348 research outputs found
Reinforcement-Driven Spread of Innovations and Fads
We propose kinetic models for the spread of permanent innovations and
transient fads by the mechanism of social reinforcement. Each individual can be
in one of M+1 states of awareness 0,1,2,...,M, with state M corresponding to
adopting an innovation. An individual with awareness k<M increases to k+1 by
interacting with an adopter. Starting with a single adopter, the time for an
initially unaware population of size N to adopt a permanent innovation grows as
ln(N) for M=1, and as N^{1-1/M} for M>1. The fraction of the population that
remains clueless about a transient fad after it has come and gone changes
discontinuously as a function of the fad abandonment rate lambda for M>1. The
fad dies out completely in a time that varies non-monotonically with lambda.Comment: 4 pages, 2 columns, 5 figures, revtex 4-1 format; revised version has
been expanded and put into iop format, with one figure adde
Influence Diffusion in Social Networks under Time Window Constraints
We study a combinatorial model of the spread of influence in networks that
generalizes existing schemata recently proposed in the literature. In our
model, agents change behaviors/opinions on the basis of information collected
from their neighbors in a time interval of bounded size whereas agents are
assumed to have unbounded memory in previously studied scenarios. In our
mathematical framework, one is given a network , an integer value
for each node , and a time window size . The goal is to
determine a small set of nodes (target set) that influences the whole graph.
The spread of influence proceeds in rounds as follows: initially all nodes in
the target set are influenced; subsequently, in each round, any uninfluenced
node becomes influenced if the number of its neighbors that have been
influenced in the previous rounds is greater than or equal to .
We prove that the problem of finding a minimum cardinality target set that
influences the whole network is hard to approximate within a
polylogarithmic factor. On the positive side, we design exact polynomial time
algorithms for paths, rings, trees, and complete graphs.Comment: An extended abstract of a preliminary version of this paper appeared
in: Proceedings of 20th International Colloquium on Structural Information
and Communication Complexity (Sirocco 2013), Lectures Notes in Computer
Science vol. 8179, T. Moscibroda and A.A. Rescigno (Eds.), pp. 141-152, 201
Layered social influence promotes multiculturality in the Axelrod model
9 pages, 4 figures, was "Robust multiculturality emerges from layered social influence". In press in Scientific Report
Timing interactions in social simulations: The voter model
The recent availability of huge high resolution datasets on human activities
has revealed the heavy-tailed nature of the interevent time distributions. In
social simulations of interacting agents the standard approach has been to use
Poisson processes to update the state of the agents, which gives rise to very
homogeneous activity patterns with a well defined characteristic interevent
time. As a paradigmatic opinion model we investigate the voter model and review
the standard update rules and propose two new update rules which are able to
account for heterogeneous activity patterns. For the new update rules each node
gets updated with a probability that depends on the time since the last event
of the node, where an event can be an update attempt (exogenous update) or a
change of state (endogenous update). We find that both update rules can give
rise to power law interevent time distributions, although the endogenous one
more robustly. Apart from that for the exogenous update rule and the standard
update rules the voter model does not reach consensus in the infinite size
limit, while for the endogenous update there exist a coarsening process that
drives the system toward consensus configurations.Comment: Book Chapter, 23 pages, 9 figures, 5 table
An in silico structural approach to critical quality attributes assessment of biopharmaceutical products
\u201cQuality by design\u201d (QbD) is a key approach in modern pharmaceutical development, applied during the development, the manufacturing and the whole life cycle of the product, included the post approval phase, for assuring the quality in terms of efficacy and safety.
In detail, QbD process includes the critical quality attributes (CQAs) assessment, providing a comprehensive understanding of the product itself and the manufacturing process. CQAs are defined as \u201call the physical, chemical, biological, or microbiological properties or characteristics that should be within an appropriate limit, range, or distribution to ensure the desired product quality\u201d (ICH Q8). They have a potential impact on bioactivity, PK, immunogenicity and safety and are associated with the drug substance and drug product. In the context of biotechnological products, the introduction of a structural investigation in an early identification of potential CQAs (pCQAs) can be very useful to QbD approach. Identification of pCQAs of biomolecules can lead the characterization process during the development phase in order to ensure the desired drug quality profile.
