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Cesaro G, Milia M, Baruzzo G, Finco G, Morandini F, Lazzarini A, Alotto P, da Cunha Carvalho de Miranda NF, Trajanoski Z, Finotello F, Di Camillo B. MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach. BIOINFORMATICS ADVANCES 2022; 2:vbac092. [PMID: 36699399 PMCID: PMC9744439 DOI: 10.1093/bioadv/vbac092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/03/2022] [Indexed: 12/10/2022]
Abstract
Motivation Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario. Results We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor-immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model.The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach. Availability and implementation MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | | | - Giacomo Baruzzo
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Giovanni Finco
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Francesco Morandini
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Alessio Lazzarini
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Piergiorgio Alotto
- Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
| | | | - Zlatko Trajanoski
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Francesca Finotello
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria,Institute of Molecular Biology, University Innsbruck, 6020 Innsbruck, Austria,Digital Science Center (DiSC), University Innsbruck, 6020 Innsbruck, Austria
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Saha S, Moon HR, Han B, Mugler A. Deduction of signaling mechanisms from cellular responses to multiple cues. NPJ Syst Biol Appl 2022; 8:48. [PMID: 36450797 PMCID: PMC9712676 DOI: 10.1038/s41540-022-00262-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/08/2022] [Indexed: 12/05/2022] Open
Abstract
Cell signaling networks are complex and often incompletely characterized, making it difficult to obtain a comprehensive picture of the mechanisms they encode. Mathematical modeling of these networks provides important clues, but the models themselves are often complex, and it is not always clear how to extract falsifiable predictions. Here we take an inverse approach, using experimental data at the cell level to deduce the minimal signaling network. We focus on cells' response to multiple cues, specifically on the surprising case in which the response is antagonistic: the response to multiple cues is weaker than the response to the individual cues. We systematically build candidate signaling networks one node at a time, using the ubiquitous ingredients of (i) up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding. In each case, our method reveals a minimal, interpretable signaling mechanism that explains the antagonistic response. Our work provides a systematic way to deduce molecular mechanisms from cell-level data.
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Affiliation(s)
- Soutick Saha
- grid.169077.e0000 0004 1937 2197Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907 USA
| | - Hye-ran Moon
- grid.169077.e0000 0004 1937 2197School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA
| | - Bumsoo Han
- grid.169077.e0000 0004 1937 2197School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA ,grid.169077.e0000 0004 1937 2197Purdue Center for Cancer Research, Purdue University, West Lafayette, IN 47907 USA
| | - Andrew Mugler
- grid.169077.e0000 0004 1937 2197Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907 USA ,grid.169077.e0000 0004 1937 2197Purdue Center for Cancer Research, Purdue University, West Lafayette, IN 47907 USA ,grid.21925.3d0000 0004 1936 9000Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260 USA
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Merino-Casallo F, Gomez-Benito MJ, Martinez-Cantin R, Garcia-Aznar JM. A mechanistic protrusive-based model for 3D cell migration. Eur J Cell Biol 2022; 101:151255. [PMID: 35843121 DOI: 10.1016/j.ejcb.2022.151255] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/15/2022] [Accepted: 07/01/2022] [Indexed: 11/17/2022] Open
Abstract
Cell migration is essential for a variety of biological processes, such as embryogenesis, wound healing, and the immune response. After more than a century of research-mainly on flat surfaces-, there are still many unknowns about cell motility. In particular, regarding how cells migrate within 3D matrices, which more accurately replicate in vivo conditions. We present a novel in silico model of 3D mesenchymal cell migration regulated by the chemical and mechanical profile of the surrounding environment. This in silico model considers cell's adhesive and nuclear phenotypes, the effects of the steric hindrance of the matrix, and cells ability to degradate the ECM. These factors are crucial when investigating the increasing difficulty that migrating cells find to squeeze their nuclei through dense matrices, which may act as physical barriers. Our results agree with previous in vitro observations where fibroblasts cultured in collagen-based hydrogels did not durotax toward regions with higher collagen concentrations. Instead, they exhibited an adurotactic behavior, following a more random trajectory. Overall, cell's migratory response in 3D domains depends on its phenotype, and the properties of the surrounding environment, that is, 3D cell motion is strongly dependent on the context.
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Affiliation(s)
- Francisco Merino-Casallo
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), Zaragoza 50018, Spain; Department of Mechanical Engineering, Universidad de Zaragoza, Zaragoza 50009, Spain
| | - Maria Jose Gomez-Benito
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), Zaragoza 50018, Spain; Department of Mechanical Engineering, Universidad de Zaragoza, Zaragoza 50009, Spain
| | - Ruben Martinez-Cantin
- Robotics, Perception and Real Time Group (RoPeRT), Aragon Institute of Engineering Research (I3A), Zaragoza 50018, Spain; Department of Computer Science and System Engineering, Universidad de Zaragoza, Zaragoza 50009, Spain
| | - Jose Manuel Garcia-Aznar
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), Zaragoza 50018, Spain; Department of Mechanical Engineering, Universidad de Zaragoza, Zaragoza 50009, Spain.
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Bergman D, Sweis RF, Pearson AT, Nazari F, Jackson TL. A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues. iScience 2022; 25:104387. [PMID: 35637730 PMCID: PMC9142654 DOI: 10.1016/j.isci.2022.104387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/30/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Agent-based models (ABMs) are a natural platform for capturing the multiple time and spatial scales in biological processes. However, these models are computationally expensive, especially when including molecular-level effects. The traditional approach to simulating this type of multiscale ABM is to solve a system of ordinary differential equations for the molecular events per cell. This significantly adds to the computational cost of simulations as the number of agents grows, which contributes to many ABMs being limited to around10 5 cells. We propose an approach that requires the same computational time independent of the number of agents. This speeds up the entire simulation by orders of magnitude, allowing for more thorough explorations of ABMs with even larger numbers of agents. We use two systems to show that the new method strongly agrees with the traditionally used approach. This computational strategy can be applied to a wide range of biological investigations.
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Affiliation(s)
- Daniel Bergman
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Randy F. Sweis
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
| | - Alexander T. Pearson
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
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Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study. Cell Death Dis 2022; 13:485. [PMID: 35597788 PMCID: PMC9124196 DOI: 10.1038/s41419-022-04939-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/11/2022] [Accepted: 05/11/2022] [Indexed: 12/14/2022]
Abstract
We present a multiscale agent-based model of ductal carcinoma in situ (DCIS) to study how key phenotypic and signaling pathways are involved in the early stages of disease progression. The model includes a phenotypic hierarchy, and key endocrine and paracrine signaling pathways, and simulates cancer ductal growth in a 3D lattice-free domain. In particular, by considering stochastic cell dedifferentiation plasticity, the model allows for study of how dedifferentiation to a more stem-like phenotype plays key roles in the maintenance of cancer stem cell populations and disease progression. Through extensive parameter perturbation studies, we have quantified and ranked how DCIS is sensitive to perturbations in several key mechanisms that are instrumental to early disease development. Our studies reveal that long-term maintenance of multipotent stem-like cell niches within the tumor are dependent on cell dedifferentiation plasticity, and that disease progression will become arrested due to dilution of the multipotent stem-like population in the absence of dedifferentiation. We have identified dedifferentiation rates necessary to maintain biologically relevant multipotent cell populations, and also explored quantitative relationships between dedifferentiation rates and disease progression rates, which may potentially help to optimize the efficacy of emerging anti-cancer stem cell therapeutics.
