1,586 research outputs found
Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models
The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project
Impact of Indirect Contacts in Emerging Infectious Disease on Social Networks
Interaction patterns among individuals play vital roles in spreading
infectious diseases. Understanding these patterns and integrating their impact
in modeling diffusion dynamics of infectious diseases are important for
epidemiological studies. Current network-based diffusion models assume that
diseases transmit through interactions where both infected and susceptible
individuals are co-located at the same time. However, there are several
infectious diseases that can transmit when a susceptible individual visits a
location after an infected individual has left. Recently, we introduced a
diffusion model called same place different time (SPDT) transmission to capture
the indirect transmissions that happen when an infected individual leaves
before a susceptible individual's arrival along with direct transmissions. In
this paper, we demonstrate how these indirect transmission links significantly
enhance the emergence of infectious diseases simulating airborne disease
spreading on a synthetic social contact network. We denote individuals having
indirect links but no direct links during their infectious periods as hidden
spreaders. Our simulation shows that indirect links play similar roles of
direct links and a single hidden spreader can cause large outbreak in the SPDT
model which causes no infection in the current model based on direct link. Our
work opens new direction in modeling infectious diseases.Comment: Workshop on Big Data Analytics for Social Computing,201
Avoiding catastrophic failure in correlated networks of networks
Networks in nature do not act in isolation but instead exchange information,
and depend on each other to function properly. An incipient theory of Networks
of Networks have shown that connected random networks may very easily result in
abrupt failures. This theoretical finding bares an intrinsic paradox: If
natural systems organize in interconnected networks, how can they be so stable?
Here we provide a solution to this conundrum, showing that the stability of a
system of networks relies on the relation between the internal structure of a
network and its pattern of connections to other networks. Specifically, we
demonstrate that if network inter-connections are provided by hubs of the
network and if there is a moderate degree of convergence of inter-network
connection the systems of network are stable and robust to failure. We test
this theoretical prediction in two independent experiments of functional brain
networks (in task- and resting states) which show that brain networks are
connected with a topology that maximizes stability according to the theory.Comment: 40 pages, 7 figure
Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty
The authors would like to thank the Spanish children and parents who participated in
the study.Polygenetic risk scores (pGRSs) consisting of adult body mass index (BMI) genetic
variants have been widely associated with obesity in children populations. The implication of
such obesity pGRSs in the development of cardio-metabolic alterations during childhood as well
as their utility for the clinical prediction of pubertal obesity outcomes has been barely investigated
otherwise. In the present study, we evaluated the utility of an adult BMI predisposing pGRS for the
prediction and pharmacological management of obesity in Spanish children, further investigating
its implication in the appearance of cardio-metabolic alterations. For that purpose, we counted
on genetics data from three well-characterized children populations (composed of 574, 96 and 124
individuals), following both cross-sectional and longitudinal designs, expanding childhood and
puberty. As a result, we demonstrated that the pGRS is strongly associated with childhood BMI
Z-Score (B = 1.56, SE = 0.27 and p-value = 1.90 × 10−8
), and that could be used as a good predictor of
obesity longitudinal trajectories during puberty. On the other hand, we showed that the pGRS is not
associated with cardio-metabolic comorbidities in children and that certain environmental factors
interact with the genetic predisposition to the disease. Finally, according to the results derived from a
weight-reduction metformin intervention in children with obesity, we discarded the utility of the
pGRS as a pharmacogenetics marker of metformin response.Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica (I + D + I), Instituto de Salud Carlos III-Health Research Funding (FONDOS FEDER)
PI1102042
PI1102059
PI1601301
PI1600871Spanish Ministry of Health, Social and Equality, General Department for Pharmacy and Health Products
EC10-243
EC10-056
EC10-281
EC10-227Regional Government of Andalusia ("Plan Andaluz de investigacion, desarrollo e innovacion (2018)")
P18-RT-2248Mapfre Foundation ("Research grants by Ignacio H. de Larramendi 2017")Instituto de Salud Carlos III
IFI17/0004
Hierarchy measure for complex networks
Nature, technology and society are full of complexity arising from the
intricate web of the interactions among the units of the related systems (e.g.,
proteins, computers, people). Consequently, one of the most successful recent
approaches to capturing the fundamental features of the structure and dynamics
of complex systems has been the investigation of the networks associated with
the above units (nodes) together with their relations (edges). Most complex
systems have an inherently hierarchical organization and, correspondingly, the
networks behind them also exhibit hierarchical features. Indeed, several papers
have been devoted to describing this essential aspect of networks, however,
without resulting in a widely accepted, converging concept concerning the
quantitative characterization of the level of their hierarchy. Here we develop
an approach and propose a quantity (measure) which is simple enough to be
widely applicable, reveals a number of universal features of the organization
of real-world networks and, as we demonstrate, is capable of capturing the
essential features of the structure and the degree of hierarchy in a complex
network. The measure we introduce is based on a generalization of the m-reach
centrality, which we first extend to directed/partially directed graphs. Then,
we define the global reaching centrality (GRC), which is the difference between
the maximum and the average value of the generalized reach centralities over
the network. We investigate the behavior of the GRC considering both a
synthetic model with an adjustable level of hierarchy and real networks.
Results for real networks show that our hierarchy measure is related to the
controllability of the given system. We also propose a visualization procedure
for large complex networks that can be used to obtain an overall qualitative
picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table
Phenotypic Variation and Bistable Switching in Bacteria
Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.
Prediction of peptide and protein propensity for amyloid formation
Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation
Ensemble approach for generalized network dismantling
Finding a set of nodes in a network, whose removal fragments the network
below some target size at minimal cost is called network dismantling problem
and it belongs to the NP-hard computational class. In this paper, we explore
the (generalized) network dismantling problem by exploring the spectral
approximation with the variant of the power-iteration method. In particular, we
explore the network dismantling solution landscape by creating the ensemble of
possible solutions from different initial conditions and a different number of
iterations of the spectral approximation.Comment: 11 Pages, 4 Figures, 4 Table
Yeast thioredoxin reductase Trr1p controls TORC1-regulated processes
The thioredoxin system plays a predominant role in the control of cellular redox status. Thioredoxin reductase fuels the system with reducing power in the form of NADPH. The TORC1 complex promotes growth and protein synthesis when nutrients, particularly amino acids, are abundant. It also represses catabolic processes, like autophagy, which are activated during starvation. We analyzed the impact of yeast cytosolic thioredoxin reductase TRR1 deletion under different environmental conditions. It shortens chronological life span and reduces growth in grape juice fermentation. TRR1 deletion has a global impact on metabolism during fermentation. As expected, it reduces oxidative stress tolerance, but a compensatory response is triggered, with catalase and glutathione increasing. Unexpectedly, TRR1 deletion causes sensitivity to the inhibitors of the TORC1 pathway, such as rapamycin. This correlates with low Tor2p kinase levels and indicates a direct role of Trr1p in its stability. Markers of TORC1 activity, however, suggest increased TORC1 activity. The autophagy caused by nitrogen starvation is reduced in the trr1Δ mutant. Ribosomal protein Rsp6p is dephosphorylated in the presence of rapamycin. This dephosphorylation diminishes in the TRR1 deletion strain. These results show a complex network of interactions between thioredoxin reductase Trr1p and the processes controlled by TOR
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