Monoclonal antibodies (mAbs), fusion proteins and antibody-drug conjugates (ADC) represent one of the most innovative class of biopharmaceuticals, due to their ability to specifically recognize unique epitopes inducing specific therapeutic responses. CQAs assessment for these biopharmaceuticals is a complex analysis due to the lack of structural information. Actually, there is only one fully-crystallized human IgG1 (PDB entry: 1HZH) and, in absence of whole structures, it is challenging to understand the impact of structural insights on the therapeutic response.
On these basis, the purpose of this study was to develop an in silico strategy to build the atomistic model of the whole structure of an IgG1, focusing on lambda and kappa light chains. To reach this goal, we used a structural chimeric approach that, using the Homology Modeling (HM) tool by MOE software, allowed us to build the full atomistic model of two therapeutic and commercially available IgG1: adalimumab (kappa chain) and avelumab (lambda chain). This allowed us to investigate structural differences between two isotypes, kappa and lambda, and understand the impact of these different characteristics on the antibody structure and function.
Our results try to fill the gap between biological and structural properties on biotechnological products, created by lack of full immunoglobulin crystal structures. Moreover, this innovative structural approach can be used in CQAs assessment during the pharmaceutical development and production phases, giving an important resource to pharmaceutical companies.
DISCLOSURES
Merck Serono, Guidonia Montecelio-Rome, Italy is an affiliate of Merck KGaA, Darmstadt, Germany.
Please note that avelumab has been approved in various countries for the treatment of metastatic Merkel cell carcinoma and in the US for treatment of advanced urothelial carcinoma progressed after platinum-containing treatment
Coevolution of Glauber-like Ising dynamics on typical networks
We consider coevolution of site status and link structures from two different
initial networks: a one dimensional Ising chain and a scale free network. The
dynamics is governed by a preassigned stability parameter , and a rewiring
factor , that determines whether the Ising spin at the chosen site flips
or whether the node gets rewired to another node in the system. This dynamics
has also been studied with Ising spins distributed randomly among nodes which
lie on a network with preferential attachment. We have observed the steady
state average stability and magnetisation for both kinds of systems to have an
idea about the effect of initial network topology. Although the average
stability shows almost similar behaviour, the magnetisation depends on the
initial condition we start from. Apart from the local dynamics, the global
effect on the dynamics has also been studied. These parameters show interesting
variations for different values of and , which helps in determining
the steady-state condition for a given substrate.Comment: 8 pages, 10 figure
From sparse to dense and from assortative to disassortative in online social networks
Inspired by the analysis of several empirical online social networks, we
propose a simple reaction-diffusion-like coevolving model, in which individuals
are activated to create links based on their states, influenced by local
dynamics and their own intention. It is shown that the model can reproduce the
remarkable properties observed in empirical online social networks; in
particular, the assortative coefficients are neutral or negative, and the power
law exponents are smaller than 2. Moreover, we demonstrate that, under
appropriate conditions, the model network naturally makes transition(s) from
assortative to disassortative, and from sparse to dense in their
characteristics. The model is useful in understanding the formation and
evolution of online social networks.Comment: 10 pages, 7 figures and 2 table
A Relational Event Approach to Modeling Behavioral Dynamics
This chapter provides an introduction to the analysis of relational event
data (i.e., actions, interactions, or other events involving multiple actors
that occur over time) within the R/statnet platform. We begin by reviewing the
basics of relational event modeling, with an emphasis on models with piecewise
constant hazards. We then discuss estimation for dyadic and more general
relational event models using the relevent package, with an emphasis on
hands-on applications of the methods and interpretation of results. Statnet is
a collection of packages for the R statistical computing system that supports
the representation, manipulation, visualization, modeling, simulation, and
analysis of relational data. Statnet packages are contributed by a team of
volunteer developers, and are made freely available under the GNU Public
License. These packages are written for the R statistical computing
environment, and can be used with any computing platform that supports R
(including Windows, Linux, and Mac).
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