Our goal was:
*to understand the classical experiments
*and to build the basic top-down models of physiological adaptation
PLAN: Top-down modelling in the bottom-up era;
Homeostasis and stabilisation;
Selye’s “Adaptation energy” – the universal currency for adaptation;
Goldstone’s critics and development of Selye’s concept;
Factor-resource models: resource, reserve and oscillating death;
Two adaptation in Selye’s experiments: Adaptation in a fitness landscape and adaptation of the fitness landscape;
Adaptation entropy and free energy;
Adaptation to load of many factors;
Questions.
Martha Stark MD – 6 Jun 2022 – The Ever-Evolving Psychodynamic Process – From...Martha Stark MD
Are you wishing that you had a better grasp of psychodynamic concepts and their application to the clinical hour? With an emphasis always on the translation of theory into practice, in this 2-hour intensive training Dr. Martha Stark will be highlighting the three major psychoanalytic schools:
(Model 1) the 1-person perspective of classical psychoanalysis – a “cognitive” approach that emphasizes “enhancement of knowledge” and “interpreting”;
(Model 2) the 1½-person perspective of self psychology – an “affective” approach that emphasizes “provision of experience” and “grieving”; and
(Model 3) the 2-person perspective of contemporary relational theory – a “relational” approach that emphasizes “engagement in relationship” and “negotiating mutual enactment.”
Martha is particularly interested in (1) how the “therapeutic process” between patient and therapist evolves over time, (2) what happens moment-to-moment in the intersubjective space between patient and therapist, (3) how healing cycles of disruption and repair can be generated when the therapist alternately challenges the patient’s defense and then supports it, and (4) how the ongoing provision of “optimal stress” can ultimately “incentivize” deep, enduring, characterological change in the patient.
In order to facilitate this advancement of the patient from psychological rigidity to psychological flexibility and from defensive reaction to adaptive response, Martha will be teaching three “optimally stressful” template statements – Model 1 “conflict statements,” Model 2 “disillusionment statements,” and Model 3 “accountability statements” – all of which are strategically designed to “precipitate disruption” in order to “trigger repair,” thereby healing unmastered early-on relational traumas and deeply embedded emotional injuries.
Martha will be presenting several brief clinical vignettes to demonstrate the transformation of “resistance” into “awareness” (Model 1), “relentless hope” into “acceptance” (Model 2), and “re-enactment” into “accountability” (Model 3).
1. Why is it important to learn about the Natural Response of RC and.pdfarshin9
1. Why is it important to learn about the Natural Response of RC and RL circuits in the time
domain before analyzing them using a method like Laplace?
Solution
natural response is defined as the response which occurs solely from initial conditions with no
other inputs. The natural response is also known as the unforced response or characteristic
response.The model differential equation for such a system is homogeneous, in that there is no
forcing term.
the homogeneous or natural response can be very simply found. It is composed of weighted sums
of functions est, where s is possibly complex (or most generally such functions multiplied by
polynomials in the time variable t). This is a statement about the solution of differential
equations. However, it is a remarkable empirical result that such differential equations well-
describe many physical systems. Said another way, the types of natural responses can be easily
observed in an experimental context, and in observations of many physical phenomena. The
natural response ties things together. A further surprising result is that real-world systems are
frequently able to be represented in terms of very simple models of first- or second-order. When
higher-order models are required, these systems have responses composed of sums of first- and
second-order responses. So it’s very worthwhile to understand the building-block first- and
second-order responses in depth.
To understand natural frequency, you have to first understand the concept of natural responseof a
system.
Natural response of a system is its response of the system to its initial state - and only initial
state.Forced response of a system is the response of the system to an input -neglecting its initial
state.
The natural frequency of a structure, depends on its physical characteristics like for example - its
mass and stiffness..
A disruptive condition that occurs in response to adverse influences from the internal or external environments
A condition in which the person responds to changes in the normal balanced state
A biological, psychological, social or chemical factor that causes physical or emotional tension and may be a factor in the etiology of certain illnesses.
Martha Stark MD – 6 Jun 2022 – The Ever-Evolving Psychodynamic Process – From...Martha Stark MD
Are you wishing that you had a better grasp of psychodynamic concepts and their application to the clinical hour? With an emphasis always on the translation of theory into practice, in this 2-hour intensive training Dr. Martha Stark will be highlighting the three major psychoanalytic schools:
(Model 1) the 1-person perspective of classical psychoanalysis – a “cognitive” approach that emphasizes “enhancement of knowledge” and “interpreting”;
(Model 2) the 1½-person perspective of self psychology – an “affective” approach that emphasizes “provision of experience” and “grieving”; and
(Model 3) the 2-person perspective of contemporary relational theory – a “relational” approach that emphasizes “engagement in relationship” and “negotiating mutual enactment.”
