What to do and not to do in advanced modelling courses

I previously introduced our in silico systems biology course. After 5 years of this course, I collected a few lessons that are probably applicable to any advanced course. Nothing very new or surprising, but worth keeping in mind when organising these teaching events.

Select the students well

Beware of the wrong expectations, and of the students who do not find what they thought they would. Disappointed students can wreak the atmosphere of a course. Beware that terminologies are different in different domains. One of the most overloaded terms is “model”. 3D structure model, Hidden markov model, general linear model, chemical kinetics model, all those are models. But they address different population. Systems Biology itself is problematic. Choose also the level of the course and stick to it when selecting the students. Even if there is not the expected number of applicant (fortunately not a problem for our in silico systems biology course anymore), do not be tempted to select inadequate candidate. Better take on less students than having a few students bored or unable to follow. Our course is advanced, and covers quite a lot of ground. We cannot expect all students to be expert in every aspect of the course. However, by selecting students who are skilled in at least one aspect of the course (and balancing the expertises), we liven up the lessons (more interesting questions and discussions) and students become themselves “associated trainers”.

More hands-on, practicals, tutorials

Students learn with their fingers. A demo will never replace an actual hands-on, where the students make the mistakes and fix them (with the help of trainers). And of course, keep the lecturers from diving in their own research and give scientific presentations. This is a course, not a conference. If needed, organise special scientific presentations a few times during the course, but not in the lessons.

Focus on concrete applications of tools

Avoid lengthy descriptions of the theoretical basis of algorithms. It is good that students learn what is under the bonnet, and can choose solutions. But (in general) they are here to learn how to use those tools for their research, not to develop the next generation of them. Two complementary approaches are 1) building toy examples, that illustrate specific uses, and 2) using famous simple examples from the literature.

Do not try to cram too much in the course

It is better to explain well a typical set of techniques, than cover inadequately the whole field. It is generally not possibly to present all the approaches used in a field of computational biology. Even a seasonned researcher in the field does not master all of them. Introduce very carefully the common basis. And then move on to a few examples of more advanced approaches. If the basics are well understood, and the students are really using the content of the course for their research, they will be able to continue training on their own.

Engage the students

It is very important that the students feel part of the course. Those events last only one week or two. The students needs to bind with the organisers, the trainers and between themselves immediatedly. Make them present their work the first day, maybe with one slide each. Organise poster sessions. Real poster sessions, where students are kept around the posters. Drinks and snacks are a good methods if they are located at the same place and keeps the students there. If you selected the students wisely (see first point), they should be interested in each other research.

Try to keep trainers around

So they can interact with students outside of their presentation/tutorials. It is very difficult. You choose the best trainers, so they are obviously very busy people. But sometimes it is better to choose better trainers than better scientists. Also select your trainers even more carefully than your students. You want good presenters, but also good interactors. Bad trainers will arrive just before their course, spend the coffee breaks reading their mails, and leave just after. Those people do not like teaching, and frankly they don’t deserve your students. Do-not hesitate to replace them, even if they are famous. Observe them also outside the classroom. This is very sad to say, but some trainers cannot behave when interacting with young adults.

These are only a few advices. I am sure there are plenty others. What are your experiences?

Advertisements

“What is systems biology” – the students talk

This year was the 5th instalment of our Wellcome-Trust / EMBL-EBI course “in silico systems biology“.

This course finds its origin a few years ago in a workshop of the EBI industry programme on “Pathways and models”. The workshop, that lasted 2 days, was praised by the attendees. However, the time limitation caused a bit of frustration and made us skip entire aspects we would have liked to cover. I therefore decided to try making it into a full-blown course with the help of Vicky Schneider then responsible of training at the EBI.

The first course, supported by EMBO, lasted 4 days. It was well received. However, we tried to cover too much, from functional genomics and network reconstruction to quantitative modelling of biological processes. Fortunately, the existence of another EBI course “Networks and pathways“, allowed us to focus on modelling. We progressively improved the programme through 1 FEBS course and 3 Wellcome-Trust advanced courses. Without boasting, the current course, co-organised with Julio Saez-Rodriguez and Laura Emery, reached almost perfection. The programme always evolves, but the changes slowed down with time, and we are now more in an optimisation/refinement phase. One of the big advantages is that we kept a core of trainers, who help improving the consistency and quality of the content. We are now happy to see our first generations of students having become active figures in systems biology. Some group leaders who attended the course in the past now send their own students every year. A forthcoming post will discuss a few things I learnt from organising those courses.

Beside the regular training, we always have a few group activities. This year, they were split in small groups at the beginning, and had to answer a few questions. One of them was …

What is systems biology?

Everyone has their own idea about that one, including myself (for more on the history, nature and challenges of systems biology). Here I provide you with the unfiltered and unclustered responses of 25 students (repetitions originate from different groups coming with the same answers):

  • Mechanisms on different levels
  • Wholistic view (tautology intended)
  • Dynamics of biological systems
  • Fun
  • Mathematical modeling
  • Insight to the systems
  • Predictions
  • Looking at the system as a whole and not per component
  • Should also be: formal, unambiguous
  • Holistic approach
  • Using modelling to answer biological questions
  • understanding dynamics of a system in terms of predictability
  • Mechanistic insight
  • A tool to complement experimental data
  • Experiments-modeling cycle leading to discovery
  • formalisms
  • Technology+bio data+ in silico
  • integrating levels of biological processes
  • reaching the experimentally unapproachable

Interesting isn’t it? At first it looks pretty much all over the place. Let-me re-order the answers and group them:

  1. Entire systems
    • Wholistic view (tautology intended)
    • Looking at the system as a whole and not per component
    • Holistic approach
  2. Mechanisms
    • Insight to the systems
    • Mechanistic insight
    • Mechanisms on different levels
    • integrating levels of biological processes
  3. Dynamics
    • Dynamics of biological systems
    • understanding dynamics of a system in terms of predictability
  4. Modeling
    • Mathematical modeling
    • Should also be: formal, unambiguous
    • formalisms
    • Using modelling to answer biological questions
  5. Complement the observation
    • A tool to complement experimental data
    • reaching the experimentally unapproachable
    • Experiments-modeling cycle leading to discovery
    • Predictions
    • Technology+bio data+ in silico
  6. And of course
    • Fun

We basically fall back on the two global positions in the field: a philosophical statement about life sciences (1,2,3), and a set of techniques (4,5). That reminds me a lot the discussions we had about molecular biology at university a few decades ago …