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Dogra P, Ramírez JR, Butner JD, Peláez MJ, Chung C, Hooda-Nehra A, Pasqualini R, Arap W, Cristini V, Calin GA, Ozpolat B, Wang Z. Translational Modeling Identifies Synergy between Nanoparticle-Delivered miRNA-22 and Standard-of-Care Drugs in Triple-Negative Breast Cancer. Pharm Res 2022; 39:511-528. [PMID: 35294699 PMCID: PMC8986735 DOI: 10.1007/s11095-022-03176-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/21/2022] [Indexed: 12/29/2022]
Abstract
Purpose Downregulation of miRNA-22 in triple-negative breast cancer (TNBC) is associated with upregulation of eukaryotic elongation 2 factor kinase (eEF2K) protein, which regulates tumor growth, chemoresistance, and tumor immunosurveillance. Moreover, exogenous administration of miRNA-22, loaded in nanoparticles to prevent degradation and improve tumor delivery (termed miRNA-22 nanotherapy), to suppress eEF2K production has shown potential as an investigational therapeutic agent in vivo. Methods To evaluate the translational potential of miRNA-22 nanotherapy, we developed a multiscale mechanistic model, calibrated to published in vivo data and extrapolated to the human scale, to describe and quantify the pharmacokinetics and pharmacodynamics of miRNA-22 in virtual patient populations. Results Our analysis revealed the dose-response relationship, suggested optimal treatment frequency for miRNA-22 nanotherapy, and highlighted key determinants of therapy response, from which combination with immune checkpoint inhibitors was identified as a candidate strategy for improving treatment outcomes. More importantly, drug synergy was identified between miRNA-22 and standard-of-care drugs against TNBC, providing a basis for rational therapeutic combinations for improved response Conclusions The present study highlights the translational potential of miRNA-22 nanotherapy for TNBC in combination with standard-of-care drugs. Supplementary Information The online version contains supplementary material available at 10.1007/s11095-022-03176-3.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, 10065, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Maria J Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Anupama Hooda-Nehra
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77230, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York, 10065, USA
| | - George A Calin
- Department of Translational Molecular Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Bulent Ozpolat
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA.
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, 10065, USA.
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Dogra P, Ramirez JR, Butner JD, Pelaez MJ, Cristini V, Wang Z. A Multiscale Model to Identify Limiting Factors in Nanoparticle-Based miRNA Delivery for Tumor Inhibition . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4230-4233. [PMID: 34892157 PMCID: PMC8712117 DOI: 10.1109/embc46164.2021.9630862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MicroRNA-based gene therapy for cancer treatment via nanoparticles (NPs) requires navigation of multiple physical and physiological barriers in order to efficiently deliver the miRNAs to the cancer cell cytoplasm. We here present a mathematical model to investigate the variability associated with tumor, NP, and miRNA characteristics, and identify the limiting factors in miRNA delivery to tumors. Through global parameter analysis, the miRNA release rate from NPs and NP degradability were found to have the most significant impact on cytosolic accumulation of miRNAs. These NP properties can be fine-tuned in order to optimize the delivery system for enhancing the efficacy of miRNA-based therapy.Clinical Relevance-Understanding the effect of nanoparticle, tumor, and miRNA characteristics in governing the efficacy of miRNA-based cancer therapy will support its clinical translation.
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Gong C, Ruiz-Martinez A, Kimko H, Popel AS. A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor-Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy. Cancers (Basel) 2021; 13:3751. [PMID: 34359653 PMCID: PMC8345161 DOI: 10.3390/cancers13153751] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/09/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022] Open
Abstract
Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer-immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials.
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Affiliation(s)
- Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
| | - Alvaro Ruiz-Martinez
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD 20878, USA;
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
- Sidney Kimmel Comprehensive Cancer Center, Department of Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
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Corradetti B, Dogra P, Pisano S, Wang Z, Ferrari M, Chen SH, Sidman RL, Pasqualini R, Arap W, Cristini V. Amphibian regeneration and mammalian cancer: Similarities and contrasts from an evolutionary biology perspective: Comparing the regenerative potential of mammalian embryos and urodeles to develop effective strategies against human cancer. Bioessays 2021; 43:e2000339. [PMID: 33751590 DOI: 10.1002/bies.202000339] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/18/2021] [Accepted: 02/23/2021] [Indexed: 12/11/2022]
Abstract
Here we review and discuss the link between regeneration capacity and tumor suppression comparing mammals (embryos versus adults) with highly regenerative vertebrates. Similar to mammal embryo morphogenesis, in amphibians (essentially newts and salamanders) the reparative process relies on a precise molecular and cellular machinery capable of sensing abnormal signals and actively reprograming or eliminating them. As the embryo's evil twin, tumor also retains common functional attributes. The immune system plays a pivotal role in maintaining a physiological balance to provide surveillance against tumor initiation or to support its initiation and progression. We speculate that susceptibility to cancer development in adult mammals may be determined by the loss of an advanced regenerative capability during evolution and believe that gaining mechanistic insights into how regenerative capacity linked to tumor suppression is postnatally lost in mammals might illuminate an as yet unrecognized route to cancer treatment.
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Affiliation(s)
- Bruna Corradetti
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, Texas, USA.,Texas A&M Health Science Center, College of Medicine, 8446 Riverside Pkwy, Bryan, TX, 77807, USA.,Swansea University Medical School, Swansea, Wales, UK
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Simone Pisano
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, Texas, USA.,Swansea University Medical School, Swansea, Wales, UK
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Mauro Ferrari
- Department of Pharmaceutics, University of Washington, Seattle, Washington, USA
| | - Shu-Hsia Chen
- Immunotherapy Research Center, Houston Methodist Research Institute, Houston, Texas, USA.,Cancer Center, Houston Methodist Research Institute, Houston, Texas, USA
| | - Richard L Sidman
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.,Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.,Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
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Tavakoli F, Sartakhti JS, Manshaei MH, Basanta D. Cancer immunoediting: A game theoretical approach. In Silico Biol 2021; 14:1-12. [PMID: 33216021 PMCID: PMC8203245 DOI: 10.3233/isb-200475] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The role of the immune system in tumor development increasingly includes the idea of cancer immunoediting. It comprises three phases: elimination, equilibrium, and escape. In the first phase, elimination, transformed cells are recognized and destroyed by immune system. The rare tumor cells that are not destroyed in this phase may then enter the equilibrium phase, where their growth is prevented by immunity mechanisms. The escape phase represents the final phase of this process, where cancer cells begin to grow unconstrained by the immune system. In this study, we describe and analyze an evolutionary game theoretical model of proliferating, quiescent, and immune cells interactions for the first time. The proposed model is evaluated with constant and dynamic approaches. Population dynamics and interactions between the immune system and cancer cells are investigated. Stability of equilibria or critical points are analyzed by applying algebraic analysis. This model allows us to understand the process of cancer development and might help us design better treatment strategies to account for immunoediting.
