Nonlinear Dynamics and Statistical Physics of Models for the Immune System

2004 ◽  
pp. 399-410
Author(s):  
Ulrich Behn ◽  
Markus Brede ◽  
Jan Richter
Keyword(s):  
Nonlinear Dynamics ◽  
Immune System ◽  
Statistical Physics

DEVELOPMENT OF THERAPEUTIC BRAIN STIMULATION TECHNIQUES WITH METHODS FROM NONLINEAR DYNAMICS AND STATISTICAL PHYSICS

2006 ◽  
Vol 16 (07) ◽  
pp. 1889-1911 ◽  
Author(s):  
PETER A. TASS ◽  
CHRISTIAN HAUPTMANN ◽  
OLEKSANDR V. POPOVYCH
Keyword(s):  
Nonlinear Dynamics ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Brain Stimulation ◽  
Brain Function ◽  
Statistical Physics ◽  
Delayed Feedback ◽  
Phase Resetting ◽  
Feedback Mechanisms

Synchronization processes may severely impair brain function, for instance, in Parkinson's disease, essential tremor or epilepsies. We present three different effectively desynchronizing stimulation techniques which have been developed with methods from nonlinear dynamics and statistical physics. These techniques exploit either stochastic phase resetting principles or complex delayed feedback mechanisms. We explain how these methods work and how they can be applied to therapeutic brain stimulation.

scholarly journals New Systematic Solution for Resolving Nonlinear Dynamics Using System Analytical Theory Based on Engineering Science

2021 ◽  
Vol 15 (6) ◽  
pp. 46
Author(s):  
Deok-soo Cha ◽  
Kyoung-il Kim
Keyword(s):  
Nonlinear Dynamics ◽  
Systems Thinking ◽  
Statistical Physics ◽  
Open Loop ◽  
Economic Systems ◽  
Analytic Method ◽  
Engineering Science ◽  
Engineering Systems ◽  
Physical Sciences ◽  
Systematic Solution

There are many nonlinear dynamics in field of non-physical sciences, such as the food chain, economic systems, or engineering systems with the characteristics of closed or open-loop systems. The problems arising from this have been resolved by the outdated chaos theory in statistical physics based on the paradigm of logical thinking. However, it was founded by classical physicists, and it is imperfect and vague, moreover, very difficult for others. Therefore, we require a perfect systematic solution based on systems thinking, such as systems analytical methods in engineering science. Surprisingly, in 2021, a non-physicist, on behalf of a physicist, has successfully resolved the problems and achieved a new solution based on systems thinking through interdisciplinary research; moreover, it has been published. Unfortunately, most physicists do not welcome it because they have no experience and it is disadvantageous to them like the Copernican theory. In addition, they have no ability to evaluate the new solution because they do not know the analytic method. Nevertheless, non-physicists are greatly welcome it, thus, there is no problem in it. Hence, non-physicists will verify it using MATLAB or simulator and apply it to all science, on behalf of physicists. If so, non-physicists will have both a logical solution and a systematic solution for resolving nonlinear dynamics.

scholarly journals Foundations of Complex Systems

2009 ◽  
Vol 17 (2) ◽  
pp. 237-248 ◽  
Author(s):  
Gregoire Nicolis ◽  
Catherine Nicolis
Keyword(s):  
Nonlinear Dynamics ◽  
Numerical Simulation ◽  
Data Analysis ◽  
Complex Systems ◽  
Statistical Physics ◽  
Fundamental Science ◽  
The Cross ◽  
Cross Fertilization ◽  
Different Levels

An approach to Complexity from the perspective of fundamental science is outlined, drawing on the cross-fertilization of concepts and tools from nonlinear dynamics, statistical physics, probability and information theories, data analysis and numerical simulation. Emphasis is placed on the intertwining between different levels of description and on the probabilistic dimension of complex systems, in connection with the issue of prediction.

Physical Sciences for Nonphysical Problems

2020 ◽  
pp. 253-268
Author(s):  
Lazaros K. Gallos
Keyword(s):  
Nonlinear Dynamics ◽  
Cardiovascular Diseases ◽  
Control Theory ◽  
Health Outcomes ◽  
Human Disease ◽  
Statistical Physics ◽  
Noncommunicable Diseases ◽  
Physical Sciences ◽  
Complex Population ◽  
And Control

To begin understanding noncommunicable diseases in a population, researchers must understand how people are connected to each other, how they interact with each other, and if there are external influences. Heterogeneity and complexity in human disease suggest new methodological and analytical ways in which the physical sciences can assist research in areas such as obesity, cardiovascular diseases, psychiatric disorders, or cancer. As it is becoming clear that human morbid states are not strictly deterministic diseases, this chapter overviews how statistical physics and nonlinear dynamics (e.g., percolation, cascades, control theory) grounded in stochastic approaches can contribute to the delineation and control of an array of complex population health outcomes.

scholarly journals Cluster-based network modeling—From snapshots to complex dynamical systems

2021 ◽  
Vol 7 (25) ◽  
pp. eabf5006
Author(s):  
Daniel Fernex ◽  
Bernd R. Noack ◽  
Richard Semaan
Keyword(s):  
Nonlinear Dynamics ◽  
Statistical Physics ◽  
Network Modeling ◽  
Network Connectivity ◽  
Dimensional Subspace ◽  
Data Driven ◽  
Lorenz Attractor ◽  
Universal Method ◽  
Time Resolved ◽  
Kolmogorov Flow

We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics. CNM describes short- and long-term behavior and is fully automatable, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields.

scholarly journals Repeats Mimic Immunostimulatory Viral Features Across a Vast Evolutionary Landscape

2021 ◽  
Author(s):  
Petr Šulc ◽  
Alexander Solovyov ◽  
Sajid A Marhon ◽  
Siyu Sun ◽  
John A LaCava ◽  
...  
Keyword(s):  
Immune System ◽  
Rna Splicing ◽  
Statistical Physics ◽  
Intron Retention ◽  
Repetitive Sequences ◽  
Evolutionary Model ◽  
Selective Forces ◽  
Viral Mimicry ◽  
Line Elements ◽  
Molecular Patterns

An emerging hallmark across many human diseases – such as cancer, autoimmune and neurodegenerative disorders – is the aberrant transcription of typically silenced repetitive elements. Once transcribed they can mimic pathogen-associated molecular patterns and bind pattern recognition receptors, thereby engaging the innate immune system and triggering inflammation in a process known as viral mimicry. Yet how to quantify pathogen mimicry, and the degree to which it is shaped by natural selection, remains a gap in our understanding of both genome evolution and the immunological basis of disease. Here we propose a theoretical framework that combines recent biological observations with statistical physics and population genetics to quantify the selective forces on virus-like features generated by repeats and integrate these forces into predictive evolutionary models. We establish that many repeat families have evolutionarily maintained specific classes of viral mimicry. We show that for HSATII and intact LINE-1 selective forces maintain CpG motifs, while for a set of SINE and LINE elements the formation of long double-stranded RNA is more prevalent than expected from a neutral evolutionary model. We validate our models by showing predicted immunostimulatory inverted SINE elements bind the MDA5 receptor under conditions of epigenetic dysregulation and that they are disproportionately present during intron retention when RNA splicing is pharmacologically inhibited. We conclude viral mimicry is a general evolutionary mechanism whereby genomes co-opt features generated by repetitive sequences to trigger the immune system, acting as a quality control system to flag genome dysregulation. We demonstrate these evolutionary principles can be learned and applied to predictive models. Our work therefore serves as a resource to identify repeats with candidate immunostimulatory features and leverage them therapeutically.

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