Martha is particularly interested in (1) how the “therapeutic process” between patient and therapist evolves over time, (2) what happens moment-to-moment in the intersubjective space between patient and therapist, (3) how healing cycles of disruption and repair can be generated when the therapist alternately challenges the patient’s defense and then supports it, and (4) how the ongoing provision of “optimal stress” can ultimately “incentivize” deep, enduring, characterological change in the patient.
In order to facilitate this advancement of the patient from psychological rigidity to psychological flexibility and from defensive reaction to adaptive response, Martha will be teaching three “optimally stressful” template statements – Model 1 “conflict statements,” Model 2 “disillusionment statements,” and Model 3 “accountability statements” – all of which are strategically designed to “precipitate disruption” in order to “trigger repair,” thereby healing unmastered early-on relational traumas and deeply embedded emotional injuries.
Martha will be presenting several brief clinical vignettes to demonstrate the transformation of “resistance” into “awareness” (Model 1), “relentless hope” into “acceptance” (Model 2), and “re-enactment” into “accountability” (Model 3).
1. Why is it important to learn about the Natural Response of RC and.pdfarshin9
1. Why is it important to learn about the Natural Response of RC and RL circuits in the time
domain before analyzing them using a method like Laplace?
Solution
natural response is defined as the response which occurs solely from initial conditions with no
other inputs. The natural response is also known as the unforced response or characteristic
response.The model differential equation for such a system is homogeneous, in that there is no
forcing term.
the homogeneous or natural response can be very simply found. It is composed of weighted sums
of functions est, where s is possibly complex (or most generally such functions multiplied by
polynomials in the time variable t). This is a statement about the solution of differential
equations. However, it is a remarkable empirical result that such differential equations well-
describe many physical systems. Said another way, the types of natural responses can be easily
observed in an experimental context, and in observations of many physical phenomena. The
natural response ties things together. A further surprising result is that real-world systems are
frequently able to be represented in terms of very simple models of first- or second-order. When
higher-order models are required, these systems have responses composed of sums of first- and
second-order responses. So it’s very worthwhile to understand the building-block first- and
second-order responses in depth.
To understand natural frequency, you have to first understand the concept of natural responseof a
system.
Natural response of a system is its response of the system to its initial state - and only initial
state.Forced response of a system is the response of the system to an input -neglecting its initial
state.
The natural frequency of a structure, depends on its physical characteristics like for example - its
mass and stiffness..
A disruptive condition that occurs in response to adverse influences from the internal or external environments
A condition in which the person responds to changes in the normal balanced state
A biological, psychological, social or chemical factor that causes physical or emotional tension and may be a factor in the etiology of certain illnesses.
hello. I am Sadiq Saleem, A graduate from the department of Physics, Karakoram International University Gilgit. I am glad to share one of the presentations I delivered. This is regarding Radiation dose equivalent, an important topic of Radiation Physics.
Email: sadiqsaleem.kiu1054@gmail.com
A behavioural model for the discussion of resilience, elasticity, and antifra...Vincenzo De Florio
Resilience is one of those "general systems attributes" that appear to play a central role in several disciplines - including ecology, business, psychology, industrial safety, microeconomics, computer networks, security, management science, cybernetics, control theory, crisis and disaster management. Resilience thus seems to be "needed" everywhere; and yet, even in the framework of a same discipline, it is not easy to define it precisely and consensually. To add to the confusion, other terms such as elasticity, change tolerance, and antifragility, although clearly related to resilience, cannot be easily differentiated.
In this talk I tackle this problem by introducing a behavioural model of resilience. I interpret resilience as the property emerging from the interaction of the behaviours produced by two "players": a system and a hosting environment. The outcome of said interaction depends on both intrinsic and extrinsic factors, including the systemic "traits" of the system but also how the system's endowment matches the requirements expressed by the behaviours of the environment. I show how the behavioural approach provides a unifying framework within which it is possible to express coherent definitions for elasticity, change tolerance, and antifragility.
Errors of Artificial Intelligence, their Correction and Simplicity Revolution...Alexander Gorban
We review and analyse biological, physical, and mathematical problems at the core of the fundamental question: how can high-dimensional brain organise reliable and fast learning in high-dimensional world of data by simple tools?
Two critical applications are reviewed: one-shot correction of errors in artificial intellectual systems and emergence of static and associative memories in ensembles of single neurons. Error correctors should be simple; not damage the existing skills of the system; allow fast non-iterative learning and correction of new mistakes without destroying the previous fixes. All these demands can be satisfied by new tools based on the concentration of measure phenomena and stochastic separation theory.