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Affiliation(s)
- Fatemeh Tavakoli
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | | | | | - David Basanta
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Yu JS, Bagheri N. Agent-Based Modeling. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11509-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Butner JD, Fuentes D, Ozpolat B, Calin GA, Zhou X, Lowengrub J, Cristini V, Wang Z. A Multiscale Agent-Based Model of Ductal Carcinoma In Situ. IEEE Trans Biomed Eng 2020; 67:1450-1461. [PMID: 31603768 PMCID: PMC8445608 DOI: 10.1109/tbme.2019.2938485] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
OBJECTIVE we present a multiscale agent-based model of Ductal Carcinoma in Situ (DCIS) in order to gain a detailed understanding of the cell-scale population dynamics, phenotypic distributions, and the associated interplay of important molecular signaling pathways that are involved in DCIS ductal invasion into the duct cavity (a process we refer to as duct advance rate here). METHODS DCIS is modeled mathematically through a hybridized discrete cell-scale model and a continuum molecular scale model, which are explicitly linked through a bidirectional feedback mechanism. RESULTS we find that duct advance rates occur in two distinct phases, characterized by an early exponential population expansion, followed by a long-term steady linear phase of population expansion, a result that is consistent with other modeling work. We further found that the rates were influenced most strongly by endocrine and paracrine signaling intensity, as well as by the effects of cell density induced quiescence within the DCIS population. CONCLUSION our model analysis identified a complex interplay between phenotypic diversity that may provide a tumor adaptation mechanism to overcome proliferation limiting conditions, allowing for dynamic shifts in phenotypic populations in response to variation in molecular signaling intensity. Further, sensitivity analysis determined DCIS axial advance rates and calcification rates were most sensitive to cell cycle time variation. SIGNIFICANCE this model may serve as a useful tool to study the cell-scale dynamics involved in DCIS initiation and intraductal invasion, and may provide insights into promising areas of future experimental research.
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A multi-scale model for determining the effects of pathophysiology and metabolic disorders on tumor growth. Sci Rep 2020; 10:3025. [PMID: 32080250 PMCID: PMC7033139 DOI: 10.1038/s41598-020-59658-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 01/17/2020] [Indexed: 11/08/2022] Open
Abstract
The search for efficient chemotherapy drugs and other anti-cancer treatments would benefit from a deeper understanding of the tumor microenvironment (TME) and its role in tumor progression. Because in vivo experimental methods are unable to isolate or control individual factors of the TME and in vitro models often do not include all the contributing factors, some questions are best addressed with systems biology mathematical models. In this work, we present a new fully-coupled, agent-based, multi-scale mathematical model of tumor growth, angiogenesis and metabolism that includes important aspects of the TME spanning subcellular-, cellular- and tissue-level scales. The mathematical model is computationally implemented for a three-dimensional TME, and a double hybrid continuous-discrete (DHCD) method is applied to solve the governing equations. The model recapitulates the distinct morphological and metabolic stages of a solid tumor, starting with an avascular tumor and progressing through angiogenesis and vascularized tumor growth. To examine the robustness of the model, we simulated normal and abnormal blood conditions, including hyperglycemia/hypoglycemia, hyperoxemia/hypoxemia, and hypercarbia/hypocarbia - conditions common in cancer patients. The results demonstrate that tumor progression is accelerated by hyperoxemia, hyperglycemia and hypercarbia but inhibited by hypoxemia and hypoglycemia; hypocarbia had no appreciable effect. Because of the importance of interstitial fluid flow in tumor physiology, we also examined the effects of hypo- or hypertension, and the impact of decreased hydraulic conductivity common in desmoplastic tumors. The simulations show that chemotherapy-increased blood pressure, or reduction of interstitial hydraulic conductivity increase tumor growth rate and contribute to tumor malignancy.
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Verma M, Bassaganya-Riera J, Leber A, Tubau-Juni N, Hoops S, Abedi V, Chen X, Hontecillas R. High-resolution computational modeling of immune responses in the gut. Gigascience 2020; 8:5513894. [PMID: 31185494 PMCID: PMC6559340 DOI: 10.1093/gigascience/giz062] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 01/19/2019] [Accepted: 05/05/2019] [Indexed: 02/07/2023] Open
Abstract
Background Helicobacter pylori causes gastric cancer in 1–2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori–colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing–driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations. Results The outputs were analyzed using 2 sequential stages: the first used a partial rank correlation coefficient regression–based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells. Conclusions The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.
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Affiliation(s)
- Meghna Verma
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.,Graduate Program in Translational Biology, Medicine and Health, Virginia Tech, Blacksburg, 1 Riverside Circle, Roanoke, VA 24016, USA
| | - Josep Bassaganya-Riera
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Andrew Leber
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Nuria Tubau-Juni
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Stefan Hoops
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Vida Abedi
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Xi Chen
- Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Perry St, Blacksburg, VA 24061, USA
| | - Raquel Hontecillas
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
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15
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Voukantsis D, Kahn K, Hadley M, Wilson R, Buffa FM. Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior. Gigascience 2019; 8:giz010. [PMID: 30715320 PMCID: PMC6423375 DOI: 10.1093/gigascience/giz010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 11/13/2018] [Accepted: 01/16/2019] [Indexed: 12/21/2022] Open
Abstract
A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behavior. Deciphering genotype-phenotype relationships has been crucial to understanding normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, typically it does not consider the physical microenvironment, which is a key determinant of phenotype. In this study, we present a novel modeling framework that enables the study of the link between genotype, signaling networks, and cell behavior in a three-dimensional microenvironment. To achieve this, we bring together Agent-Based Modeling, a powerful computational modeling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modeling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrient availability, and their interactions. Using cancer as a model system, we illustrate how this framework delivers a unique opportunity to identify determinants of single-cell behavior, while uncovering emerging properties of multi-cellular growth. This framework is freely available at http://www.microc.org.
