Table 3

Approaches towards understanding 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 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.

Loewe BMC Systems Biology 2009 3:27   doi:10.1186/1752-0509-3-27