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Robust heteroclinic networks are invariant sets that can appear as attractors in symmetrically coupled or otherwise constrained dynamical systems. These networks may have a very complicated structure that is poorly understood and determined…

Adaptation and Self-Organizing Systems · Physics 2015-06-12 Peter Ashwin , Claire Postlethwaite

Time-varying graph signal recovery has been widely used in many applications, including climate change, environmental hazard monitoring, and epidemic studies. It is crucial to choose appropriate regularizations to describe the…

Signal Processing · Electrical Eng. & Systems 2024-05-17 Weihong Guo , Yifei Lou , Jing Qin , Ming Yan

Bond graphs can be used to build thermodynamically-compliant hierarchical models of biomolecular systems. As bond graphs have been widely used to model, analyse and synthesise engineering systems, this paper suggests that they can play the…

Molecular Networks · Quantitative Biology 2016-06-06 Peter Gawthrop , Edmund Crampin

In this work we establish that finite directed graphs give rise to semiflows on the power set of their nodes. We analyze the topological dynamics for semiflows on finite directed graphs by characterizing Morse decompositions, recurrence…

Dynamical Systems · Mathematics 2020-05-29 José Ayala , Wolfgang Kliemann

Biological and social networks have recently attracted enormous attention between physicists. Among several, two main aspects may be stressed: A non trivial topology of the graph describing the mutual interactions between agents exists…

Statistical Mechanics · Physics 2015-05-19 Adriano Barra , Elena Agliari

Protein structure prediction and folding are fundamental to understanding biology, with recent deep learning advances reshaping the field. Diffusion-based generative models have revolutionized protein design, enabling the creation of novel…

Machine Learning · Computer Science 2025-10-01 Yogesh Verma , Markus Heinonen , Vikas Garg

Despite substantial progress in non-equilibrium physics, steady-state (s.s.) probabilities remain intractable to analysis. For a Markov process, s.s. probabilities can be expressed in terms of transition rates using the Matrix-Tree theorem…

Statistical Mechanics · Physics 2021-09-07 Ugur Cetiner , Jeremy Gunawardena

In this article, we present a novel approach to reconstruct the topology of networked linear dynamical systems with latent nodes. The network is allowed to have directed loops and bi-directed edges. The main approach relies on the unique…

Systems and Control · Electrical Eng. & Systems 2021-06-29 Mishfad S. V. , Harish Doddi , Murti V. Salapaka

We propose a conceptually novel method of reconstructing the topology of dynamical networks. By examining the correlation between the variable of one node and the derivative of another node, we derive a simple matrix equation yielding the…

Data Analysis, Statistics and Probability · Physics 2015-06-11 Zoran Levnajić

We propose a novel method for topological analysis of unweighted graphs which is based on \textit{persistent homology}. The proposed method maps the input graph to a complete weighted graph where the weighting function maps each edge to a…

Algebraic Topology · Mathematics 2020-07-31 Padraig Corcoran

We propose a new and effective means for designing stable and fast-folding polypeptide sequences using a cumulant expansion of the molecular partition function. This method is unique in that $T_{Z}$, the ``cumulant design temperature''…

Condensed Matter · Physics 2007-05-23 Michael P. Morrissey , Eugene I. Shakhnovich

Protein folding produces characteristic and functional three-dimensional structures from unfolded polypeptides or disordered coils. The emergence of extraordinary complexity in the protein folding process poses astonishing challenges to…

Biomolecules · Quantitative Biology 2013-08-14 Kelin Xia , Guo-Wei Wei

Background: Many attempts have been made to resolve in time the folding of model proteins in computer simulations. Different computational approaches have emerged. Some of these approaches suffer from the insensitivity to the geometrical…

Statistical Mechanics · Physics 2007-05-23 Nikolay V. Dokholyan , Sergey V. Buldyrev , H. Eugene Stanley , Eugene I. Shakhnovich

We present a technique for calculating free-energy profiles for the nucleation of multicomponent structures that contain as many species as building blocks. We find that a key factor is the topology of the graph describing the connectivity…

Soft Condensed Matter · Physics 2015-01-12 William M. Jacobs , Aleks Reinhardt , Daan Frenkel

To provide a phenomenological theory for the various interesting transitions in restructuring networks we employ a statistical mechanical approach with detailed balance satisfied for the transitions between topological states. This enables…

Statistical Mechanics · Physics 2007-05-23 Imre Derenyi , Illes Farkas , Gergely Palla , Tamas Vicsek

Random heteropolymers do not display the typical equilibrium properties of globular proteins, but are the starting point to understand the physics of proteins and, in particular, to describe their non-native states. So far, they have been…

Biomolecules · Quantitative Biology 2015-06-03 Guido Tiana , Ludovico Sutto

A family of fast sampling methods is introduced here for molecular simulations of systems having rugged free energy landscapes. The methods represent a generalization of a strategy consisting of adjusting a model for the free energy as a…

Computational Physics · Physics 2022-02-07 Pablo F. Zubieta Rico , Juan J. de Pablo

It has recently been demonstrated that many biological networks exhibit a scale-free topology where the probability of observing a node with a certain number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has been…

Molecular Networks · Quantitative Biology 2009-11-11 Eric J. Deeds , Orr Ashenberg , Eugene I. Shakhnovich

Online social networks are a dominant medium in everyday life to stay in contact with friends and to share information. In Twitter, users can connect with other users by following them, who in turn can follow back. In recent years,…

Social and Information Networks · Computer Science 2022-05-06 Christoph Schweimer , Christine Gfrerer , Florian Lugstein , David Pape , Jan A. Velimsky , Robert Elsässer , Bernhard C. Geiger

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insight by isolating the essential degrees of freedom that dictate the…

Statistical Mechanics · Physics 2023-04-12 Shriram Chennakesavalu , David J. Toomer , Grant M. Rotskoff
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