Biosystems最新文献

Recently, a new genetic code with 62 sense codons, coding for 21 amino acids, and only 2 termination codons has been identified in archaea. The authors argue that the appearance of this variant of the genetic code is due to the relatively recent and complete recoding of all UAG stop codons to codons encoding for pyrrolysine. I re-evaluate this discovery by presenting arguments that favour the early, i.e. ancestral, appearance of this variant of the genetic code during the origin of the genetic code itself. These arguments are capable of supporting that during the origin of the organization of the genetic code, at least two versions of the genetic code evolved in the domain of the Archaea. Thus, the genetic code would not be absolutely universal.

Any homeostatic protometabolism would have required orchestration of disparate biochemical pathways into integrated circuits. Extraordinarily specific molecular assemblies were also required at the right time and place. Assembly Theory conflated with its cousins-Complexity Theory, Chaos theory, Quantum Mechanics, Irreversible Nonequilibrium Thermodynamics and Molecular Evolution theory- collectively have great naturalistic appeal in hopes of their providing the needed exquisite steering and controls. They collectively offer the best hope of circumventing the need for active selection required to formally orchestrate bona fide formal organization (as opposed to the mere self-ordering of chaos theory) (Abel and Trevors, 2006b). This paper focuses specifically on AT's contribution to naturalistic life-origin models.

This article revisits Artificial Neural Networks (NNs) through the lens of Evolutionary Dynamics. The two most important features of NNs are shown to reflect the two most general processes of Evolutionary Dynamics. This overlap may serve as a new and powerful connection between NNs and Evolutionary Dynamics, which encompasses a body of knowledge that has been built over multiple centuries and has been expanded to inspire applications across a vast range of disciplines. Consequently, NNs should also be applicable across the same range of disciplines-that is, much more broadly than initially envisioned. The article concludes by opening questions about NN dynamics, based on the new connection to Evolutionary Dynamics.

Cancers during oncogenic progression hold information in epigenetic memory which allows flexible encoding of malignant phenotypes and more rapid reaction to the environment when compared to purely mutation-based clonal evolution mechanisms. Cancer memory describes a proposed mechanism by which complex information such as metastasis phenotypes, therapy resistance and interaction patterns with the tumor environment might be encoded at multiple levels via mechanisms used in memory formation in the brain and immune system (e.g. single-cell epigenetic changes and distributed state modifications in cellular ensembles). Carcinogenesis might hence be the result of physiological multi-level learning mechanisms unleashed by defined heritable oncogenic changes which lead to tumor-specific loss of goal state integration into the whole organism. The formation of cancer memories would create and bind new levels of individuality within the host organism into the entity we call cancer. Translational implications of cancer memory are that cancers could be engaged at higher organizational levels (e.g. be "trained" for memory extinction) and that compounds that are known to interfere with memory processes could be investigated for their potential to block cancer memory formation or recall. It also suggests that diagnostic measures should extend beyond sequencing approaches to functional diagnosis of cancer physiology.

This article presents a refinement of theoretical explanations of the main stages of linguistic and cognitive evolution in anthropogenesis. The concepts of language, consciousness, self-consciousness, the self, the unconscious, the subconscious, and the relation between free will and determinism remain at the center of active and complex debates in philosophy and neuroscience. A basic theoretical apparatus comprising the central concepts of "concern" and "providing structure" (an extension of the biological concept of "adaptation") develops the paradigm of the extended evolutionary synthesis. Challenge-threats and challenge-opportunities are invariably associated with concerns pertaining to sustenance, safety, sexuality, parenthood, status, and emotional support. The consolidation of successful behavioral tries (tries), in response to these challenges occurs through the formation of a variety of providing structures including practices, abilities, and attitudes. These structures are formed through mechanisms of interactive rituals and internalization. These novel practices facilitate the transformation of both techno-natural environmental niches and group niches. The emergence of new structures gives rise to new challenges and concerns, which in turn necessitate undertaking of new tries. In the context of African multiregionalism, hominin groups and populations that experienced favorable periods of demographic growth, active migration, genetic, technological, and skill exchange also underwent significant demographic disasters. During the most unfavorable bottleneck periods only the most advanced groups, populations and species survived. The achieved potential for these abilities was consolidated as complexes of innate assignments in gene pools through the Baldwin effect and the multilevel selection. This logic provides an explanation for the main stages of language and speech complication (from holophrases and articulation to complex syntax), as well as the emergence of new abilities of consciousness (from the expansion of attention field to self-consciousness and the "I"-structure).

Antimicrobial resistance is one of the most significant healthcare challenges of our times. Multidrug or combination therapies are sometimes required to treat severe infections; for example, the current protocols to treat pulmonary tuberculosis combine several antibiotics. However, combination therapy is usually based on lengthy empirical trials, and it is difficult to predict its efficacy. We propose a new tool to identify antibiotic synergy or antagonism and optimize combination therapies. Our model explicitly incorporates the mechanisms of individual drug action and estimates their combined effect using a mechanistic approach. By quantifying the impact on growth and death of a bacterial population, we can identify optimal combinations of multiple drugs. Our approach also allows for the investigation of the drugs' actions and the testing of theoretical hypotheses. We demonstrate the utility of this tool with in vitro Escherichia coli data using a combination of ampicillin and ciprofloxacin. In contrast to previous interpretations, our model finds a slight synergy between the antibiotics. Our mechanistic model allows investigating possible causes of the synergy.