Use and Abuse of Entropy in Biology: A Case for Caliber

Application of a novel numerical simulation to biochemical reaction systems

Recent advancements in omics and single-cell analysis highlight the necessity of numerical methods for managing the complexity of biological data. This paper introduces a simulation program for biochemical reaction systems based on the natural number simulation (NNS) method. This novel approach ensures the equitable treatment of all molecular entities, such as DNA, proteins, H2O, and hydrogen ions (H+), in biological systems. Central to NNS is its use of stoichiometric formulas, simplifying the modeling process and facilitating efficient and accurate simulations of diverse biochemical reactions. The advantage of this method is its ability to manage all molecules uniformly, ensuring a balanced representation in simulations. Detailed in Python, NNS is adept at simulating various reactions, ranging from water ionization to Michaelis-Menten kinetics and complex gene-based systems, making it an effective tool for scientific and engineering research.

Unveiling the genetic tapestry: Rare disease genomics of spinal muscular atrophy and phenylketonuria proteins

Rare diseases, defined by their low prevalence, present significant challenges, including delayed detection, expensive treatments, and limited research. This study delves into the genetic basis of two noteworthy rare diseases in Saudi Arabia: Phenylketonuria (PKU) and Spinal Muscular Atrophy (SMA). PKU, resulting from mutations in the phenylalanine hydroxylase (PAH) gene, exhibits geographical variability and impacts intellectual abilities. SMA, characterized by motor neuron loss, is linked to mutations in the survival of motor neuron 1 (SMN1) gene. Recognizing the importance of unveiling signature genomics in rare diseases, we conducted a quantitative study on PAH and SMN1 proteins of multiple organisms by employing various quantitative techniques to assess genetic variations. The derived signature-genomics contributes to a deeper understanding of these critical genes, paving the way for enhanced diagnostics for disorders associated with PAH and SMN1.

The calculus of codes - From entropy, complexity, and information to life

Exploring the core components that define living systems and their operational mechanisms within emerging biological entities is a complex endeavor. In the realm of biological systems literature, the terms matter, energy, information, complexity, and entropy are frequently referenced. However, possessing these concepts alone does not guarantee a comprehensive understanding or the ability to reconstruct the intricate nature of life. This study aims to illuminate the trajectory of these organic attributes, presenting a theoretical framework that delves into the integrated role of these concepts in biology. We assert that Code Biology serves as a pivotal steppingstone for unraveling the mechanisms underlying life. Biological codes (BCs) emerge not only from the interplay of matter and energy but also from Information. Contrary to deriving information from the former elements, we propose that information holds its place as a fundamental physical aspect. Consequently, we propose a continuum perspective called Calculus of Fundamentals involving three fundamentals: Matter, Energy, and Information, to depict the dynamics of BCs. To achieve this, we emphasize the necessity of studying Entropy and Complexity as integral organic descriptors. This perspective also facilitates the introduction of a mathematical theoretical framework that aids in comprehending continuous changes, the driving dynamics of biological fundamentals. We posit that Energy, Matter, and Information constitute the essential building blocks of living systems, and their interactions are governed by Entropy and Complexity analyses, redefined as biological descriptors. This interdisciplinary perspective of Code Biology sheds light on the intricate interplay between the controversial phenomenon of life and advances the idea of constructing a theory rooted in information as an organic fundamental.

A Catastrophic Biodiversity Loss in the Environment Is Being Replicated on the Skin Microbiome: Is This a Major Contributor to the Chronic Disease Epidemic?

There has been a catastrophic loss of biodiversity in ecosystems across the world. A similar crisis has been observed in the human gut microbiome, which has been linked to "all human diseases affecting westernized countries". This is of great importance because chronic diseases are the leading cause of death worldwide and make up 90% of America's healthcare costs. Disease development is complex and multifactorial, but there is one part of the body's interlinked ecosystem that is often overlooked in discussions about whole-body health, and that is the skin microbiome. This is despite it being a crucial part of the immune, endocrine, and nervous systems and being continuously exposed to environmental stressors. Here we show that a parallel biodiversity loss of 30-84% has occurred on the skin of people in the developed world compared to our ancestors. Research has shown that dysbiosis of the skin microbiome has been linked to many common skin diseases and, more recently, that it could even play an active role in the development of a growing number of whole-body health problems, such as food allergies, asthma, cardiovascular diseases, and Parkinson's, traditionally thought unrelated to the skin. Damaged skin is now known to induce systemic inflammation, which is involved in many chronic diseases. We highlight that biodiversity loss is not only a common finding in dysbiotic ecosystems but also a type of dysbiosis. As a result, we make the case that biodiversity loss in the skin microbiome is a major contributor to the chronic disease epidemic. The link between biodiversity loss and dysbiosis forms the basis of this paper's focus on the subject. The key to understanding why biodiversity loss creates an unhealthy system could be highlighted by complex physics. We introduce entropy to help understand why biodiversity has been linked with ecosystem health and stability. Meanwhile, we also introduce ecosystems as being governed by "non-linear physics" principles-including chaos theory-which suggests that every individual part of any system is intrinsically linked and implies any disruption to a small part of the system (skin) could have a significant and unknown effect on overall system health (whole-body health). Recognizing the link between ecosystem health and human health allows us to understand how crucial it could be to maintain biodiversity across systems everywhere, from the macro-environment we inhabit right down to our body's microbiome. Further, in-depth research is needed so we can aid in the treatment of chronic diseases and potentially change how we think about our health. With millions of people currently suffering, research to help mitigate the crisis is of vital importance.