In several words, the stochastic separation theorems state that for an essentially high-dimensional distributions a random point can be separated from a random set by Fisher's linear discriminant with high probability. The number of points in this set can grow exponentially with dimension. Different versions of stochastic separation theorems use different definitions of `random sets' and `essentially high-dimensional distributions' but the essence of these definitions is simple: sets with very small (vanishing) volume should not have high probability even for large dimension.
The talk is based on the work: A.N. Gorban, V.A. Makarov, I.Y. Tyukin, The unreasonable effectiveness of small neural ensembles in high-dimensional brain. Physics of Life Reviews, 2019, https://doi.org/10.1016/j.plrev.2018.09.005
Do Fractional Norms and Quasinorms Help to Overcome the Curse of Dimensiona...Alexander Gorban
A talk given at IJCNN2019, Budapest
Curse of dimensionality (Bellman, 1957);
Blessing of dimensionality (Kainen, 1997).
For a random sample in high-dimensional space:
(i) Concentration of distances: Distances between almost all pairs of points are almost equal;
(ii) Quasiorthogonality: Vectors of the sample are almost orthogonal (after centralization);
(iii) Stochastic separation: Almost every point is linearly separable from the set of all other points --
With high probability,
for a wide class of distributions
and even for exponentially large samples.
Do fractional norms can compensate curse of dimensionality??? (Hypothesis of C.C. Aggarwal, 2001)
Analysis of dozens of datasets gives the answer:
NO! Fractional quasinorms do not help to overcome the curse of dimensionality in classification problem.
More Related Content
Similar to Adaptation free energy: The third generation of models of physiological adaptation
hello. I am Sadiq Saleem, A graduate from the department of Physics, Karakoram International University Gilgit. I am glad to share one of the presentations I delivered. This is regarding Radiation dose equivalent, an important topic of Radiation Physics.
Email: sadiqsaleem.kiu1054@gmail.com
A behavioural model for the discussion of resilience, elasticity, and antifra...Vincenzo De Florio
Resilience is one of those "general systems attributes" that appear to play a central role in several disciplines - including ecology, business, psychology, industrial safety, microeconomics, computer networks, security, management science, cybernetics, control theory, crisis and disaster management. Resilience thus seems to be "needed" everywhere; and yet, even in the framework of a same discipline, it is not easy to define it precisely and consensually. To add to the confusion, other terms such as elasticity, change tolerance, and antifragility, although clearly related to resilience, cannot be easily differentiated.
In this talk I tackle this problem by introducing a behavioural model of resilience. I interpret resilience as the property emerging from the interaction of the behaviours produced by two "players": a system and a hosting environment. The outcome of said interaction depends on both intrinsic and extrinsic factors, including the systemic "traits" of the system but also how the system's endowment matches the requirements expressed by the behaviours of the environment. I show how the behavioural approach provides a unifying framework within which it is possible to express coherent definitions for elasticity, change tolerance, and antifragility.
Errors of Artificial Intelligence, their Correction and Simplicity Revolution...Alexander Gorban
We review and analyse biological, physical, and mathematical problems at the core of the fundamental question: how can high-dimensional brain organise reliable and fast learning in high-dimensional world of data by simple tools?
Two critical applications are reviewed: one-shot correction of errors in artificial intellectual systems and emergence of static and associative memories in ensembles of single neurons. Error correctors should be simple; not damage the existing skills of the system; allow fast non-iterative learning and correction of new mistakes without destroying the previous fixes. All these demands can be satisfied by new tools based on the concentration of measure phenomena and stochastic separation theory.
In several words, the stochastic separation theorems state that for an essentially high-dimensional distributions a random point can be separated from a random set by Fisher's linear discriminant with high probability. The number of points in this set can grow exponentially with dimension. Different versions of stochastic separation theorems use different definitions of `random sets' and `essentially high-dimensional distributions' but the essence of these definitions is simple: sets with very small (vanishing) volume should not have high probability even for large dimension.
The talk is based on the work: A.N. Gorban, V.A. Makarov, I.Y. Tyukin, The unreasonable effectiveness of small neural ensembles in high-dimensional brain. Physics of Life Reviews, 2019, https://doi.org/10.1016/j.plrev.2018.09.005
Do Fractional Norms and Quasinorms Help to Overcome the Curse of Dimensiona...Alexander Gorban
A talk given at IJCNN2019, Budapest
Curse of dimensionality (Bellman, 1957);
Blessing of dimensionality (Kainen, 1997).
For a random sample in high-dimensional space:
(i) Concentration of distances: Distances between almost all pairs of points are almost equal;
(ii) Quasiorthogonality: Vectors of the sample are almost orthogonal (after centralization);
(iii) Stochastic separation: Almost every point is linearly separable from the set of all other points --
With high probability,
for a wide class of distributions
and even for exponentially large samples.
Do fractional norms can compensate curse of dimensionality??? (Hypothesis of C.C. Aggarwal, 2001)
Analysis of dozens of datasets gives the answer:
NO! Fractional quasinorms do not help to overcome the curse of dimensionality in classification problem.
