F. Eugene Yates

Homeokinetics/Homeodynamics: A Physical Heuristic for Life and Complexity

This essay addresses the puzzlement, the missing piece, sensed when attempts are made to build a bridge from the synchronic, informational genotype to the diachronic, dynamic phenotype—a regular mapping that seems to be extraphysical. There is no formal, dynamic foundation for the bridge. Albert Einstein, Max Delbrück, and Erwin Schrödinger all expressed acute awareness of limitations of contemporary physics when considering biology because physics addresses much simpler sysems. As a proposed remedy, a new physical heuristic, homeokinetics, developed by Arthur Iberall and Harry Soodak (and later recast for biology by me as homeodynamics) is introduced here as a foundation for comprehending energy flows and transformations in complex systems, including those in metabolic networks of living systems. Their individual dynamic stability is flexible and marginal—it must allow for adaptations and changes in physiological and behavioral states to occur in an orderly fashion as external circumstances change. At the population level, stability must allow for evolvability of chemical networks that have energized terrestrial living systems for about 3.9 billion years. Homeokinetics/homeodynamics emphasizes that persistent, marginally stable metabolic networks, as open thermodynamic systems, necessarily organize energy processing as cyclic, physical action modes. Conceptually, that organization is under 2 kinds of biological time pressure—time as a cycle that daily closes the thermodynamic books and time as an arrow orthogonally pressing the cyles into the future, creating joint time as a helix. In most animals, after maturity, the helix is additionally shaped into a tapered ellipsoid by a senesence process that gains influence as dynamic degrees of freedom are frozen out by the constructions of development.

Ultradian Sleep Rhythms of Lateral Eeg, Autonomic, and Cardiovascular Activity Are Coupled in Humans

This study compared the dynamics of multiple systems during sleep with earlier results during waking rest. Three consecutive nights of data were collected from three healthy adults for 10 variables: left and right central EEGs; the nasal cycle (NC); beat-to-beat measures of CO, SV, HR, SBP, DBP, MAP, and hemoglobin-oxygen saturation. Time series analysis detected periods at 280–300, 215–275, 165–210, 145–160, 105–140, 70–100, and 40–65 min bins with the greatest spectral power in longer periods. We found significance across subjects with all parameters at 280–300, 105–140 (except left EEG power, left minus right EEG power, and HR), 70- 100, and 40–65 min. Significant periods were reported earlier during waking for the NC, pituitary hormones, cate-cholamines, insulin, and cardiovascular function in five bins at 220–340, 170–215, 115–145, 70–100, and 40–65 min, with 115–145, 70–100, and 40–65 min common across all variables. These results suggest that lateral EEG power during sleep has a common pacemaker (the hypothalamus), or a mutually entrained pacemaker, with the cardiovascular and autonomic nervous systems (ANS), and that the waking ultradians of the neuroendocririe and fuel regulatory hormones may also be coupled to lateral EEG

Ultradian rhythms of alternating cerebral hemispheric EEG dominance are coupled to rapid eye movement and non-rapid eye movement stage 4 sleep in humans

Objective: To replicate the left minus right (L-R) hemisphere EEG power shifts coupled to rapid eye movement (REM) and non-rapid eye movement (NREM) sleep observed in 1972 by Goldstein (Physiol Behav (1972) 811), and to characterize the L-R EEG power spectra for total EEG, delta, theta, alpha and beta bands.Background: Ultradian alternating cerebral hemispheric dominance rhythms are observed using EEG during both waking and sleep, and with waking cognition. The question of whether this cerebral rhythm is coupled to the REM-NREM sleep cycle and the basic rest-activity cycle (BRAC) deserves attention.Methods: L-R EEG signals for ten young, normal adult males were converted to powers and the means were normalized, smoothed and subtracted. Sleep hypnograms were compared with L-R EEGs, and spectra were computed for C3, C4 and L-R EEG powers.Results: Significant peaks were found for all C3, C4 and L-R frequency bands at the 280-300, 75-125, 55-70 and 25-50 min bins, with power dominating in the 75-125 min bin. L-R EEG rhythms were observed for all bands. Greater right hemisphere EEG dominance was found during NREM stage 4 sleep, and greater left during REM for total EEG, delta and alpha bands (Chi-squares, P<0.001). Theta was similar, but not significant (P=0.163), and beta was equivocal.Conclusions: Earlier ultradian studies show that lateral EEG and L-R EEG power have a common pacemaker, or a mutually entrained pacemaker with the autonomic, cardiovascular, neuroendocrine and fuel-regulatory hormone systems. These results for L-R EEG coupling to sleep stages and multi-variate relations may present a new perspective for Kleitmans BRAC and for diagnosing variants of pathopsychophysiological states.

A skeleton of physical ideas for the dynamics of complex systems

We define complex systems, from molecular ensembles and networks to galactic clusters, as fluid-plastic-elastic systems in which bulk/shear viscosity ratios are large, with the consequence that interactions among parts at any level lead to internalization of action (energy × time) and to variable process delays between external inputs and outputs. Living systems are notably complex by this measure. Complex systems can be comprehended dynamically through an extended statistical mechanics, irreversible thermodynamics, and nonlinear mechanics, in the form of an electrohydrodynamic field physics. Chemical reactivity (making, breaking, or exchanging bonds) is subsumed in the generalized mechanics as a diffusive process. In this paper we apply this physical construct to the problems of dynamic regulation and coordination among parts in the forms of life found in the more than 300 families of flowering plants. We choose to use these particular plants rather than vertebrates, mammals, or man, as exemplars of our theory to show how effective “chemical languages” can be, without the obfuscating arrangements of nervous systems and muscles designed to support locomotion.

Variability of pressures and rates in the human cardiovascular system - Is it chaos or old fashioned noise?

We discuss various traditional and new methods of modelling time series data to reveal dynamic characteristics. We consider cases in which measurements represent signals from a deterministic generator contaminated by additive noise or multiplicative noise. The properties of chaotic dynamics (nonlinear deterministic mechanisms) are also discussed. Fluctuations in cardiovascular data, as demonstrated in 24-hour ambulatory monitoring records of blood pressures, heart rate and core temperature, are then examined from these various perspectives, including spectral analysis. The pointwise correlation dimension calculated on R-R intervals on the 24-hour electrocardiogram (Holter record) is shown to reveal variations in physiological state that cannot be seen as clearly by spectral analysis. We conclude that both periodic models (simple systems) and chaotic models (complex systems) are useful in that different groups of subjects can be compared by either analysis. Neither model by itself, using standard algorithms, has the capability of identifying the mechanism actually generating the time history. Thus, we answer the question posed in the title of this paper as follows: It is difficult to decide on the basis of models of data only.

On Varieties of Information

The word “information” bears a heavy burden of theory and facts of wide range. It invokes associated notions of messages, communication, computation, complexity, knowledge, semantics (significant meanings), intelligence, and memory and languages, among many others. A tour through its domain requires many major stops, often having the form of a question. They include the following: What are the chief kinds of information? Does nature necessarily consist of matter, energy, and information? Is Shannon (selective) Information theory the best primitive (syntactic) form? Is the universe a computer, and if so, analog, digital, or quantum? Are information (message, communication) entropy and thermodynamic entropy closely related concepts? How do our perceptions relate to information? How do we reconcile the apparent causal decoupling between brains and minds? Are some mental processes in principle computationally not simulatable by a Turing machine? This article addresses each question in turn and offers some suggestions as to their resolution. It provides a basis for evaluating conceptions of information in discussions of intelligence from first principles.