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The unicellular life cycle of cancer and Entamoeba is uniquely similar, and thus contradicts the molecular phylostratigraphic theory for the origin of cancer. This deep relationship between the two cell systems is supported by the "amoeba model", which provides a greater understanding of the biology of cancer from the evolutionary perspective. [9]
Developmental psychobiology posed this question since the lack of knowledge about the precise coordination of all cells, even those not related anatomically, in space and time during the embryonic period does not allow us to understand what forces at the cellular level coordinate four very general classes of tissue deformation, namely: tissue ...
Example of a biological network between genes and proteins that controls entry into S phase. However, with knowledge of network interactions and a set of parameters for the proteins and protein interactions (usually obtained through empirical research), it is often possible to construct a model of the network as a dynamical system .
Biological processes are regulated by many means; examples include the control of gene expression, protein modification or interaction with a protein or substrate molecule. Homeostasis: regulation of the internal environment to maintain a constant state; for example, sweating to reduce temperature
In cellular biology, P-bodies, or processing bodies, are distinct foci formed by phase separation within the cytoplasm of a eukaryotic cell consisting of many enzymes involved in mRNA turnover. [1] P-bodies are highly conserved structures and have been observed in somatic cells originating from vertebrates and invertebrates , plants and yeast .
From molecular and cellular information processing networks to ecologies, economies and brains, life computes. Despite ubiquitous agreement on this fact going back as far as von Neumann automata and McCulloch–Pitts neural nets , we so far lack principles to understand rigorously how computation is done in living, or active, matter".
The paths of the agents through the network and/or their final positions represent potential solutions to the problem. For instance, in the system described by Nicolau et al., [6] mobile molecular motor filaments are detected at the "exits" of a network encoding the NP-complete problem SUBSET SUM. All exits visited by filaments represent ...
Machine learning in environmental metagenomics can help to answer questions related to the interactions between microbial communities and ecosystems, e.g. the work of Xun et al., in 2021 [50] where the use of different machine learning methods offered insights on the relationship among the soil, microbiome biodiversity, and ecosystem stability.