My inaugural lecture about the future of applied mathematics. It was delivered in 2006 in Leicester, UK. I analysed the change of era in science, the main locomotive problems and tried to explain the reasons of "Unreasonable Effectiveness of Mathematics".
New universal Lyapunov functions for nonlinear kineticsAlexander Gorban
ABSTRACT f-divergences provide us by a rich family of the Lyapunov functions for master equation (Rényi–Csiszár–Morimoto H-theorem). For nonlinear mass action law kinetics the set of the known universal (independent of constants) Lyapunov functions is much poorer. For most of reaction mechanisms we know the only universal Lyapunov function, the classical free energy (even under the assumption of detailed balance). In this talk, I present a new rich family of universal Lyapunov function. They can be constructed for any given reaction mechanism (linear or nonlinear) and generalized mass action law.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Adaptation free energy: The third generation of models of physiological adaptation
1. Adaptation free energy:
The third generation of models
of physiological adaptation
Alexander N. Gorban
University of Leicester
(joint work with Tatiana A. Tyukina)
2. Plan
• Top-down modelling in the bottom-up era;
• Homeostasis and stabilisation;
• Selye’s “Adaptation energy” – the universal currency for
adaptation;
• Goldstone’s critics and development of Selye’s concept;
• Factor-resource models: resource, reserve and oscillating
death;
• Two adaptation in Selye’s experiments: Adaptation in a
fitness landscape and adaptation of the fitness landscape;
• Adaptation entropy and free energy;
• Adaptation to load of many factors;
• Questions.
3. Between bottom-up and top-down
http://learnandreturn.com/
https://navicell.curie.fr/navicell/
The ‘top-down’
modelling:
• Describes the world in
terms of dynamics,
interaction and control
of macroscopic
processes.
• Starts from the most
general phenomena and
aggregated variables. The ‘bottom-up’
modelling:
• Describes the world in
terms of elementary
processes.
• Starts from the level
of functioning and
interaction of individual
entities.
4. Homeostasis is, in its essence,
automatic stabilisation of the body:
• “Whenever conditions are such as to affect
the organism harmfully, factors appear within
the organism itself that protect or restore its
disturbed balance” (Cannon W.B. The wisdom
of the body. 1932)
Comfort
zone
This idea in engineering,
centrifugal governor, 1788A formal scheme
Any change in equilibrium
prompts an opposing reaction
in the responding system
Le Chatelier's principle (1898)
in chemistry (and beyond)
6. Beyond elementary
one-parametric loops
• Decomposition into relatively independent subsystems
(functional systems, PCA, ICA, lumping, …);
• Restructuring of the systems under stress;
• Dynamics of complex systems;
• Robustness and canalization;
• ……………….
• Small Models–Qualitative Insights and Large Models–
Quantitative Insights (mechanisms driven versus data
driven models).
• Tools: Combination of system analysis, dynamics,
control theory, and modern data mining.
7. Cannon’s world – the first generation of
adaptation models is developed far enough
1. Organism is represented as a structure of
relatively independent systems (groups of
parameters);
2. This decomposition is dynamical and may
change under the load of harmful factors;
3. Homeostasis is provided by a rich structure of
feedback loops;
More and more detailed mechanisms of regulation
are revealed and supported by the data-driven
approach.
9. General Adaptation Syndrome
(H. Selye, 1926)
“Experiments on rats show that if the organism is severely damaged by
acute nonspecific nocuous agents such as exposure to cold, surgical injury,
production of spinal shock,…, excessive muscular exercise, or intoxications with sublethal
doses of diverse drugs…, a typical syndrome appears, the symptoms of which
are independent of the nature of the damaging agent or the
pharmacological type of the drug employed, and represent rather a
response to damage as such.”
10. …during adaptation to a certain stimulus
the resistance to other stimuli decreases.
Am. J. Physiol. 123 (1938), 758-765.
11. Selye’s “Adaptation energy” – the
universal currency for adaptation
(and the term was a political mistake of Selye because
everybody asked him to demonstrate the physical nature of this
“energy”; an abstract “adaptation resource” may be better)
12. Selye’s conclusion
• These findings are tentatively interpreted by the
assumption that the resistance of the organism to
various damaging stimuli is dependent on its
adaptability.
• This adaptability is conceived to depend upon
adaptation energy of which the organism possesses
only a limited amount, so that if it is used for
adaptation to a certain stimuli will necessarily
decrease.
• We conclude that adaptation to any stimulus, is always
acquired at a cost, namely, at the cost of adaptation
energy.
14. There are several different and apparently contradictory answers; yet, in
different circumstances each of these answers is probably true:
1. If an individual is failing to adapt to a disease he may succeed in so doing, if
he is exposed to a totally different mild stimulus (such as slight fall of oxygen
tension).
