The Multiple Dimensions of Networks in Cancer: A Perspective

Axenie, Cristian; Bauer, R.; Martínez, María Rodríguez

doi:10.3390/sym13091559

Cited by 8 publications

(4 citation statements)

References 132 publications

(138 reference statements)

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“…Having patient-specific parameters, this model has also been validated partially to illustrate its clinical relevance. We propose here an aggregation of local diffusion phenomena to be included as part of the dose-effect relationship, i.e., (3). The effect surface depends not only on the γ and σ parameters characterizing drug-patient interaction but also on local effects in molecular binding between drugs and targeted treatment of the cancer tissue.…”

Section: Regional Anomalous Diffusionmentioning

confidence: 99%

“…The processes that govern tumor growth have been investigated and translated into mathematical models that allow analyzing of the interactions on the cancer site, and predict the evolution of the tissue and treatment outcome across multiple scales [3,4]. Monitoring pre-and post-treatment of tumorous tissue requires recursive alterations to the initially planned therapy profiles, depending on the patient's therapeutic response [5].…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Regional Control with Anomalous Diffusion of Multi-Drug Combined Cancer Therapy for Volume Predictions

Ionescu

Ghita

2022

Symmetry

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Symmetry breaking in the anatomical lung is triggered by tumorigenesis and disrupted by delivering single or multiple drugs to stop the progression of the tumor and treat cancer. In this study, a prior model of combined drug therapy is augmented to introduce tissue heterogeneity when the drug is applied in multi-drug therapy of lung cancer. Patient-related drug resistance and synergy are investigated as a function of diffusion intensity as drug molecules reach the tumor site. The results indicate that diffusion of drug molecules plays an important role next to other factors such as patient sensitivity to the drug and drug synergy effects. We conclude that the minimal model provides meaningful predictions on tumor growth at the intermediate mesoscale level. With such models at hand, it is now possible to employ model-based control algorithms to optimize the dose profiles in terms of time and amount. In this paper, we present a theoretical framework for control employing networked game theory optimality. Specific situations are discussed in terms of finding optimality at Nash equilibrium in relation to patient response and drug synergy effects.

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Section: Regional Anomalous Diffusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Based Regional Control with Anomalous Diffusion of Multi-Drug Combined Cancer Therapy for Volume Predictions

Ionescu

Ghita

2022

Symmetry

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“…Network theory is widely used to investigate, model, and analyze biological factors, including tumors, ncRNAs, and infectious diseases [1][2] [3]. Feedback vertex set (FVS), a graph theory concept applied to the identification of important nodes in a network [4] [5], is a set of nodes such that if they and the incoming edges to them are deleted, the original graph becomes an acyclic graph.…”

Section: Introductionmentioning

confidence: 99%

Computational Method for k-Distance Limited Minimum Feedback Vertex Set and Its Application to Biological Networks

Nakashima,

Akutsu

2024

Preprint

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Background Feedback vertex set (FVS), a graph theory concept, is used to identify critical nodes in network and system control. Extended versions of FVS are continuously explored and have found applications in biological data analysis and system control. Then, the minimum feedback vertex set (MFVS), defined as the FVS with the smallest number of elements among all feedback vertex sets, is important when controlling a system with the least number of nodes. Results In this paper, we introduce the concept of the k-distance limited Minimum Feedback Vertex Set (kMFVS) as an extension to MFVS. We present a novel approach for obtaining the exact solutions for kMFVS using Integer Linear Programming (ILP). Simulations offer a means to determine the value of k for kMFVS in random graphs, depending on the number of nodes in the graph. Applying kMFVS to real biological data, we show that compared to MFVS, kMFVS improves recall by using only information about the network structure. Conclusion kMFVS can extract important nodes (genes) from pure network structures better than MFVS. Fur- thermore, we prove that in a discrete-time network, by controlling nodes chosen as kMFVS, the system can be transitioned to an arbitrary static steady state in at most k discrete times. Despite the computational complexity of the kMFVS problem, the proposed ILP-based method is capable of identifying exact optimal solutions even for scale-free graphs with approximately 3000 nodes.

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“…Computational analysis and modelling can help better understand the origins [5], progression [6] as well as treatment options [7] of cancer. The latter includes recent proposals of artificial intelligence (AI) based approaches to targeted therapeutics, directed at the control of Boolean network models [8] of signalling and regulatory networks, namely rule-based machine learning [9], reinforcement learning [10,11], and deep reinforcement learning [12] to address larger networks.…”

Section: Introductionmentioning

confidence: 99%

A Structural Characterisation of the Mitogen-Activated Protein Kinase Network in Cancer

et al. 2022

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Gene regulatory networks represent collections of regulators that interact with each other and with other molecules to govern gene expression. Biological signalling networks model how signals are transmitted and how activities are coordinated in the cell. The study of the structure of such networks in complex diseases such as cancer can provide insights into how they function, and consequently, suggest suitable treatment approaches. Here, we explored such topological characteristics in the example of a mitogen-activated protein kinase (MAPK) signalling network derived from published studies in cancer. We employed well-established techniques to conduct network analyses, and collected information on gene function as obtained from large-scale public databases. This allowed us to map topological and functional relationships, and build hypotheses on this network’s functional consequences. In particular, we find that the topology of this MAPK network is highly non-random, modular and robust. Moreover, analysis of the network’s structure indicates the presence of organisational features of cancer hallmarks, expressed in an asymmetrical manner across communities of the network. Finally, our results indicate that the organisation of this network renders it problematic to use treatment approaches that focus on a single target. Our analysis suggests that multi-target attacks in a well-orchestrated manner are required to alter how the network functions. Overall, we propose that complex network analyses combined with pharmacological insights will help inform on future treatment strategies, exploiting structural vulnerabilities of signalling and regulatory networks in cancer.

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The Multiple Dimensions of Networks in Cancer: A Perspective

Cited by 8 publications

References 132 publications

Model-Based Regional Control with Anomalous Diffusion of Multi-Drug Combined Cancer Therapy for Volume Predictions

Model-Based Regional Control with Anomalous Diffusion of Multi-Drug Combined Cancer Therapy for Volume Predictions

Computational Method for k-Distance Limited Minimum Feedback Vertex Set and Its Application to Biological Networks

A Structural Characterisation of the Mitogen-Activated Protein Kinase Network in Cancer

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