117

This already shows: our selection of persons can only be exemplary. Especially in bio­

informatics, it would fill a book of its own if one wanted to acknowledge all the important

contributions of even just the important people.

With this overview we can see that systems biology can be traced back to clear basic

principles (first part), but is also characterised in a very interdisciplinary way by important

principles from neighbouring fields (second part: presentation of the five research person­

alities in systems biology).

But how do we use these insights in practice? Modeling software is available for this

purpose, but we only understand its results if we do not forget the principles and keep them

in mind.

9.6

Which Systems Biology Software Can I Use?

As we have already learned, one typically proceeds in two steps. First, one assembles the

necessary components for the system description and then proceeds to a systems biology

modeling of the dynamics, i.e. the time course in a semiquantitative model. Semiquantitative

here also means that we learn from the model the sequence of processes, i.e. what is stron­

ger and what is weaker, but not the absolute strength of the signals or the exact “kinetics”,

i.e. the precise pace of the processes. This requires yet more data, especially experiments

that accurately measure the speed. These data can then be used to incorporate them into

the models as accurately as possible. This is then the final third step, the exact mathemati­

cal modelling. There are numerous ways to do this (see also the nice textbook “Systems

Biology” by Klipp et al. (2016)). Here, only particularly well-known and easy-to-use tools

can be mentioned, without claiming to be exhaustive. Above, we have already presented

some tools that can be used for metabolic modeling, but which also work well for signal

cascades:

In particular, modeling with the convenient programming languages R and MATLAB

is recommended. For the R language, as well as for MATLAB, there is an R Systems

Biology Suite, and for the evaluation of gene expression data and systems biology based

on it, there is the Bioconductor Software package, which also uses R.

CellNetAnalyzer, COPASI, COBRA (Table 4.2) and Odefy (Krumsiek et  al.

2010) should be mentioned here. SQUAD (di Cara et al. 2007) and Jimena (Karl and

Dandekar 2015) have also been mentioned.

9.6  Which Systems Biology Software Can I Use?