determinar el vínculo entre el marketing relacional y la lealtad del cliente del segmento B2B de una empresa de telecomunicaciones, Arequipa, 2020. En cuanto al tipo de investigación es básica, a nivel relacional; El método de la investigación fue el hipotético deductivo. Para la recolección de datos se empleó la técnica de la encuesta y como instrumento el cuestionario con preguntas desarrolladas de acuerdo a las dimensiones e indicadores de cada variable, planteándose una escala de valoración de nunca, casi nunca, a veces, casi siempre y siempre. La población estuvo conformada por 384 clientes del segmento B2B y muestra lo constituyeron los 192 clientes; luego se elaboró la interpretación de los resultados por medio de la estadística descriptiva. Los resultados indican que, existe relación significativa, directa y leve entre la variable marketing relacional y la variable lealtad del cliente, al ser el p-valor de cero e inferiores al límite de 0,05 de margen de error. Esto se corrobora cuando el 31.8% que señala que existe un regular marketing relacional a su vez indica que mantiene una lealtad promedio, quedando así corroborada la hipótesis de investigación planteada
Computationally designed libraries of fluorescent proteins evaluated by preservation and diversity of function
To determine which of seven library design algorithms best introduces new protein function without destroying it altogether, seven combinatorial libraries of green fluorescent protein variants were designed and synthesized. Each was evaluated by distributions of emission intensity and color compiled from measurements made in vivo. Additional comparisons were made with a library constructed by error-prone PCR. Among the designed libraries, fluorescent function was preserved for the greatest fraction of samples in a library designed by using a structure-based computational method developed and described here. A trend was observed toward greater diversity of color in designed libraries that better preserved fluorescence. Contrary to trends observed among libraries constructed by error-prone PCR, preservation of function was observed to increase with a library's average mutation level among the four libraries designed with structure-based computational methods
Beyond inverse Ising model: structure of the analytical solution for a class of inverse problems
I consider the problem of deriving couplings of a statistical model from
measured correlations, a task which generalizes the well-known inverse Ising
problem. After reminding that such problem can be mapped on the one of
expressing the entropy of a system as a function of its corresponding
observables, I show the conditions under which this can be done without
resorting to iterative algorithms. I find that inverse problems are local (the
inverse Fisher information is sparse) whenever the corresponding models have a
factorized form, and the entropy can be split in a sum of small cluster
contributions. I illustrate these ideas through two examples (the Ising model
on a tree and the one-dimensional periodic chain with arbitrary order
interaction) and support the results with numerical simulations. The extension
of these methods to more general scenarios is finally discussed.Comment: 15 pages, 6 figure
Computational complexity of the landscape I
We study the computational complexity of the physical problem of finding
vacua of string theory which agree with data, such as the cosmological
constant, and show that such problems are typically NP hard. In particular, we
prove that in the Bousso-Polchinski model, the problem is NP complete. We
discuss the issues this raises and the possibility that, even if we were to
find compelling evidence that some vacuum of string theory describes our
universe, we might never be able to find that vacuum explicitly.
In a companion paper, we apply this point of view to the question of how
early cosmology might select a vacuum.Comment: JHEP3 Latex, 53 pp, 2 .eps figure
Transition states in protein folding kinetics: Modeling Phi-values of small beta-sheet proteins
Small single-domain proteins often exhibit only a single free-energy barrier,
or transition state, between the denatured and the native state. The folding
kinetics of these proteins is usually explored via mutational analysis. A
central question is which structural information on the transition state can be
derived from the mutational data. In this article, we model and structurally
interpret mutational Phi-values for two small beta-sheet proteins, the PIN and
the FBP WW domain. The native structure of these WW domains comprises two
beta-hairpins that form a three-stranded beta-sheet. In our model, we assume
that the transition state consists of two conformations in which either one of
the hairpins is formed. Such a transition state has been recently observed in
Molecular Dynamics folding-unfolding simulations of a small designed
three-stranded beta-sheet protein. We obtain good agreement with the
experimental data (i) by splitting up the mutation-induced free-energy changes
into terms for the two hairpins and for the small hydrophobic core of the
proteins, and (ii) by fitting a single parameter, the relative degree to which
hairpin 1 and 2 are formed in the transition state. The model helps to
understand how mutations affect the folding kinetics of WW domains, and
captures also negative Phi-values that have been difficult to interpret.Comment: 27 pages, 6 pages, 3 tables; to appear in Biophys.
Searching for simplicity: Approaches to the analysis of neurons and behavior
What fascinates us about animal behavior is its richness and complexity, but
understanding behavior and its neural basis requires a simpler description.
Traditionally, simplification has been imposed by training animals to engage in
a limited set of behaviors, by hand scoring behaviors into discrete classes, or
by limiting the sensory experience of the organism. An alternative is to ask
whether we can search through the dynamics of natural behaviors to find
explicit evidence that these behaviors are simpler than they might have been.
We review two mathematical approaches to simplification, dimensionality
reduction and the maximum entropy method, and we draw on examples from
different levels of biological organization, from the crawling behavior of C.
elegans to the control of smooth pursuit eye movements in primates, and from
the coding of natural scenes by networks of neurons in the retina to the rules
of English spelling. In each case, we argue that the explicit search for
simplicity uncovers new and unexpected features of the biological system, and
that the evidence for simplification gives us a language with which to phrase
new questions for the next generation of experiments. The fact that similar
mathematical structures succeed in taming the complexity of very different
biological systems hints that there is something more general to be discovered
On Side-Chain Conformational Entropy of Proteins
The role of side-chain entropy (SCE) in protein folding has long been speculated about but is still not fully understood. Utilizing a newly developed Monte Carlo method, we conducted a systematic investigation of how the SCE relates to the size of the protein and how it differs among a protein's X-ray, NMR, and decoy structures. We estimated the SCE for a set of 675 nonhomologous proteins, and observed that there is a significant SCE for both exposed and buried residues for all these proteins—the contribution of buried residues approaches ∼40% of the overall SCE. Furthermore, the SCE can be quite different for structures with similar compactness or even similar conformations. As a striking example, we found that proteins' X-ray structures appear to pack more “cleverly” than their NMR or decoy counterparts in the sense of retaining higher SCE while achieving comparable compactness, which suggests that the SCE plays an important role in favouring native protein structures. By including a SCE term in a simple free energy function, we can significantly improve the discrimination of native protein structures from decoys
Pairwise maximum entropy models for studying large biological systems: when they can and when they can't work
One of the most critical problems we face in the study of biological systems
is building accurate statistical descriptions of them. This problem has been
particularly challenging because biological systems typically contain large
numbers of interacting elements, which precludes the use of standard brute
force approaches. Recently, though, several groups have reported that there may
be an alternate strategy. The reports show that reliable statistical models can
be built without knowledge of all the interactions in a system; instead,
pairwise interactions can suffice. These findings, however, are based on the
analysis of small subsystems. Here we ask whether the observations will
generalize to systems of realistic size, that is, whether pairwise models will
provide reliable descriptions of true biological systems. Our results show
that, in most cases, they will not. The reason is that there is a crossover in
the predictive power of pairwise models: If the size of the subsystem is below
the crossover point, then the results have no predictive power for large
systems. If the size is above the crossover point, the results do have
predictive power. This work thus provides a general framework for determining
the extent to which pairwise models can be used to predict the behavior of
whole biological systems. Applied to neural data, the size of most systems
studied so far is below the crossover point
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