The role of stochasticity in biological communication processes

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making

The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

Biology in the 21st century: Natural selection is cognitive selection

Natural selection has a formal definition as the natural process that results in the survival and reproductive success of individuals or groups best adjusted to their environment, leading to the perpetuation of those genetic qualities best suited to that organism's environmental niche. Within conventional Neo-Darwinism, the largest source of those variations that can be selected is presumed to be secondary to random genetic mutations. As these arise, natural selection sustains adaptive traits in the context of a 'struggle for existence'. Consequently, in the 20th century, natural selection was generally portrayed as the primary evolutionary driver. The 21st century offers a comprehensive alternative to Neo-Darwinian dogma within Cognition-Based Evolution. The substantial differences between these respective evolutionary frameworks have been most recently articulated in a revision of Crick's Central Dogma, a former centerpiece of Neo-Darwinism. The argument is now advanced that the concept of natural selection should also be comprehensively reappraised. Cognitive selection is presented as a more precise term better suited to 21st century biology. Since cognition began with life's origin, natural selection represents cognitive selection.

Stochasticity in genetics and gene regulation

Development from fertilized egg to functioning multi-cellular organism requires precision. There is no precision, and often no survival, without plasticity. Plasticity is conferred partly by stochastic variation, present inherently in all biological systems. Gene expression levels fluctuate ubiquitously through transcription, alternative splicing, translation and turnover. Small differences in gene expression are exploited to trigger early differentiation, conferring distinct function on selected individual cells and setting in motion regulatory interactions. Non-selected cells then acquire new functions along the spatio-temporal developmental trajectory. The differentiation process has many stochastic components. Meiotic segregation, mitochondrial partitioning, X-inactivation and the dynamic DNA binding of transcription factor assemblies-all exhibit randomness. Non-random X-inactivation generally signals deleterious X-linked mutations. Correct neural wiring, such as retina to brain, arises through repeated confirmatory activity of connections made randomly. In immune system development, both B-cell antibody generation and the emergence of balanced T-cell categories begin through stochastic trial and error followed by functional selection. Aberrant selection processes lead to immune dysfunction. DNA sequence variants also arise through stochastic events: some involving environmental fluctuation (radiation or presence of pollutants), or genetic repair system malfunction. The phenotypic outcome of mutations is also fluid. Mutations may be advantageous in some circumstances, deleterious in others. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Integrative vision in cancer research, prevention and therapy

There is already a significant global initiative to explore the synergy between traditional medicine (TM) and oncology, for holistic best care of cancer patients. Integrative oncology clinics have emerged with operational efficiency. What is needed now is an integrative vision that inspires to seamlessly coordinate the trans-system efforts in cancer research and rapidly translate the positive outcomes into prevention and treatment of cancer. The current dominant paradigm to consider TM only for complementary and alternative adjunct usage cannot inspire state-of-the art research and development on TM leads and serendipitous discoveries. Ayurvedic concepts of Vyadhi-kshamatwa (Immune resistance), Shatkriyakala (Six stages of a disease) and Hetuviparya Chikitsa (Reversal of pathogenetic factors) need to be synergized with ayurvedic pharmacoepidemiology, reverse pharmacology, observational therapeutics, ayurgenomics, ayurvedic biology, and reverse ayurceutics. Such a paradigm-shifting vision may lead to pragmatic translational research/practice and system obstacles and novel bridges in Integrative Oncology.

Infectious Virus Tracking by Fluorescent Live Cell Imaging in Primary Cells

To successfully infect a cell, HIV-1 has to overcome several host barriers while exploiting cellular cofactors. HIV-1 infection is highly inefficient with the great majority of viral particles not being able to successfully integrate into the target cell genome. Nonproductive HIV-1 particles are degraded or accumulated in cellular compartments. Thus, it becomes hard to distinguish between viral behaviors that lead to effectively infecting the cell from the ones that do not by using traditional methods. Here, we describe the infectious virus tracking method that detects and quantifies individual fluorescent viral particles over time and links viral particle behavior to its infectivity. This method employs live-cell imaging at ultra-low MOIs to detect the outcome of infection for every HIV-1 particle.