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Affiliation(s)
- Dimitrios Voukantsis
- Computational Biology and Integrative Genomics, MRC/CRUK Oxford Institute, Departmemt of Oncology, University of Oxford, Old Road Campus, Oxford, Oxfordshire, OX3 7DQ, UK
| | - Kenneth Kahn
- Computational Biology and Integrative Genomics, MRC/CRUK Oxford Institute, Departmemt of Oncology, University of Oxford, Old Road Campus, Oxford, Oxfordshire, OX3 7DQ, UK
- Academic Information Technology Research Team, University of Oxford, 13 Bambury Road, Oxford, Oxfordshire, OX2 6NN, UK
| | - Martin Hadley
- Academic Information Technology Research Team, University of Oxford, 13 Bambury Road, Oxford, Oxfordshire, OX2 6NN, UK
| | - Rowan Wilson
- Academic Information Technology Research Team, University of Oxford, 13 Bambury Road, Oxford, Oxfordshire, OX2 6NN, UK
| | - Francesca M Buffa
- Computational Biology and Integrative Genomics, MRC/CRUK Oxford Institute, Departmemt of Oncology, University of Oxford, Old Road Campus, Oxford, Oxfordshire, OX3 7DQ, UK
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16
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Wang Z, Deisboeck TS. Dynamic Targeting in Cancer Treatment. Front Physiol 2019; 10:96. [PMID: 30890944 PMCID: PMC6413712 DOI: 10.3389/fphys.2019.00096] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 01/25/2019] [Indexed: 12/18/2022] Open
Abstract
With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.
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Affiliation(s)
- Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Thomas S Deisboeck
- Department of Radiology, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
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17
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Jarrett AM, Lima EABF, Hormuth DA, McKenna MT, Feng X, Ekrut DA, Resende ACM, Brock A, Yankeelov TE. Mathematical models of tumor cell proliferation: A review of the literature. Expert Rev Anticancer Ther 2018; 18:1271-1286. [PMID: 30252552 PMCID: PMC6295418 DOI: 10.1080/14737140.2018.1527689] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
INTRODUCTION A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. Expert commentary: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.
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Affiliation(s)
- Angela M Jarrett
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
| | - Ernesto A B F Lima
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - David A Hormuth
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
| | - Matthew T McKenna
- c Department of Biomedical Engineering , Vanderbilt University , Nashville , USA
| | - Xinzeng Feng
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - David A Ekrut
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - Anna Claudia M Resende
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- d Department of Computational Modeling , National Laboratory for Scientific Computing , Petrópolis , Brazil
| | - Amy Brock
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
- e Department of Biomedical Engineering , The University of Texas at Austin , Austin , USA
| | - Thomas E Yankeelov
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
- e Department of Biomedical Engineering , The University of Texas at Austin , Austin , USA
- f Department of Diagnostic Medicine , The University of Texas at Austin , Austin , USA
- g Department of Oncology , The University of Texas at Austin , Austin , USA
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18
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Oduola WO, Li X. Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System. Cancer Inform 2018; 17:1176935118790262. [PMID: 30083052 PMCID: PMC6073835 DOI: 10.1177/1176935118790262] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 06/16/2018] [Indexed: 12/16/2022] Open
Abstract
Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.
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Affiliation(s)
- Wasiu Opeyemi Oduola
- Department of Electrical and Computer Engineering (ECE), Prairie View A&M University, Prairie View, TX, USA
| | - Xiangfang Li
- Department of Electrical and Computer Engineering (ECE), Prairie View A&M University, Prairie View, TX, USA
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19
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Gong C, Milberg O, Wang B, Vicini P, Narwal R, Roskos L, Popel AS. A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition. J R Soc Interface 2018; 14:rsif.2017.0320. [PMID: 28931635 PMCID: PMC5636269 DOI: 10.1098/rsif.2017.0320] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/30/2017] [Indexed: 12/11/2022] Open
Abstract
When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.
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Affiliation(s)
- Chang Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Oleg Milberg
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | | | | | | | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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20
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Zangooei MH, Habibi J. Hybrid multiscale modeling and prediction of cancer cell behavior. PLoS One 2017; 12:e0183810. [PMID: 28846712 PMCID: PMC5573302 DOI: 10.1371/journal.pone.0183810] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 08/13/2017] [Indexed: 12/03/2022] Open
Abstract
Background Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. Methods In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Results Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Conclusion Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.
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Affiliation(s)
| | - Jafar Habibi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
- * E-mail:
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21
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Li XL, Oduola WO, Qian L, Dougherty ER. Integrating Multiscale Modeling with Drug Effects for Cancer Treatment. Cancer Inform 2016; 14:21-31. [PMID: 26792977 PMCID: PMC4712979 DOI: 10.4137/cin.s30797] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 11/08/2015] [Accepted: 11/15/2015] [Indexed: 12/12/2022] Open
Abstract
In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.
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Affiliation(s)
- Xiangfang L. Li
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Wasiu O. Oduola
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Lijun Qian
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Edward R. Dougherty
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
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22
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Gardiner BS, Wong KKL, Joldes GR, Rich AJ, Tan CW, Burgess AW, Smith DW. Discrete Element Framework for Modelling Extracellular Matrix, Deformable Cells and Subcellular Components. PLoS Comput Biol 2015; 11:e1004544. [PMID: 26452000 PMCID: PMC4599884 DOI: 10.1371/journal.pcbi.1004544] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 09/09/2015] [Indexed: 01/13/2023] Open
Abstract
This paper presents a framework for modelling biological tissues based on discrete particles. Cell components (e.g. cell membranes, cell cytoskeleton, cell nucleus) and extracellular matrix (e.g. collagen) are represented using collections of particles. Simple particle to particle interaction laws are used to simulate and control complex physical interaction types (e.g. cell-cell adhesion via cadherins, integrin basement membrane attachment, cytoskeletal mechanical properties). Particles may be given the capacity to change their properties and behaviours in response to changes in the cellular microenvironment (e.g., in response to cell-cell signalling or mechanical loadings). Each particle is in effect an ‘agent’, meaning that the agent can sense local environmental information and respond according to pre-determined or stochastic events. The behaviour of the proposed framework is exemplified through several biological problems of ongoing interest. These examples illustrate how the modelling framework allows enormous flexibility for representing the mechanical behaviour of different tissues, and we argue this is a more intuitive approach than perhaps offered by traditional continuum methods. Because of this flexibility, we believe the discrete modelling framework provides an avenue for biologists and bioengineers to explore the behaviour of tissue systems in a computational laboratory. Modelling is an important tool in understanding the behaviour of biological tissues. In this paper we advocate a new modelling framework in which cells and tissues are represented by a collection of particles with associated properties. The particles interact with each other and can change their behaviour in response to changes in their environment. We demonstrate how the propose framework can be used to represent the mechanical behaviour of different tissues with much greater flexibility as compared to traditional continuum based methods.
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Affiliation(s)
- Bruce S. Gardiner
- School of Engineering and Information Technology, Murdoch University, Perth, Australia
- * E-mail:
| | - Kelvin K. L. Wong
- Engineering Computational Biology, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - Grand R. Joldes
- Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Perth, Australia
| | - Addison J. Rich
- Engineering Computational Biology, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - Chin Wee Tan
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
| | - Antony W. Burgess
- Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- Department of Surgery, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - David W. Smith
- Engineering Computational Biology, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
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23
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Frieboes HB, Smith BR, Wang Z, Kotsuma M, Ito K, Day A, Cahill B, Flinders C, Mumenthaler SM, Mallick P, Simbawa E, AL-Fhaid AS, Mahmoud SR, Gambhir SS, Cristini V. Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma. PLoS One 2015; 10:e0129433. [PMID: 26061425 PMCID: PMC4464754 DOI: 10.1371/journal.pone.0129433] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 05/09/2015] [Indexed: 12/20/2022] Open
Abstract
We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.
