Biosystems最新文献

Freedom, safety, and ease of movement are innate human urges attributed to conscience along with many other preferences such as attractiveness (beauty), economy, and life. This article addresses the physics basis of the innate urge to have freedom. It unveils the connection between animal freedom and the universal (constructal) tendency toward easier movement and greater access in all evolutionary systems throughout nature (animate & inanimate). The demonstration is made with a model of lack of freedom in animal movement: a man who walks his dog on a leash. When two animals are coerced to move at the same speed, their combined effort (the spent power) is greater than when they move freely, and independently. When the speed of the couple is dictated by the big body (man), the big one walks freely, and the small one must run. Participants in organized movement (life, society) are not equal. All participants move with less effort when they are not coerced to move the same way. The implications of this part of physics (nature) are numerous and help unify the animal realm with the design and evolution of human society. If you want diversity, give the population freedom, not prescriptions.

In this work we present an analysis of the dinucleotide occurrences in the three codon sites 1–2, 2–3 and 1–3, based on a computation of the codon usage of three large sets of bacterial, archaeal and eukaryotic genes using the same method that identified a maximal $C^3$ self-complementary trinucleotide circular code $X$ in genes of bacteria and eukaryotes in 1996 (Arquès and Michel, 1996). Surprisingly, two dinucleotide circular codes are identified in the codon sites 1–2 and 2–3. Furthermore, these two codes are shifted versions of each other. Moreover, the dinucleotide code in the codon site 1–3 is circular, self-complementary and contained in the projection of $X$ onto the 1st and 3rd bases, i.e. by cutting the middle base in each codon of $X$. We prove several results showing that the circularity and the self-complementarity of trinucleotide codes is induced by the circularity and the self-complementarity of its dinucleotide cut codes. Finally, we present several evolutionary approaches for an emergence of trinucleotide codes from dinucleotide codes.

We attempt in this article to formulate a conceptual and testable framework weaving Cosmos, Mind and Matter into a whole. We build on three recent discoveries, each requiring more evidence: i. The particles of the Standard Model, SU(3) x SU(2) x U(1), are formally capable of collective autocatalysis. This leads us to ask what roles such autocatalysis may have played in Cosmogenesis, and in trying to answer, Why our Laws? Why our Constants? A capacity of the particles of SU(3) x SU(2) x U(1) for collective autocatalysis may be open to experimental test, stunning if confirmed. ii. Reasonable evidence now suggests that matter can expand spacetime. The first issue is to establish this claim at or beyond 5 sigma if that can be done. If true, this process may elucidate Dark Matter, Dark Energy and Inflation and require alteration of Einstein's Field Equations. Cosmology would be transformed. iii. Evidence at 6.49 Sigma suggests that mind can alter the outcome of the two-slit experiment. If widely and independently verified, the foundations of quantum mechanics must be altered. Mind plays a role in the universe. That role may include Cosmic Mind.

Our considerations concern

1. Ontologically Real Potentia and the Unmanifest; 2. Nonlocality as Fundamental; 3. Res potentia, Res extensa, and Actualization; 4. Mind and Qualia, Mind is not in Spacetime; 5. Quantum Vacuum = Potentia not in Spacetime = Mind not in Spacetime; 6. Mind can Actualize Potentia; 7. The emergence of the classical world; 8. Co-evolution of evermore complex matter; 9. Why “My Mind”?; 10. Each embodied mind is coupled bilaterally to the Quantum Vacuum that is Cosmic Mind; 11. Responsible Free Will.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

