On hydrodynamic interpretation of folding of an α-helical protein

Andryushchenko, V. A.; Chekmarev, S. F.

doi:10.1134/s0869864316060184

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Interestingly, most focal adhesion complexes and tyrosine receptor kinases mediating the initiation and progression of cancer metastasis depend on the structural conformation of SH3 domains (e.g., Src/FAK/Crk/Cas pathways) ( Figure 1) (83). Likewise, the folding dynamics of the villin subdomain HP-35 protein in a FRET (Forster resonance energy transfer) experiment was shown to obey the -model of turbulence with many orders (scales) of turbulent flow transitions for the eddies in the 3D conformational space (84,85).…”

Section: Turbulencementioning

confidence: 97%

Cancer: A Turbulence Problem

Uthamacumaran

2019

Preprint

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As we transition towards an era of Computational Medicine, our mathematical models of cancer dynamics must be revised. As such, recent evidence supports the perspective that cancermicroenvironment interactions consist of turbulent flows and strange attractor dynamics. Cancer pattern formation, invasion-growth dynamics, protein folding kinetics, stem cell fate bifurcations and metastatic invasion are discussed within the context of hydrodynamical turbulence. Cancer is presented as a three-dimensional Navier-Stokes equations global regularity, smoothness and existence problem. POSTULATE:Cancer stem cells are strange attractors on the Waddington energy landscape governed by turbulence dynamics. EMERGENCECancers are complex systems. The Hanahan-Weinberg 'hallmarks' are an inadequate representation of these dynamical systems. Complex systems are systems in which the interactions of its elements and parts cannot be deduced due to interdependence. Some general characteristics of complex systems include nonlinearity, emergence, computational irreducibility, unpredictability, nonequilibrium statistical mechanics and intractability. In simple terms, the concerted whole cannot be defined by the sum of its interacting parts. For example, the flow of exosomes between cancer cells are emergent characteristics of tumor ecosystems. It is impossible to understand exosomes without complex systems approaches. Exosomes are heterogeneous nano-scaled packets of information forming complex long-range communication networks. Along with ctDNA (circulating tumor DNA), exosomes are emerging targets for early tumor detection from liquid biopsies of patients (1, 2). Human embryonic stem cells-derived exosomes have shown capability of reprogramming malignant cancer phenotypes to benign-like fates, indicating exosomes are potent phenotype reprogramming machineries (3). However, the identity of cancer stem cells remains ambiguous. It is debated whether cancer stem cells conform a small subset of the tumor hierarchy or whether all cancer cells are potentially stem cells (i.e., have the potency of phenotypic plasticity and unlimited replication). In attempt to reconcile such problems, precision oncology is transitioning towards the use of artificial intelligence and machine learning algorithms in guiding clinical decision-making (4). Machine intelligence is emerging as a powerful tool in deciphering cancer ecosystems.Each tumor exhibits spatio-temporal heterogeneity and emergent properties that are unpredictable. For example, certain malignant melanomas spontaneously regress, a property not recognized as a hallmark (5). Another example, PEDF (pigment epithelium-derived factor) upregulated by extracellular vesicle bodies-mediated EGFRvIII promotes the self-renewal and propagation of GSCs (glioma stem cells) (6, 7). However, PEDF is an anti-angiogenic factor and GSCs require vascular interactions for development. Such complex feedback loops are emergent properties of complex systems. Certain tumors exhibit vasculogenic mimicry, the spontaneous form...

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Section: Turbulencementioning

confidence: 97%

Cancer: A Turbulence Problem

Uthamacumaran

2019

Preprint

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“…Turbulence dynamics have also been proposed to explain the Kolmogorov's power law decay observed in the frequency spectra of computationally simulated protein folding models. One of the studied proteins, Src kinases, are crucial drivers of cancer metastases and focal adhesion dynamics with the ECM (extracellular matrix) (Kalgin and Chekmarev, 2011 [119]; Andryuschenko and Chekmarev, 2016 [120]; Chekmarev, 2018 [121]). The Src kinases and associated adhesion proteins have shown capability of reprogramming cancer cells to benignity by restoring a subset of miRNAs to normal levels (Kourtidis et al, 2015 [122]).…”

Section: Nonlinear Dynamics Fractals and Chaosmentioning

confidence: 99%

A Review of Complex Systems Approaches to Cancer Networks

Uthamacumaran¹

2020

ComplexSystems

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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.

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“…As mentioned, we are currently adherent to the central dogma of systems biology, visualizing protein folding as a stochastic process. However, the simulated protein folding transitions of the SH3-domain (Src Homology 3) protein was shown to obey the vortex dynamics and hydrodynamical equations of Kolmogorov's statistical theory of turbulence [63 , 64] . The spatial flow distributions of the probability fluxes were determined to be self-similar (Kolmogorov-Richardson cascades) with a fractal dimension that decreases toward the native state, indicating that paradoxically the flow becomes more turbulent at more stable protein conformations [63 , 64] .…”

Section: Chemical Turbulence In Pattern Formationmentioning

confidence: 99%

“…However, the simulated protein folding transitions of the SH3-domain (Src Homology 3) protein was shown to obey the vortex dynamics and hydrodynamical equations of Kolmogorov's statistical theory of turbulence [63 , 64] . The spatial flow distributions of the probability fluxes were determined to be self-similar (Kolmogorov-Richardson cascades) with a fractal dimension that decreases toward the native state, indicating that paradoxically the flow becomes more turbulent at more stable protein conformations [63 , 64] . To shed some context to fluid turbulence, the Kolmogorov-Richardson cascade observed in these protein folding simulations describes experimentally observed isotropic turbulence of an incompressible fluid, where large-scale flow structures (eddies and vortices) decay into smaller fractal structures.…”

Section: Chemical Turbulence In Pattern Formationmentioning

confidence: 99%