The Linear Fitness Correlate Hypothesis. This hypothesis states that it is possible to define a computable fitness correlate (f
) based on a comprehensive systems biology model that is proportional to a particular observable fitness correlate (f
) like survival, fecundity or growth rate. The resulting adaptive landscape is of type 'L-1D' (see Figure 2). Mutants (yellow squares) with values below the wildtype value can be constructed by introducing deleterious mutations of known effects. Mutants with values above the wildtype can be difficult to obtain in natural environments for fitness correlates that closely follow fitness (the wild type is optimised for these). Artificial environments can solve this problem, as wild types are less adapted here, leaving more room for optimisation. Once calibrated by such mutants, in silico estimates can capture very small effects more precisely than direct observations with their accompanying experimental errors. See text for more explanations.