(title taken from a wonderful book here)
Last year we ran a
second year undergraduate computer practical in Biosciences introducing students to
the ways in which computer simulations can be used to model plant
growth. There are several neat scientific and practical ideas here.
The students find that the diverse and complex range of beautiful
plant forms can be mimicked by simulating simple, iterated rules (as
in the pic). But the deeper idea is that these forms are not just
mimicked by iterated rules – they genuinely emerge from such rules,
not represented in a computer but in the biological language of the
genome. This is the first time many students have met the idea that
computational modelling allows a scientist to "play god" – they can make whatever changes they like to the rules and explore the
effect on the simulated plants they grow. They also get to grips with
algorithmic thinking – a highly transferrable skill given the
expansion of coding and computational approaches across sectors.
Figure 1: L-Studio, simulating plant growth in computers from simple iterated rules. (top) An introductory exercise modelling a highly reduced model plant using a small number of growth rules. (bottom) A more involved simulation, based again around repeated application of simple rules, giving a fairly natural-looking plant structure.
The class is about
50 students, working in pairs or small groups with a computer. They
are given some introductory exercises on “L-systems” (have a play e.g. here!), then given increasingly complicated structures to play with –
including some famous fractals – and finally meet the translation
to plant forms. In this way they learn to think algorithmically in a
more abstract sense before the connection to biology is driven home.
The exercises start fairly prescriptive, reproducing given
structures. They then progress to a more investigative mode – given
a particular plant form, how would you change its growth rules to,
for example, outcompete tall neighbours, or disperse seeds more
broadly? The final, exploratory, mode is the most interesting, where
the students are given free rein, and design, adapt, and compare
their own plant “designs”.
There are typically
two members of staff and a handful of PhD students or postdocs acting
in a TA capacity. We help with the initial setup – smoothing the
way to this (unnatural for some) way of working with biological model
systems in a computer. We then engage in a more scientific way with
the groups, posing extension questions, guiding through questioning
(careful not to just recite the solution to a given problem). In the
final exploratory mode, we reduce the formality and jointly discuss
scientific extensions and applications with the students and
encourage the social aspect of the comparison and collaboration.
This class is an
interesting one from a pedagogical point of view. The mode of
learning shifts through the course of the two-hour session, from
prescriptive to exploratory. Typically the class is very split in
their uptake of this unfamiliar way of thinking. Some students love
it, particularly the open-ended parts, and stick around to the end
taking pictures of their plants and sending them to friends. Some
frequently question the “point” of the class – a common
question because logistics often mean they meet this class before
lectures that naturally set up the modelling perspective. However, it
usually just takes a few minutes of one-on-one discussion –
illustrated with examples of our own research using computational
modelling – to convince students of the utility both of the science
and of the transferrable skill acquisition. One of my most personally
rewarding experiences was with a student who started out almost
aggressively sceptical about the point of these models – and
whether the class would get him a better degree. After a discussion
of the science and the transferrable value of computational
modelling, he completely switched around and was interested in
further pursuit of these topics. Other students are less polarised –
they view the class as a box to be ticked. The final section of the
class has an interesting influence here, where the enthusiasm of some
groups rubs off onto the box-tickers. As such, there’s an
interesting dynamic of teaching staff acting to catalyse the spread
of enthusiasm – and of information – that emerges from the
students’ own exploration.
