17,127 research outputs found
Outcome prediction in mathematical models of immune response to infection
Clinicians need to predict patient outcomes with high accuracy as early as
possible after disease inception. In this manuscript, we show that
patient-to-patient variability sets a fundamental limit on outcome prediction
accuracy for a general class of mathematical models for the immune response to
infection. However, accuracy can be increased at the expense of delayed
prognosis. We investigate several systems of ordinary differential equations
(ODEs) that model the host immune response to a pathogen load. Advantages of
systems of ODEs for investigating the immune response to infection include the
ability to collect data on large numbers of `virtual patients', each with a
given set of model parameters, and obtain many time points during the course of
the infection. We implement patient-to-patient variability in the ODE
models by randomly selecting the model parameters from Gaussian distributions
with variance that are centered on physiological values. We use logistic
regression with one-versus-all classification to predict the discrete
steady-state outcomes of the system. We find that the prediction algorithm
achieves near accuracy for , and the accuracy decreases with
increasing for all ODE models studied. The fact that multiple steady-state
outcomes can be obtained for a given initial condition, i.e. the basins of
attraction overlap in the space of initial conditions, limits the prediction
accuracy for . Increasing the elapsed time of the variables used to train
and test the classifier, increases the prediction accuracy, while adding
explicit external noise to the ODE models decreases the prediction accuracy.
Our results quantify the competition between early prognosis and high
prediction accuracy that is frequently encountered by clinicians.Comment: 14 pages, 7 figure
Electrostatic Steering Accelerates C3d:CR2 Association.
Electrostatic effects are ubiquitous in protein interactions and are found to be pervasive in the complement system as well. The interaction between complement fragment C3d and complement receptor 2 (CR2) has evolved to become a link between innate and adaptive immunity. Electrostatic interactions have been suggested to be the driving factor for the association of the C3d:CR2 complex. In this study, we investigate the effects of ionic strength and mutagenesis on the association of C3d:CR2 through Brownian dynamics simulations. We demonstrate that the formation of the C3d:CR2 complex is ionic strength-dependent, suggesting the presence of long-range electrostatic steering that accelerates the complex formation. Electrostatic steering occurs through the interaction of an acidic surface patch in C3d and the positively charged CR2 and is supported by the effects of mutations within the acidic patch of C3d that slow or diminish association. Our data are in agreement with previous experimental mutagenesis and binding studies and computational studies. Although the C3d acidic patch may be locally destabilizing because of unfavorable Coulombic interactions of like charges, it contributes to the acceleration of association. Therefore, acceleration of function through electrostatic steering takes precedence to stability. The site of interaction between C3d and CR2 has been the target for delivery of CR2-bound nanoparticle, antibody, and small molecule biomarkers, as well as potential therapeutics. A detailed knowledge of the physicochemical basis of C3d:CR2 association may be necessary to accelerate biomarker and drug discovery efforts
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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
The Immune System: the ultimate fractionated cyber-physical system
In this little vision paper we analyze the human immune system from a
computer science point of view with the aim of understanding the architecture
and features that allow robust, effective behavior to emerge from local sensing
and actions. We then recall the notion of fractionated cyber-physical systems,
and compare and contrast this to the immune system. We conclude with some
challenges.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
Structure Learning in Nested Effects Models
Nested Effects Models (NEMs) are a class of graphical models introduced to
analyze the results of gene perturbation screens. NEMs explore noisy subset
relations between the high-dimensional outputs of phenotyping studies, e.g. the
effects showing in gene expression profiles or as morphological features of the
perturbed cell.
In this paper we expand the statistical basis of NEMs in four directions:
First, we derive a new formula for the likelihood function of a NEM, which
generalizes previous results for binary data. Second, we prove model
identifiability under mild assumptions. Third, we show that the new formulation
of the likelihood allows to efficiently traverse model space. Fourth, we
incorporate prior knowledge and an automated variable selection criterion to
decrease the influence of noise in the data
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