Biosystems最新文献

This article proposes an evolutionary trajectory for the development of biological energy producing systems. Six main stages of energy producing system evolution are described, from early evolutionary pyrite-pulled mechanism through the Last Universal Common Ancestor (LUCA) to contemporary systems. We define the Last Pure Chemical Entity (LPCE) as the last completely non-enzymatic entity. LPCE could have had some life-like properties, but lacked genetic information carriers, thus showed greater instability and environmental dependence than LUCA. A double bubble model is proposed for compartmentalization and cellularization as a prerequisite to both highly efficient protein synthesis and transmembrane ion-gradient. The article finds that although LUCA predominantly functioned anaerobically, it was a non-exclusive anaerobe, and sulfur dominated metabolism preceded phosphate dominated one.

I analyzed the polyphyletic origin of glycyl-tRNA synthetase (GlyRS) and lysyl-tRNA synthetase (LysRS), making plausible the following implications. The fact that the genetic code needed to evolve aminoacyl-tRNA synthetases (ARSs) only very late would be in perfect agreement with a late origin, in the main phyletic lineages, of both GlyRS and LysRS. Indeed, as suggested by the coevolution theory, since the genetic code was structured by biosynthetic relationships between amino acids and as these occurred on tRNA-like molecules which were evidently already loaded with amino acids during its structuring, this made possible a late origin of ARSs. All this corroborates the coevolution theory of the origin of the genetic code to the detriment of theories which would instead predict an early intervention of the action of ARSs in organizing the genetic code. Furthermore, the assembly of the GlyRS and LysRS protein domains in main phyletic lineages is itself at least evidence of the possibility that ancestral genes were assembled using pieces of genetic material that coded these protein domains. This is in accordance with the exon theory of genes which postulates that ancestral exons coded for protein domains or modules that were assembled to form the first genes. This theory is exemplified precisely in the evolution of both GlyRS and LysRS which occurred through the assembly of protein domains in the main phyletic lineages, as analyzed here. Furthermore, this late assembly of protein domains of these proteins into the two main phyletic lineages, i.e. a polyphyletic origin of both GlyRS and LysRS, appears to corroborate the progenote evolutionary stage for both LUCA and at least the first part of the evolutionary stages of the ancestor of bacteria and that of archaea. Indeed, this polyphyletic origin would imply that the genetic code was still evolving because at least two ARSs, i.e. proteins that make the genetic code possible today, were still evolving. This would imply that the evolutionary stages involved were characterized not by cells but by protocells, that is, by progenotes because this is precisely the definition of a progenote. This conclusion would be strengthened by the observation that both GlyRS and LysRS originating in the phyletic lineages leading to bacteria and archaea, would demonstrate that, more generally, proteins were most likely still in rapid and progressive evolution. Namely, a polyphyletic origin of proteins which would qualify at least the initial phase of the evolutionary stage of the ancestor of bacteria and that of archaea as stages belonging to the progenote.

Over more than the past century, reports that chromosomes in Eukaryotes are linked have been published. Recently this has been confirmed by micromanipulation. The chromolinkers are DNAse sensitive, as has been previously reported. The arguments for and against chromolinkers have been reviewed, and a call for definitive research made, because if chromolinkers do exist, the whole basis for nuclear DNA genetics may require revision.

The prevailing consensus in scientific literature underscores the mutualistic bond between the microbiota and the human host, suggesting a finely tuned coevolutionary partnership that enhances the fitness of both parties. This symbiotic relationship has been extensively studied, with certain bacterial attributes being construed as hallmarks of natural selection favoring the benefit of the human host. Some scholars go as far as equating the intricate interplay between humans and their intestinal microbiota to that of endosymbiotic relationships, even conceptualizing microbiota as an integral human organ.

However, amidst the prevailing narrative of bacterial species being categorized as beneficial or detrimental to human health, a critical oversight often emerges – the inherent functional diversity within bacterial strains. Such reductionist perspectives risk oversimplifying the complex dynamics at play within the microbiome. Recent genomic analysis at the strain level is highly limited, which is surprising given that strain information provides critical data about selective pressures in the intestine. These pressures appear to focus more on the well-being of bacteria rather than human health. Connected to this is the extent to which animals depend on metabolic activity from intestinal bacteria, which varies widely across species. While omnivores like humans exhibit lower dependency, certain herbivores rely entirely on bacterial activity and have developed specialized compartments to house these bacteria.

In biological systems, solitary organisms or eusocial groups, the metabolic rate often scales allometrically with systems’ size, when they are inactive, and the scaling becomes nearly isometric when the systems are active. Here I propose a hypothesis attempting to offer a departing point for a general joint understanding of the difference in the scaling powers between inactive and active states. When the system is inactive, there exist inactive components, which consume less energy than the active ones, and the larger the system is, the larger the fraction of the inactive components, which leads to sublinear scaling. When the system is active, most inactive components are activated, which leads to nearly isometric scaling. I hypothesize that the disproportional fraction of the inactive components is caused by the diffusants screening in the complex transportation network. I.e., when metabolites or information diffuses in the system, due to the physical limitation of the network structure and the diffusant’s physical feature, not all the components can equally receive the diffusants so that these components are inactive. Using the mammalian pulmonary system, ant colonies, and other few systems as examples, I discuss how the screening leads to the allometric and isometric metabolic scaling powers in inactive and active states respectively. It is noteworthy that there are a few exceptions, in which the metabolic rate of the system has an isometric scaling relationship with size at rest. I show that these exceptions not only do not disapprove the hypothesis, but actually support it.

