Cancer onset and progression: A genome-wide, nonlinear dynamical systems perspective on onconetworks

Qu, Kun; Haidar, Alaa Abi; Fan, Jiangyuan; Ensman, Lisa; Tuncay, Kağan; Jolly, Michael S.; Ortoleva, P.

doi:10.1016/j.jtbi.2006.12.002

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…To achieve a state-transition, no single point mutation is sufficient, and a cumulative effect linked to mutations will occur only if a critical state of the system as a whole is reached. It is worth of noting that the transition beyond that critical point “may be prevented or reversed by simultaneously manipulating a number of factors in the extracellular medium” [76]. Indeed, If multiple molecular elements must be tuned simultaneously to change cell phenotype, then it should be hypothesized that only a stimulus, outfitted with pleiotropic property, would perform that task, mainly based on stochastic fluctuations that enable transition from one attractor (phenotype) to another; that model may explain the genome-wide adaptability to environmental changes without requiring specific molecular signaling transducers [77] and why switching in between different cell fates can be triggered by changes in extracellular matrix structure, by inducing cell shape modification, and by adding aspecific chemical substances, electrical ion flows, and magnetic or gravitational fields [78–80].…”

Section: Physical Cues Drive Cells Differentiation and Fatementioning

confidence: 99%

Tumor and the Microenvironment: A Chance to Reframe the Paradigm of Carcinogenesis?

Bizzarri

Cucina

2014

BioMed Research International

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The somatic mutation theory of carcinogenesis has eventually accumulated an impressive body of shortfalls and paradoxes, as admittedly claimed by its own supporters given that the cell-based approach can hardly explain the emergence of tissue-based processes, like cancer. However, experimental data and alternatives theories developed during the last decades may actually provide a new framework on which cancer research should be reframed. Such issue may be fulfilled embracing new theoretical perspectives, taking the cells-microenvironment interplay as the privileged level of observation and assuming radically different premises as well as new methodological frameworks. Within that perspective, the tumor microenvironment cannot be merely considered akin to new “factor” to be added to an already long list of “signaling factors”; microenvironment represents the physical-biochemical support of the morphogenetic field which drives epithelial cells towards differentiation and phenotype transformation, according to rules understandable only by means of a systems biology approach. That endeavour entails three fundamental aspects: general biological premises, the level of observation (i.e., the systems to which we are looking for), and the principles of biological organization that would help in integrating and understanding experimental data.

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Section: Physical Cues Drive Cells Differentiation and Fatementioning

confidence: 99%

Tumor and the Microenvironment: A Chance to Reframe the Paradigm of Carcinogenesis?

Bizzarri

Cucina

2014

BioMed Research International

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“…Explaining this variability requires a dynamic perspective, emphasizing the interactions among multiple factors across levels and over time (Grizzi & Chiriva‐Internati, ; Qu et al, ). For example, Cheng and colleagues (2008) performed a deep analysis of the molecular profiles of 161 colorectal cancers.…”

Section: Stability From Variabilitymentioning

confidence: 99%

The Science of the Individual

Rose¹,

Rouhani²,

Fischer³

2013

Mind Brain and Education

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Our goal is to establish a science of the individual, grounded in dynamic systems, and focused on the analysis of individual variability. Our argument is that individuals behave, learn, and develop in distinctive ways, showing patterns of variability that are not captured by models based on statistical averages. As such, any meaningful attempt to develop a science of the individual necessarily begins with an account of the individual variability that is pervasive in all aspects of behavior, and at all levels of analysis. Using examples from fields as diverse as education and medicine, we show how starting with individual variability, not statistical averages, helped researchers discover two sources of ordered variability-pathways and contexts-that have implications for theory, research, and practice in multiple disciplines. We conclude by discussing three broad challenges-data, models, and the nature of science-that must be addressed to ensure that the science of the individual reaches its full potential.

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“…Cancer is a complex and multifactorial disease in which gene mutations in signaling, 1-3 metabolic [4][5][6] and gene regulation 7,8 networks accumulate in a dynamic process, leading to deregulation of cell death and/or proliferation. Most of the components of the underlying biological networks have been studied for decades using traditional approaches in biology such as reductionism, linear logic and conceptual models (i.e., a drawing or qualitative representation of the biological mechanism).…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and analysis of cancer cellular network motifs

Cloutier

Wang

2011

Integr. Biol.

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With the advent of high-throughput biology, we now routinely scan cells and organisms at practically all levels, from genome to protein, metabolism, signaling and other cellular functions. This methodology allowed biological studies to move from a reductionist approach, such as isolation of specific pathways and mechanisms, to a more integrative approach, where biological systems are seen as a network of interconnected components that provide specific outputs and functions in response to stimuli. Recent literature on biological networks demonstrates two important concepts that we will consider in this review: (i) cellular pathways are highly interconnected and should not be studied separately, but as a network; (ii) simple, recurrent feedback motifs within the network can produce very specific functions that favor their modular use. The first theme differs from the traditional approach in biology because it provides a framework (i.e., the network view) in which large datasets are analyzed with an unbiased view. The second theme (feedback motifs) shows the importance of locally analyzing the dynamic properties of biological networks in order to better understand their functionality. We will review these themes with examples from cell signaling networks, gene regulatory networks and metabolic pathways. The deregulation of cellular networks (metabolism, signaling etc.) is involved in cancer, but the size of the networks and resulting non-linear behavior do not allow for intuitive reasoning. In that context, we argue that the qualitative classification of the 'building blocs' of biological networks (i.e. the motifs) in terms of dynamics and functionality will be critical to improve our understanding of cancer biology and rationalize the wealth of information from high-throughput experiments. From the examples highlighted in this review, it is clear that dynamic feedback motifs can be used to provide a unified view of various cellular processes involved in cancer and this will be critical for future research on personalized and predictive cancer therapies.

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Cancer onset and progression: A genome-wide, nonlinear dynamical systems perspective on onconetworks

Cited by 12 publications

References 24 publications

Tumor and the Microenvironment: A Chance to Reframe the Paradigm of Carcinogenesis?

Tumor and the Microenvironment: A Chance to Reframe the Paradigm of Carcinogenesis?

The Science of the Individual

Dynamic modeling and analysis of cancer cellular network motifs

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