52

Table 4.2  Programs for metabolic modeling

Metatool

https://pinguin.biologie.uni-­jena.de/bioinformatik/networks/metatool/

YANA

https://www.bioinfo.biozentrum.uni-­wuerzburg.de/computing/

yanasquare/

CellNetAnalyzer

https://www2.mpi-­magdeburg.mpg.de/projects/cna/cna.html

COPASI

https://copasi.org/

Flux Balance Analysis https://systemsbiology.ucsd.edu/Downloads/FluxBalanceAnalysis

COBRA Toolbox

https://opencobra.github.io/

CNA also uses Boolean networks as well as multi-digit logic and interaction graphs and

can thus also model signal networks and regulation. The stable system states are deter­

mined and the dynamics are simulated with differential equations (via a so-called plugin,

an additional program that uses the software ODEfy). Finally, one can also consider net­

work properties such as the signal network length and any feedback loops that may be

present.

The COPASI “Biochemical System Simulator” allows to analyze biochemical net­

works in their structure and dynamics (Kühnel et al. 2008; Kent et al. 2012; Bergmann

et al. 2016). It is also possible to read in models (in SBML format) and model the network

using differential equations (“ODEs”) or stochastic (“Gillespie’s stochastic simulation”),

so that random events (e.g. nutrient supply) can be simulated well.

Flux Balance Analysis (FBA) is the software of the world-famous old master of meta­

bolic simulations, Bernhard Palsson. You can also model metabolic and, with extensions,

signal networks.

The COBRA toolbox (Kent et al. 2012) is useful for metabolic modeling and signaling

cascades. A detailed tutorial, including the starting metabolic model for E. coli, is avail­

able and a whole community of users and developers. Orth et al. (2010) introduce an

instructive E. coli metabolism model in a separate paper.

Conclusion

Metabolism is fundamental to the nutrition, growth and reproduction of all living

beings. Metabolic modelling allows us to look at this in detail. Bioinformatics first uses

biochemical knowledge and databases such as KEGG to determine the set of all

enzymes involved. It is then possible to calculate (see exercises and tutorials) which

metabolic pathways and enzyme chains keep the metabolites in a network in equilib­

rium (flux balance analysis), which of these are also no longer decomposable (elemen­

tary mode analysis) and which of these are sufficient to represent all real metabolic

situations by combining a few pure flux modes (extreme pathway analysis).

In order to calculate the flux strength, one needs further data, e.g. gene expression

data and software (e.g. YANA programs). Further analyses look at metabolic control

(metabolic control theory) and describe the rates (kinetics) of the enzymes involved in

more detail. This is mathematically complex, but leads to deeper insights into their

regulation and function.

4  Modeling Metabolism and Finding New Antibiotics