The textbook conceptualization of phenotype creation, “genotype (G) + environment (E) + genotype & environment interactions (GE) $\mapsto$ phenotype (Ph)”, is modeled with open quantum systems theory (OQST) or more generally with adaptive dynamics theory (ADT). The model is quantum-like, i.e., it is not about quantum physical processes in biosystems. Generally such modeling is about applications of the quantum formalism and methodology outside of physics. Macroscopic biosystems, in our case genotypes and phenotypes, are treated as information processors which functioning matches the laws of quantum information theory. Phenotypes are the outputs of the $E$-adaptation processes described by the quantum master equation, Gorini–Kossakowski–Sudarshan–Lindblad equation (GKSL). Its stationary states correspond to phenotypes. We highlight the class of GKSL dynamics characterized by the camel-like graphs of (von Neumann) entropy: in the process of $E$-adaptation phenotype’s state entropy (disorder) first increases and then falls down — a stable and well-ordered phenotype is created. Traits, an organism’s phenotypic characteristics, are modeled within the quantum measurement theory, as generally unsharp observables given by positive operator valued measures (POVMs. This paper is also a review on the methods and mathematical apparatus of quantum information biology.

In this paper we propose a control theory of manipulating holograms in Quantum Brain Dynamics (QBD) involving our subjective experiences, i.e. qualia. We begin with the Lagrangian density in QBD and extend our theory to a hierarchical model involving multiple layers covering the neocortex. We adopt reservoir computing approach or morphological computation to manipulate waveforms of holograms involving our subjective experiences. Numerical simulations performed indicate that the convergence to target waveforms of holograms is realized by external electric fields in QBD in a hierarchy. Our theory can be applied to non-invasive neuronal stimulation of the neocortex and adopted to check whether or not our brain adopts the language of holography. In case the protocol in a brain is discovered and the brain adopts the language of holography, our control theory will be applied to develop virtual reality devices by which our subjective experiences provided by the five senses in the form of qualia are manipulated non-invasively. Then, the information content of qualia might be directly transmitted into our brain without passing through sensory organs.

Focusing on the opposing ways of thinking of philosophers and scientists to explain the generation of form in biological development, I show that today's controversies over explanations of early development bear fundamental similarities to the dichotomy of preformation theory versus epigenesis in Greek antiquity. They are related to the acceptance or rejection of the idea of a physical form of what today would be called information for the generating of the embryo as a necessary pre-requisite for specific development and heredity.

As a recent example, I scrutinize the dichotomy of genomic causality versus self-organization in 20th and 21st century theories of the generation of form. On the one hand, the generation of patterns and form, as well as the constant outcome in development, are proposed to be causally related to something that is "preformed" in the germ cells, the nucleus of germ cells, or the genome. On the other hand, it is proposed that there is no pre-existing form or information, and development is seen as a process where genuinely new characters emerge from formless matter, either by immaterial "forces of life," or by physical-chemical processes of self-organization.

I also argue that these different ways of thinking and the research practices associated with them are not equivalent, and maintain that it is impossible to explain the generation of form and constant outcome of development without the assumption of the transmission of pre-existing information in the form of DNA sequences in the genome. Only in this framework of "preformed" information can "epigenesis" in the form of physical and chemical processes of self-organization play an important role.

A large hindrance to analyzing information in genetic or protein sequence data has been a lack of a mathematical framework for doing so. In this paper, we present a multinomial probability space $X$ as a general foundation for multicategory discrete data, where categories refer to variants/alleles of biosequences. The external information that is infused in order to generate a sample of such data is quantified as a distance on $X$ between the prior distribution of data and the empirical distribution of the sample. A number of distances on $X$ are treated. All of them have an information theoretic interpretation, reflecting the information that the sampling mechanism provides about which variants that have a selective advantage and therefore appear more frequently compared to prior expectations. This includes distances on $X$ based on mutual information, conditional mutual information, active information, and functional information. The functional information distance is singled out as particularly useful. It is simple and has intuitive interpretations in terms of 1) a rejection sampling mechanism, where functional entities are retained, whereas non-functional categories are censored, and 2) evolutionary waiting times. The functional information is also a quasi-metric on $X$, with information being measured in an asymmetric, mountainous landscape. This quasi-metric property is also retained for a robustified version of the functional information distance that allows for mutations in the sampling mechanism. The functional information quasi-metric has been applied with success on bioinformatics data sets, for proteins and sequence alignment of protein families.