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Table 3 Approaches towards understanding biology.

From: A framework for evolutionary systems biology

latin meaning strength weakness analogy1
in ratio analytic model well understood, precise predictions or approximations; can falsify intuitions and hint at simulation errors; can explain data if mechanistic limited to simple models by mathematical tractability hard, dry bone
in silico simulations of more realistic models can be very realistic; can use more observations than analytic models to make better predictions; can falsify approximations and intuitions; can explain data if mechanistic sometimes too hard to understand; computing can be costly; some heuristic models can predict data without explaining flesh
in vitro experiment without anything alive precise molecular observation and manipulation possibilities; can falsify models can be expensive; extrapolation to in vivo is not always possible; complexity limits food to eat
in vivo laboratory experiment with living cells controlled environment allows specific manipulations; can falsify models relevance for natural settings not always clear; limited mechanistic understanding water to drink
in natura observation of organisms in their natural setting get information on actual natural processes; can falsify models either only historic or usually limited by ~3 year funding periods; limited mechanistic understanding air to breathe
in tuitio ask good questions very cheap and fast; all ideas start here is no scientific proof in itself spirit with good ideas
  1. 1 As analogy, think of a biologist made of flesh and blood. Just bones and flesh are dead unless they breathe air, drink water and eat food. This illustrates that good theoretical models need to be designed to incorporate experimental data in order to 'come alive'. A good intuition is needed to develop such models. Heuristic models can be very good in predicting observations, but true understanding grows only when models reflect true causalities.