Building on and extending existing definitions of robustness and evolvability, we propose and utilize new formal definitions, with matching measures, of robustness and evolvability of systems with genotypes and corresponding phenotypes. We explain and show how these measures are more general and more representative of the concepts they stand for, than the commonly used/referenced measures originally proposed by Wagner. Further, a versatile digital modeling approach (BNK) is proposed that is inspired by NK systems. However, unlike NK systems, BNK incorporates a genotype and a phenotype, in addition to fitness. We develop and apply an Evolutionary Algorithm to a BNK-modeled system to find different types of perfect oscillators. We then map the resulting oscillating systems to possible genetic circuit realizations. Continuing with the synthetic biology theme, we also investigate the effect of noise in DNA synthesis on the predicted functionality of a DNA-based biosensor (i.e., its robustness), and we carry out a theoretical assessment of the evolvability of different types of ribozymes, undergoing directed evolution.

The pan-cancer initiative aims to study the origin patterns of cancer cell, the processes of carcinogenesis, and the signaling pathways from a perspective that spans across different types of cancer. The construction of the pan-cancer related gene regulatory network is helpful to excavate the commonalities in regulatory relationships among different types of cancers. It also aids in understanding the mechanisms behind cancer occurrence and development, which is of great scientific significance for cancer prevention and treatment. In light of the high dimension and large sample size of pan-cancer omics data, a causal pan-cancer gene regulation network inference algorithm based on stochastic complexity is proposed. With the network construction strategy of local first and then global, the stochastic complexity is used in the conditional independence test and causal direction inference for the candidate adjacent node set of the target nodes. This approach aims to decrease the time complexity and error rate of causal network learning. By applying this algorithm to the sample data of seven types of cancers in the TCGA database, including breast cancer, lung adenocarcinoma, and so on, the pan-cancer related causal regulatory networks are constructed, and their biological significance is verified. The experimental results show that this algorithm can eliminate the redundant regulatory relationships effectively and infer the pan-cancer regulatory network more accurately (https://github.com/LindeEugen/CNI-SC).

Fungal mycelium networks are large scale biological networks along which nutrients, metabolites flow. Recently, we discovered a rich spectrum of electrical activity in mycelium networks, including action-potential spikes and trains of spikes. Reasoning by analogy with animals and plants, where travelling patterns of electrical activity perform integrative and communicative mechanisms, we speculated that waves of electrical activity transfer information in mycelium networks. Using a new discrete space–time model with emergent radial spanning-tree topology, hypothetically comparable mycelial morphology and physically comparable information transfer, we provide physical arguments for the use of such a model, and by considering growing mycelium network by analogy with growing network of matter in the cosmic web, we develop mathematical models and theoretical concepts to characterise the parameters of the information transfer.

At any moment in time, evolution is faced with a formidable challenge: refining the already highly optimised design of biological species, a feat accomplished through all preceding generations. In such a scenario, the impact of random changes (the method employed by evolution) is much more likely to be harmful than advantageous, potentially lowering the reproductive fitness of the affected individuals. Our hypothesis is that ageing is, at least in part, caused by the cumulative effect of all the experiments carried out by evolution to improve a species’ design. These experiments are almost always unsuccessful, as expected given their pseudorandom nature, cause harm to the body and ultimately lead to death. This hypothesis is consistent with the concept of “terminal addition”, by which nature is biased towards adding innovations at the end of development. From the perspective of evolution as an optimisation algorithm, ageing is advantageous as it allows to test innovations during a phase when their impact on fitness is present but less pronounced. Our inference suggests that ageing has a key biological role, as it contributes to the system’s evolvability by exerting a regularisation effect on the fitness landscape of evolution.

As development varies greatly across the tree of life, it may seem difficult to suggest a model that proposes a single mechanism for understanding collective cell behaviors and the coordination of tissue formation. Here we propose a mechanism called differentiation waves, which unify many disparate results involving developmental systems from across the tree of life. We demonstrate how a relatively simple model of differentiation proceeds not from function-related molecular mechanisms, but from so-called differentiation waves. A phenotypic model of differentiation waves is introduced, and its relation to molecular mechanisms is proposed. These waves contribute to a differentiation tree, which is an alternate way of viewing cell lineage and local action of the molecular factors. We construct a model of differentiation wave-related molecular mechanisms (genome, epigenome, and proteome) based on bioinformatic data from the nematode Caenorhabditis elegans. To validate this approach across different modes of development, we evaluate protein expression across different types of development by comparing Caenorhabditis elegans with several model organisms: fruit flies (Drosophila melanogaster), yeast (Saccharomyces cerevisiae), and mouse (Mus musculus). Inspired by gene regulatory networks, two Models of Interactive Contributions (fully-connected MICs and ordered MICs) are used to suggest potential genomic contributions to differentiation wave-related proteins. This, in turn, provides a framework for understanding differentiation and development.