Data Overview

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System Biology Models

Abstract	Model Name	Number of Species	Number of Parameters
visibility_off	Teusink2000_Glycolysis	26	15
Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Teusink,B et al.: Eur J Biochem 2000 Sep;267(17):5313-29. The model reproduces the steady-state fluxes and metabolite concentrations of the branched model as given in Table 4 of the paper. It is derived from the model on JWS online, but has the ATP consumption in the succinate branch with the same stoichiometrie as in the publication. The model was successfully tested on copasi v.4.4(build 26). For Vmax values, please note that there is a conversion factor of approx. 270 to convert from U/mg-protein as shown in Table 1 of the paper to mmol/(min*L_cytosol). The equilibrium constant for the ADH reaction in the paper is given for the reverse reaction (Keq = 1.45*10 4 ). The value used in this model is for the forward reaction: 1/Keq = 6.9*10 -5 . Vmax parameters values used (in [mM/min] except VmGLT): VmGLT 97.264 mmol/min VmGLK 226.45 VmPGI 339.667 VmPFK 182.903 VmALD 322.258 VmGAPDH_f 1184.52 VmGAPDH_r 6549.68 VmPGK 1306.45 VmPGM 2525.81 VmENO 365.806 VmPYK 1088.71 VmPDC 174.194 VmG3PDH 70.15 The result of the G6P steady state concentration (marked in red) differs slightly from the one given in table 4. of the publication Results for steady state: orig. article this model Fluxes[mM/min]   Glucose  88  88  Ethanol  129  129  Glycogen  6  6  Trehalose  4.8  4.8  (G6P flux through trehalose branch) Glycerol  18.2  18.2  Succinate  3.6  3.6  Conc.[mM]   G6P  1.07  1.03  F6P  0.11  0.11  F1,6P  0.6  0.6  DHAP  0.74  0.74  3PGA  0.36  0.36  2PGA  0.04  0.04  PEP  0.07  0.07  PYR  8.52  8.52  AcAld  0.17  0.17  ATP  2.51  2.51  ADP  1.29  1.29  AMP  0.3  0.3  NAD  1.55  1.55  NADH  0.04  0.04  Authors of the publication also mentioned a few misprints in the original article: in the kinetic law for ADH : the species a should denote NAD and b Ethanol the last term in the equation should read bpq /( K ib K iq K p ) in the kinetic law for PFK : R = 1 + λ 1 + λ 2 + g r λ 1 λ 2 equation L should read: L = L0*(..) 2 *(..) 2 *(..) 2 not L = L0*(..) 2 *(..) 2 *(..) To make the model easier to curate, the species ATP , ADP and AMP were added. These are calculated via assignment rules from the active phosphate species, P , and the sum of all AXP , SUM_P . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
visibility_off	Dwivedi2014 - Crohns IL6 Disease model - Anti-IL6R Antibody	44	53
Dwivedi2014 - Crohns IL6 Disease model - Anti-IL6R Antibody This model is comprised of four models: [BIOMD0000000534] Healthy Volunteer model [BIOMD0000000535] Crohn's Disease - IL-6 Antibody [BIOMD0000000536] Crohn's Disease - sgp130FC [BIOMD0000000537] Crohn's Disease - IL-6Ra Antibody Possible avenues for Interleukin-6 (IL-6) inhibition in treating Crohn's disease are compared here. Each model refers to separate ligands. The system simulates differential activity of the ligands on the signalling of IL-6. This affects Signal Transducer and Activator of Transcription 3 (STAT3) activity on the production of biomarker C-Reactive Protein (CRP) expression. Figures referring to this Crohn's Disease model are 3a, 4d, 4e, 4f and 5b. This model is described in the article: A multiscale model of interleukin-6-mediated immune regulation in Crohn's disease and its application in drug discovery and development. Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C. CPT Pharmacometrics Syst Pharmacol 2014; 3: e89 Abstract: In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014. This model is hosted on BioModels Database and identified by: BIOMD0000000537. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
visibility_off	Tang2020 - Estimation of transmission risk of COVID-19 and impact of public health interventions	8	16
Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71–7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.

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