Search results
Results from the WOW.Com Content Network
Biological constraints are factors which make populations resistant to evolutionary change. One proposed definition of constraint is "A property of a trait that, although possibly adaptive in the environment in which it originally evolved, acts to place limits on the production of new phenotypic variants."
The approach based on optimality models in biology is sometimes called optimality theory. [1] Optimal behavior is defined as an action that maximizes the difference between the costs and benefits of that decision. Three primary variables are used in optimality models of behavior: decisions, currency, and constraints. [2]
The primal constraint graph or simply primal graph (also the Gaifman graph) of a constraint satisfaction problem is the graph whose nodes are the variables of the problem and an edge joins a pair of variables if the two variables occur together in a constraint. [1] The primal constraint graph is in fact the primal graph of the constraint ...
The constraints belong to two subsets from a biological perspective; boundary constraints that limit nutrient uptake/excretion and internal constraints that limit the flux through reactions within the organism. In mathematical terms, the application of constraints can be considered to reduce the solution space of the FBA model.
Developmental constraints are limitations on phenotypic variability (or absence of variation) caused by the inherent structure and dynamics of the developmental system. [1] Constraints are a bias against a certain ontogenetic trajectory, and consequently are thought to limit adaptive evolution.
Bergmann's rule - Penguins on the Earth (mass m, height h) [1] Bergmann's rule is an ecogeographical rule that states that, within a broadly distributed taxonomic clade, populations and species of larger size are found in colder environments, while populations and species of smaller size are found in warmer regions.
There are four primary types of data used to quantify stabilizing selection in a population. The first type of data is an estimation of fitness of different phenotypes within a single generation.
Learning constraints representing these partial evaluation is called graph-based learning. It uses the same rationale of graph-based backjumping. These methods are called "graph-based" because they are based on pairs of variables in the same constraint, which can be found from the graph associated to the constraint satisfaction problem.