Self-organization: the fundament of cell biology

Wedlich‐Söldner, Roland; Betz, Timo

doi:10.1098/rstb.2017.0103

Cited by 55 publications

(44 citation statements)

References 17 publications

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“…Self-organization is a hallmark feature of CAS. 25,30 Several key clinical observations reveal the importance of a complexity model for homeopathic healing. These observations include:…”

Section: Complexity Complex Systems and Networkmentioning

confidence: 99%

“…There is no external hierarchical or controlling agent that predetermines the emergent order. 28,30,40 Self-organized criticality A property of far-from-equilibrium, non-linear, dynamical systems (such as the brain) in which the system attains a critical state and spontaneously shifts into a new state space (attractor) with self-similar behavior patterns in space and/or time. Examples in nature include avalanches and landslides.…”

Section: Emergencementioning

confidence: 99%

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The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

Bell

2019

Homeopathy

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Background The concepts of complex systems science enhance the understanding of how people develop and recover from disease. Living systems (human beings, animals, and plants) are self-organizing complex adaptive systems (CAS): that is, interconnected networks. CAS maintain life by initiating and carrying out non-linear dynamical changes to optimize survival fitness and function in the context of an ever-changing environment. Aims In Part 1 of this two-part paper, we relate concepts from complex systems science to homeopathic healing. The systemic changes of homeopathic healing involve adaptive patterns of responses to salient signals (similia) for reversing disease patterns and generating emergent multi-symptom healing over time. Methods and Results This narrative review relates homeopathic clinical practice theory to complex systems and network research. Homeopathic medicines communicate individually salient environmental information to the organism, with effects that are multi-system and indirect. The body's defense mechanisms recognize the self-similar information that the correctly chosen simillimum medicine at low dose conveys as a weak external/internal environmental stressor or danger signal (hormetin) to mobilize neural and cellular defenses. The body networks then use endogenous cell to cell signaling and amplify the small magnitude signal information. The results are disproportionately large: that is, non-linear, adaptive, modifications across the inter-connected self-organized biological networks/sub-systems of the body. CAS amplification mechanisms for small or weak signals include stochastic resonance, time-dependent sensitization, and hormesis. Conclusions The body as a complex system has the capacity for self-organization, emergence and self-similarity over global (overall health and wellbeing) and local (organ) levels of organization. These features are key for future research on the systemic healing that evolves over time during individualized homeopathic treatment.

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“…Self-organization is a hallmark feature of CAS. 25,30 Several key clinical observations reveal the importance of a complexity model for homeopathic healing. These observations include:…”

Section: Complexity Complex Systems and Networkmentioning

confidence: 99%

Section: Emergencementioning

confidence: 99%

The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

Bell

2019

Homeopathy

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“…system-level) order. Many micro-and macro-scale phenomena are orchestrated by self-organising principles [11]. In schooling fish, for example, adoption of the predominant movement direction by individual fish leads to eventual emergence of synchronised swimming.…”

Section: Self-organisation: a Decentralized Decision-making Platformmentioning

confidence: 99%

Coupled cycling and regulation of metazoan morphogenesis

Rezaei-Lotfi

Farahani

2020

Cell Div

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Metazoan animals are characterized by restricted phenotypic heterogeneity (i.e. morphological disparity) of organisms within various species, a feature that contrasts sharply with intra-species morphological diversity observed in the plant kingdom. Robust emergence of morphogenic blueprint in metazoan animals reflects restricted autonomy of individual cells in adoption of fate outcomes such as differentiation. Fates of individual cells are linked to and influenced by fates of neighboring cells at the population level. Such coupling is a common property of all self-organising systems and propels emergence of order from simple interactions between individual cells without supervision by external directing forces. As a consequence of coupling, expected functional relationship between the constituent cells of an organ system is robustly established concurrent with multiple rounds of cell division during morphogenesis. Notably, the molecular regulation of multicellular coupling during morphogenic self-organisation remains largely unexplored. Here, we review the existing literature on multicellular self-organisation with particular emphasis on recent discovery that β-catenin is the key coupling factor that programs emergence of multi-cellular self-organisation by regulating synchronised cycling of individual cells.

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“…Further, the aging markers interact with each other [9], and synergistically coordinate the aging process, herein generating the self-organization system [10] of aging, where particular bio-markers regulate the aging process in different age groups, respectively. Although tissues become disordered reflected by a functional decline during aging in general (often evaluated by increased entropies [11]), a series of aging markers perform particular / ordered functions in special aging stages / age groups.…”

Section: Introductionmentioning

confidence: 99%

The self-organization model reveals systematic characteristics of aging

Wang

Huang

Li³

et al. 2020

Theor Biol Med Model

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Background: Aging is a fundamental biological process, where key bio-markers interact with each other and synergistically regulate the aging process. Thus aging dysfunction will induce many disorders. Finding aging markers and re-constructing networks based on multi-omics data (i.e. methylation, transcriptional and so on) are informative to study the aging process. However, optimizing the model to predict aging have not been performed systemically, although it is critical to identify potential molecular mechanism of aging related diseases.Methods: This paper aims to model the aging self-organization system using a series of supervised learning methods, and study complex molecular mechanisms of aging at system level: i.e. optimizing the aging network; summarizing interactions between aging markers; accumulating patterns of aging markers within module; finding order-parameters in the aging self-organization system. Results: In this work, the normal aging process is modeled based on multi-omics profiles across different tissues. In addition, the computational pipeline aims to model aging self-organizing systems and study the relationship between aging and related diseases (i.e. cancers), thus provide useful indicators of aging related diseases and could help to improve prediction abilities of diagnostics. Conclusions:The aging process could be studied thoroughly by modelling the self-organization system, where key functions and the crosstalk between aging and cancers were identified.

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Self-organization: the fundament of cell biology

Abstract: One contribution of 15 to a theme issue 'Selforganization in cell biology'.

Cited by 55 publications

References 17 publications

The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

Coupled cycling and regulation of metazoan morphogenesis

The self-organization model reveals systematic characteristics of aging

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