2. In the process of adapting to this new stimulus he may acquire the power of
reacting more intensely to all stimuli.
3. As a result of a severe stimulus an individual may not be able to adapt
successfully to a second severe stimulus (such as a disease).
4. If he is already adapting successfully to a disease this adaptation may fail
when he is exposed to a second severe stimulus.
5. In some diseases (those of Adaptation) exposure to a fresh severe stimulus
may cure the disease. Here, too, exposure to an additional stressor will bring
him nearer to death but the risk may be justifiable if it is likely to re-mould
the adaptive mechanism to a normal form.
S. Afr. Med. J. 26 (1952), 88-92, 106-109.
An attempt has been made to decide how one stimulus will
affect an individual's power to respond to a different stimulus.
15. Goldstone found evidences suggesting
that previous adaptation strengthens the
individual to resist future stressors
• Goldstone proposed the conception of a constant
production or income of Adaptation Energy which
may be stored (up to a limit), as a capital reserve
of adaptation.
• He showed that this conception best explains the
clinical and Selye's own laboratory findings.
• It is possible that, had Selye's experimental
animals been asked to spend adaptation at a
lesser rate (below their energy income), they
might have coped successfully with their stressor
indefinitely.
16. Selye’s picture of adaptation
Stressor
Stressor
Stressor
Exhausting
…………………………………………….
Death
18. Selye’s axioms of Adaptation Energy (AE)
1. AE is a finite supply, presented at birth.
2. As a protective mechanism, there is some upper limit to the
amount of AE that an individual can use at any discrete moment
in time. It can be focused on one activity, or divided among
other activities designed to respond to multiply occupational
challenges.
3. There is a threshold of AE activation that must be present to
potentiate an occupational response.
4. AE is active at two levels of awareness: a primary level at which
creating the response occurs at a high awareness level, with
high usage of finite supply of adaptation energy; and a
secondary level at which the response creation is being
processing at a sub-awareness level, with a lower energy
expenditure.
(Following Schkade & Schultz, 2003)
19. Goldstone’s axiom 1’
• Adaptation Energy can be created, though the
income of this energy is slower in old age;
• It can also be stored as Adaptation Capital,
though the storage capacity has a fixed limit.
• If an individual spends his Adaptation Energy
faster than he creates it, he will have to draw
on his capital reserve;
• When this is exhausted he dies.
21. Simplest resource dynamics
(“superficial AE”)
R0 – AE
storage
capacity r0 – AE
level
Stressor
f – stressor
intensity
𝜓=f-r – non-compensated
stressor intensity
ℎ 𝜓 - the Heaviside
step function
𝑑𝑟
𝑑𝑡
= −𝑘 𝑑 𝑟 + 𝑘𝑟0 𝜓ℎ 𝜓 ;
𝑑𝑟0
𝑑𝑡
= −𝑘 𝑑 𝑟0 − 𝑘𝑟0 𝜓ℎ 𝜓 + 𝑘 𝑝𝑟 𝑅0 − 𝑟0 .
Degradation
Supply for stressor
neutralization
Production
Input
A system
Output
Sensor
Feedback
system
Feedback
22. Correction is necessary
𝑑𝑟
𝑑𝑡
= −𝑘 𝑑 𝑟 + 𝑘𝑟0 𝜓ℎ 𝜓 ;
𝑑𝑟0
𝑑𝑡
= −𝑘 𝑑 𝑟0 − 𝑘𝑟0 𝜓ℎ 𝜓 + 𝑘 𝑝𝑟 𝑅0 − 𝑟0 .
Degradation
Supply for stressor
neutralization
Production
For large f
𝑟0≈
𝑘 𝑝𝑟 𝑅0
𝑘𝑓
; 𝑟 ≈
𝑘𝑟0 𝑓
𝑘 𝑑
≈
𝑘 𝑝𝑟 𝑅0
𝑘 𝑑
When f→∞ no crises appear. Immortality is possible.
Something is wrong…
23. Correction is necessary:
threshold of death
𝑑𝑟
𝑑𝑡
= −𝑘 𝑑 𝑟 + 𝑘𝑟0 𝜓ℎ 𝜓 ;
𝑑𝑟0
𝑑𝑡
= −𝑘 𝑑 𝑟0 − 𝑘𝑟0 𝜓ℎ 𝜓 + 𝑘 𝑝𝑟 𝑅0 − 𝑟0 .
