Cancer systems biology and modeling: Microscopic scale and multiscale approaches

Masoudi‐Nejad, Ali; Bidkhori, Gholamreza; Ashtiani, Saman Hosseini; Najafi, Ali; Bozorgmehr, Joseph Hannon; Wang, Edwin

doi:10.1016/j.semcancer.2014.03.003

Cited by 35 publications

(23 citation statements)

References 66 publications

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“…As we did not have specific detailed data about our patients that would allow us to use current tumor growth models (11-13) (this would be the case for most standard clinical practice situations), we took advantage of Fisher information processing, which is a valid approach to obtain the relationship between tumor mass data from clinical radiology and time t baseline till appearance of the tumor mass (clinical presentation).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Total Hepatocellular Cancer Lifespan, Including Both Clinically Evident and Preclinical Development, Using Combined Network Phenotyping Strategy and Fisher Information Analysis

Pančoška

Skála

Nešetřil

et al. 2015

Seminars in Oncology

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We previously showed that for hepatocellular cancer prognostication, disease parameters need to be considered within a total personal clinical context. This requires preserving the coherence of data values, observed simultaneously for each patient during baseline diagnostic evaluation. Application of Network Phenotyping Strategy provided quantitative descriptors of these patient coherences. Combination of these descriptors with Fisher information about the patient tumor mass and the histogram of the tumor masses in the whole cohort, permitted estimation of the time from disease onset until clinical diagnosis (tbaseline). We found faster growth of smaller tumors having total masses <70 (80% of cohort) which involved ~3 times more interacting cellular processes than were observed for slower growing larger tumors (20% of cohort) with total masses >70. Combining the clinical survival and tbaseline normalized all HCC patients to a common 1045 days of mean total disease duration (tbaseline plus post diagnosis survival). We also found a simple relationship between the baseline clinical status, tbaseline and survival. Every difference between individual patient baseline clinical profiles and special coherent clinical status (HL1) reduced the above common overall survival by 65 days. In summary, we showed that HCC patients with any given tumor can best have their tumor biology understood, when account is taken of the total clinical and liver contexts, and with knowing the point in the tumor history when an HCC diagnosis is made. This ability to compute the tbaseline from standard clinical data brings us closer to calculating survival from diagnosis of individual HCC patients.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Total Hepatocellular Cancer Lifespan, Including Both Clinically Evident and Preclinical Development, Using Combined Network Phenotyping Strategy and Fisher Information Analysis

Pančoška

Skála

Nešetřil

et al. 2015

Seminars in Oncology

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“…Here, we highlight some of the more commonly used approaches ( Table 2 ), which have been discussed in greater detail in prior reviews, although various other formalisms exist. These include data‐driven approaches (common in systems biology), ordinary differential equations (common in PK and PD and engineering), and approaches that include spatial effects (derived from engineering).…”

Section: Stage 3 Representing the Biology: Developing The Model Strumentioning

confidence: 99%

A Six‐Stage Workflow for Robust Application of Systems Pharmacology

Gadkar¹,

Kirouac²,

Mager

et al. 2016

CPT Pharmacom & Syst Pharma

107

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Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

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“…Mathematical and computational models can conceivably establish relationships among cancer cell proliferation, intrusion, and microenvironment attributes, provided they can be calibrated and validated with in vitro and in vivo experimental data. Multiscale modeling is one of the effective approaches to use these data in investigations of cancerous cell behavior in tissues and in modeling the overall tumor growth phenomenon [1]. …”

Section: Introductionmentioning

confidence: 99%

A fully coupled space–time multiscale modeling framework for predicting tumor growth

Rahman

Feng

Yankeelov

et al. 2017

Computer Methods in Applied Mechanics and Engineering

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Most biological systems encountered in living organisms involve highly complex heterogeneous multi-component structures that exhibit different physical, chemical, and biological behavior at different spatial and temporal scales. The development of predictive mathematical and computational models of multiscale events in such systems is a major challenge in contemporary computational biomechanics, particularly the development of models of growing tumors in humans. The aim of this study is to develop a general framework for tumor growth prediction by considering major biological events at tissue, cellular, and subcellular scales. The key to developing such multiscale models is how to bridge spatial and temporal scales that range from 10−3 to 103 mm in space and from 10−6 to 107 s in time. In this paper, a fully coupled space–time multiscale framework for modeling tumor growth is developed. The framework consists of a tissue scale model, a model of cellular activities, and a subcellular transduction signaling pathway model. The tissue, cellular, and subcellular models in this framework are solved using partial differential equations for tissue growth, agent-based model for cellular events, and ordinary differential equations for signaling transduction pathway as a network at subcellular scale. The model is calibrated using experimental observations. Moreover, this model is biologically-driven from a signaling pathway, volumetrically-consistent between cellular and tissue scale in terms of tumor volume evolution in time, and a biophysically-sound tissue model that satisfies all conservation laws. The results show that the model is capable of predicting major characteristics of tumor growth such as the morphological instability, growth patterns of different cell phenotypes, compact regions of the higher cell density at the tumor region, and the reduction of growth rate due to drug delivery. The predicted treatment outcomes show a reduction in proliferation at different rates in response to different drug dosages. Moreover, the results of several 3D applications to tumor growth and the evolution of cellular and subcellular events are presented.

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Cancer systems biology and modeling: Microscopic scale and multiscale approaches

Cited by 35 publications

References 66 publications

Evaluation of Total Hepatocellular Cancer Lifespan, Including Both Clinically Evident and Preclinical Development, Using Combined Network Phenotyping Strategy and Fisher Information Analysis

Evaluation of Total Hepatocellular Cancer Lifespan, Including Both Clinically Evident and Preclinical Development, Using Combined Network Phenotyping Strategy and Fisher Information Analysis

A Six‐Stage Workflow for Robust Application of Systems Pharmacology

A fully coupled space–time multiscale modeling framework for predicting tumor growth

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