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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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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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Miningou N, Blackwell KT. The road to ERK activation: Do neurons take alternate routes? Cell Signal 2020; 68:109541. [PMID: 31945453 PMCID: PMC7127974 DOI: 10.1016/j.cellsig.2020.109541] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 01/11/2020] [Accepted: 01/12/2020] [Indexed: 01/29/2023]
Abstract
The ERK cascade is a central signaling pathway that regulates a wide variety of cellular processes including proliferation, differentiation, learning and memory, development, and synaptic plasticity. A wide range of inputs travel from the membrane through different signaling pathway routes to reach activation of one set of output kinases, ERK1&2. The classical ERK activation pathway beings with growth factor activation of receptor tyrosine kinases. Numerous G-protein coupled receptors and ionotropic receptors also lead to ERK through increases in the second messengers calcium and cAMP. Though both types of pathways are present in diverse cell types, a key difference is that most stimuli to neurons, e.g. synaptic inputs, are transient, on the order of milliseconds to seconds, whereas many stimuli acting on non-neural tissue, e.g. growth factors, are longer duration. The ability to consolidate these inputs to regulate the activation of ERK in response to diverse signals raises the question of which factors influence the difference in ERK activation pathways. This review presents both experimental studies and computational models aimed at understanding the control of ERK activation and whether there are fundamental differences between neurons and other cells. Our main conclusion is that differences between cell types are quite subtle, often related to differences in expression pattern and quantity of some molecules such as Raf isoforms. In addition, the spatial location of ERK is critical, with regulation by scaffolding proteins producing differences due to colocalization of upstream molecules that may differ between neurons and other cells.
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Affiliation(s)
- Nadiatou Miningou
- Department of Chemistry and Biochemistry, George Mason University, Fairfax, VA 22030, United States of America
| | - Kim T Blackwell
- Interdisciplinary Program in Neuroscience and Bioengineering Department, George Mason University, Fairfax, VA 22030, United States of America.
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Antonopoulos M, Dionysiou D, Stamatakos G, Uzunoglu N. Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study. BMC Bioinformatics 2019; 20:442. [PMID: 31455206 PMCID: PMC6712764 DOI: 10.1186/s12859-019-2997-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/16/2019] [Indexed: 01/10/2023] Open
Abstract
Background Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. Results This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. Conclusion In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.
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Affiliation(s)
- Markos Antonopoulos
- 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
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Nikolaos Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
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6
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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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7
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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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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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9
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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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10
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Identification of crucial parameters in a mathematical multiscale model of glioblastoma growth. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:437094. [PMID: 24899919 PMCID: PMC4034489 DOI: 10.1155/2014/437094] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Accepted: 03/22/2014] [Indexed: 02/08/2023]
Abstract
Glioblastomas are highly malignant brain tumours. Mathematical models and their analysis
provide a tool to support the understanding of the development of these tumours as well as
the design of more effective treatment strategies. We have previously developed a multiscale
model of glioblastoma progression that covers processes on the cellular and molecular scale.
Here, we present a novel nutrient-dependent multiscale sensitivity analysis of this model
that helps to identify those reaction parameters of the molecular interaction network that influence
the tumour progression on the cellular scale the most. In particular, those parameters
are identified that essentially determine tumour expansion and could be therefore used as potential
therapy targets. As indicators for the success of a potential therapy target, a deceleration
of the tumour expansion and a reduction of the tumour volume are employed.
From the results, it can be concluded that no single parameter variation results in a less
aggressive tumour. However, it can be shown that a few combined perturbations of two
systematically selected parameters cause a slow-down of the tumour expansion velocity accompanied
with a decrease of the tumour volume. Those parameters are primarily linked to
the reactions that involve the microRNA-451 and the thereof regulated protein MO25.
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11
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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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12
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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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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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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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15
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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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Neelamegham S, Liu G. Systems glycobiology: biochemical reaction networks regulating glycan structure and function. Glycobiology 2011; 21:1541-53. [PMID: 21436236 DOI: 10.1093/glycob/cwr036] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
There is a growing use of bioinformatics based methods in the field of Glycobiology. These have been used largely to curate glycan structures, organize array-based experimental data and display existing knowledge of glycosylation-related pathways in silico. Although the cataloging of vast amounts of data is beneficial, it is often a challenge to gain meaningful mechanistic insight from this exercise alone. The development of specific analysis tools to query the database is necessary. If these queries can integrate existing knowledge of glycobiology, new insights may be gained. Such queries that couple biochemical knowledge and mathematics have been developed in the field of Systems Biology. The current review summarizes the current state of the art in the application of computational modeling in the field of Glycobiology. It provides (i) an overview of experimental and online resources that can be used to construct glycosylation reaction networks, (ii) mathematical methods to formulate the problem including a description of ordinary differential equation and logic-based reaction networks, (iii) optimization techniques that can be applied to fit experimental data for the purpose of model reconstruction and for evaluating unknown model parameters, (iv) post-simulation analysis methods that yield experimentally testable hypotheses and (v) a summary of available software tools that can be used by non-specialists to perform many of the above functions.
