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Article Excerpt
Computer simulations can facilitate the building of models of natural phenomena in research, such as in the molecular life sciences. In order to introduce molecular life science students to the use of computer simulations for model building, a digital case was developed in which students build a model of a pattern formation process in developmental biology using experimental data and computer simulations. For the development of a pedagogical approach, several design principles were used with respect to a suitable model-building method and with respect to increasing the students' understanding of (biological) systems. The case was then developed using this approach. Additional software components have been developed to provide sufficient feedback and support for students who work with the simulations. The case has been evaluated in three 3rd-year undergraduate courses at Wageningen University in the Netherlands and at the University of Zurich in Switzerland. Students appreciated working with the case and answered most exam questions about the contents of the case relatively well.
INTRODUCTION
Computer simulations can play an important role in science education. For example, they are well suited for a form of discovery learning (de Jong & van Joolingen, 1998), where the main task of the learner is to infer, through experimentation, characteristics of the model that underlie the simulation and are unknown to the learner. Scientific discovery learning with computer simulations can lead to more "intuitive" knowledge than expository teaching and it can lead to the mastery of discovery skills (de Jong & van Joolingen, 1998). However, students who employ this kind of scientific discovery learning using computer simulations do not learn how computer simulations can be applied in actual research to facilitate the building of models of natural phenomena.
The molecular life sciences constitute a research area in which computer simulations, as well as other quantitative methods, are rapidly gaining importance (Knight, 2002; Lander, 2004; Pennisi, 2003). For example, numerical simulations can be employed to discover novel biological principles (Eldar et al., 2002). In order to better prepare molecular life science students for quantitative research, curriculum adjustments that are aimed at a better integration of biology and quantitative thinking are required (Bialek & Botstein, 2004; Committee on Undergraduate Biology Education to Prepare Research Scientists for the 21st Century, 2003). For the integration of quantitative thinking into existing biology courses, it is important that the added value of quantitative thinking for biology research is illustrated, that no more mathematical knowledge is required than the current molecular life science students already have, and that students are supported in working with the quantitative methods, such that they do not get too distracted from biology (Aegerter-Wilmsen & Bisseling, 2005).
One example where numerical simulations have been employed in research to discover novel biological principles constitutes a pattern formation process during the early development of the fruit fly Drosophila, the formation of a gradient of the protein Decapentaplegic (Dpp) (Eldar et al., 2002). According to this model, a stable, dynamic Dpp gradient emerges from processes at the molecular level in combination with a specific distribution of the molecules among different regions of the embryo prior to the gradient formation. In particular, diffusion rate differences between free Dpp and a complex of Dpp and another protein, Short gastrulation (Sog), are essential for the formation of the Dpp gradient (Eldar et al., 2002).
It is worthwhile for undergraduate students who follow courses in developmental biology to become acquainted with this model for a number of reasons. First, protein gradients play a crucial role in development; therefore, it is important that students be introduced to mechanisms for the formation of a gradient. Second, diffusion rate differences are crucial in the model and diffusion rate differences are predicted to be important in other pattern forming processes (Koch & Meinhardt, 1994; Turing, 1952). Third, the Dpp gradient formation illustrates that interactions and properties at the molecular level can contribute to an emerging pattern at the embryo level. These show that emergent behavior can be important in developmental biology. Finally, the gradient forming mechanism is robust against concentration fluctuations of most of the participating proteins. This is an important biological implication of the model, since this enables embryos to develop normally even if the protein levels are not tightly controlled.
This paper describes the development and evaluation of a digital case in which students are engaged in building a model for the Dpp gradient formation using computer simulations.
DISCUSSION
Design Principles
The learning material aims to achieve several goals. Upon working with the material, students should be aware of the value that simulations add to research. Furthermore, they must know how certain biological models can be converted into a set of (partial) differential equations from a conceptual point of view (see Figure 3). For example, they must know that interactions among molecules can be represented by specific terms in (partial) differential equations, but they do not have to be able to formulate such terms themselves. Nor, do they have to be able to program anything themselves. Since experimental results are essential for model building, students should also be able to employ experimental results to test certain aspects of a model. Finally, after working with the learning material, students should understand the mechanism by which the Dpp gradient is formed. By this we mean that they should be able to describe, in their own words, how the gradient is formed under wild-type (normal) conditions as well as explain the behavior of the biological system under different experimental conditions.
In research, a rather simple model to describe the formation of the Dpp gradient was published initially (Biehs, Francois, & Bier, 1996). Later, however, it was shown that this simple model was seriously flawed (Ashe & Levine, 1999). It then took 3 years before the current, conceptually different, model was published (Eldar et al., 2002). This shows that the formulation of this new model was certainly not trivial. Compared to researchers, students have little experience with the interpretation of experimental data and building models. Therefore, in order to offer students the opportunity to participate in building a model of the gradient formation themselves, we found it necessary to offer considerable support. The cognitive apprenticeship model (Collins, Brown, & Newman, 1989) is a pedagogical model for such support. In order to structure this support, a pedagogical approach was developed based on a number of design principles with respect to a suitable general model-building method and...
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