Entropy and the arrow of time in population dynamics

The concept of entropy in statistical physics is related to the existence of irreversible macroscopic processes. In this work, we explore a recently introduced entropy formula for a class of stochastic processes with more than one absorbing state that is extensively used in population genetics models. We will consider the Moran process as a paradigm for this class, and will extend our discussion to other models outside this class. We will also discuss the relation between non-extensive entropies in physics and epistasis (i.e., when the effects of different alleles are not independent) and the role of symmetries in population genetic models.

Interdependent Autonomous Human-Machine Systems: The Complementarity of Fitness, Vulnerability and Evolution

For the science of autonomous human-machine systems, traditional causal-time interpretations of reality in known contexts are sufficient for rational decisions and actions to be taken, but not for uncertain or dynamic contexts, nor for building the best teams. First, unlike game theory where the contexts are constructed for players, or machine learning where contexts must be stable, when facing uncertainty or conflict, a rational process is insufficient for decisions or actions to be taken; second, as supported by the literature, rational explanations cannot disaggregate human-machine teams. In the first case, interdependent humans facing uncertainty spontaneously engage in debate over complementary tradeoffs in a search for the best path forward, characterized by maximum entropy production (MEP); however, in the second case, signified by a reduction in structural entropy production (SEP), interdependent team structures make it rationally impossible to discern what creates better teams. In our review of evidence for SEP-MEP complementarity for teams, we found that structural redundancy for top global oil producers, replicated for top global militaries, impedes interdependence and promotes corruption. Next, using UN data for Middle Eastern North African nations plus Israel, we found that a nation's structure of education is significantly associated with MEP by the number of patents it produces; this conflicts with our earlier finding that a U.S. Air Force education in air combat maneuvering was not associated with the best performance in air combat, but air combat flight training was. These last two results exemplify that SEP-MEP interactions by the team's best members are made by orthogonal contributions. We extend our theory to find that competition between teams hinges on vulnerability, a complementary excess of SEP and reduced MEP, which generalizes to autonomous human-machine systems.

Calorimetric Measurements of Biological Interactions and Their Relationships to Finite Time Thermodynamics Parameters

A description and examination of the potential for calorimetry for use in exploring the entropy flows in biological and or reacting systems is presented. A calorimeter operation background is provided, and two case studies are investigated using a transient numerical simulation. The first case describes a single cell calorimeter containing a single phase material excited by heat generation source function such as joule heating. The second case is a reacting system. The basic observation parameter, the temperature, cannot be used to separate the entropy property changes and the rate of entropy production in the second case. The calculated transient response can be further analyzed to determine the equilibrium constant once the reaction equation and stoichiometric constants are specified which allows entropy property changes and the rate of entropy production to be determined. In a biological community, the equivalent of the reaction equation and a definition of an equilibrium constant are not available for all systems. The results for the two cases illustrate that using calorimetry measurements to identify the entropy flows in biological community activities requires further work to establish a framework similar to that chemical reacting systems that are based on an equilibrium type parameter.

Entropy Perspectives of Molecular and Evolutionary Biology

Attempts to find and quantify the supposed low entropy of organisms and its preservation are revised. The absolute entropy of the mixed components of non-living biomass (approximately −1.6 × 10³ J K⁻¹ L⁻¹) is the reference to which other entropy decreases would be ascribed to life. The compartmentation of metabolites and the departure from the equilibrium of metabolic reactions account for reductions in entropy of 1 and 40–50 J K⁻¹ L⁻¹, respectively, and, though small, are distinctive features of living tissues. DNA and proteins do not supply significant decreases in thermodynamic entropy, but their low informational entropy is relevant for life and its evolution. No other living feature contributes significantly to the low entropy associated with life. The photosynthetic conversion of radiant energy to biomass energy accounts for most entropy (2.8 × 10⁵ J K⁻¹ carbon kg⁻¹) produced by living beings. The comparatively very low entropy produced in other processes (approximately 4.8 × 10² J K⁻¹ L⁻¹ day⁻¹ in the human body) must be rapidly exported outside as heat to preserve low entropy decreases due to compartmentation and non-equilibrium metabolism. Enzymes and genes are described, whose control minimizes the rate of production of entropy and could explain selective pressures in biological evolution and the rapid proliferation of cancer cells.

Statistical Physics of Evolving Systems

Evolution is customarily perceived as a biological process. However, when formulated in terms of physics, evolution is understood to entail everything. Based on the axiom of everything comprising quanta of actions (e.g., quanta of light), statistical physics describes any system evolving toward thermodynamic balance with its surroundings systems. Fluxes of quanta naturally select those processes leveling out differences in energy as soon as possible. This least-time maxim results in ubiquitous patterns (i.e., power laws, approximating sigmoidal cumulative curves of skewed distributions, oscillations, and even the regularity of chaos). While the equation of evolution can be written exactly, it cannot be solved exactly. Variables are inseparable since motions consume driving forces that affect motions (and so on). Thus, evolution is inherently a non-deterministic process. Yet, the future is not all arbitrary but teleological, the final cause being the least-time free energy consumption itself. Eventually, trajectories are computable when the system has evolved into a state of balance where free energy is used up altogether.

On Conditional Tsallis Entropy

There is no generally accepted definition for conditional Tsallis entropy. The standard definition of (unconditional) Tsallis entropy depends on a parameter α that converges to the Shannon entropy as α approaches 1. In this paper, we describe three proposed definitions of conditional Tsallis entropy suggested in the literature—their properties are studied and their values, as a function of α, are compared. We also consider another natural proposal for conditional Tsallis entropy and compare it with the existing ones. Lastly, we present an online tool to compute the four conditional Tsallis entropies, given the probability distributions and the value of the parameter α.