Biological Relativity Requires Circular Causality but Not Symmetry of Causation: So, Where, What and When Are the Boundaries?

ALDH2 polymorphism and myocardial infarction: From alcohol metabolism to redox regulation

Acute myocardial infarction (AMI) remains a leading cause of death worldwide. Increased formation of reactive oxygen species (ROS) during the early reperfusion phase is thought to trigger lipid peroxidation and disrupt redox homeostasis, leading to myocardial injury. Whilst the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2) is chiefly recognised for its central role in ethanol metabolism, substantial experimental evidence suggests an additional cardioprotective role for ALDH2 independent of alcohol intake, which mitigates myocardial injury by detoxifying breakdown products of lipid peroxidation including the reactive aldehydes, malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE). Epidemiological evidence suggests that an ALDH2 mutant variant with reduced activity that is highly prevalent in the East Asian population increases AMI risk. Additional studies have uncovered a strong association between coronary heart disease and this ALDH2 mutant variant. It appears this enzyme polymorphism (in particular, in ALDH2*2/2 carriers) has the potential to have wide-ranging effects on thiol reactivity, redox tone and therefore numerous redox-related signaling processes, resilience of the heart to cope with lifestyle-related and environmental stressors, and the ability of the whole body to achieve redox balance. In this review, we summarize the journey of ALDH2 from a mitochondrial reductase linked to alcohol metabolism, via pre-clinical studies aimed at stimulating ALDH2 activity to reduce myocardial injury to clinical evidence for its protective role in the heart.

Situating physiology within evolutionary theory

Traditionally defined as the science of the living, or as the field that beyond anatomical structure and bodily form studies functional organization and behaviour, physiology has long been excluded from evolutionary research. The main reason for this exclusion is that physiology has a presential and futuristic outlook on life, while evolutionary theory is traditionally defined as the study of natural history. In this paper, I re-evaluate these classic science divisions and situate physiology within the history of the evolutionary sciences, as well as within debates on the Extended Evolutionary Synthesis and the need for a Third Way of Evolution. I then briefly point out how evolutionary physiology in particular contributes to research on function, causation, teleonomy, agency and cognition.

Uncovering the determinants of brain functioning, behavior and their interplay in the light of context

Notwithstanding the huge progress in molecular and cellular neuroscience, our ability to understand the brain and develop effective treatments promoting mental health is still limited. This can be partially ascribed to the reductionist, deterministic and mechanistic approaches in neuroscience that struggle with the complexity of the central nervous system. Here, I introduce the Context theory of constrained systems proposing a novel role of contextual factors and genetic, molecular and neural substrates in determining brain functioning and behavior. This theory entails key conceptual implications. First, context is the main driver of behavior and mental states. Second, substrates, from genes to brain areas, have no direct causal link to complex behavioral responses as they can be combined in multiple ways to produce the same response and different responses can impinge on the same substrates. Third, context and biological substrates play distinct roles in determining behavior: context drives behavior, substrates constrain the behavioral repertoire that can be implemented. Fourth, since behavior is the interface between the central nervous system and the environment, it is a privileged level of control and orchestration of brain functioning. Such implications are illustrated through the Kitchen metaphor of the brain. This theoretical framework calls for the revision of key concepts in neuroscience and psychiatry, including causality, specificity and individuality. Moreover, at the clinical level, it proposes treatments inducing behavioral changes through contextual interventions as having the highest impact to reorganize the complexity of the human mind and to achieve a long-lasting improvement in mental health.

Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development

The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)-mental models that graphically represent causal relationships between a system's factors-are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure-finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect-possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

Impoverished Conceptions of Gene Causation and Therapy in Developmental Neurology

We offer a primer to the modifiability of genetic neurological disease, particularly during development. One goal is to harness several unexpected observations made in the course of experimental gene modification or therapy into an explanatory conceptual context based on biological first principles. To this end, we anchor growing, disparate reports of unusual or untoward effects to a plausible framework wherein genes exhibit different degrees of modifiability and may result, when mutated or therapeutically modified, in unsuspected consequences. We propose that genetic pathogenic variant effects and modifiability depend on the number and complexity of associated protein-protein or higher-order interactions. Thus, gene malleability may range from that characteristic of the favorably modifiable primarily structural genes that subserve relatively invariant or circumscribed phenomena such as cell shape to that typical of some transcription factors, which are less functionally predictable when altered. The latter may be expressed developmentally, in compartmentalized manner, or only intermittently and yet exert vastly ramified influences sometimes circumscribed only to select species. We also argue that genetic diseases may steer the organism toward often poorly understood biological end points and co-opt multiple processes into hardly modifiable biology. Addition or modification of genes to approximate a normal state not previously experienced by the organism may lead to further aberration due to extraneous interference with the native biology of the disease state. Therefore, an understanding as perspicuous as possible of gene function, regulation, modifiability, and biological directionality down to seemingly minute but disease-relevant consequences is a prerequisite to intervention. Although we provide some groundwork steps to such an understanding, this may occasionally prove unattainable.

Globally invariant behavior of oncogenes and random genes at population but not at single cell level

Cancer is widely considered a genetic disease. Notably, recent works have highlighted that every human gene may possibly be associated with cancer. Thus, the distinction between genes that drive oncogenesis and those that are associated to the disease, but do not play a role, requires attention. Here we investigated single cells and bulk (cell-population) datasets of several cancer transcriptomes and proteomes in relation to their healthy counterparts. When analyzed by machine learning and statistical approaches in bulk datasets, both general and cancer-specific oncogenes, as defined by the Cancer Genes Census, show invariant behavior to randomly selected gene sets of the same size for all cancers. However, when protein-protein interaction analyses were performed, the oncogenes-derived networks show higher connectivity than those relative to random genes. Moreover, at single-cell scale, we observe variant behavior in a subset of oncogenes for each considered cancer type. Moving forward, we concur that the role of oncogenes needs to be further scrutinized by adopting protein causality and higher-resolution single-cell analyses.

Is information the other face of causation in biological systems?

Is information the other face of causation? This issue cannot be clarified without discussing how these both are related to physical laws, logic, computation, networks, bio-signaling, and the mind-body problem. The relation between information and causation is also intrinsically linked to many other concepts in complex systems theory such as emergence, self-organization, synergy, criticality, and hierarchy, which in turn involve various notions such as observer-dependence, dimensionality reduction, and especially downward causation. A canonical example proposed for downward causation is the collective behavior of the whole system at a macroscale that may affect the behavior of each its member at a microscale. In neuroscience, downward causation is suggested as a strong candidate to account for mental causation (free will). However, this would be possible only on the condition that information might have causal power. After introducing the Causal Equivalence Principle expanding the relativity principle for coarse-grained and fine-grained linear causal chains, and a set-theoretical definition of multiscale nested hierarchy composed of modular ⊂-chains, it is shown that downward causation can be spurious. It emerges only in the eyes of an observer, though, due to information that could not be obtained by "looking" exclusively at the behavior of a system at a microscale. On the other hand, since biological systems are hierarchically organized, this information gain is indicative of how information can be a function of scale in these systems and a prerequisite for scale-dependent emergence of cognition and consciousness in neural networks.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology

Is data-driven analysis sufficient for understanding the COVID-19 pandemic and for justifying public health regulations? In this paper, we argue that such analysis is insufficient. Rather what is needed is the identification and implementation of over-arching hypothesis-related and/or theory-based rationales to conduct effective SARS-CoV2/COVID-19 (Corona) research. To that end, we analyse and compare several published recommendations for conceptual and methodological frameworks in medical research (e.g., public health, preventive medicine and health promotion) to current research approaches in medical Corona research. Although there were several efforts published in the literature to develop integrative conceptual frameworks before the COVID-19 pandemic, such as social ecology for public health issues and systems thinking in health care, only a few attempts to utilize these concepts can be found in medical Corona research. For this reason, we propose nested and integrative systemic modelling approaches to understand Corona pandemic and Corona pathology. We conclude that institutional efforts for knowledge integration and systemic thinking, but also for integrated science, are urgently needed to avoid or mitigate future pandemics and to resolve infection pathology.

