scholarly journals Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

2021 ◽  
Author(s):  
Mahnoor Naseer Gondal ◽  
Safee Ullah Chaudhary
Keyword(s):  
Systems Biology ◽  
In Silico ◽  
Clinical Decision Making ◽  
Cancer Therapeutics ◽  
Simulation Software ◽  
Therapeutic Interventions ◽  
Cancer Data ◽  
Multi Scale ◽  
Cancer Systems Biology ◽  
Scale Models

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

scholarly journals Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

2021 ◽  
Vol 11 ◽  
Author(s):  
Mahnoor Naseer Gondal ◽  
Safee Ullah Chaudhary
Keyword(s):  
Systems Biology ◽  
Clinical Oncology ◽  
In Silico ◽  
Clinical Decision Making ◽  
Cancer Therapeutics ◽  
Simulation Software ◽  
Cancer Data ◽  
Multi Scale ◽  
Cancer Systems Biology ◽  
Scale Models

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

scholarly journals The openCARP Simulation Environment for Cardiac Electrophysiology

2021 ◽  
Author(s):  
Gernot Plank ◽  
Axel Loewe ◽  
Aurel Neic ◽  
Christoph Augustin ◽  
Yung-Lin Huang ◽  
...  
Keyword(s):  
In Silico ◽  
Cardiac Electrophysiology ◽  
Medical Specialty ◽  
Simulation Software ◽  
Simulation Environment ◽  
Training Material ◽  
Multi Scale ◽  
Cell Tissue ◽  
Body Level ◽  
Community Standard

AbstractBackground and ObjectiveCardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community.Methods and ResultsopenCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential.ConclusionAs an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.

scholarly journals Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher Schölzel ◽  
Valeria Blesius ◽  
Gernot Ernst ◽  
Andreas Dominik
Keyword(s):  
Systems Biology ◽  
Software Engineering ◽  
Software Reuse ◽  
Best Practice ◽  
Graphical Representation ◽  
Cardiac Conduction ◽  
Modeling Languages ◽  
Multi Scale ◽  
Modular Model ◽  
Scale Models

AbstractReuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.

Developing a Framework for Multi-Scale Modeling of the Digital Patient

2017 ◽  
Vol 5 (2) ◽  
pp. 21-33
Author(s):  
Charles Donald Combs
Keyword(s):  
Systems Biology ◽  
Professional Education ◽  
Public Health Care ◽  
Biological Processes ◽  
Multi Scale ◽  
Device Development ◽  
Theoretical Approaches ◽  
Scale Modeling ◽  
Scale Models ◽  
Multi Scale Modeling

The Digital Patient is an analytic platform that has the potential to transform personal and public health care, pharmaceutical and device development, research, and patient and professional education. It is the ultimate Big Data project in healthcare; however, its power will derive not from the volume of data, but from the integration of disparate sources of data into valid and reliable intelligence—about biological processes, social context and treatment efficacy. That integration, in turn, is largely dependent on the evolving theoretical approaches known as systems biology and convergence that lead to the successful meshing of multi-scale models. This paper provides an overview of the digital patient, the domains of systems biology and multi-scale modeling, the evolving efforts to promote convergence and the implications for personalized medicine.

Recent Nano-based Therapeutic Intervention of Bioactive Sesquiterpenes: Prospects in Cancer Therapeutics

2020 ◽  
Vol 26 (11) ◽  
pp. 1138-1144 ◽  
Author(s):  
Mohammad A. Ansari ◽  
Khan F. Badrealam ◽  
Asrar Alam ◽  
Saba Tufail ◽  
Gulshan Khalique ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
Essential Oils ◽  
Plant Species ◽  
Bioactive Compounds ◽  
Therapeutic Intervention ◽  
Cancer Therapeutics ◽  
Therapeutic Interventions ◽  
Pharmacological Intervention ◽  
Clinical Applicability ◽  
Near Future

: In the recent scenario, nanotechnology-based therapeutics intervention has gained tremendous impetus all across the globe. Nano-based pharmacological intervention of various bioactive compounds has been explored on an increasing scale. Sesquiterpenes are major constituents of essential oils (EOs) present in various plant species which possess intriguing therapeutic potentials. However, owing to their poor physicochemical properties; they have pharmacological limitations. Recent advances in nano-based therapeutic interventions offer various avenues to improve their therapeutic applicability. Reckoning with these, the present review collates various nano-based therapeutic intervention of sesquiterpenes with prospective potential against various debilitating diseases especially cancer. In our viewpoint, considering the burgeoning advancement in the field of nanomedicine; in the near future, the clinical applicability of these nano-formulated sesquiterpenes can be foreseen with great enthusiasm.

scholarly journals The Winter Habitat Selection of Red Deer (Cervus elaphus) Based on a Multi-Scale Model

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2454
Author(s):  
Yue Sun ◽  
Yanze Yu ◽  
Jinhao Guo ◽  
Minghai Zhang
Keyword(s):  
Habitat Selection ◽  
Environmental Factors ◽  
Cervus Elaphus ◽  
Red Deer ◽  
Scale Model ◽  
Winter Habitat ◽  
Multi Scale ◽  
Optimal Scale ◽  
Scale Models ◽  
Selection Of

Single-scale frameworks are often used to analyze the habitat selections of species. Research on habitat selection can be significantly improved using multi-scale models that enable greater in-depth analyses of the scale dependence between species and specific environmental factors. In this study, the winter habitat selection of red deer in the Gogostaihanwula Nature Reserve, Inner Mongolia, was studied using a multi-scale model. Each selected covariate was included in multi-scale models at their “characteristic scale”, and we used an all subsets approach and model selection framework to assess habitat selection. The results showed that: (1) Univariate logistic regression analysis showed that the response scale of red deer to environmental factors was different among different covariate. The optimal scale of the single covariate was 800–3200 m, slope (SLP), altitude (ELE), and ratio of deciduous broad-leaved forests were 800 m in large scale, except that the farmland ratio was 200 m in fine scale. The optimal scale of road density and grassland ratio is both 1600 m, and the optimal scale of net forest production capacity is 3200 m; (2) distance to forest edges, distance to cement roads, distance to villages, altitude, distance to all road, and slope of the region were the most important factors affecting winter habitat selection. The outcomes of this study indicate that future studies on the effectiveness of habitat selections will benefit from multi-scale models. In addition to increasing interpretive and predictive capabilities, multi-scale habitat selection models enhance our understanding of how species respond to their environments and contribute to the formulation of effective conservation and management strategies for ungulata.

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