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Biological applications of bifurcation theory provide a framework for understanding the behavior of biological networks modeled as dynamical systems.In the context of a biological system, bifurcation theory describes how small changes in an input parameter can cause a bifurcation or qualitative change in the behavior of the system.
Scientific modelling is an activity that produces models representing empirical objects, phenomena, and physical processes, to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate.
In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality.
Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology -based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach ( holism instead of the more traditional reductionism ) to biological research.
Evolutionary game theory analyses Darwinian mechanisms with a system model with three main components – population, game, and replicator dynamics. The system process has four phases: 1) The model (as evolution itself) deals with a population (Pn). The population will exhibit variation among competing individuals. In the model this competition ...
A more complex model may consist of several sub-models, e.g. micro-climate conditions given macro-climate conditions, body temperature given micro-climate conditions, fitness or other biological rates (e.g. survival, fecundity) given body temperature (thermal performance curves), resource or energy requirements, and population dynamics ...
In evolutionary biology and bio-medicine, the model is used to detect the presence of structured genetic variation in a group of individuals. The model assumes that alleles carried by individuals under study have origin in various extant or past populations. The model and various inference algorithms allow scientists to estimate the allele ...
While this model has seen success in machine-learning applications, it is a poor model for real (biological) neurons, because it lacks time-dependence in input and output. When an input is switched on at a time t and kept constant thereafter, biological neurons emit a spike train.