Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

Imle, Andrea; Kumberger, Peter; Schnellbächer, Nikolas D.; Fehr, Jana; Carrillo-Bustamante, Paola; Aleš, Janez; Schmidt, P.; Ritter, Christian; Godinez, William J.; Müller, Bárbara; Rohr, Karl; Hamprecht, Fred A.; Schwarz, Ulrich S.; Graw, Frederick; Fackler, Oliver T.

doi:10.1038/s41467-019-09879-3

Cited by 71 publications

(115 citation statements)

References 68 publications

(85 reference statements)

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“…In order to help understand complex pathogenrelated processes, computational models were developed for viral 46,47 bacterial, 48 parasitic 49 and fungal pathogens. 50 The bioinformatics tools (Table 3) are used to identify possible epitopes for vaccine formulation.…”

Section: Immunoinformatics and Infectious Diseasementioning

confidence: 99%

<p>Immunoinformatics and Vaccine Development: An Overview</p>

Oli

Obialor

Ifeanyichukwu

et al. 2020

ITT

175

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The use of vaccines have resulted in a remarkable improvement in global health. It has saved several lives, reduced treatment costs and raised the quality of animal and human lives. Current traditional vaccines came empirically with either vague or completely no knowledge of how they modulate our immune system. Even at the face of potential vaccine design advance, immune-related concerns (as seen with specific vulnerable populations, cases of emerging/reemerging infectious disease, pathogens with complex lifecycle and antigenic variability, need for personalized vaccinations, and concerns for vaccines' immunological safety -specifically vaccine likelihood to trigger non-antigen-specific responses that may cause autoimmunity and vaccine allergy) are being raised. And these concerns have driven immunologists toward research for a better approach to vaccine design that will consider these challenges. Currently, immunoinformatics has paved the way for a better understanding of some infectious disease pathogenesis, diagnosis, immune system response and computational vaccinology. The importance of this immunoinformatics in the study of infectious diseases is diverse in terms of computational approaches used, but is united by common qualities related to host-pathogen relationship. Bioinformatics methods are also used to assign functions to uncharacterized genes which can be targeted as a candidate in vaccine design and can be a better approach toward the inclusion of women that are pregnant into vaccine trials and programs. The essence of this review is to give insight into the need to focus on novel computational, experimental and computation-driven experimental approaches for studying of host-pathogen interactions and thus making a case for its use in vaccine development.

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Section: Immunoinformatics and Infectious Diseasementioning

confidence: 99%

<p>Immunoinformatics and Vaccine Development: An Overview</p>

Oli

Obialor

Ifeanyichukwu

et al. 2020

ITT

175

View full text Add to dashboard Cite

show abstract

“…gene expression, signal transduction and multicellular systems (e.g. Imle et al , 2019 ; Lenive et al , 2016 ; Picchini, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…Contrarily, in likelihood-free methods, particularly ABC, it is easy to disregard any noise due to the unnecessity of even formulating a likelihood and the various inherent approximation levels, so that error sources can be difficult to pinpoint from the result. In the past, it has repeatedly not been included in ABC analyses ( Eriksson et al , 2019 ; Imle et al , 2019 ; Jagiella et al , 2017 ; Lenive et al , 2016 ; Toni et al , 2009 ). Asymptotic unbiasedness of ABC is however granted only if the data-generation process is perfectly reproduced.…”

Section: Introductionmentioning

confidence: 99%

Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation

Schälte

Hasenauer

2020

Bioinformatics

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Motivation Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, as it allows analyzing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, as ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC. Results We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling-based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, Markov jump processes and stochastically interacting agents, and noise models including normal, Laplace and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications. Availability and implementation The developed algorithms are made publicly available as part of the open-source python toolbox pyABC (https://github.com/icb-dcm/pyabc). Supplementary information Supplementary data are available at Bioinformatics online.

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“…gene expression, signal transduction, and multi-cellular systems (e.g. Lenive et al (2016); Picchini (2014); Imle et al (2019)).…”

Section: Introductionmentioning

confidence: 99%

“…Contrarily, in likelihood-free methods, particularly ABC, it is easy to disregard any noise due to the unnecessity of even formulating a likelihood and the various inherent approximation levels, so that error sources can be difficult to pinpoint from the result. In the past, it has repeatedly not been included in ABC analyses (Toni et al, 2009;Lenive et al, 2016;Jagiella et al, 2017;Imle et al, 2019;Eriksson et al, 2019). Asymptotic unbiasedness of ABC is however granted only if the data-generation process is perfectly reproduced.…”

Section: Introductionmentioning

confidence: 99%

Efficient Exact Inference for Dynamical Systems with Noisy Measurements using Sequential Approximate Bayesian Computation

Schälte

Hasenauer

2020

Preprint

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Motivation: Approximate Bayesian Computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, since it allows analysing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, since ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC. Results:We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, 1 Markov jump processes, and stochastically interacting agents, and noise models including normal, Laplace, and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications.Availability: The developed algorithms are made publicly available as part of the opensource python toolbox pyABC (https://github.com/icb-dcm/pyabc).

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Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

Cited by 71 publications

References 68 publications

<p>Immunoinformatics and Vaccine Development: An Overview</p>

<p>Immunoinformatics and Vaccine Development: An Overview</p>

Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation

Efficient Exact Inference for Dynamical Systems with Noisy Measurements using Sequential Approximate Bayesian Computation

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