Degradation
Supply for stressor
neutralization
Production
AE production
should decrease
for large non-
compensated
stressors 𝜓=f-r
×W
W is the “well-being” coefficient (fitness)
𝑊 = 𝑊 𝜓 = 𝑤
𝜓
𝜓0
; 𝑤 𝑥 = 0 𝑖𝑓 𝑥 > 1
Threshold appears: If 𝑓 > 𝜓0
then a threshold 𝜃 > 0 exists:
if 𝑟 0 + 𝑟0 0 < 𝜃 then
𝑟 𝑡 + 𝑟0 𝑡 → 0 as 𝑡 → ∞
ψ=f-r
W(ψ)
𝜓 = 𝜓0 – the critical value of the non-compensated stressor intensity
1
If 𝑓 < 𝜓0 then life is possible (W>0) without adaptation (with r=0)
Non-compensated
stressors 𝜓=f-r
25. Reserve
The upper border 𝑟.
If r0 crosses this
border and goes up
– close reserve
The lower border r
If r0 crosses this
border and goes
down – open reserve
Resource barrel,
capacity R0
Reserve barrel,
capacity Rrv
𝑟0
𝐵 𝑜/𝑐
𝐵 𝑜/𝑐=1
𝐵 𝑜/𝑐=0
Hysteresis of reserve supply:
𝐵 𝑜/𝑐=0 – reserve is closed
𝐵 𝑜/𝑐=1 – reserve is open
Resource
level r0
Reserve
level rrv
26. Resource & reserve model
The simplest dynamical model has three
real variables and one Boolean
If reserve is open then r0 < ṝ. It closes when r0 =ṝ. If
reserve is closed then r0 > r. It opens when r0 = r.
𝑑𝑟
𝑑𝑡
= −𝑘 𝑑 𝑟 + 𝑘𝑟0 𝜓ℎ 𝜓 ;
𝑑𝑟0
𝑑𝑡
= −𝑘 𝑑 𝑟0 − 𝑘𝑟0 𝜓ℎ 𝜓 + 𝑘 𝑝𝑟 𝑟rv 𝐵 𝑜/𝑐 𝑅0 − 𝑟0 + 𝑘 𝑝𝑟 𝑅0 − 𝑟0 𝑊;
𝑑𝑟rv
𝑑𝑡
= −𝑘 𝑑 𝑟rv
− 𝑘 𝑝𝑟 𝑟rv 𝐵 𝑜/𝑐 𝑅0 − 𝑟0 + 𝑘 𝑝𝑟1 𝑅rv
− 𝑟rv
𝑊
27. Null-isoclines on 𝑟, 𝑟0 plane for
𝐵 𝑜/𝑐=0 and 𝑓 < 𝜓0
r0
fW=0
dr/dt=0
dr0/dt=0
r0=R0
The safe situation.
• Equilibrium is unique and stable.
• The “death border” W=0 is in the
negative area.
• It is unattainable from the
positively invariant rectangular
0 ≤ 𝑟 ≤ 𝑓, 0 ≤ 𝑟0 ≤ 𝑅0
r
28. Null-isoclines on 𝑟, 𝑟0 plane for
𝐵 𝑜/𝑐=0 and 𝑓 > 𝜓0
r0
W=0
dr/dt=0
dr0/dt=0
r0=R0
S
U
r
f
Life area
(positively
invariant)
r
Death Separatrix
29. Stabilisation by reserve
𝑟 (reserve off)
𝑟 (reserve on)
r0
W=0
dr/dt=0
dr0/dt=0
r0=R0
S
U
Modified
isocline
dr0/dt=0
Three types of stabilisation:
1. Removing dangerous
borders or reduction of
their attainability regions;
2. Stabilisation of unstable
equilibria;
3. Stable oscillations instead
of deaths.
31. Stable oscillations for
various initial conditions
Metastable oscillations for various
initial conditions (oscillating death)
32. Oscillating mortality after cancer surgery
operation (high severity cohort)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Days after operation
Deaths
Deaths
33. Oscillating mortality for trauma
(low severity cohort)
Daily coefficient of mortality -- evaluated probability of a patient to die
on day t under condition that he survived during days [1,t-1]:
a) for NISS severities 1-8,
b) for NISS severity 9,
c) for the whole dataset (monotonically decreases).
NISS= New Injury Severity Score
E.M. Mirkes, T.J. Coats, J. Levesley, A.N. Gorban, Handling missing data in large healthcare
dataset: a case study of unknown trauma outcomes, Computers in Biology and Medicine
75 (2016), 203-216.
35. I do not understand:
is training possible?
Something seems to be wrong!
𝜓0 may be non-constant.
If I stay here for time T then 𝜓0 may increase
ψ=f-r
W(ψ)
𝜓 = 𝜓0
1
ψ=f-r
W(ψ)
𝜓 = 𝜓0
1
Training is increasing of 𝜓0. This process consumes AE. The rate
of this process depends on available AE and on the well-being W.
One more simple ODE for dynamics of 𝜓0 should be added.