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Affiliation(s)
- Hermann B. Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, 40202, United States of America
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, United States of America
- Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, United States of America
| | - Bryan R. Smith
- Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA, 94305, United States of America
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, United States of America
| | - Masakatsu Kotsuma
- Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA, 94305, United States of America
| | - Ken Ito
- Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA, 94305, United States of America
| | - Armin Day
- Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, United States of America
| | - Benjamin Cahill
- Department of Bioengineering, University of Louisville, Louisville, KY, 40202, United States of America
| | - Colin Flinders
- Department of Biological Chemistry, University of California at Los Angeles, Los Angeles, CA, 90095, United States of America
- Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA, 90033, United States of America
| | - Shannon M. Mumenthaler
- Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA, 90033, United States of America
| | - Parag Mallick
- Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA, 94305, United States of America
| | - Eman Simbawa
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - A. S. AL-Fhaid
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - S. R. Mahmoud
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Sanjiv S. Gambhir
- Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA, 94305, United States of America
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, United States of America
- Department of Materials Science & Engineering, and Bio-X, Stanford University, Stanford, CA, 94305, United States of America
| | - Vittorio Cristini
- Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, United States of America
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM, 87131, United States of America
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24
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Wang Z, Deisboeck TS, Cristini V. Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models. IET Syst Biol 2015; 8:191-7. [PMID: 25257020 DOI: 10.1049/iet-syb.2013.0026] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
There are two challenges that researchers face when performing global sensitivity analysis (GSA) on multiscale 'in silico' cancer models. The first is increased computational intensity, since a multiscale cancer model generally takes longer to run than does a scale-specific model. The second problem is the lack of a best GSA method that fits all types of models, which implies that multiple methods and their sequence need to be taken into account. In this study, the authors therefore propose a sampling-based GSA workflow consisting of three phases - pre-analysis, analysis and post-analysis - by integrating Monte Carlo and resampling methods with the repeated use of analysis of variance; they then exemplify this workflow using a two-dimensional multiscale lung cancer model. By accounting for all parameter rankings produced by multiple GSA methods, a summarised ranking is created at the end of the workflow based on the weighted mean of the rankings for each input parameter. For the cancer model investigated here, this analysis reveals that extracellular signal-regulated kinase, a downstream molecule of the epidermal growth factor receptor signalling pathway, has the most important impact on regulating both the tumour volume and expansion rate in the algorithm used.
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Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Thomas S Deisboeck
- Department of Radiology, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Vittorio Cristini
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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25
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Sakkalis V, Sfakianakis S, Tzamali E, Marias K, Stamatakos G, Misichroni F, Ouzounoglou E, Kolokotroni E, Dionysiou D, Johnson D, McKeever S, Graf N. Web-based workflow planning platform supporting the design and execution of complex multiscale cancer models. IEEE J Biomed Health Inform 2015; 18:824-31. [PMID: 24808225 DOI: 10.1109/jbhi.2013.2297167] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Significant Virtual Physiological Human efforts and projects have been concerned with cancer modeling, especially in the European Commission Seventh Framework research program, with the ambitious goal to approach personalized cancer simulation based on patient-specific data and thereby optimize therapy decisions in the clinical setting. However, building realistic in silico predictive models targeting the clinical practice requires interactive, synergetic approaches to integrate the currently fragmented efforts emanating from the systems biology and computational oncology communities all around the globe. To further this goal, we propose an intelligent graphical workflow planning system that exploits the multiscale and modular nature of cancer and allows building complex cancer models by intuitively linking/interchanging highly specialized models. The system adopts and extends current standardization efforts, key tools, and infrastructure in view of building a pool of reliable and reproducible models capable of improving current therapies and demonstrating the potential for clinical translation of these technologies.
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26
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Wang Z, Butner JD, Cristini V, Deisboeck TS. Integrated PK-PD and agent-based modeling in oncology. J Pharmacokinet Pharmacodyn 2015; 42:179-89. [PMID: 25588379 DOI: 10.1007/s10928-015-9403-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Accepted: 01/08/2015] [Indexed: 01/11/2023]
Abstract
Mathematical modeling has become a valuable tool that strives to complement conventional biomedical research modalities in order to predict experimental outcome, generate new medical hypotheses, and optimize clinical therapies. Two specific approaches, pharmacokinetic-pharmacodynamic (PK-PD) modeling, and agent-based modeling (ABM), have been widely applied in cancer research. While they have made important contributions on their own (e.g., PK-PD in examining chemotherapy drug efficacy and resistance, and ABM in describing and predicting tumor growth and metastasis), only a few groups have started to combine both approaches together in an effort to gain more insights into the details of drug dynamics and the resulting impact on tumor growth. In this review, we focus our discussion on some of the most recent modeling studies building on a combined PK-PD and ABM approach that have generated experimentally testable hypotheses. Some future directions are also discussed.
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Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, USA
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27
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Wang Z, Kerketta R, Chuang YL, Cristini V. Development of a diffusion-based mathematical model for predicting chemotherapy effects. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:2480-3. [PMID: 25570493 DOI: 10.1109/embc.2014.6944125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Mathematical modeling of drug transport can complement current experimental and clinical investigations to understand drug resistance mechanisms, which eventually will help to develop patient-specific chemotherapy treatments. In this paper, we present a general time- and space-dependent mathematical model based on diffusion theory for predicting chemotherapy outcome. This model has two important parameters: the blood volume fraction and radius of blood vessels divided by drug diffusion penetration length. Model analysis finds that a larger ratio of the radius of blood vessel to diffusion penetration length resulted in to a larger fraction of tumor killed, thereby leading to a better treatment outcome. Clinical translation of the model can help quantify and predict the optimal dosage size and frequency of chemotherapy for individual patients.
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28
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Johnson D, Connor AJ, McKeever S, Wang Z, Deisboeck TS, Quaiser T, Shochat E. Semantically linking in silico cancer models. Cancer Inform 2014; 13:133-43. [PMID: 25520553 PMCID: PMC4260769 DOI: 10.4137/cin.s13895] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 10/15/2014] [Accepted: 10/16/2014] [Indexed: 01/23/2023] Open
Abstract
Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.
