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  1. Book: Modeling biomolecular site dynamics

    Hlavacek, William S.

    methods and protocols

    (Methods in molecular biology ; 1945 ; Springer protocols)

    2019  

    Author's details edited by William S. Hlavacek
    Series title Methods in molecular biology ; 1945
    Springer protocols
    Collection
    Language English
    Size xix, 423 Seiten, Illustrationen
    Publisher Humana Press
    Publishing place New York, NY
    Publishing country United States
    Document type Book
    HBZ-ID HT020032783
    ISBN 978-1-4939-9100-6 ; 9781493991020 ; 1-4939-9100-0 ; 1493991027
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 7042 -1945- verfügbar in Königswinter Königswinter

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  2. Article ; Online: Differential contagiousness of respiratory disease across the United States.

    Mallela, Abhishek / Lin, Yen Ting / Hlavacek, William S

    Epidemics

    2023  Volume 45, Page(s) 100718

    Abstract: The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, ... ...

    Abstract The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, R
    MeSH term(s) Humans ; United States/epidemiology ; Bayes Theorem ; COVID-19/epidemiology ; Basic Reproduction Number
    Language English
    Publishing date 2023-09-22
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2467993-8
    ISSN 1878-0067 ; 1755-4365
    ISSN (online) 1878-0067
    ISSN 1755-4365
    DOI 10.1016/j.epidem.2023.100718
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Bayesian inference using qualitative observations of underlying continuous variables.

    Mitra, Eshan D / Hlavacek, William S

    Bioinformatics (Oxford, England)

    2020  Volume 36, Issue 10, Page(s) 3177–3184

    Abstract: Motivation: Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a ... ...

    Abstract Motivation: Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a statistical interpretation of the objective functions used in optimization.
    Results: We formulated likelihood functions suitable for performing Bayesian UQ using qualitative observations of underlying continuous variables or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for immunoglobulin E (IgE) receptor signaling using synthetic qualitative and quantitative datasets. Remarkably, estimates of parameter values derived from the qualitative data were nearly as consistent with the assumed ground-truth parameter values as estimates derived from the lower throughput quantitative data. These results provide further motivation for leveraging qualitative data in biological modeling.
    Availability and implementation: The likelihood functions presented here are implemented in a new release of PyBioNetFit, an open-source application for analyzing Systems Biology Markup Language- and BioNetGen Language-formatted models, available online at www.github.com/lanl/PyBNF.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Bayes Theorem ; Likelihood Functions ; Software ; Systems Biology ; Uncertainty
    Language English
    Publishing date 2020-02-07
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btaa084
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2374: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article: Parameter Estimation and Uncertainty Quantification for Systems Biology Models.

    Mitra, Eshan D / Hlavacek, William S

    Current opinion in systems biology

    2019  Volume 18, Page(s) 9–18

    Abstract: Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quantification before reliable predictions become possible. We review currently available ... ...

    Abstract Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quantification before reliable predictions become possible. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.
    Language English
    Publishing date 2019-11-06
    Publishing country England
    Document type Journal Article
    ISSN 2452-3100
    ISSN 2452-3100
    DOI 10.1016/j.coisb.2019.10.006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Differential contagiousness of respiratory disease across the United States

    Mallela, Abhishek / Lin, Yen Ting / Hlavacek, William S

    medRxiv

    Abstract: The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, denoted R_0. The value of R_0 gives the expected number of new cases generated by an infectious person in a wholly susceptible ... ...

    Abstract The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, denoted R_0. The value of R_0 gives the expected number of new cases generated by an infectious person in a wholly susceptible population and depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we estimated region-specific R_0 values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. Our estimates range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases.
    Keywords covid19
    Language English
    Publishing date 2022-09-17
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2022.09.15.22279948
    Database COVID19

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  6. Article: Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah).

    Miller, Ely F / Neumann, Jacob / Chen, Ye / Mallela, Abhishek / Lin, Yen Ting / Hlavacek, William S / Posner, Richard G

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period ...

    Abstract During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. To investigate the causes of this difference, we used a compartmental model accounting for distinct periods of non-pharmaceutical interventions (NPIs
    Language English
    Publishing date 2023-02-16
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.02.15.23285971
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah).

    Miller, Ely F / Neumann, Jacob / Chen, Ye / Mallela, Abhishek / Lin, Yen Ting / Hlavacek, William S / Posner, Richard G

    PLOS global public health

    2023  Volume 3, Issue 6, Page(s) e0001490

    Abstract: During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period ...

