Better visualization of micro-level structures and processes can greatly enhance student understanding of key biological functions such as the central dogma. Previous research has demonstrated a need of introducing novel methods to increase student understanding of these concepts. The intention of this report is to show how computational modeling programs (CMPs) can be successfully used as an innovative method of teaching biology concepts that occur at a molecular level. The use of computers and web-based lessons are not new topics in secondary education studies but there is not an abundance of research related to computational modeling alone. We began by researching the many studies that have already indicated the benefits of using computers in the classroom with an emphasis on CMPs and simulations. Of these, we focused mostly on the ones that showed increased student engagement and influenced understanding of core science concepts. Based on the literature reviewed, a framework for curriculum designed around CMPs is proposed. Lastly, a model lesson is discussed to provide an example of how these professional grade tools can be employed in the classroom. This report provides a basis for the continued development of constructivist curriculum built around the use of professional grade computational tools in secondary science classrooms. / text

The aim of this study is to prepare and verify a molecular biological teaching cycle. This cycle is focused on hands-on activities, critical thinking and student's activity. A total of 99 pupils from two elementary schools (9th grade) and one multi-year grammar school (equivalent to primary schools) participated in the verification. They helped to create an attractive learning cycle from molecular biology, which can be used directly without major modifications and low cost.

This masters's thesis contains description of designed and implemented tool for classification of plant microRNA without genome. Properties of mature and star sequences in microRNA duplexes are used. Implemented method is based on clustering of RNA sequences (with CD-HIT) to mainly reduce their count. Selected representants from each clusters are classified using support vector machine. Performance of classification is more than 96% (based on cross-validation method using the training data).