From information to physics to biology

Commentary to "The gene: An appraisal" by Keith Baverstock. PBMB, Volume 164, September 2021, Pages 46-62. NOTE: this short and informal commentary constructively criticizes the very interesting approach in the paper by a brief survey of the work that a few of us develop since several years. I will first recall the very pertinent critique of the Modern Synthesis and the genocentric approach presented in the paper, then suggest a methodological (and theoretical) critique of the approach by K. Baverstock and hint to alternatives paths that are compatible, but "extend" the physics for biology presented by the author. The purposes and the space allowed force a limited number of references and technical details. These may be found in the references contained in the few papers quoted below that are not the most nor the only representative contributions to the that work, but are inserted as a source of references or as synthetic presentations of our views.

Neurobiological reduction: From cellular explanations of behavior to interventions

Scientific reductionism, the view that higher level functions can be explained by properties at some lower-level or levels, has been an assumption of nervous system analyses since the acceptance of the neuron doctrine in the late 19th century, and became a dominant experimental approach with the development of intracellular recording techniques in the mid-20th century. Subsequent refinements of electrophysiological approaches and the continual development of molecular and genetic techniques have promoted a focus on molecular and cellular mechanisms in experimental analyses and explanations of sensory, motor, and cognitive functions. Reductionist assumptions have also influenced our views of the etiology and treatment of psychopathologies, and have more recently led to claims that we can, or even should, pharmacologically enhance the normal brain. Reductionism remains an area of active debate in the philosophy of science. In neuroscience and psychology, the debate typically focuses on the mind-brain question and the mechanisms of cognition, and how or if they can be explained in neurobiological terms. However, these debates are affected by the complexity of the phenomena being considered and the difficulty of obtaining the necessary neurobiological detail. We can instead ask whether features identified in neurobiological analyses of simpler aspects in simpler nervous systems support current molecular and cellular approaches to explaining systems or behaviors. While my view is that they do not, this does not invite the opposing view prevalent in dichotomous thinking that molecular and cellular detail is irrelevant and we should focus on computations or representations. We instead need to consider how to address the long-standing dilemma of how a nervous system that ostensibly functions through discrete cell to cell communication can generate population effects across multiple spatial and temporal scales to generate behavior.

Network Physiology of Exercise: Beyond Molecular and Omics Perspectives

Molecular Exercise Physiology and Omics approaches represent an important step toward synthesis and integration, the original essence of Physiology. Despite the significant progress they have introduced in Exercise Physiology (EP), some of their theoretical and methodological assumptions are still limiting the understanding of the complexity of sport-related phenomena. Based on general principles of biological evolution and supported by complex network science, this paper aims to contrast theoretical and methodological aspects of molecular and network-based approaches to EP. After explaining the main EP challenges and why sport-related phenomena cannot be understood if reduced to the molecular level, the paper proposes some methodological research advances related to the type of studied variables and measures, the data acquisition techniques, the type of data analysis and the assumed relations among physiological levels. Inspired by Network Physiology, Network Physiology of Exercise provides a new paradigm and formalism to quantify cross-communication among diverse systems across levels and time scales to improve our understanding of exercise-related phenomena and opens new horizons for exercise testing in health and disease.

