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Long Branch Attraction  Form of systematic error (tree-building error) in which dis­

tantly related taxa are incorrectly considered to be closely related or closely related to be

unrelated, resulting from comparing sequences of different lengths or when a single

sequence is quite long and the taxa have different numbers of mutations.

Markov Chain  This is the name given to a random process (Markov chain; also Markov

process, after Andrei Andreyevich Markov; other spellings: Markov chain, Markoff chain,

Markof chain). A good example is the random results of dice rolls. By knowing only a

limited past history (e.g. the last three throws), it will only be possible to make predictions

about future developments that are as good as those made by knowing the entire past his­

tory of all previous throws: Each new throw has a random result, and the probability of a

given number of dice is always one-sixth.

Markov Process  see Markov chain.

Mathematical Modelling  Mathematical modelling describes the representation of

experimental data with mathematical equations. Here, there are the Boolean/discrete,

quantitative and semi-quantitative methods. In principle, these methods consider the nodes

(proteins) of a network according to their activation state, i.e. either activated (on; maxi­

mally activated = 1) or inhibited (off; maximally inhibited = 0). According to the initial

state (how strongly is the node activated/deactivated), the further temporal course, i.e. how

does the state of the node change over time, is calculated for each individual node of the

network. In this way, the behavior or the network interconnection can be examined in

more detail, whereby corresponding network effects, i.e. the respective effect of a node,

also become clear. Boolean modeling always considers the on/off (1/0) state of a system,

i.e., the node is either activated (on; 1) or inhibited (off; 0). Quantitative modeling is useful

for kinetic data, such as Michaelis–Menten kinetics (example software: PottersWheel).

Here, the system state of a network is considered using exact concentrations and mathe­

matical differential equations, but this requires information about the kinetics.

Semiquantitative modeling combines both methods, which enables one to consider the

system state in the interval between 0 and 1, which can also be done without knowledge

about the kinetics (example software: SQUAD and Jimena). 

Maximum Likelihood Method  Phylogenetic method in which the most probable path­

way is calculated for all mutations (every single mutation is taken into account) (very

computationally intensive and time-consuming, but particularly accurate). 

Medical Informatics  In common parlance, this is computer support in the clinic. In par­

ticular, this includes computers in intensive care monitoring and anaesthesia, the elec­

tronic infrastructure for patient documentation (doctor’s letters, findings, electronic

medical records) and expert systems (such as databases on antidotes for poisoning or

infections) as well as educational software (e.g. for anaesthesia, anatomy). In contrast, the

modelling of diseases would be directly attributed to bioinformatics.

18  Glossary