Vision of LIFE - Volume 17 - No. 3

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more than they expected to achieve. At the
beginning of class it’s always ‘oh’ and ‘ah’ and
‘how am I ever going to manage it’ and at the
end you can really look back and say ‘Yes,
now I know it! And I can do it!’. I think that is
what I like most.
What kind of courses are you teaching at
the moment?
I’m teaching a big masters course called
Analysis of Metabolic Networks. It is about
the application of engineering principles to
microbial cells, or in other words, learning
how we can find out what is going on inside
the cell based on extracellular measurements.
For example, can we predict how a process
will run? It is applying mathematics and
engineering to biology and by that learning
about the microbe.
And currently I have an elective course
running, called Systems Biology, also a
Masters course. I do that one together with
Greg Bokinsky (nanobiology). We both have
very different views on systems biology, Greg
comes from the experimental side and I come
from the modeling side, so doing that together
is great fun.
“I like to see people grow. They achieve
more than they expected to achieve”
Whether for your own research group, or
for all students; research and disappointing
results can be very stressful, how do you
advise students to stay motivated?
First of all, good research does not depend on
good results. We can plan good research, but
we can not plan good results. Every experiment
is a learning curve. Sometimes you may look
back and say ‘well I would not mind to have
spared doing these failed experiments’, but
there is still always a learning experience in
it. Besides, you will never make the same
mistake again. Sometimes you just have to
change the viewpoint and realise what you
have actually learned from the experiment
and most of the time you can generate a
positive result from that.
“Every experiment is a learning curve”
More journals are coming up that not
only allow for success stories but also for
experiments that just have good data and
good learnings in them. Scientific Reports for
example, a journal that has as only criterion
that the data quality of your experiment
is good. It is not important whether you
generated completely new insights or whether
you have a big story to tell.
In our current culture we have a bit of a
tendency of overselling results. Something
you will also see in your Masters. When you
study publications, titles are very catchy
and promising, but from what is actually in
the article you often cannot proof that it is
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