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Affiliation(s)
- David Johnson
- Department of Computing, Imperial College London, London, UK. ; Data Science Institute, Imperial College London, London, UK
| | - Anthony J Connor
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Steve McKeever
- Department of Informatics and Media, Uppsala University, Uppsala, Sweden. ; St. Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO), St. Petersburg, Russian Federation
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, USA
| | - Thomas S Deisboeck
- Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Tom Quaiser
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center, Penzberg, Germany
| | - Eliezer Shochat
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center, Basel, Switzerland
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29
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Finley SD, Chu LH, Popel AS. Computational systems biology approaches to anti-angiogenic cancer therapeutics. Drug Discov Today 2014; 20:187-97. [PMID: 25286370 DOI: 10.1016/j.drudis.2014.09.026] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Revised: 08/05/2014] [Accepted: 09/29/2014] [Indexed: 01/06/2023]
Abstract
Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.
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Affiliation(s)
- Stacey D Finley
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
| | - Liang-Hui Chu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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30
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Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS. Simulating cancer growth with multiscale agent-based modeling. Semin Cancer Biol 2014; 30:70-8. [PMID: 24793698 DOI: 10.1016/j.semcancer.2014.04.001] [Citation(s) in RCA: 132] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 03/18/2014] [Accepted: 04/04/2014] [Indexed: 01/01/2023]
Abstract
There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.
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Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Joseph D Butner
- Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA
| | - Romica Kerketta
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Vittorio Cristini
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA; Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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31
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Wang C, Tang Z, Zhao Y, Yao R, Li L, Sun W. Three-dimensional
in vitro
cancer models: a short review. Biofabrication 2014; 6:022001. [DOI: 10.1088/1758-5082/6/2/022001] [Citation(s) in RCA: 109] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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32
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Andrieux G, Le Borgne M, Théret N. An integrative modeling framework reveals plasticity of TGF-β signaling. BMC SYSTEMS BIOLOGY 2014; 8:30. [PMID: 24618419 PMCID: PMC4007780 DOI: 10.1186/1752-0509-8-30] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 03/03/2014] [Indexed: 11/10/2022]
Abstract
Background The TGF-β transforming growth factor is the most pleiotropic cytokine controlling a broad range of cellular responses that include proliferation, differentiation and apoptosis. The context-dependent multifunctional nature of TGF-β is associated with complex signaling pathways. Differential models describe the dynamics of the TGF-β canonical pathway, but modeling the non-canonical networks constitutes a major challenge. Here, we propose a qualitative approach to explore all TGF-β-dependent signaling pathways. Results Using a new formalism, CADBIOM, which is based on guarded transitions and includes temporal parameters, we have built the first discrete model of TGF-β signaling networks by automatically integrating the 137 human signaling maps from the Pathway Interaction Database into a single unified dynamic model. Temporal property-checking analyses of 15934 trajectories that regulate 145 TGF-β target genes reveal the association of specific pathways with distinct biological processes. We identify 31 different combinations of TGF-β with other extracellular stimuli involved in non-canonical TGF-β pathways that regulate specific gene networks. Extensive analysis of gene expression data further demonstrates that genes sharing CADBIOM trajectories tend to be co-regulated. Conclusions As applied here to TGF-β signaling, CADBIOM allows, for the first time, a full integration of highly complex signaling pathways into dynamic models that permit to explore cell responses to complex microenvironment stimuli.
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Affiliation(s)
| | | | - Nathalie Théret
- INSERM U1085, IRSET, Université de Rennes 1, 2 avenue Pr Léon Bernard, 35043 Rennes, France.
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33
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Masoudi-Nejad A, Meshkin A. WITHDRAWN: Cancer modeling: The holonic agent-based approach. Semin Cancer Biol 2014:S1044-579X(14)00032-7. [PMID: 24598694 DOI: 10.1016/j.semcancer.2014.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 02/19/2014] [Accepted: 02/20/2014] [Indexed: 11/22/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Alireza Meshkin
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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34
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Rekhi R, Qutub AA. Systems approaches for synthetic biology: a pathway toward mammalian design. Front Physiol 2013; 4:285. [PMID: 24130532 PMCID: PMC3793170 DOI: 10.3389/fphys.2013.00285] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Accepted: 09/19/2013] [Indexed: 01/08/2023] Open
Abstract
We review methods of understanding cellular interactions through computation in order to guide the synthetic design of mammalian cells for translational applications, such as regenerative medicine and cancer therapies. In doing so, we argue that the challenges of engineering mammalian cells provide a prime opportunity to leverage advances in computational systems biology. We support this claim systematically, by addressing each of the principal challenges to existing synthetic bioengineering approaches—stochasticity, complexity, and scale—with specific methods and paradigms in systems biology. Moreover, we characterize a key set of diverse computational techniques, including agent-based modeling, Bayesian network analysis, graph theory, and Gillespie simulations, with specific utility toward synthetic biology. Lastly, we examine the mammalian applications of synthetic biology for medicine and health, and how computational systems biology can aid in the continued development of these applications.
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Affiliation(s)
- Rahul Rekhi
- Department of Bioengineering, Rice University Houston, TX, USA
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35
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Wang Z, Deisboeck TS. Mathematical modeling in cancer drug discovery. Drug Discov Today 2013; 19:145-50. [PMID: 23831857 DOI: 10.1016/j.drudis.2013.06.015] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 06/25/2013] [Accepted: 06/27/2013] [Indexed: 12/20/2022]
Abstract
Mathematical models have the potential to help discover new therapeutic targets and treatment strategies. In this review, we discuss how the latest developments in mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. We give special attention to two modeling approaches: network-based modeling and multiscale modeling, because they have begun to show promise in facilitating the process of effective cancer drug discovery. Both modeling approaches are integrated with a variety of experimental methods to ensure proper parameterization and to maximize their predictive value. We also discuss several challenges faced in modeling-based drug discovery.
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Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA
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36
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Johnson D, McKeever S, Stamatakos G, Dionysiou D, Graf N, Sakkalis V, Marias K, Wang Z, Deisboeck TS. Dealing with diversity in computational cancer modeling. Cancer Inform 2013; 12:115-24. [PMID: 23700360 PMCID: PMC3653811 DOI: 10.4137/cin.s11583] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.
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Affiliation(s)
- David Johnson
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Steve McKeever
- Department of Informatics and Media, Uppsala University, Uppsala, Sweden
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Dimitra Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Norbert Graf
- Department of Paediatric Haematology and Oncology, Saarland University Hospital, Homburg, Germany
| | - Vangelis Sakkalis
- Institute of Computer Science at the Foundation for Research and Technology—Hellas, Heraklion, Crete, Greece
| | - Konstantinos Marias
- Institute of Computer Science at the Foundation for Research and Technology—Hellas, Heraklion, Crete, Greece
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, USA
| | - Thomas S. Deisboeck
- Harvard-MIT (HST), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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37
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Das H, Wang Z, Niazi MKK, Aggarwal R, Lu J, Kanji S, Das M, Joseph M, Gurcan M, Cristini V. Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer. PLoS One 2013; 8:e61398. [PMID: 23620747 PMCID: PMC3631240 DOI: 10.1371/journal.pone.0061398] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Accepted: 03/08/2013] [Indexed: 11/18/2022] Open
Abstract
Molecular-focused cancer therapies, e.g., molecularly targeted therapy and immunotherapy, so far demonstrate only limited efficacy in cancer patients. We hypothesize that underestimating the role of biophysical factors that impact the delivery of drugs or cytotoxic cells to the target sites (for associated preferential cytotoxicity or cell signaling modulation) may be responsible for the poor clinical outcome. Therefore, instead of focusing exclusively on the investigation of molecular mechanisms in cancer cells, convection-diffusion of cytotoxic molecules and migration of cancer-killing cells within tumor tissue should be taken into account to improve therapeutic effectiveness. To test this hypothesis, we have developed a mathematical model of the interstitial diffusion and uptake of small cytotoxic molecules secreted by T-cells, which is capable of predicting breast cancer growth inhibition as measured both in vitro and in vivo. Our analysis shows that diffusion barriers of cytotoxic molecules conspire with γδ T-cell scarcity in tissue to limit the inhibitory effects of γδ T-cells on cancer cells. This may increase the necessary ratios of γδ T-cells to cancer cells within tissue to unrealistic values for having an intended therapeutic effect, and decrease the effectiveness of the immunotherapeutic treatment.