Proposal to Consider Chemical/Physical Microenvironment as a New Therapeutic Off-Target Approach

The molecular revolution could lead drug discovery from chance observation to the rational design of new classes of drugs that could simultaneously be more effective and less toxic. Unfortunately, we are witnessing some failure in this sense, and the causes of the crisis involve a wide range of epistemological and scientific aspects. In pharmacology, one key point is the crisis of the paradigm the “magic bullet”, which is to design therapies based on specific molecular targets. Drug repurposing is one of the proposed ways out of the crisis and is based on the off-target effects of known drugs. Here, we propose the microenvironment as the ideal place to direct the off-targeting of known drugs. While it has been extensively investigated in tumors, the generation of a harsh microenvironment is also a phenotype of the vast majority of chronic diseases. The hostile microenvironment, on the one hand, reduces the efficacy of both chemical and biological drugs; on the other hand, it dictates a sort of “Darwinian” selection of those cells armed to survive in such hostile conditions. This opens the way to the consideration of the microenvironment as a convenient target for pharmacological action, with a clear example in proton pump inhibitors.

Modern physiology vindicates Darwin's Dream

New Findings

What is the topic of this review?

Revisiting the 2013 article ‘Physiology is rocking the foundations of evolutionary biology’.

What advances does it highlight?

The discovery that the genome is not isolated from the soma and the environment, and that there is no barrier preventing somatic characteristics being transmitted to the germline, means that Darwin's pangenetic ideas become relevant again.

Abstract

Charles Darwin spent the last decade of his life collaborating with physiologists in search of the biological processes of evolution. He viewed physiology as the way forward in answering fundamental questions about inheritance, acquired characteristics, and the mechanisms by which organisms could achieve their ends and survival. He collaborated with 19th century physiologists, notably John Burdon‐Sanderson and George Romanes, in his search for the mechanisms of transgenerational inheritance. The discovery that the genome is not isolated from the soma and the environment, and that there is no barrier preventing somatic characteristics being transmitted to the germline, means that Darwin's pangenetic ideas become relevant again. It is time for 21st century physiology to come to the rescue of evolutionary biology. This article outlines research lines by which this could be achieved.

A Sedentary and Unhealthy Lifestyle Fuels Chronic Disease Progression by Changing Interstitial Cell Behaviour: A Network Analysis

Managing chronic diseases, such as heart disease, stroke, diabetes, chronic lung disease and Alzheimer’s disease, account for a large proportion of health care spending, yet they remain in the top causes of premature mortality and are preventable. It is currently accepted that an unhealthy lifestyle fosters a state of chronic low-grade inflammation that is linked to chronic disease progression. Although this is known to be related to inflammatory cytokines, how an unhealthy lifestyle causes cytokine release and how that in turn leads to chronic disease progression are not well known. This article presents a theory that an unhealthy lifestyle fosters chronic disease by changing interstitial cell behavior and is supported by a six-level hierarchical network analysis. The top three networks include the macroenvironment, social and cultural factors, and lifestyle itself. The fourth network includes the immune, autonomic and neuroendocrine systems and how they interact with lifestyle factors and with each other. The fifth network identifies the effects these systems have on the microenvironment and two types of interstitial cells: macrophages and fibroblasts. Depending on their behaviour, these cells can either help maintain and restore normal function or foster chronic disease progression. When macrophages and fibroblasts dysregulate, it leads to chronic low-grade inflammation, fibrosis, and eventually damage to parenchymal (organ-specific) cells. The sixth network considers how macrophages change phenotype. Thus, a pathway is identified through this hierarchical network to reveal how external factors and lifestyle affect interstitial cell behaviour. This theory can be tested and it needs to be tested because, if correct, it has profound implications. Not only does this theory explain how chronic low-grade inflammation causes chronic disease progression, it also provides insight into salutogenesis, or the process by which health is maintained and restored. Understanding low-grade inflammation as a stalled healing process offers a new strategy for chronic disease management. Rather than treating each chronic disease separately by a focus on parenchymal pathology, a salutogenic strategy of optimizing interstitial health could prevent and mitigate multiple chronic diseases simultaneously.

