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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
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]
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 computers use biologically derived molecules — such as DNA and/or proteins — to perform digital or real computations. The development of biocomputers has been made possible by the expanding new science of nanobiotechnology.
For example, EU-backed SpiNNaker and BrainScaleS, Stanford's Neurogrid, IBM's TrueNorth, and Qualcomm's Zeroth. TrueNorth is a brain-inspired chip that IBM has been developing for nearly 10 years. The US DARPA program has been funding IBM to develop pulsed neural network chips for intelligent processing since 2008.
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.
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Natural computing, [1] [2] also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute.
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