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Affiliation(s)
- Hiranmoy Das
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail: (HD); (MG); (VC)
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - M. Khalid Khan Niazi
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
| | - Reeva Aggarwal
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Jingwei Lu
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Suman Kanji
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Manjusri Das
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Matthew Joseph
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Metin Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail: (HD); (MG); (VC)
| | - Vittorio Cristini
- Department of Pathology, University of New Mexico, Albuquerque, New Mexico, United States of America
- Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America
- * E-mail: (HD); (MG); (VC)
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Abstract
This Teaching Resource provides lecture notes, slides, and a problem set for a lecture introducing the mathematical concepts and interpretation of partial least squares regression (PLSR) that were part of a course entitled "Systems Biology: Mammalian Signaling Networks." PLSR is a multivariate regression technique commonly applied to analyze relationships between signaling or transcriptional data and cellular behavior.
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Affiliation(s)
- Pamela K Kreeger
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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39
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Wang Z, Sagotsky J, Taylor T, Shironoshita P, Deisboeck TS. Accelerating cancer systems biology research through Semantic Web technology. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2012. [PMID: 23188758 DOI: 10.1002/wsbm.1200] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute's caBIG, so users can interact with the DMR not only through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers' intellectual property.
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Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, USA
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40
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Abstract
Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.
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Affiliation(s)
- Thomas S Deisboeck
- Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts 02129
| | - Zhihui Wang
- Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts 02129
| | - Paul Macklin
- Division of Mathematics, University of Dundee, Dundee DD1 4HN, United Kingdom
| | - Vittorio Cristini
- Department of Pathology, University of New Mexico, Albuquerque, New Mexico 87131.,Department of Chemical and Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131]
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41
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Wang Z, Bordas V, Deisboeck TS. Identification of Critical Molecular Components in a Multiscale Cancer Model Based on the Integration of Monte Carlo, Resampling, and ANOVA. Front Physiol 2011; 2:35. [PMID: 21779251 PMCID: PMC3132643 DOI: 10.3389/fphys.2011.00035] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2011] [Accepted: 06/20/2011] [Indexed: 11/13/2022] Open
Abstract
To date, parameters defining biological properties in multiscale disease models are commonly obtained from a variety of sources. It is thus important to examine the influence of parameter perturbations on system behavior, rather than to limit the model to a specific set of parameters. Such sensitivity analysis can be used to investigate how changes in input parameters affect model outputs. However, multiscale cancer models require special attention because they generally take longer to run than does a series of signaling pathway analysis tasks. In this article, we propose a global sensitivity analysis method based on the integration of Monte Carlo, resampling, and analysis of variance. This method provides solutions to (1) how to render the large number of parameter variation combinations computationally manageable, and (2) how to effectively quantify the sampling distribution of the sensitivity index to address the inherent computational intensity issue. We exemplify the feasibility of this method using a two-dimensional molecular-microscopic agent-based model previously developed for simulating non-small cell lung cancer; in this model, an epidermal growth factor (EGF)-induced, EGF receptor-mediated signaling pathway was implemented at the molecular level. Here, the cross-scale effects of molecular parameters on two tumor growth evaluation measures, i.e., tumor volume and expansion rate, at the microscopic level are assessed. Analysis finds that ERK, a downstream molecule of the EGF receptor signaling pathway, has the most important impact on regulating both measures. The potential to apply this method to therapeutic target discovery is discussed.
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Affiliation(s)
- Zhihui Wang
- Harvard-MIT Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, MA, USA
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42
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Krepkin K, Costa J. Defining the role of cooperation in early tumor progression. J Theor Biol 2011; 285:36-45. [PMID: 21745479 DOI: 10.1016/j.jtbi.2011.06.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2011] [Revised: 05/30/2011] [Accepted: 06/27/2011] [Indexed: 12/14/2022]
Abstract
Competition among cells has long been recognized as an important part of carcinogenesis. However, the role of cellular cooperation in cancer has been largely ignored. In this work, we investigated the role of cooperation in early tumor progression using a mathematical and agent-based modeling approach. We hoped to learn how the introduction of cooperative cells into a cell population would affect the dynamics of the system. We modeled the stem cell compartment of tissue using a spatial structure organized into cell patches, with stem cells able to replicate or leave the stem cell compartment through apoptosis or differentiation. The cells could also acquire mutations in three oncogenes and two tumor suppressor genes. Cooperative cells in our model provided a cooperative signal that increased the fitness of their immediate neighbors, but did not affect their own fitness. Running simulations of the model, we found that if cooperative cells are introduced into a cell population, they steadily proliferate and confer a growth advantage to the entire population. This leads us to conclude that providing a cooperative signal is likely to be under positive selective pressure. When cooperative ability and mutation are concurrently present in the same cells, the overall cell population experiences a significant growth advantage, much greater than with cooperation or mutation alone. This growth advantage is diminished if cells with only oncogene/tumor suppressor mutations are also present in the population, suggesting that the optimal scenario for tumor growth would be for cooperative cells to take over a cell population, and then for mutations in oncogenes and tumor suppressors to arise on a background of cooperation. We predict that cooperation is particularly important in the very early stages of carcinogenesis, when tissue is morphologically and histologically normal. Our results have implications for the screening and early diagnosis of cancer.
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Affiliation(s)
- Konstantin Krepkin
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA.
