scholarly journals The Multiple Dimensions of Networks in Cancer: A Perspective

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1559
Author(s):  
Cristian Axenie ◽  
Roman Bauer ◽  
María Rodríguez Martínez
Keyword(s):  
Multiple Scales ◽  
Predictive Modelling ◽  
Agent Based ◽  
Multiple Dimensions ◽  
Computational Oncology ◽  
Modelling Analysis ◽  
Issue Networks ◽  
Multiple Processes ◽  
Network Approaches ◽  
Therapy Design

This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

scholarly journals A 3D Agent-Based Model of Lung Fibrosis

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 90
Author(s):  
Nicolò Cogno ◽  
Roman Bauer ◽  
Marco Durante
Keyword(s):  
Lung Fibrosis ◽  
Computational Models ◽  
Multiple Scales ◽  
Inflammatory Diseases ◽  
Cell Damage ◽  
Heterogeneous Systems ◽  
Chronic Obstructive ◽  
Agent Based Model ◽  
Agent Based ◽  
Radiation Induced

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).

scholarly journals Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

2020 ◽  
Author(s):  
Cristian Axenie ◽  
Daria Kurz
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Tumor Growth ◽  
Predictive Modelling ◽  
Growth Patterns ◽  
Growth Behaviour ◽  
Computational Oncology ◽  
Chemotherapy Protocol ◽  
Clinical Advantage ◽  
Personalized Chemotherapy

AbstractMathematical and computational oncology has increased the pace of cancer research towards the advancement of personalized therapy. Serving the pressing need to exploit the large amounts of currently underutilized data, such approaches bring a significant clinical advantage in tailoring the therapy. CHIMERA is a novel system that combines mechanistic modelling and machine learning for personalized chemotherapy and surgery sequencing in breast cancer. It optimizes decision-making in personalized breast cancer therapy by connecting tumor growth behaviour and chemotherapy effects through predictive modelling and learning. We demonstrate the capabilities of CHIMERA in learning simultaneously the tumor growth patterns, across several types of breast cancer, and the pharmacokinetics of a typical breast cancer chemotoxic drug. The learnt functions are subsequently used to predict how to sequence the intervention. We demonstrate the versatility of CHIMERA in learning from tumor growth and pharmacokinetics data to provide robust predictions under two, typically used, chemotherapy protocol hypotheses.

scholarly journals Multiscale dynamics of COVID-19 and model-based recommendations for 105 countries

2020 ◽  
Author(s):  
Jithender J. Timothy ◽  
Vijaya Holla ◽  
Günther Meschke
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Computational Modelling ◽  
Real Data ◽  
Mitigation Measures ◽  
Agent Based ◽  
Multiscale Dynamics ◽  
Large Scale Analysis ◽  
Disease Spreading ◽  
Country Specific

We analyse the dynamics of COVID-19 using computational modelling at multiple scales. For large scale analysis, we propose a 2-scale lattice extension of the classical SIR-type compartmental model with spatial interactions called the Lattice-SIRQL model. Computational simulations show that global quantifiers are not completely representative of the actual dynamics of the disease especially when mitigation measures such as quarantine and lockdown are applied. Furthermore, using real data of confirmed COVID-19 cases, we calibrate the Lattice-SIRQL model for 105 countries. The calibrated model is used to make country specific recommendations for lockdown relaxation and lockdown continuation. Finally, using an agent-based model we analyse the influence of cluster level relaxation rate and lockdown duration on disease spreading.

Hugh Heclo, “Issue Networks and the Executive Establishment”

2016 ◽  
Author(s):  
Volker Schneider
Keyword(s):  
Public Policy ◽  
Public Administration ◽  
Policy Making ◽  
Cognitive Perspective ◽  
Agent Based ◽  
Relational Perspective ◽  
Modern Politics ◽  
Issue Networks ◽  
Dynamic Perspective

This chapter comments on Hugh Heclo’s 1978 paper “Issue Networks and the Executive Establishment,” an innovative analysis of modern politics based on four analytical perspectives of public policy: an actor-centered or agent-based dynamic perspective, a relational perspective, a cultural or cognitive perspective, and a long-term perspective. Widely regarded as a classic in policy analysis and public administration, Heclo’s paper uses the concept of “issue networks” to describe the highly intricate and diversified webs of influence that shape modern American policy-making. This chapter discusses Heclo’s concept of issue networks within the context of the American situation in government and public administration, as well as its impact on fields such as political science.

Computational Simulation Modeling in Population Health Research and Policy

2020 ◽  
pp. 171-184
Author(s):  
Nathaniel Osgood
Keyword(s):  
Dynamic Modeling ◽  
Multiple Scales ◽  
Computational Simulation ◽  
Discrete Event ◽  
Intervention Strategies ◽  
Agent Based ◽  
Event Simulation ◽  
Modeling Process ◽  
Trade Offs ◽  
Model Dynamics

Dynamic modeling provides a powerful tool for enabling faster learning in a complex and uncertain world. Within this contribution, we briefly survey three prominent dynamic modeling traditions—agent-based modeling, system dynamics, and discrete event simulation. Each such tradition offers unique combinations of strengths and limitations and is further distinguished by emphasis of different sets of modeling goals and norms. This chapter discusses such trade-offs between such methods, with a particular emphasis on the key distinction between aggregate and individual-based approaches, which has widespread practical ramifications. The authors further note the advent of hybrid dynamic modeling approaches, which provide unique levels of flexibility in addressing diverse intervention strategies and generative pathways at multiple scales and the capacity for the model representation to adapt with the learning and evolving understanding of key elements of model dynamics that constitute a key outcome of the modeling process.

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