Recentering neuroscience on behavior: The interface between brain and environment is a privileged level of control of neural activity

Despite the huge and constant progress in the molecular and cellular neuroscience fields, our capability to understand brain alterations and treat mental illness is still limited. Therefore, a paradigm shift able to overcome such limitation is warranted. Behavior and the associated mental states are the interface between the central nervous system and the living environment. Since, in any system, the interface is a key regulator of system organization, behavior is proposed here as a unique and privileged level of control and orchestration of brain structure and activity. This view has relevant scientific and clinical implications. First, the study of behavior represents a singular starting point for the investigation of neural activity in an integrated and comprehensive fashion. Second, behavioral changes, accomplished through psychotherapy or environmental interventions, are expected to have the highest impact to specifically reorganize the complexity of the human mind and thus achieve a solid and long-lasting improvement in mental health.

George Orwell, objectivity, and the reality behind illusions

Illusions are commonly defined as departures of our percepts from the veridical representation of objective, common-sense reality. However, it has been claimed recently that this definition lacks validity, for example, on the grounds that external reality cannot possibly be represented truly by our sensory systems, and indeed may even be a fiction. Here, I first demonstrate how novelist George Orwell warned that such denials of objective reality are dangerous mistakes, in that they can lead to the suppression and even the atrophy of independent thought and critical evaluation. Second, anti-realists assume their opponents hold a fully reductionist metaphysics, in which fundamental physics describes the only ground truth, thereby placing it beyond direct human sensory observation. In contrast, I point to a more recent and commonly used alternative, non-reductive metaphysics. This ascribes real existence to many levels of dynamic systems of information, emerging progressively from the subatomic to the biological, psychological, social, and ecological. Within such a worldview the notion of objective reality is valid, it comes in part within the range of our senses, and thus a definition of illusions as kinds of deviations from veridical perception becomes possible again.

Role of the Circadian Clock "Death-Loop" in the DNA Damage Response Underpinning Cancer Treatment Resistance

Here, we review the role of the circadian clock (CC) in the resistance of cancer cells to genotoxic treatments in relation to whole-genome duplication (WGD) and telomere-length regulation. The CC drives the normal cell cycle, tissue differentiation, and reciprocally regulates telomere elongation. However, it is deregulated in embryonic stem cells (ESCs), the early embryo, and cancer. Here, we review the DNA damage response of cancer cells and a similar impact on the cell cycle to that found in ESCs—overcoming G1/S, adapting DNA damage checkpoints, tolerating DNA damage, coupling telomere erosion to accelerated cell senescence, and favouring transition by mitotic slippage into the ploidy cycle (reversible polyploidy). Polyploidy decelerates the CC. We report an intriguing positive correlation between cancer WGD and the deregulation of the CC assessed by bioinformatics on 11 primary cancer datasets (rho = 0.83; p < 0.01). As previously shown, the cancer cells undergoing mitotic slippage cast off telomere fragments with TERT, restore the telomeres by ALT-recombination, and return their depolyploidised offspring to telomerase-dependent regulation. By reversing this polyploidy and the CC “death loop”, the mitotic cycle and Hayflick limit count are thus again renewed. Our review and proposed mechanism support a life-cycle concept of cancer and highlight the perspective of cancer treatment by differentiation.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Active Inferants: An Active Inference Framework for Ant Colony Behavior

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally.

Heterochromatin Networks: Topology, Dynamics, and Function (a Working Hypothesis)

Open systems can only exist by self-organization as pulsing structures exchanging matter and energy with the outer world. This review is an attempt to reveal the organizational principles of the heterochromatin supra-intra-chromosomal network in terms of nonlinear thermodynamics. The accessibility of the linear information of the genetic code is regulated by constitutive heterochromatin (CHR) creating the positional information in a system of coordinates. These features include scale-free splitting-fusing of CHR with the boundary constraints of the nucleolus and nuclear envelope. The analysis of both the literature and our own data suggests a radial-concentric network as the main structural organization principle of CHR regulating transcriptional pulsing. The dynamic CHR network is likely created together with nucleolus-associated chromatin domains, while the alveoli of this network, including springy splicing speckles, are the pulsing transcription hubs. CHR contributes to this regulation due to the silencing position variegation effect, stickiness, and flexible rigidity determined by the positioning of nucleosomes. The whole system acts in concert with the elastic nuclear actomyosin network which also emerges by self-organization during the transcriptional pulsing process. We hypothesize that the the transcriptional pulsing, in turn, adjusts its frequency/amplitudes specified by topologically associating domains to the replication timing code that determines epigenetic differentiation memory.