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Tang J, Enderling H, Becker-Weimann S, Pham C, Polyzos A, Chen CY, Costes SV. Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling. Integr Biol (Camb) 2011; 3:408-21. [PMID: 21373705 PMCID: PMC4009383 DOI: 10.1039/c0ib00092b] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We introduce an agent-based model of epithelial cell morphogenesis to explore the complex interplay between apoptosis, proliferation, and polarization. By varying the activity levels of these mechanisms we derived phenotypic transition maps of normal and aberrant morphogenesis. These maps identify homeostatic ranges and morphologic stability conditions. The agent-based model was parameterized and validated using novel high-content image analysis of mammary acini morphogenesis in vitro with focus on time-dependent cell densities, proliferation and death rates, as well as acini morphologies. Model simulations reveal apoptosis being necessary and sufficient for initiating lumen formation, but cell polarization being the pivotal mechanism for maintaining physiological epithelium morphology and acini sphericity. Furthermore, simulations highlight that acinus growth arrest in normal acini can be achieved by controlling the fraction of proliferating cells. Interestingly, our simulations reveal a synergism between polarization and apoptosis in enhancing growth arrest. After validating the model with experimental data from a normal human breast line (MCF10A), the system was challenged to predict the growth of MCF10A where AKT-1 was overexpressed, leading to reduced apoptosis. As previously reported, this led to non growth-arrested acini, with very large sizes and partially filled lumen. However, surprisingly, image analysis revealed a much lower nuclear density than observed for normal acini. The growth kinetics indicates that these acini grew faster than the cells comprising it. The in silico model could not replicate this behavior, contradicting the classic paradigm that ductal carcinoma in situ is only the result of high proliferation and low apoptosis. Our simulations suggest that overexpression of AKT-1 must also perturb cell-cell and cell-ECM communication, reminding us that extracellular context can dictate cellular behavior.
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Affiliation(s)
- Jonathan Tang
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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44
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Dada JO, Mendes P. Multi-scale modelling and simulation in systems biology. Integr Biol (Camb) 2011; 3:86-96. [DOI: 10.1039/c0ib00075b] [Citation(s) in RCA: 129] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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45
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Wang Z, Bordas V, Deisboeck TS. Discovering Molecular Targets in Cancer with Multiscale Modeling. Drug Dev Res 2010; 72:45-52. [PMID: 21572568 DOI: 10.1002/ddr.20401] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Multiscale modeling is increasingly being recognized as a promising research area in computational cancer systems biology. Here, exemplified by two pioneering studies, we attempt to explain why and how such a multiscale approach paired with an innovative cross-scale analytical technique can be useful in identifying high-value molecular therapeutic targets. This novel, integrated approach has the potential to offer a more effective in silico framework for target discovery and represents an important technical step towards systems medicine.
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Affiliation(s)
- Zhihui Wang
- Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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Wang Z, Bordas V, Sagotsky J, Deisboeck TS. Identifying therapeutic targets in a combined EGFR-TGFβR signalling cascade using a multiscale agent-based cancer model. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2010; 29:95-108. [PMID: 21147846 DOI: 10.1093/imammb/dqq023] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Applying a previously developed non-small cell lung cancer model, we assess 'cross-scale' the therapeutic efficacy of targeting a variety of molecular components of the epidermal growth factor receptor (EGFR) signalling pathway. Simulation of therapeutic inhibition and amplification allows for the ranking of the implemented downstream EGFR signalling molecules according to their therapeutic values or indices. Analysis identifies mitogen-activated protein kinase and extracellular signal-regulated kinase as top therapeutic targets for both inhibition and amplification-based treatment regimen but indicates that combined parameter perturbations do not necessarily improve the therapeutic effect of the separate parameter treatments as much as might be expected. Potential future strategies using this in silico model to tailor molecular treatment regimen are discussed.
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Affiliation(s)
- Zhihui Wang
- Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital-East, 13th Street, Charlestown, MA 02129, USA
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A mathematical framework for agent based models of complex biological networks. Bull Math Biol 2010; 73:1583-602. [PMID: 20878493 DOI: 10.1007/s11538-010-9582-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2010] [Accepted: 09/06/2010] [Indexed: 10/19/2022]
Abstract
Agent-based modeling and simulation is a useful method to study biological phenomena in a wide range of fields, from molecular biology to ecology. Since there is currently no agreed-upon standard way to specify such models, it is not always easy to use published models. Also, since model descriptions are not usually given in mathematical terms, it is difficult to bring mathematical analysis tools to bear, so that models are typically studied through simulation. In order to address this issue, Grimm et al. proposed a protocol for model specification, the so-called ODD protocol, which provides a standard way to describe models. This paper proposes an addition to the ODD protocol which allows the description of an agent-based model as a dynamical system, which provides access to computational and theoretical tools for its analysis. The mathematical framework is that of algebraic models, that is, time-discrete dynamical systems with algebraic structure. It is shown by way of several examples how this mathematical specification can help with model analysis. This mathematical framework can also accommodate other model types such as Boolean networks and the more general logical models, as well as Petri nets.
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Poplawski NJ, Shirinifard A, Agero U, Gens JS, Swat M, Glazier JA. Front instabilities and invasiveness of simulated 3D avascular tumors. PLoS One 2010; 5:e10641. [PMID: 20520818 PMCID: PMC2877086 DOI: 10.1371/journal.pone.0010641] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2009] [Accepted: 04/13/2010] [Indexed: 12/21/2022] Open
Abstract
We use the Glazier-Graner-Hogeweg model to simulate three-dimensional (3D), single-phenotype, avascular tumors growing in an homogeneous tissue matrix (TM) supplying a single limiting nutrient. We study the effects of two parameters on tumor morphology: a diffusion-limitation parameter defined as the ratio of the tumor-substrate consumption rate to the substrate-transport rate, and the tumor-TM surface tension. This initial model omits necrosis and oxidative/hypoxic metabolism effects, which can further influence tumor morphology, but our simplified model still shows significant parameter dependencies. The diffusion-limitation parameter determines whether the growing solid tumor develops a smooth (noninvasive) or fingered (invasive) interface, as in our earlier two-dimensional (2D) simulations. The sensitivity of 3D tumor morphology to tumor-TM surface tension increases with the size of the diffusion-limitation parameter, as in 2D. The 3D results are unexpectedly close to those in 2D. Our results therefore may justify using simpler 2D simulations of tumor growth, instead of more realistic but more computationally expensive 3D simulations. While geometrical artifacts mean that 2D sections of connected 3D tumors may be disconnected, the morphologies of 3D simulated tumors nevertheless correlate with the morphologies of their 2D sections, especially for low-surface-tension tumors, allowing the use of 2D sections to partially reconstruct medically-important 3D-tumor structures.
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Affiliation(s)
- Nikodem J Poplawski
- Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, Indiana, USA.
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Kreeger PK, Lauffenburger DA. Cancer systems biology: a network modeling perspective. Carcinogenesis 2010; 31:2-8. [PMID: 19861649 PMCID: PMC2802670 DOI: 10.1093/carcin/bgp261] [Citation(s) in RCA: 232] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2009] [Revised: 10/17/2009] [Accepted: 10/18/2009] [Indexed: 12/28/2022] Open
Abstract
Cancer is now appreciated as not only a highly heterogenous pathology with respect to cell type and tissue origin but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multivariate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straightforward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article, we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments and effects of molecular therapeutics.
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Affiliation(s)
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Building 16, Room 343, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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