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.

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability

INTRODUCTION

The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata.

METHODOLOGY

To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms.

RESULTS

Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models.

CONCLUSIONS

This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

Biological evolution requires an emergent, self-organizing principle

In this perspective review, we assess fundamental flaws in Darwinian evolution, including its modern versions. Fixed mutations 'explain' microevolution but not macroevolution including speciation events and the origination of all the major body plans of the Cambrian explosion. Complex, multifactorial change is required for speciation events and inevitably requires self-organization beyond what is accomplished by known mechanisms. The assembly of ribosomes and ATP synthase are specific examples. We propose their origin is a model for what is unexplained in biological evolution. Probability of evolution is modeled in Section 9 and values are absurdly improbable. Speciation and higher taxonomic changes become exponentially less probable as the number of required, genetically-based events increase. Also, the power required of the proposed selection mechanism (survival of the fittest) is nil for any biological advance requiring multiple changes, because they regularly occur in multiple generations (different genomes) and would not be selectively conserved by the concept survival of the fittest (a concept ultimately centered on the individual). Thus, survival of the fittest cannot 'explain' the origin of the millions of current and extinct species. We also focus on the inadequacies of laboratory chemistry to explain the complex, required biological self-organization seen in cells. We propose that a 'bioelectromagnetic' field/principle emerges in living cells. Synthesis by self-organization of massive molecular complexes involves biochemical responses to this emergent field/principle. There are ramifications for philosophy, science, and religion. Physics and mathematics must be more strongly integrated with biology and integration should receive dedicated funding with special emphasis for medical applications; treatment of cancer and genetic diseases are examples.

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.

Making use of noise in biological systems

Disorder and noise are inherent in biological systems. They are required to provide systems with the advantages required for proper functioning. Noise is a part of the flexibility and plasticity of biological systems. It provides systems with increased routes, improves information transfer, and assists in response triggers. This paper reviews recent studies on noise at the genome, cellular, and whole organ levels. We focus on the need to use noise in system engineering. We present some of the challenges faced in studying noise. Optimizing the efficiency of complex systems requires a degree of variability in their functions within certain limits. Constrained noise can be considered a method for improving system robustness by regulating noise levels in continuously dynamic settings. The digital pill-based artificial intelligence (AI)-based platform is the first to implement second-generation AI comprising variability-based signatures. This platform enhances the efficacy of the therapeutic regimens. Systems requiring variability and mechanisms regulating noise are mandatory for understanding biological functions.

Re-analysis of publicly available methylomes using signal detection yields new information

Cytosine methylation is an epigenetic mark that participates in regulation of gene expression and chromatin stability in plants. Advancements in whole genome sequencing technologies have enabled investigation of methylome dynamics under different conditions. However, the computational methods for analyzing bisulfite sequence data have not been unified. Contention remains in the correlation of differentially methylated positions with the investigated treatment and exclusion of noise, inherent to these stochastic datasets. The prevalent approaches apply Fisher's exact test, logistic, or beta regression, followed by an arbitrary cut-off for differences in methylation levels. A different strategy, the MethylIT pipeline, utilizes signal detection to determine cut-off based on a fitted generalized gamma probability distribution of methylation divergence. Re-analysis of publicly available BS-seq data from two epigenetic studies in Arabidopsis and applying MethylIT revealed additional, previously unreported results. Methylome repatterning in response to phosphate starvation was confirmed to be tissue-specific and included phosphate assimilation genes in addition to sulfate metabolism genes not implicated in the original study. During seed germination plants undergo major methylome reprogramming and use of MethylIT allowed us to identify stage-specific gene networks. We surmise from these comparative studies that robust methylome experiments must account for data stochasticity to achieve meaningful functional analyses.

Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells

The Cancer Stem Model allows for a dynamical description of cancer colonies which accounts for the existence of different families of cells, namely stem cells, highly proliferating and quasi-immortal, and differentiated cells, both undergoing cellular processes under numerous activated pathways. In the present work, we investigate a dynamical model numerically, as a system of coupled differential equations, and include a plasticity mechanism, of differentiated cells turning into a stem state if the stem concentration drops low. We are particularly interested in the stability of the model once we introduce stochastically evolving parameters, associated with environmental and cellular intrinsic variabilities, as well as the response of the model after introducing a drug therapy. As long as we stay within the characteristic time scale of the system, defined on the base of the needed time for the trajectories to converge on stable states, we observe that the system remains stable for the main parameters evolving stochastically according to white noise. As for the drug treatments, we discuss a model both for the kinetics and the dynamics of the substance in the organism, and then consider the impact of different types of therapies in a few particular examples, outlining some interesting mechanisms, such as the tumor growth paradox, that possibly impact the outcome of therapy significantly.

Cellular and Natural Viral Engineering in Cognition-Based Evolution

Neo-Darwinism conceptualizes evolution as the continuous succession of predominately random genetic variations disciplined by natural selection. In that frame, the primary interaction between cells and the virome is relegated to host-parasite dynamics governed by selective influences. Cognition-Based Evolution regards biological and evolutionary development as a reciprocating cognition-based informational interactome for the protection of self-referential cells. To sustain cellular homeorhesis, cognitive cells collaborate to assess the validity of ambiguous biological information. That collective interaction involves coordinate measurement, communication, and active deployment of resources as Natural Cellular Engineering. These coordinated activities drive multicellularity, biological development, and evolutionary change. The virome participates as the vital intercessory among the cellular domains to ensure their shared permanent perpetuation. The interactions between the virome and the cellular domains represent active virocellular cross-communications for the continual exchange of resources. Modular genetic transfers between viruses and cells carry bioactive potentials. Those exchanges are deployed as nonrandom flexible tools among the domains in their continuous confrontation with environmental stresses. This alternative framework fundamentally shifts our perspective on viral-cellular interactions, strengthening established principles of viral symbiogenesis. Pathogenesis can now be properly appraised as one expression of a range of outcomes between cells and viruses within a larger conceptual framework of Natural Viral Engineering as a co-engineering participant with cells. It is proposed that Natural Viral Engineering should be viewed as a co-existent facet of Natural Cellular Engineering within Cognition-Based Evolution.

Natural selection according to Darwin: cause or effect?

In the 1940s, the 'modern synthesis' (MS) of Darwinism and genetics cast genetic mutation and recombination as the source of variability from which environmental events naturally select the fittest, such 'natural selection' constituting the cause of evolution. Recent biology increasingly challenges this view by casting genes as followers and awarding the leading role in the genesis of adaptations to the agency and plasticity of developing phenotypes-making natural selection a consequence of other causal processes. Both views of natural selection claim to capture the core of Darwin's arguments in On the Origin of Species. Today, historians largely concur with the MS's reading of Origin as a book aimed to prove natural selection the cause (vera causa) of adaptive change. This paper finds the evidence for that conclusion wanting. I undertake to examine the context and meaning of all Darwin's known uses of the phrase vera causa, documenting in particular Darwin's resistance to the pressure to prove natural selection a vera causa in letters written early in 1860. His resistance underlines the logical dependence of natural selection, an unobservable phenomenon, on the causal processes producing the observable events captured by the laws of inheritance, variation, and the struggle for existence, established in Chapters 1-3 of Origin.

Cellular Darwinism: Regulatory networks, stochasticity, and selection in cancer development

There are strong parallels between the evolutionary origin of species within populations of organisms and new concepts for the origin of cancers within cell populations in the tissues of the body. The analogy is that cancers can be regarded as a new somatic species developing within the host organism. In both cases, understanding the processes involved requires a multi-scale analysis, including higher-level control of genetic and epigenetic changes. A key to developing successful therapeutic strategies will be to identify the processes that control heterogeneity in tissues. These include processes outside the currently dominant theory of evolution, i.e. the Modern Synthesis. Specifically, organisms can partially direct both genetic and epigenetic changes through the harnessing of chance. The loci and rates of mutation and of genome reorganisation are forms of targeted functional reorganisation of genomes. They are more likely to result in functional reorganisations compared to the slow accumulation of point mutations.