36. ψ=f-r
W(ψ)
𝜓 = 𝜓0
1
ψ=f-r
W(ψ)
𝜓 = 𝜓0
1
Detraining
If I stay in the area with small 𝜓 for sufficiently long time
then 𝜓0 decreases
37. Dynamics of adaptation
with training effects
𝑑𝑟
𝑑𝑡
= −𝑘 𝑑 𝑟 + 𝑘𝑟0
𝜓
𝜓0
ℎ 𝜓 − 𝑘training
𝜓
𝜓0
𝑤
𝜓
𝜓0
𝑟;
𝑑𝑟0
𝑑𝑡
= −𝑘 𝑑 𝑟0 − 𝑘𝑟0
𝜓
𝜓0
ℎ 𝜓 + 𝑘 𝑝𝑟 𝑟rv 𝐵 𝑜/𝑐 𝑅0 − 𝑟0 + 𝑘 𝑝𝑟 𝑅0 − 𝑟0 𝑤
𝜓
𝜓0
;
𝑑𝑟rv
𝑑𝑡
= −𝑘 𝑑 𝑟rv − 𝑘 𝑝𝑟 𝑟rv 𝐵 𝑜/𝑐 𝑅0 − 𝑟0 + 𝑘 𝑝𝑟1 𝑅rv − 𝑟rv 𝑤
𝜓
𝜓0
;
1
𝜓0
𝑑𝜓0
𝑑𝑡
= −𝑘detraining + 𝛼𝑘training
𝜓
𝜓0
𝑤
𝜓
𝜓0
𝑟.
𝛼>0 – efficiency of AE
supply for training
The last equation generalises the popular models of athletic training and
performance, see, for example, a review in
Clarke DC, Skiba PF. Rationale and resources for teaching the mathematical
modeling of athletic training and performance. Advances in physiology education.
2013 Jun 1;37(2):134-52.
Rescaling of AE
demand and
supply
38. Adaptation to several factors
(Selye’s experiments, 1938)
Am. J. Physiol. 123 (1938), 758-765.
𝑓1
𝑓2
𝜓0,2 for 𝑓2
𝜓0,1 for 𝑓1
𝑓1
𝑓2
𝜓0,2 for 𝑓2
𝜓0,1 for 𝑓1
𝑓1
𝑓2
𝜓0,2 for 𝑓2
𝜓0,1 for 𝑓1
OR BUT NOT
THIS
Comfort zone
VOLUME MATTERS
39. Volume matters
𝑓1
𝑓2
𝜓0,2 for 𝑓2
𝜓0,1 for 𝑓1
𝑓1
𝑓2
𝜓0,2 for 𝑓2
𝜓0,1 for 𝑓1
𝑓1
𝑓2
𝜓0,2 for 𝑓2
𝜓0,1 for 𝑓1
OR BUT NOT
THIS
Comfort zone
𝑉𝑜𝑙 ∝
𝑖
𝜓0,𝑖 ; ln(𝑉𝑜𝑙) =
𝑖
ln 𝜓0,𝑖 + ⋯
ln(𝑉𝑜𝑙) is entropy, the main term is additive in the logarithms
of the comfort zone widths ln 𝜓0,𝑖 for various stressors
40. Pumping of adaptation energy
into an entropic reservoir
𝑑𝑟
𝑑𝑡
= ⋯ − 𝑘training
𝜓
𝜓0
𝑤
𝜓
𝜓0
𝑟;
…………………………………………………………………….
1
𝜓0
𝑑𝜓0
𝑑𝑡
= −𝑘detraining + 𝛼𝑘training
𝜓
𝜓0
𝑤
𝜓
𝜓0
𝑟.
• Training is a medium-term adaptation process with transformation of
the adaptation energy into entropic form, ln 𝜓0.
• Following Selye, this transformation is irreversible and the transformed
energy cannot be spent to other purposes.
• Life lengths of this entropic form is determined by the detraining
constant 𝑘detraining .
• According to sport medicine models, 𝑘detraining may be rather small
and 1/𝑘detraining~10 weeks in some cases.
41. Stressor
f –stressor
intensity
𝜓=f-r – non-compensated
stressor
r – Assigned AE
AE
𝜓
𝜓0
- rescaling
ln 𝜓0
Entropic AE reservoir
(inconvertible)
Feedback
The “adaptation shield” metaphor
should be modified
Reserve
Short-time protection
Training
Superficial
free AE
(convertible)
Deep AE
42. Distribution of adaptation resource
for neutralization of several factors
(Adaptation of adaptation
to many stressors)
43. Definition of deep questions:
A question is deep if it allows at
least two answers which are true
but contradict each other.
(Scientific folklore)
44. Adaptive systems under load of
many factors: do they become
more or less similar under stress?
Both answers are correct simultaneously:
1. They become more similar because stress!
2. They become less similar because stress!
This is a deep question.
