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Cell-based models are mathematical models that represent biological cells as discrete entities. Within the field of computational biology they are often simply called agent-based models [1] of which they are a specific application and they are used for simulating the biomechanics of multicellular structures such as tissues. to study the influence of these behaviors on how tissues are organised ...
The adenylate energy charge is an index used to measure the energy status of biological cells.. ATP or Mg-ATP is the principal molecule for storing and transferring energy in the cell : it is used for biosynthetic pathways, maintenance of transmembrane gradients, movement, cell division, etc...
The dynamic energy budget (DEB) theory is a formal metabolic theory which provides a single quantitative framework to dynamically describe the aspects of metabolism (energy and mass budgets) of all living organisms at the individual level, based on assumptions about energy uptake, storage, and utilization of various substances.
The original model proposed by Graner and Glazier contains cells of two types, with different adhesion energies for cells of the same type and cells of a different type. Each cell type also has a different contact energy with the medium, and the cell volume is assumed to remain close to a target value. The Hamiltonian is formulated as:
The eukaryotic cell cycle is very complex and is one of the most studied topics, since its misregulation leads to cancers. It is possibly a good example of a mathematical model as it deals with simple calculus but gives valid results. Two research groups [1] [2] have produced several models of the cell cycle simulating several organisms. They ...
At present, the practice of cell culture remains based on varying combinations of single or multiple cell structures in 2D. [65] Currently, there is an increase in use of 3D cell cultures in research areas including drug discovery, cancer biology, regenerative medicine, nanomaterials assessment and basic life science research.
Energy-based generative neural networks [1] [2] is a class of generative models, which aim to learn explicit probability distributions of data in the form of energy-based models, the energy functions of which are parameterized by modern deep neural networks.
Only after the first sugar has been exhausted do the cells switch to the second. At the time of the "diauxic shift", there is often a lag period during which cells produce the enzymes needed to metabolize the second sugar. Monod later put aside his work on diauxic growth and focused on the lac operon model of gene expression, which led to a ...