scholarly journals Exploring biomolecular energy landscapes

2017 ◽  
Vol 53 (52) ◽  
pp. 6974-6988 ◽  
Author(s):  
Jerelle A. Joseph ◽  
Konstantin Röder ◽  
Debayan Chakraborty ◽  
Rosemary G. Mantell ◽  
David J. Wales
Keyword(s):  
Potential Energy ◽  
Structure Prediction ◽  
Energy Landscape ◽  
Energy Landscapes ◽  
Computational Framework ◽  
Feature Article ◽  
Thermodynamics And Kinetics ◽  
Potential Energy Landscape ◽  
Kinetics Of

This feature article presents the potential energy landscape perspective, which provides both a conceptual and computational framework for structure prediction, and decoding the global thermodynamics and kinetics of biomolecules.

scholarly journals Decoding the energy landscape: extracting structure, dynamics and thermodynamics

2012 ◽  
Vol 370 (1969) ◽  
pp. 2877-2899 ◽  
Author(s):  
David J. Wales
Keyword(s):  
Potential Energy ◽  
Energy Surface ◽  
Condensed Matter ◽  
Energy Landscape ◽  
Local Minima ◽  
Atomic And Molecular Clusters ◽  
Computational Framework ◽  
Interparticle Forces ◽  
Structure Dynamics ◽  
Potential Energy Landscape

Describing a potential energy surface in terms of local minima and the transition states that connect them provides a conceptual and computational framework for understanding and predicting observable properties. Visualizing the potential energy landscape using disconnectivity graphs supplies a graphical connection between different structure-seeking systems, which can relax efficiently to a particular morphology. Landscapes involving competing morphologies support multiple potential energy funnels, which may exhibit characteristic heat capacity features and relaxation time scales. These connections between the organization of the potential energy landscape and structure, dynamics and thermodynamics are common to all the examples presented, ranging from atomic and molecular clusters to biomolecules and soft and condensed matter. Further connections between motifs in the energy landscape and the interparticle forces can be developed using symmetry considerations and results from catastrophe theory.

scholarly journals An energy landscape approach to locomotor transitions in complex 3D terrain

2020 ◽  
Vol 117 (26) ◽  
pp. 14987-14995 ◽  
Author(s):  
Ratan Othayoth ◽  
George Thoms ◽  
Chen Li
Keyword(s):  
Potential Energy ◽  
Complex Terrain ◽  
Statistical Physics ◽  
Energy Landscape ◽  
Three Dimensional ◽  
Single Mode ◽  
Physical Interaction ◽  
Landscape Approach ◽  
3D Terrain ◽  
Potential Energy Landscape

Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Despite this, far more mechanistic understanding of terrestrial locomotion has been on how to generate and stabilize around near–steady-state movement in a single mode. We still know little about how locomotor transitions emerge from physical interaction with complex terrain. Consequently, robots largely rely on geometric maps to avoid obstacles, not traverse them. Recent studies revealed that locomotor transitions in complex three-dimensional (3D) terrain occur probabilistically via multiple pathways. Here, we show that an energy landscape approach elucidates the underlying physical principles. We discovered that locomotor transitions of animals and robots self-propelled through complex 3D terrain correspond to barrier-crossing transitions on a potential energy landscape. Locomotor modes are attracted to landscape basins separated by potential energy barriers. Kinetic energy fluctuation from oscillatory self-propulsion helps the system stochastically escape from one basin and reach another to make transitions. Escape is more likely toward lower barrier direction. These principles are surprisingly similar to those of near-equilibrium, microscopic systems. Analogous to free-energy landscapes for multipathway protein folding transitions, our energy landscape approach from first principles is the beginning of a statistical physics theory of multipathway locomotor transitions in complex terrain. This will not only help understand how the organization of animal behavior emerges from multiscale interactions between their neural and mechanical systems and the physical environment, but also guide robot design, control, and planning over the large, intractable locomotor-terrain parameter space to generate robust locomotor transitions through the real world.

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