How it may occur? See the next slide
45. Correlations and variance in crisis
The typical picture:
Cor↑ Var ↑ stress; (correlations increase – more similarity;
variance increases – more differences)
Cor ↓ Var ↓ recovering;
Cor ↓ Var ↑ approaching the disadaptation catastrophe.
Axes correspond to attributes, normalized to the unite variance in the comfort state.
46. Example
a) Correlation graphs of lipid metabolism for newborn babies.
• Vertices – fractions of lipids, solid lines – correlation coefficient
between fractions |rij| ≥ 0.5, dashed lines 0.5 > |rij| ≥ 0.25.
• Upper row – Far North (FN), lower row – the temperate belt of
Siberia (TBS).
• From the left to the right: 1st-3rd days, 4th-6th days, 7th-10th days.
b) The weight of the correlation graphs (solid lines) and the variance
(dashed lines)
Far North (FN)
Temperate belt (TBS)
Days
Gorban, Smirnova, 1987
ji
ijrG
47. The gene regulatory networks formed by the 50 genes best
discriminating Atrial fibrillation patients from control
(microarray data, Censi, Giuliani, Bartolini, Calcagnini, 2011)
48. Financial crisis
2007: 30 larges
companies from
FTSE,
correlations
between daily
closing price
log-returns in
sliding windows
Gorban, Smirnova, Tykina, 2010
49. Distribution of resources for
neutralisation of several factors
Assume that adaptation should maximize a fitness
function W which depends on the compensated
values of factors,
ψi = fi − airi
for the given amount of available resource:
The structure of solution depends on the properties
of function W.
51. Adaptation acts as a cooper
that repairs Liebig’s barrel
• If the system satisfies the Law of the Minimum, then the adaptation
process makes the tension produced by different factors more
uniform.
• Adaptation decreases the effect from the limiting factor and hides
manifestations of the Law of the Minimum.
• The cooper starts to repair Liebig’s barrel from the shortest stave
and after reparation the staves are more uniform than they were
before.
• After adaptation, the factors become equally important and the
dimension of the “data cloud” increases but its variance decreases.
52. Several references
• A.N. Gorban, V.T. Manchuk, E.V. Smirnova. Dynamics of physiological
parameters correlations and the ecological-evolutionary principle of
polyfactoriality. The Problems of Ecological Monitoring and
Ecosystem Modelling, V. 10 (1987), 187–198.
• K.R. Sedov, A.N. Gorban, E.V. Smirnova, V.T. Manchuk, E.N.
Shalamova. Correlation adaptometry as a method of screening of
the population. Vestn. Akad Med Nauk SSSR, 10 (1988), 69–75.
• F. Longin, B. Solnik. Is the correlation in international equity returns
constant: 1960-1990? J. Internat. Money and Finance, 14, No. 1
(1995), 3–26.
• R.N. Mantegna, H.E. Stanley. An introduction to econophysics:
correlations and complexity in finance. Cambridge University Press,
Cambridge, 1999.
• M. Scheffer, et al. Anticipating critical transitions. Science 338, no.
6105 (2012), 344-348.
53.
54. Take-home messages
1. Adaptation Energy, resource and reserve
• Selye’s “Adaptation energy” is an abstract adaptation
resource, the universal currency for adaptation.
• It can be defined through its place in the mathematical
“factor-resources” models.
• We should introduce two types (at least) of the
adaptation resource supply: from “checking account”
of the superficial resource and from “saving account”
of the reserve.
• Existence of these two types determines rich family of
dynamical regimes including limiting cycles and
oscillating death.
55. Take-home messages
2. Adaptation entropy
• Analysis of training models leads to introduction
of ENTROPIC form of adaptation energy.
• This energy, is irreversibly pumped into the
entropic reservoir, extends the volume of
comfort zone and cannot be reassigned.
• Models with adaptation energy and adaptation
entropy capture main phenomenological effects
and can be used in the top-down modelling of
physiological adaptation.
56. Take-home messages
3. Interaction on various stressors
• In ensembles of multifactor multidimensional systems
under stress, both correlations and variance increase.
• This behaviour is supported by many observations in
ecological physiology, medicine, economics and finance and
may serve for early diagnosis of crises.
• It is determined by the (generalised) Liebig’s organization of
the system of stressors (harmful factors): Fitness is a
quasiconcave function of factors’ pressure.
• The opposite organization with quasiconvex fitness
(synergy of stressors) leads to opposite behaviour: under
stress, correlations may be destroyed but variance
increases.
57. Take-home messages
4. It is a great pleasure to read the
classical papers
Our goal was:
• to understand the classical experiments
• and to build the basic top-down models of
physiological adaptation
TO DO: dynamical models with many stressors:
there exist too many possibilities. Discrimination is
needed.