The Physics-Biology continuum challenges darwinism: Evolution is directed by the homeostasis-dependent bidirectional relation between genome and phenotype

The physics-biology continuum relies on the fact that life emerged from prebiotic molecules. Here, I argue that life emerged from the coupling between nucleic acid and protein synthesis during which proteins (or proto-phenotypes) maintained the physicochemical parameter equilibria (or proto-homeostasis) in the proximity of their encoding nucleic acids (or proto-genomes). This protected the proto-genome physicochemical integrity (i.e., atomic composition) from environmental physicochemical constraints, and therefore increased the probability of reproducing the proto-genome without variation. From there, genomes evolved depending on the biological activities they generated in response to environmental fluctuations. Thus, a genome maintaining homeostasis (i.e., internal physicochemical parameter equilibria), despite and in response to environmental fluctuations, maintains its physicochemical integrity and has therefore a higher probability to be reproduced without variation. Consequently, descendants have a higher probability to share the same phenotype than their parents. Otherwise, the genome is modified during replication as a consequence of the imbalance of the internal physicochemical parameters it generates, until new mutation-deriving biological activities maintain homeostasis in offspring. In summary, evolution depends on feedforward and feedback loops between genome and phenotype, as the internal physicochemical conditions that a genome generates ─ through its derived phenotype in response to environmental fluctuations ─ in turn either guarantee its stability or direct its variation. Evolution may not be explained by the Darwinism-derived, unidirectional principle (random mutations-phenotypes-natural selection) but rather by the bidirectional relationship between genome and phenotype, in which the phenotype in interaction with the environment directs the evolution of the genome it derives from.

Seeking Insights into Aging Through Yeast Mitochondrial Electrophysiology

During aging, mitochondrial membrane potential, a key indicator for bioenergetics of cells, depolarizes in a wide range of species-from yeasts, plants to animals. In humans, the decline of mitochondrial activities can impact the high-energy-consuming organs, such as the brain and heart, and increase the risks of age-linked diseases. Intriguingly, a mild depolarization of mitochondria has lifespan-extending effects, suggesting an important role played by bioelectricity during aging. However, the underpinning biophysical mechanism is not very well understood due in part to the difficulties associated with a multiscale process. Budding yeast Saccharomyces cerevisiae could provide a model system to bridge this knowledge gap and provide insights into aging. In this perspective, we overview recent studies on the yeast mitochondrial membrane electrophysiology and aging and call for more electrochemical and biophysical studies on aging.

The Quest for System-Theoretical Medicine in the COVID-19 Era

Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the limits of MSM: the neglect of the bio-psycho-social systemic nature of humans and their context as the object of individual therapeutic and population-oriented interventions. COVID-19 illustrates how a medical problem requires a transdisciplinary approach in epidemiology, pathology, internal medicine, public health, environmental medicine, and socio-economic modeling. Regarding the need for conceptual integration of these different kinds of knowledge we suggest the application of general system theory (GST). This approach endorses an organism-centered view on health and disease, which according to Ludwig von Bertalanffy who was the founder of GST, we call Organismal Systems Medicine (OSM). We argue that systems science offers wider applications in the field of pathology and can contribute to an integrative systems medicine by (i) integration of evidence across functional and structural differentially scaled subsystems, (ii) conceptualization of complex multilevel systems, and (iii) suggesting mechanisms and non-linear relationships underlying the observed phenomena. We underline these points with a proposal on multi-level systems pathology including neurophysiology, endocrinology, immune system, genetics, and general metabolism. An integration of these areas is necessary to understand excess mortality rates and polypharmacological treatments. In the pandemic era this multi-level systems pathology is most important to assess potential vaccines, their effectiveness, short-, and long-time adverse effects. We further argue that these conceptual frameworks are not only valid in the COVID-19 era but also important to be integrated in a medicinal curriculum.