    Abstract During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.
    Language English
    Publishing date 2023-06-21
    Publishing country United States
    Document type Journal Article
    ISSN 2767-3375
    ISSN (online) 2767-3375
    DOI 10.1371/journal.pgph.0001490
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Using RuleBuilder to Graphically Define and Visualize BioNetGen-Language Patterns and Reaction Rules.

    Suderman, Ryan / Fricke, G Matthew / Hlavacek, William S

    Methods in molecular biology (Clifton, N.J.)

    2019  Volume 1945, Page(s) 33–42

    Abstract: RuleBuilder is a tool for drawing graphs that can be represented by the BioNetGen language (BNGL), which is used to formulate mathematical, rule-based models of biochemical systems. BNGL provides an intuitive plain text, or string, representation of such ...

    Abstract RuleBuilder is a tool for drawing graphs that can be represented by the BioNetGen language (BNGL), which is used to formulate mathematical, rule-based models of biochemical systems. BNGL provides an intuitive plain text, or string, representation of such systems, which is based on a graphical formalism. Reactions are defined in terms of graph-rewriting rules that specify the necessary intrinsic properties of the reactants, a transformation, and a rate law. Rules also contain contextual constraints that restrict application of the rule. In some cases, the specification of contextual constraints can be verbose, making a rule difficult to read. RuleBuilder is designed to ease the task of reading and writing individual reaction rules or other BNGL patterns required for model formulation. The software assists in the reading of existing models by converting BNGL strings of interest into a graph-based representation composed of nodes and edges. RuleBuilder also enables the user to construct de novo a visual representation of BNGL strings using drawing tools available in its interface. As objects are added to the drawing canvas, the corresponding BNGL string is generated on the fly, and objects are similarly drawn on the fly as BNGL strings are entered into the application. RuleBuilder thus facilitates construction and interpretation of rule-based models.
    MeSH term(s) Algorithms ; Computer Simulation ; Models, Biological ; Models, Theoretical ; Signal Transduction/genetics ; Software
    Language English
    Publishing date 2019-04-03
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-9102-0_2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks.

    Lin, Yen Ting / Feng, Song / Hlavacek, William S

    The Journal of chemical physics

    2019  Volume 150, Issue 24, Page(s) 244101

    Abstract: Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit time. Here, we present a new acceleration algorithm based on adaptive and ... ...

    Abstract Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit time. Here, we present a new acceleration algorithm based on adaptive and heterogeneous scaling of reaction rates and stoichiometric coefficients. The algorithm is conceptually related to the commonly used idea of accelerating a stochastic simulation by considering a subvolume λΩ (0 < λ < 1) within a system of interest, which reduces the number of reaction events per unit time occurring in a simulation by a factor 1/λ at the cost of greater error in unbiased estimates of first moments and biased overestimates of second moments. Our new approach offers two unique benefits. First, scaling is adaptive and heterogeneous, which eliminates the pitfall of overaggressive scaling. Second, there is no need for an a priori classification of populations as discrete or continuous (as in a hybrid method), which is problematic when discreteness of a chemical species changes during a simulation. The method requires specification of only a single algorithmic parameter, N
    Language English
    Publishing date 2019-06-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3113-6
    ISSN 1089-7690 ; 0021-9606
    ISSN (online) 1089-7690
    ISSN 0021-9606
    DOI 10.1063/1.5096774
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 4' 49/16: Show issues

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  10. Article ; Online: Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States.

    Mallela, Abhishek / Chen, Ye / Lin, Yen Ting / Miller, Ely F / Neumann, Jacob / He, Zhili / Nelson, Kathryn E / Posner, Richard G / Hlavacek, William S

    Bulletin of mathematical biology

    2024  Volume 86, Issue 3, Page(s) 31

    Abstract: To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model ...

    Abstract To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x  to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.
    MeSH term(s) United States/epidemiology ; Humans ; SARS-CoV-2/genetics ; COVID-19/epidemiology ; COVID-19/prevention & control ; Mathematical Concepts ; Models, Biological ; Vaccination
    Language English
    Publishing date 2024-02-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 184905-0
    ISSN 1522-9602 ; 0007-4985 ; 0092-8240
    ISSN (online) 1522-9602
    ISSN 0007-4985 ; 0092-8240
    DOI 10.1007/s11538-024-01258-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.B 754: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    ab Jg. 2022: Lesesaal (EG)

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