Towards performance guarantees for emergent behavior

The self-assembly of foldamers into macrocycles is a simple approach to non-biological higher-order structure. Previous work on the co-assembly of ortho-phenylene foldamers with rod-shaped linkers has shown that folding and self-assembly affect each other; that is, the combination leads to new emergent behavior, such as access to otherwise unfavorable folding states. To this point this relationship has been passive. Here, we demonstrate control of self-assembly by manipulating the foldamers’ conformational energy surfaces. A series of o-phenylene decamers and octamers have been assembled into macrocycles using imine condensation. Product distributions were analyzed by gel-permeation chromatography and molecular geometries extracted from a combination of NMR spectroscopy and computational chemistry. The assembly of o-phenylene decamers functionalized with alkoxy groups or hydrogens gives both [2+2] and [3+3] macrocycles. The mixture results from a subtle balance of entropic and enthalpic effects in these systems: the smaller [2+2] macrocycles are entropically favored but require the oligomer to misfold, whereas a perfectly folded decamer fits well within the larger [3+3] macrocycle that is entropically disfavored. Changing the substituents to fluoro groups, however, shifts assembly quantitatively to the [3+3] macrocycle products, even though the structural changes are well-removed from the functional groups directly participating in bond formation. The electron-withdrawing groups favor folding in these systems by strengthening arene–arene stacking interactions, increasing the enthalpic penalty to misfolding. The architectural changes are substantial even though the chemical perturbation is small: analogous o-phenylene octamers do not fit within macrocycles when perfectly folded, and quantitatively misfold to give small macrocycles regardless of substitution. Taken together, these results represent both a high level of structural control in structurally complex foldamer systems and the demonstration of large-amplitude structural changes as a consequence of a small structural effects.

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Models of decision support in socio-cyber-physical systems

Information and Control Systems ◽

10.31799/1684-8853-2019-3-55-70 ◽

2019 ◽

pp. 55-70 ◽

Cited By ~ 1

Author(s):

A. V. Smirnov ◽

T. V. Levashova

Keyword(s):

Decision Making ◽

Decision Support ◽

Situation Awareness ◽

Cyber Physical Systems ◽

Emergent Behavior ◽

Emergent Properties ◽

Decision Making Process ◽

Physical Systems ◽

Decision Support Models ◽

Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

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Optimization-Based Wireless Network Architectures: Complexity, Decentralization and Performance Guarantees

10.21236/ada564105 ◽

2011 ◽

Author(s):

R. Srikant

Keyword(s):

Wireless Network ◽

Network Architectures ◽

Performance Guarantees ◽

And Performance

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Computational Methods for Structure-to-Function Analysis of Diet-Derived Catechins-Mediated Targeting of In Vitro Vasculogenic Mimicry

Cancer Informatics ◽

10.1177/11769351211009229 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110092

Author(s):

Abicumaran Uthamacumaran ◽

Narjara Gonzalez Suarez ◽

Abdoulaye Baniré Diallo ◽

Borhane Annabi

Keyword(s):

Machine Learning ◽

Cancer Cells ◽

Structural Changes ◽

Function Analysis ◽

Vasculogenic Mimicry ◽

Machine Learning Algorithms ◽

Emergent Behavior ◽

Molecular Signature ◽

Ovarian Cancer Cells

Background: Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science. Procedures: In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers. Results: These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes. Conclusions: Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

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