Biosystems最新文献

Owing to recent advancements in brain science and AI, researchers tend to focus on the concept of self-organized criticality or the edge of chaos. On the other hand, quantum cognition, which is rooted in quantum mechanics, is promising for resolving various cognitive illusions. However, until recently, no connection between criticality and quantum mechanics was proposed. Gunji et al. (2024) recently introduced a linkage termed quantum logic automata, which encompasses not only quantum logic but also criticality characterized by power-law distributions. While quantum logic automata can be derived from various structures, only one of them has been proposed and discussed. Here, we define another type of quantum logic automata involving quantum logic and demonstrate that symmetric quantum logic automata lead to complex Class IV-like patterns and power-law distributions. Our findings support the association between criticality and quantum theory.

The classification of amino acids has proven to be a useful tool for understanding the importance of sequence in protein function. The reduced amino acid alphabets are an example of these classifications, which, when built from physicochemical, structural and quantum characteristics of the amino acids, allow it to simplify the representation of the sequences, being useful in the modelling, design and understanding of proteins. So, an objective selection of amino acids properties is important, due classes formed in a reduced alphabet depend on the descriptors used for classification. In this research, based on a careful selection of descriptors for the 20 amino acids, through techniques such as the information content index and hierarchical cluster analysis with ties in proximity, 20,871,586 reduced amino acid alphabets were constructed. This large collection of reduced alphabets was been used to interpret alterations in the function of three proteins: N-carbamylase, Luciferase, and PI3K, caused by amino acid changes in their sequences. For this, the similar and different descriptors linked to these mutations were studied. Properties such as volume, hydrophobicity, charge and autocorrelation can be associated with variations in the behaviour of these proteins, while the frequency in specific secondary structures, the Gibbs free energy and some topological and quantum properties can be considered as the causes of preventing the deactivation of protein function. This work offers the most complete collection of reduced alphabets that promise to be a useful tool for the interpretation of alterations caused by amino acid mutations in the protein sequence.

Enzymatic reactions are essential for most cellular reactions and ubiquitous in living organisms. In the present study, we explore the pivotal role of the reverse reaction in enzymatic reactions. It is a powerful noise-buffering motif. By SSA (stochastic simulation algorithm), a remarkable 32% reduction of product CV (coefficient of variation) was observed. To better understand the causes, we split the upstream noise. The product CV reduction is more than 35% for the noise inherited from the enzyme but merely 6%–21% for that from the substrate. It implies that the system applies different strategies to different upstream noises. We identified two leading causes responsible for noise attenuation. A cell is well designed to control its intracellular noise, and to acquire wisdom from nature is always enjoyable.

We usually accept that consciousness is in the brain. This statement corresponds to a Neurocentrist view. However, with all the physical and physiological data currently available, a convincing explanation of how consciousness emerges has not been given this topic is aborded by Anil Seth (2021). Because of this, a natural question arises: Is consciousness really in the brain or not? If the answer is no, this corresponds to the Embodied perspective. We cannot discriminate between these two points of view because we cannot identify how the organism processes the information. If we try to measure information processing in the brain, then the Neurocentrist view is unavoidable. For example, the information integration theory of Tononi's research group and the global work area theory developed by Dehaene and Baars, focus solely on the brain without considering aspects of Embodied vision (See Tononi, 2021; Dehaene, 2021). In this article, we propose an index based on Shannon's entropy, capable of identifying the leading processing elements acting: Are they mainly inner or external? In order to validate it, we performed simulations with networks accounting for different amounts of internal and outer layers. Since Shannon's entropy is an abstract measure of the information content, this index is not dependent on the physical network nor the proportion of different layers. Therefore, we validate the index as free of bias. This index is a way to discriminate between Embodied from Neurocentrist hypotheses.

The attention system underwent important evolutionary changes and specializations in the human genus. In fact, our outstanding social and technological complexity strictly depends on our attentional ability, which is sustained, intentional, and conscious. Attention, intention, and awareness are key features for what can be defined a mindful cognition, and we may wonder whether a specific combination of these cognitive traits may be the result of a natural selective process, or else an accidental by-product of mental complexity. In this article, basic concepts in evolutionary anthropology are reviewed, to consider whether positive, neutral, or negative selective forces might have influenced the evolution of a mindful cognitive ability. At present, all these alternatives are potentially supported by different kinds of evidence. Hybrid hypotheses, considering stabilizing mechanisms or distinct social roles and intra-specific variation, are also likely. An evolutionary approach to the cognitive abilities involved in attention and awareness can reveal potentialities, limitations, and drawbacks of our individual and collective natural behaviors, especially when dealing with the evolution of the human consciousness.

The genetic code plays a central role in all living organisms and its modeling is important for describing and understanding involved the coding rules. There are many approaches to modeling various aspects of the genetic code. One of the simple and successful mathematical tools for modeling the similarity both between codons and between amino acids, is the ultrametrics and especially the $p$-adic distance. This article contains an overview of ultrametric ($p$-adic) modeling of genetic information, and its translation to proteins using the genetic code.