Journal: Cancers

Article Title: Disentangling ERBB Signaling in Breast Cancer Subtypes—A Model-Based Analysis

doi: 10.3390/cancers14102379

Figure Lengend Snippet: Model-based proliferation predictions. ( a ) Schematic overview of the proliferation prediction process. Signaling features were simulated based on the calibrated ODE model and used as input for the linear proliferation model to predict condition-specific proliferation responses. Single treatments were used for model calibration to predict the response to co-treatments. ( b ) Proliferation predictions of MCF7 cells pre-treated with a single or a combination of drugs, followed by a stimulation with the indicated ligand. Data are displayed as log2 fold change relative to the ligand-only control. ( c ) Cell viability data were experimentally collected and used to assess the predictive power of the model for co-treatment predictions. Single treatments used for model calibration are shown in grey, while validation co-treatments are colored. Drugs are abbreviated for co-treatment denotation: Cet—cetuximab, Erlo—erlotinib, Tras—trastuzumab, Pert—pertuzumab and Lum—lumretuzumab. Data points are displayed as the fold change relative to the ligand control.

Article Snippet: The ODE model we developed in the present study is available on BioModels [ ] under the identifier MODEL2205030001 in SBML and PEtab formats [ ].

Techniques: Control, Biomarker Discovery

Journal: International Journal of Molecular Sciences

Article Title: Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases

doi: 10.3390/ijms20020421

Figure Lengend Snippet: Overview of recent mechanistic computational models that were developed to investigate miR-mediated pathways in human disease with a focus on the analysis of time-course kinetics.

Article Snippet: miR-140 , Osteoarthritis , b Mechanistic network model using ODE * , miR production and degradation , Simulate the protective effect of miR-140 under various combinations of cytokine stimulation , [ ] Available in BioModels 2 .

Techniques: Expressing, Construct, Control, Gene Expression

Journal: International Journal of Pharmaceutics: X

Article Title: Autoencoder-based inverse design and surrogate-based optimization of an integrated wet granulation manufacturing process

doi: 10.1016/j.ijpx.2024.100287

Figure Lengend Snippet: Model type, inputs and outputs for each process model used to develop the flowsheet model.

Article Snippet: The granule dissolution process was modelled using the ordinary differential equation (ODE) model based on the Noyes-Whitney ( ) and Nernst-Brunner ( ) equation, where the concentration balance of the bulk solute and the mass balance dissolving granule is denoted by Eq. and Eq., respectively. (19) d C b , i dt = k i C sat − C b , i = s 1 , s 2 (20) d m i dt = − k i A i C sat − C b , i = s 1 , s 2 Here, C b is the bulk concentration, C sat is the saturation concentration, k is the mass transfer coefficient, and A is the surface area.

Techniques: Dissolution

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Towards Quantitative Systems Pharmacology Models of Chemotherapy‐Induced Neutropenia

doi: 10.1002/psp4.12191

Figure Lengend Snippet: An overview of granulopoiesis. As with all blood cells, neutrophils begin as hematopoietic stem cells (HSCs, orange circle) in the bone marrow (pale yellow background), where they develop. HSCs are capable of self‐renewal and are subject to cell death (dashed arrows). HSCs may also differentiate into one of the blood cell lines, including the neutrophils (purple circles). After commitment to the neutrophil lineage, cells undergo a period of proliferative expansion at the end of which they no longer divide. Postmitotic neutrophils then mature, growing in size and gaining receptors. At the end of the maturation process, cells are then stored in the bone marrow reservoir from which they egress to reach the circulation (pale red background) before removal (by margination or death). G‐CSF acts to modulate the rate of exit from the marrow reservoir, increase the rates of maturation and proliferation, and to modulate the rate of differentiation into the neutrophil lineage (G‐CSF actions represented by blue vertical arrows).

Article Snippet: Stochastic , Krinner et al . , ODE granulopoiesis model + stochasticity , 60.

Techniques:

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Towards Quantitative Systems Pharmacology Models of Chemotherapy‐Induced Neutropenia

doi: 10.1002/psp4.12191

Figure Lengend Snippet: Schematic representation of the production of circulating neutrophils in the bone marrow and the interaction of the system with G‐CSF. Hematopoietic stem cells (HSCs‐Q) enter the neutrophil lineage, the other blood lines, or are removed from the HSC pool. Differentiated HSCs undergo successive divisions during the proliferative phase. Cells then mature before being stored in the marrow reservoir, or dying off during maturation. Neutrophils remain in the reservoir until they are removed randomly or enter the circulation, where they disappear rapidly from the blood. Freely circulating G‐CSF may bind to receptors on the neutrophils. The concentration of bound G‐CSF drives its pharmacodynamic effects. The concentration of G‐CSF bound to mature neutrophils, G 2 , determines the rate of release from the marrow reservoir. The concentration of G‐CSF bound to neutrophil precursors, assumed proportional to G 1 , the concentration of freely circulating G‐CSF, determines the rate of differentiation from the HSCs, the speed of maturation, and the rate of proliferation. For all four effects, speed and rates increase with increasing G‐CSF concentration. Figure reproduced from “A mathematical model of granulopoiesis incorporating the negative feedback dynamics and kinetics of G‐CSF/neutrophil binding and internalization,” Bull. Math. Biol ., 78, 2016, p. 2308, Craig, M., Humphries, A.R., and Mackey, M.C. with the permission of Springer.

Article Snippet: Stochastic , Krinner et al . , ODE granulopoiesis model + stochasticity , 60.

Techniques: Concentration Assay, Binding Assay

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Towards Quantitative Systems Pharmacology Models of Chemotherapy‐Induced Neutropenia

doi: 10.1002/psp4.12191

Figure Lengend Snippet: Summary of discussed models by discipline and type

Article Snippet: Stochastic , Krinner et al . , ODE granulopoiesis model + stochasticity , 60.

Techniques:

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: NONMEM Tutorial Part I: Description of Commands and Options, With Simple Examples of Population Analysis

doi: 10.1002/psp4.12404

Figure Lengend Snippet: List of built‐in models as of NONMEM 7.4 (PREDPP guide VI 1 )

Article Snippet: ADVAN9 , GENERAL NONLINEAR MODEL: LSODI ODE SOLVER, AND DIFFERENTIAL‐ALGEBRAIC EQUATIONS (ADE) , TRANS1 , , General nonlinear model with equilibrium compartments (ordinary and algebraic differential equations, LSODI1).

Techniques: