1
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Beik SP, Harris LA, Kochen MA, Sage J, Quaranta V, Lopez CF. Unified tumor growth mechanisms from multimodel inference and dataset integration. PLoS Comput Biol 2023; 19:e1011215. [PMID: 37406008 DOI: 10.1371/journal.pcbi.1011215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/25/2023] [Indexed: 07/07/2023] Open
Abstract
Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.
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Affiliation(s)
- Samantha P Beik
- Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Leonard A Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, United States of America
- Interdisciplinary Graduate Program in Cell & Molecular Biology, University of Arkansas, Fayetteville, Arkansas, United States of America
- Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Michael A Kochen
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Julien Sage
- Departments of Pediatrics, Stanford University, Stanford, California, United States of America
- Departments of Genetics, Stanford University, Stanford, California, United States of America
| | - Vito Quaranta
- Program in Chemical and Physical Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Carlos F Lopez
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, United States of America
- Altos Laboratories, Redwood City, California, United States of America
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2
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Kostoglou M, Karapantsios T, Petala M, Roilides E, Dovas CI, Papa A, Metallidis S, Stylianidis E, Lytras T, Paraskevis D, Koutsolioutsou-Benaki A, Panagiotakopoulos G, Tsiodras S, Papaioannou N. The COVID-19 pandemic as inspiration to reconsider epidemic models: A novel approach to spatially homogeneous epidemic spread modeling. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:9853-9876. [PMID: 36031972 DOI: 10.3934/mbe.2022459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Epidemic spread models are useful tools to study the spread and the effectiveness of the interventions at a population level, to an epidemic. The workhorse of spatially homogeneous class models is the SIR-type ones comprising ordinary differential equations for the unknown state variables. The transition between different states is expressed through rate functions. Inspired by -but not restricted to- features of the COVID-19 pandemic, a new framework for modeling a disease spread is proposed. The main concept refers to the assignment of properties to each individual person as regards his response to the disease. A multidimensional distribution of these properties represents the whole population. The temporal evolution of this distribution is the only dependent variable of the problem. All other variables can be extracted by post-processing of this distribution. It is noteworthy that the new concept allows an improved consideration of vaccination modeling because it recognizes vaccination as a modifier of individuals response to the disease and not as a means for individuals to totally defeat the disease. At the heart of the new approach is an infection age model engaging a sharp cut-off. This model is analyzed in detail, and it is shown to admit self-similar solutions. A hierarchy of models based on the new approach, from a generalized one to a specific one with three dominant properties, is derived. The latter is implemented as an example and indicative results are presented and discussed. It appears that the new framework is general and versatile enough to simulate disease spread processes and to predict the evolution of several variables of the population during this spread.
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Affiliation(s)
- Margaritis Kostoglou
- Laboratory of Chemical and Environmental Technology, Department of Chemistry, Aristotle University of Thessaloniki, 54 124 Thessaloniki, 54124, Greece
| | - Thodoris Karapantsios
- Laboratory of Chemical and Environmental Technology, Department of Chemistry, Aristotle University of Thessaloniki, 54 124 Thessaloniki, 54124, Greece
| | - Maria Petala
- Laboratory of Environmental Engineering & Planning, Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
| | - Emmanuel Roilides
- Infectious Diseases Unit and 3rd Department of Pediatrics, Aristotle University School of Health Sciences, Hippokration Hospital, Thessaloniki, 54642, Greece
| | - Chrysostomos I Dovas
- Faculty of Veterinary Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
| | - Anna Papa
- Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
| | - Simeon Metallidis
- Department of Haematology, First Department of Internal Medicine, Faculty of Medicine, AHEPA General Hospital, Aristotle University of Thessaloniki, Thessaloniki, 54636, Greece
| | - Efstratios Stylianidis
- School of Spatial Planning and Development, Faculty of Engineering, Aristotle University of Thessaloniki, 54124, Greece
| | - Theodoros Lytras
- National Public Health Organization, Athens, Greece
- European University Cyprus, Nicosia, Cyprus
| | | | - Anastasia Koutsolioutsou-Benaki
- Department of Environmental Health, Directory of Epidemiology and Prevention of Non-Communicable Diseases and Injuries, National Public Health Organization, Athens, Greece
| | | | | | - Nikolaos Papaioannou
- Faculty of Veterinary Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
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3
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Győrgy R, Kostoglou M, Mantalaris A, Georgiadis MC. Development of a multi-scale model to simulate Mesenchymal Stem Cell osteogenic differentiation within hydrogels in a rotating wall bioreactor. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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4
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Wu G, Xiu H, Luo H, Ding Y, Li Y. A mathematical model for cell cycle control: graded response or quantized response. Cell Cycle 2022; 21:820-834. [PMID: 35107036 PMCID: PMC8973363 DOI: 10.1080/15384101.2022.2031770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/03/2022] [Accepted: 01/17/2022] [Indexed: 02/04/2023] Open
Abstract
Cell cycle is an important and complex biological system. A lot of efforts have been put in understanding cell cycle arrest for its vital role in clinical therapies. The cell-cycle-arrest outcomes upon stimulation are complicated. The response could be stringent or relaxed, and graded or quantized. A model fully addressing various cell-cycle-arrest outcomes is to be developed. Here, we developed a mathematical model of cell cycle control incorporating distinct characteristics of various cell-cycle-arrest outcomes. The model can simulate two typical properties of cell cycle arrest, quantized and graded. We also characterized the inheritable quiescence and refractory state, which were crucial in long-term response of the population. Then, we monitored cells respond to multiple stimulations, and the results indicated that cells responded to stimulations with small interval did not induce significantly sustained cell cycle arrest as the existence of refractory state. Our work will benefit fundamental research and make efforts to predicting outcomes of clinical therapeutics.
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Affiliation(s)
- Guoyu Wu
- School of Clinical Pharmacy, Guangdong Pharmaceutical University, Guangdong, China
- Key Specialty of Clinical Pharmacy, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangdong Pharmaceutical University, Guangzhou, China
- CONTACT Guoyu Wu
| | - Huiyu Xiu
- School of Clinical Pharmacy, Guangdong Pharmaceutical University, Guangdong, China
| | - Haiying Luo
- School of Clinical Pharmacy, Guangdong Pharmaceutical University, Guangdong, China
| | - Yu Ding
- School of Clinical Pharmacy, Guangdong Pharmaceutical University, Guangdong, China
| | - Yuchao Li
- MegaLab, MegaRobo Technologies Co., Ltd, Beijing, China
- Yuchao Li
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5
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Harris LA, Beik S, Ozawa PMM, Jimenez L, Weaver AM. Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 17:24-34. [PMID: 32642602 PMCID: PMC7343346 DOI: 10.1016/j.coisb.2019.09.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.
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Affiliation(s)
- Leonard A. Harris
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Samantha Beik
- Cancer Biology Graduate Program, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Patricia M. M. Ozawa
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Lizandra Jimenez
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alissa M. Weaver
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
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Győrgy R, Klontzas ME, Kostoglou M, Panoskaltsis N, Mantalaris A, Georgiadis MC. Capturing Mesenchymal Stem Cell Heterogeneity during Osteogenic Differentiation: An Experimental–Modeling Approach. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b01988] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Romuald Győrgy
- Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
| | - Michail E. Klontzas
- Biological Systems Engineering Laboratory, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
- School of Medicine, Emory University, Atlanta, Georgia 30332, United States
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Margaritis Kostoglou
- Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Nicki Panoskaltsis
- School of Medicine, Emory University, Atlanta, Georgia 30332, United States
- Department of Haematology, Imperial College London, London SW7 2AZ, United Kingdom
| | - Athanasios Mantalaris
- Biological Systems Engineering Laboratory, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Michael C. Georgiadis
- Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
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Tissot T, Thomas F, Roche B. Non-cell-autonomous effects yield lower clonal diversity in expanding tumors. Sci Rep 2017; 7:11157. [PMID: 28894191 PMCID: PMC5593982 DOI: 10.1038/s41598-017-11562-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 08/21/2017] [Indexed: 01/07/2023] Open
Abstract
Recent cancer research has investigated the possibility that non-cell-autonomous (NCA) driving tumor growth can support clonal diversity (CD). Indeed, mutations can affect the phenotypes not only of their carriers (“cell-autonomous”, CA effects), but also sometimes of other cells (NCA effects). However, models that have investigated this phenomenon have only considered a restricted number of clones. Here, we designed an individual-based model of tumor evolution, where clones grow and mutate to yield new clones, among which a given frequency have NCA effects on other clones’ growth. Unlike previously observed for smaller assemblages, most of our simulations yield lower CD with high frequency of mutations with NCA effects. Owing to NCA effects increasing competition in the tumor, clones being already dominant are more likely to stay dominant, and emergent clones not to thrive. These results may help personalized medicine to predict intratumor heterogeneity across different cancer types for which frequency of NCA effects could be quantified.
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Affiliation(s)
- Tazzio Tissot
- CREEC/MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 64501, 34394, Montpellier, Cedex 5, France.
| | - Frédéric Thomas
- CREEC/MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 64501, 34394, Montpellier, Cedex 5, France
| | - Benjamin Roche
- CREEC/MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 64501, 34394, Montpellier, Cedex 5, France.,Unité mixte internationale de Modélisation Mathématique et Informatique des Systèmes Complexes. (UMI IRD/UPMC UMMISCO), 32 Avenue Henri Varagnat, 93143, Bondy Cedex, France
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8
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Identification of optimal strategies for state transition of complex biological networks. Biochem Soc Trans 2017; 45:1015-1024. [PMID: 28733488 DOI: 10.1042/bst20160419] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 04/23/2017] [Accepted: 05/25/2017] [Indexed: 11/17/2022]
Abstract
Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor. In addition, the minimum time for the state transition was determined by defining and solving a time-optimal control problem. Moreover, time-dependent variable profiles for state transitions were achieved rather than constant values commonly adopted in previous studies. Furthermore, the optimization method allows multiple controls to be simultaneously adjusted to drive the system out of an undesired attractor. Optimization improved the results of the parameter perturbation method, thus providing a valuable guidance for experimental design.
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9
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Samarasinghe S, Ling H. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks. Biosystems 2017; 153-154:6-25. [PMID: 28174135 DOI: 10.1016/j.biosystems.2017.01.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 12/01/2016] [Accepted: 01/23/2017] [Indexed: 11/16/2022]
Abstract
In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced parameters and protein concentrations similar to the original RNN system. Results thus demonstrated the reliability of the proposed RNN method for modelling relatively large networks by modularisation for practical settings. Advantages of the method are its ability to represent accurate continuous system dynamics and ease of: parameter estimation through training with data from a practical setting, model analysis (40% faster than ODE), fine tuning parameters when more data are available, sub-model extension when new elements and/or interactions come to light and model expansion with addition of sub-models.
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Affiliation(s)
- S Samarasinghe
- Integrated Systems Modelling Group, Lincoln University, New Zealand.
| | - H Ling
- Integrated Systems Modelling Group, Lincoln University, New Zealand
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Kostoglou M, Fuentes-Garí M, García-Münzer D, Georgiadis MC, Panoskaltsis N, Pistikopoulos EN, Mantalaris A. A comprehensive mathematical analysis of a novel multistage population balance model for cell proliferation. Comput Chem Eng 2016. [DOI: 10.1016/j.compchemeng.2016.02.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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11
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Savvopoulos S, Misener R, Panoskaltsis N, Pistikopoulos EN, Mantalaris A. A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia. IEEE Trans Biomed Eng 2016; 63:2396-2404. [PMID: 26929022 DOI: 10.1109/tbme.2016.2533658] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Chronic lymphocytic leukemia (CLL) is the most common peripheral blood and bone marrow cancer in the developed world. This manuscript proposes mathematical model equations representing the disease dynamics of B-cell CLL. We interconnect delay differential cell cycle models in each of the tumor-involved disease centers using physiologically relevant cell migration. We further introduce five hypothetical case studies representing CLL heterogeneity commonly seen in clinical practice and demonstrate how the proposed CLL model framework may capture disease pathophysiology across patient types. We conclude by exploring the capacity of the proposed temporally- and spatially distributed model to capture the heterogeneity of CLL disease progression. By using global sensitivity analysis, the critical parameters influencing disease trajectory over space and time are: 1) the initial number of CLL cells in peripheral blood, the number of involved lymph nodes, the presence and degree of splenomegaly; 2) the migratory fraction of nonproliferating as well as proliferating CLL cells from bone marrow into blood and of proliferating CLL cells from blood into lymph nodes; and 3) the parameters inducing nonproliferative cells to proliferate. The proposed model offers a practical platform that may be explored in future personalized patient protocols once validated.
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12
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A systematic framework for the design, simulation and optimization of personalized healthcare: Making and healing blood. Comput Chem Eng 2015. [DOI: 10.1016/j.compchemeng.2015.03.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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13
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Fuentes-Garí M, Misener R, Georgiadis MC, Kostoglou M, Panoskaltsis N, Mantalaris A, Pistikopoulos EN. Selecting a Differential Equation Cell Cycle Model for Simulating Leukemia Treatment. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01150] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
| | | | | | | | - Nicki Panoskaltsis
- Centre
for Haematology, Imperial College London, Northwick Park Campus, HA1
3LY, London, U.K
| | | | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
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