Health and Disease Are Dynamic Complex-Adaptive States Implications for Practice and Research

Interoception, the ability to convey one's overall physiological state, allows people to describe their health along an experiential continuum, from excellent, very good, good, fair to poor. Each health state reflects a distinct pattern of one's overall function. This assay provides a new frame of understanding health and disease as complex-adaptive system states of the person as-a-whole. It firstly describes how complex patterns can emerge from simple equations. It then discusses how clinical medicine in certain domains has started to explore the pattern characteristics resulting in the heterogeneity of disease, and how this better understanding has improved patient management. The experiential state of health can be surprising to the observer-some are in good health with disabling disease, others are in poor health without the evidence of any. The main part of the assay describes the underlying complexity principles that contribute to health, and synthesizes available evidence from various research perspectives to support the philosophic/theoretical proposition of the complex-adaptive nature of health. It shows how health states arise from complex-adaptive system dynamics amongst the variables of a hierarchically layered system comprising the domains of a person's macro-level external environment to his nano-level biological blueprint. The final part suggests that the frame of health as a dynamic complex-adaptive state defines a new paradigm, and outlines ways of translating these expanded understandings to clinical practice, future research, and health system design.

Breath profile as composite biomarkers for lung cancer diagnosis

OBJECTIVES

Lung cancer is continuously the leading cause of cancer related death, resulting from the lack of specific symptoms at early stage. A large-scale screening method may be the key point to find asymptomatic patients, leading to the reduction of mortality.

METHODS

An alternative method combining breath test and a machine learning algorithm is proposed. 236 breath samples were analyzed by TD-GCMS. Breath profile of each sample is composed of 308 features extracted from chromatogram. Gradient boost decision trees algorithm was employed to recognize lung cancer patients. Bootstrap is performed to simulate real diagnostic practice, with which we evaluated the confidence of our methods.

RESULTS

An accuracy of 85 % is shown in 6-fold cross validations. In statistical bootstrap, 72 % samples are marked as "confident", and the accuracy of confident samples is 93 % throughout the cross validations.

CONCLUSION

We have proposed such a non-invasive, accurate and confident method that might contribute to large-scale screening of lung cancer. As a consequence, more asymptomatic patients with early lung cancer may be detected.

How the Toxin got its Toxicity

Venom systems are functional and ecological traits, typically used by one organism to subdue or deter another. A predominant subset of their constituent molecules—“toxins”—share this ecological function and are therefore molecules that mediate interactions between organisms. Such molecules have been referred to as “exochemicals.” There has been debate within the field of toxinology concerning the evolutionary pathways leading to the “recruitment” of a gene product for a toxic role within venom. We review these discussions and the evidence interpreted in support of alternate pathways, along with many of the most popular models describing the origin of novel molecular functions in general. We note that such functions may arise with or without gene duplication occurring and are often the consequence of a gene product encountering a novel “environment,” i.e., a range of novel partners for molecular interaction. After stressing the distinction between “activity” and “function,” we describe in detail the results of a recent study which reconstructed the evolutionary history of a multigene family that has been recruited as a toxin and argue that these results indicate that a pluralistic approach to understanding the origin of novel functions is advantageous. This leads us to recommend that an expansive approach be taken to the definition of “neofunctionalization”—simply the origins of a novel molecular function by any process—and “recruitment”—the “weaponization” of a molecule via the acquisition of a toxic function in venom, by any process. Recruitment does not occur at the molecular level or even at the level of gene expression, but only when a confluence of factors results in the ecological deployment of a physiologically active molecule as a toxin. Subsequent to recruitment, the evolutionary regime of a gene family may shift into a more dynamic form of “birth-and-death.” Thus, recruitment leads to a form of “downwards causation,” in which a change at the ecological level at which whole organisms interact leads to a change in patterns of evolution at the genomic level.