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Affiliation(s)
- Sriram Neelamegham
- Department of Chemical and Biological Engineering, and The NY State Center for Excellence in Bioinformatics and Life Sciences, State University of New York, Buffalo, NY 14260, USA.
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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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Wang Z, Birch CM, Sagotsky J, Deisboeck TS. Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model. ACTA ACUST UNITED AC 2009; 25:2389-96. [PMID: 19578172 DOI: 10.1093/bioinformatics/btp416] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor beta (TGFbeta) has been integrated into our previously developed in silico model as a second extrinsic input in addition to epidermal growth factor (EGF). The main aim of this study is to investigate how the effects of individual and combinatorial change in EGF and TGFbeta concentrations at the molecular level alter tumor growth dynamics on the multi-cellular level, specifically tumor volume and expansion rate. Our simulation results show that separate EGF and TGFbeta fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGFbeta signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGFbeta concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGFbeta profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGFbeta do not impact the resulting multi-cellular response substantially, while outside these concentration ranges, a change at the molecular level will substantially alter either tumor volume or tumor expansion rate, or both. By evaluating tumor growth dynamics across different scales, we show that, under certain conditions, therapeutic targeting of only one signaling pathway may be insufficient. Potential implications of these in silico results for future clinico-pharmacological applications are discussed.
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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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Walker DC, Southgate J. The virtual cell--a candidate co-ordinator for 'middle-out' modelling of biological systems. Brief Bioinform 2009; 10:450-61. [PMID: 19293250 DOI: 10.1093/bib/bbp010] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Understanding the functioning of biological systems depends on tackling complexity spanning spatial scales from genome to organ to whole organism. The basic unit of life, the cell, acts to co-ordinate information received across these scales and processes the myriad of signals to produce an integrated cellular response. Cells interact with and respond to other cells through direct or indirect contact, resulting in emergent structure and function of tissues and organs. Systems biology has traditionally used either a 'top-down' or 'bottom-up' approach. However, neither approach takes account of heterogeneity or 'noise', which is an inherent feature of cellular behaviour and may have significant impact on system level behaviour. We review existing approaches to modelling that use cellular automata or agent-based methodologies, where individual cells are represented as equivalent virtual entities governed by simple rules. These paradigms allow a direct one-to-one mapping between real and virtual cells that can be exploited in terms of acquiring parameters from experimental systems, or for model validation. Such models are inherently extensible and can be integrated with other modelling modalities (e.g. partial or ordinary differential equations) to model multi-scale phenomena. Alternatively, hierarchical agent models may be used to explore the functions of biological systems across temporal and spatial scales. This review examines individual-based models and the application of the paradigm to explore multi-scale phenomena in biology. In so doing, it demonstrates how cellular-based models have begun to play an important role in the development of 'middle-out' models, but with considerable potential for future development.
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Affiliation(s)
- Dawn C Walker
- Department of Computer Science at the University of Sheffield
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Deisboeck TS, Wang Z. A new concept for cancer therapy: out-competing the aggressor. Cancer Cell Int 2008; 8:19. [PMID: 19077276 PMCID: PMC2628876 DOI: 10.1186/1475-2867-8-19] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2008] [Accepted: 12/12/2008] [Indexed: 11/23/2022] Open
Abstract
Cancer expansion depends on host organ conditions that permit growth. Since such microenvironmental nourishment is limited we argue here that an autologous, therapeutically engineered and faster metabolizing cell strain could potentially out-compete native cancer cell populations for available resources which in turn should contain further cancer growth. This hypothesis aims on turning cancer progression, and its microenvironmental dependency, into a therapeutic opportunity. To illustrate our concept, we developed a three-dimensional computational model that allowed us to investigate the growth dynamics of native tumor cells mixed with genetically engineered cells that exhibit a higher proliferation rate. The simulation results confirm in silico efficacy of such therapeutic cells to combating cancer cells on site in that they can indeed control tumor growth once their proliferation rate exceeds a certain level. While intriguing from a theoretical perspective, this bold, innovative ecology-driven concept bears some significant challenges that warrant critical discussion in the community for further refinement.
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Affiliation(s)
- Thomas S Deisboeck
- Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Zhihui Wang
- Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
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