scholarly journals A Review of Complex Systems Approaches to Cancer Networks

2020 ◽  
Vol 29 (4) ◽  
pp. 779-835
Author(s):  
A. Uthamacumaran ◽  
Keyword(s):  
Complex Systems ◽  
Network Science ◽  
Expression Patterns ◽  
Computational System ◽  
Information Dynamics ◽  
Learning Network ◽  
Algorithmic Information ◽  
Cancer Networks ◽  
Cancer Network ◽  
Learning Architectures

Cancers remain the leading cause of disease-related pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.

scholarly journals A Complex Systems Analysis of Cancer Networks

2019 ◽  
Author(s):  
Abicumaran Uthamacumaran
Keyword(s):  
Complex Systems ◽  
Systems Analysis ◽  
Expression Patterns ◽  
Computational System ◽  
Information Dynamics ◽  
Learning Network ◽  
Algorithmic Information ◽  
Cancer Networks ◽  
Cancer Network ◽  
Learning Architectures

Cancers remain the lead cause of disease-related, pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system with intractable algorithmic complexity. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple, strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep Learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

scholarly journals Cognitive Networks: Interactivity, Intersubjectivity, and Synergy

2018 ◽  
Vol 20 (1) ◽  
pp. 52-78
Author(s):  
Helena Knyazeva
Keyword(s):  
Complex Systems ◽  
Cognitive Science ◽  
Cognitive Processes ◽  
Network Science ◽  
Cognitive Maps ◽  
Cognitive Networks ◽  
Point Of View ◽  
Network Structures ◽  
Cognitive Coherence ◽  
Cognitive Niches

Some properties of cognitive networks are discussed in the article in the context of the modern achievements of the network science. It is the study in network structures and their surprising properties that gives a new impetus to the development of the theory of complex systems (synergetics). The analysis of cognitive processes from the point of view of the network structures that arise in them not only fits with such concepts already existing in cognitive science and epistemology, as cognitive niches, cognitive maps, cognitive coherence, etc.), but also brings some new aspects to the understanding of interactivity, intersubjectivity, synergy in cognition and creative activities, empathy.

scholarly journals Patient participation in cancer network governance: a six-year case study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dominique Tremblay ◽  
Nassera Touati ◽  
Susan Usher ◽  
Karine Bilodeau ◽  
Marie-Pascale Pomey ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Participation ◽  
Collaborative Governance ◽  
Network Governance ◽  
Local Network ◽  
Natural Setting ◽  
Participation In Decision Making ◽  
Cancer Networks ◽  
Cancer Network

Abstract Background Patient participation in decision-making has become a hallmark of responsive healthcare systems. Cancer networks in many countries have committed to involving people living with and beyond cancer (PLC) at multiple levels. However, PLC participation in network governance remains highly variable for reasons that are poorly understood. This study aims to share lessons learned regarding mechanisms that enable PLC participation in cancer network governance. Methods This multiple case study, using a qualitative approach in a natural setting, was conducted over six years in three local cancer networks within the larger national cancer network in Quebec (Canada), where PLC participation is prescribed by the Cancer Directorate. Data were collected from multiple sources, including individual and focus group interviews (n = 89) with policymakers, managers, clinicians and PLC involved in national and local cancer governance committees. These data were triangulated and iteratively analysed according to a framework based on functions of collaborative governance in the network context. Results We identify three main mechanisms that enable PLC participation in cancer network governance: (1) consistent emphasis on patient-centred care as a network objective; (2) flexibility, time and support to translate mandated PLC representation into meaningful participation; and (3) recognition of the distinct knowledge of PLC in decision-making. The shared vision of person-centred care facilitates PLC participation. The quality of participation improves through changes in how committee meetings are conducted, and through the establishment of a national committee where PLC can pool their experience, develop skills and establish a common voice on priority issues. PLC knowledge is especially valued around particular challenges such as designing integrated care trajectories and overcoming barriers to accessing care. These three mechanisms interact to enable PLC participation in governance and are activated to varying extents in each local network. Conclusions This study reveals that mandating PLC representation on governance structures is a powerful context element enabling participation, but that it also delineates which governance functions are open to influence from PLC participation. While the activation of mechanisms is context dependent, the insights from this study in Quebec are transferable to cancer networks in other jurisdictions seeking to embed PLC participation in decision-making.

scholarly journals Understanding complex systems: When Big Data meets network science

2015 ◽  
Vol 57 (4) ◽  
Author(s):  
Ingo Scholtes
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Complex Systems ◽  
Computer Science ◽  
Network Science ◽  
Grand Challenge ◽  
Research Activities

AbstractBetter understanding and controlling complex systems has become a grand challenge not only for computer science, but also for the natural and social sciences. Many of these systems have in common that they can be studied from a network perspective. Consequently methods from network science have proven instrumental in their analysis. In this article, I introduce the macroscopic perspective that is at the heart of network science. Summarizing my recent research activities, I discuss how a combination of this perspective with Big Data methods can improve our understanding of complex systems.