Network Physiology of Exercise: Vision and Perspectives

The basic theoretical assumptions of Exercise Physiology and its research directions, strongly influenced by reductionism, may hamper the full potential of basic science investigations, and various practical applications to sports performance and exercise as medicine. The aim of this perspective and programmatic article is to: (i) revise the current paradigm of Exercise Physiology and related research on the basis of principles and empirical findings in the new emerging field of Network Physiology and Complex Systems Science; (ii) initiate a new area in Exercise and Sport Science, Network Physiology of Exercise (NPE), with focus on basic laws of interactions and principles of coordination and integration among diverse physiological systems across spatio-temporal scales (from the sub-cellular level to the entire organism), to understand how physiological states and functions emerge, and to improve the efficacy of exercise in health and sport performance; and (iii) to create a forum for developing new research methodologies applicable to the new NPE field, to infer and quantify nonlinear dynamic forms of coupling among diverse systems and establish basic principles of coordination and network organization of physiological systems. Here, we present a programmatic approach for future research directions and potential practical applications. By focusing on research efforts to improve the knowledge about nested dynamics of vertical network interactions, and particularly, the horizontal integration of key organ systems during exercise, NPE may enrich Basic Physiology and diverse fields like Exercise and Sports Physiology, Sports Medicine, Sports Rehabilitation, Sport Science or Training Science and improve the understanding of diverse exercise-related phenomena such as sports performance, fatigue, overtraining, or sport injuries.

The Causal Closure of Physics in Real World Contexts

The causal closure of physics is usually discussed in a context free way. Here I discuss it in the context of engineering systems and biology, where strong emergence takes place due to a combination of upwards emergence and downwards causation (Ellis, Emergence in Solid State Physics and Biology, 2020, arXiv:2004.13591). Firstly, I show that causal closure is strictly limited in terms of spatial interactions because these are cases that are of necessity strongly interacting with the environment. Effective Spatial Closure holds ceteris parabus, and can be violated by Black Swan Events. Secondly, I show that causal closure in the hierarchy of emergence is a strictly interlevel affair, and in the cases of engineering and biology encompasses all levels from the social level to the particle physics level. However Effective Causal Closure can usefully be defined for a restricted set of levels, and one can experimentally determine Effective Theories that hold at each level. This does not however imply those effective theories are causally complete by themselves. In particular, the particle physics level is not causally complete by itself in the contexts of solid state physics (because of interlevel wave-particle duality), digital computers (where algorithms determine outcomes), or biology (because of time dependent constraints). Furthermore Inextricably Intertwined Levels occur in all these contexts.

Phenomenal Consciousness and Emergence: Eliminating the Explanatory Gap

The role of emergence in the creation of consciousness has been debated for over a century, but it remains unresolved. In particular there is controversy over the claim that a "strong" or radical form of emergence is required to explain phenomenal consciousness. In this paper we use some ideas of complex system theory to trace the emergent features of life and then of complex brains through three progressive stages or levels: Level 1 (life), Level 2 (nervous systems), and Level 3 (special neurobiological features), each representing increasing biological and neurobiological complexity and ultimately leading to the emergence of phenomenal consciousness, all in physical systems. Along the way we show that consciousness fits the criteria of an emergent property-albeit one with extreme complexity. The formulation Life + Special neurobiological features → Phenomenal consciousness expresses these relationships. Then we consider the implications of our findings for some of the philosophical conundrums entailed by the apparent "explanatory gap" between the brain and phenomenal consciousness. We conclude that consciousness stems from the personal life of an organism with the addition of a complex nervous system that is ideally suited to maximize emergent neurobiological features and that it is an example of standard ("weak") emergence without a scientific explanatory gap. An "experiential" or epistemic gap remains, although this is ontologically untroubling.