scholarly journals Scale-free structure of cancer networks and their vulnerability to hub-directed combination therapy

2020 ◽  
Author(s):  
Andrew X. Chen ◽  
Christopher J. Zopf ◽  
Jerome Mettetal ◽  
Wen Chyi Shyu ◽  
Joseph Bolen ◽  
...  
Keyword(s):  
Combination Therapies ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Effective Combination ◽  
Scale Free Networks ◽  
New Strategy ◽  
Cancer Networks ◽  
Law Degree ◽  
Cancer Network ◽  
Lethal Genes

AbstractBackgroundThe effectiveness of many targeted therapies is limited by toxicity and the rise of drug resistance. A growing appreciation of the inherent redundancies of cancer signaling has led to a rise in the number of combination therapies under development, but a better understanding of the overall cancer network topology would provide a conceptual framework for choosing effective combination partners. In this work, we explore the scale-free nature of cancer protein-protein interaction networks in 14 indications. Scale-free networks, characterized by a power-law degree distribution, are known to be resilient to random attack on their nodes, yet vulnerable to directed attacks on their hubs (their most highly connected nodes).ResultsConsistent with the properties of scale-free networks, we find that lethal genes are associated with ∼5-fold higher protein connectivity partners than non-lethal genes. This provides a biological rationale for a hub-centered combination attack. Our simulations show that combinations targeting hubs can efficiently disrupt 50% of network integrity by inhibiting less than 1% of the connected proteins, whereas a random attack can require inhibition of more than 30% of the connected proteins.ConclusionsWe find that the scale-free nature of cancer networks makes them vulnerable to focused attack on their highly connected protein hubs. Thus, we propose a new strategy for designing combination therapies by targeting hubs in cancer networks that are not associated with relevant toxicity networks.

scholarly journals Emergence and Algorithmic Information Dynamics of Systems and Observers

2021 ◽  
Author(s):  
Felipe S. Abrahão ◽  
Hector Zenil
Keyword(s):  
Perturbation Analysis ◽  
Formal Theory ◽  
Previous Knowledge ◽  
Information Costs ◽  
Information Dynamics ◽  
Formal Knowledge ◽  
Causal Processes ◽  
Algorithmic Information ◽  
Generative Processes ◽  
Strong Emergence

Previous work has shown that perturbation analysis in algorithmic information dynamics can uncover generative causal processes of finite objects and quantify each of its element's information contribution to computably constructing the objects. One of the challenges for defining emergence is that the dependency on the observer's previous knowledge may cause a phenomenon to present itself as emergent for one observer at the same time that reducible for another observer. Thus, in order to quantify emergence of algorithmic information in computable generative processes, perturbation analyses may inherit such a problem of the dependency on the observer's previous formal knowledge. In this sense, by formalizing the act of observing as mutual perturbations, the emergence of algorithmic information becomes invariant, minimal, and robust to information costs and distortions, while it indeed depends on the observer. Then, we demonstrate that the unbounded increase of emergent algorithmic information implies asymptotically observer-independent emergence, which eventually overcomes any formal theory that any observer might devise. In addition, we discuss weak and strong emergence and analyze the concepts of observer-dependent emergence and asymptotically observer-independent emergence found in previous definitions and models in the literature of deterministic dynamical and computable systems.

scholarly journals Statistical physics of network structure and information dynamics

2021 ◽  
Author(s):  
Arsham Ghavasieh ◽  
Manlio De Domenico
Keyword(s):  
Machine Learning ◽  
Statistical Physics ◽  
Quantum Physics ◽  
Network Science ◽  
Information Science ◽  
Wide Spectrum ◽  
Quantum Statistical Mechanics ◽  
Information Dynamics ◽  
Statistical Field ◽  
The Rich

Abstract In the last two decades, network science has proven to be an invaluable tool for the analysis of empirical systems across a wide spectrum of disciplines, with applications to data structures admitting a representation in terms of complex networks. On the one hand, especially in the last decade, an increasing number of applications based on geometric deep learning have been developed to exploit, at the same time, the rich information content of a complex network and the learning power of deep architectures, highlighting the potential of techniques at the edge between applied math and computer science. On the other hand, studies at the edge of network science and quantum physics are gaining increasing attention, e.g., because of the potential applications to quantum networks for communications, such as the quantum Internet. In this work, we briefly review a novel framework grounded on statistical physics and techniques inspired by quantum statistical mechanics which have been successfully used for the analysis of a variety of complex systems. The advantage of this framework is that it allows one to define a set of information-theoretic tools which find widely used counterparts in machine learning and quantum information science, while providing a grounded physical interpretation in terms of a statistical field theory of information dynamics. We discuss the most salient theoretical features of this framework and selected applications to protein-protein interaction networks, neuronal systems, social and transportation networks, as well as potential novel applications for quantum network science and machine learning.

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