Models In Life Sciences Research Articles

Modelling in the scientific approach to teaching life and earth sciences: Views and practices of Moroccan teachers

Modeling is a fundamental tool in the teaching and learning processes of life and earth sciences. It serves as an investigative instrument that enables students to test hypotheses and solve scientific problems. This paper presents the findings of a survey conducted among 96 Moroccan life and earth sciences teachers teaching students at the primary, junior high and high school levels. The study aims to highlight the role of modelling in life sciences instruction and explore teachers' understanding, approaches and perceptions towards models and modeling practices. A questionnaire-based methodology was employed to collect data on teachers' awareness of the significance of models in life sciences and the instructional approaches that are adopted in these models. The findings reveal Moroccan teachers' clear grasp of models' significance in teaching life sciences. Moreover, they employ similar approaches to models and modeling practices. The research highlights instructors' awareness of models' potential to increase the effectiveness and appeal of scientific instruction. It establishes modeling's pivotal role in instructing life sciences, emphasizing the need to incorporate modeling activities into the curriculum to nurture students' scientific inquiry and problem-solving skills. The study's practical implications suggest the value of training programs and professional development initiatives for teachers to promote model use in life sciences instruction. Enhancing teachers' knowledge and pedagogical strategies related to modeling can enrich science education leading to increased student engagement and achievement in the life sciences.

Precision Medicine: Implications for value chains and business models in life sciences

Precision Medicine (PM) is expected to be one of the biggest revolutions of the upcoming years and not only in the healthcare industry. PM aims to prevent and treat disease on the basis of a person's unique genetic makeup, lifestyle habits, and environment. While the topic has attracted huge attention from scholars in the biomedical field of research, in contrast, literature in the field of business studies is almost absent. This in itself is surprising: this revolution implies a profound impact on the business models of companies and institutions as it pushes a new paradigm of sustainable development for healthcare and welfare systems at the global level. However, little is known about whether, to what extent, and in what direction these dynamics are impacting on the way organisations and value chains are managed, from research to care delivery. Our study addresses this gap: we discuss both theoretical arguments and empirical evidence from a multiple case study survey. Findings show that PM is a multi-faceted phenomenon grounded on novel forms of innovation eco-systems and bundled-based models. We reveal a dramatic shift in the value chain, which is moving upstream from recovery and surgery to prevention and monitoring.

MAGPIE: Simplifying access and execution of computational models in the life sciences.

Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.

Language: A fresh concept to integrate syntactic and semantic information in life sciences

Several fields in biology tend to view the concept of information from one or the other of two extreme positions. Exclusionists base their stance of total rejection on gene-centrism and gene-determinism, typified by the recently-established endo-Darwinist school of life sciences. At the other end of the spectrum, there is total acceptance, as in the newly developed information-centred paradigms that populate biosemiotics. We propose in this paper to split the informational concepts into two irreducible (but linked) poles: the syntactic (concerned with the quantification of the information structure or complexity in a system), and the semantic (concerned with the organization rules and causality weights of interactions in a system). We claim that the past and present uses of the concept could then be classified as various degrees of oscillation between the two poles. The concept of language presents itself as a good tool with which to bridge the syntactic and the semantic poles, combining as it does the form-related and the meaning-related aspects of information, while methodologically supporting formal grammatical models in life sciences. We aim to show, at the same time, that neither of these poles alone can suffice to efficiently and holistically describe, model, and predict natural phenomena.

Delay differential operators and some solvable models in life sciences

Using the methods of the spectral theory of differential operators in Hilbert spaces L2-solvability of some models arising in mathematical biology are investigated. Particularly, concrete solvable models are given

Life sciences through mathematical models

This article discusses some of the basic principles of mathematical modeling in life sciences, and in particular the special features that make the modeling task fundamentally different from the traditional reductive modeling. The intricacies of the modeling in living systems are elucidated by simple and tractable examples that underline the problems of parametric models and the issue of non-scalability of the models.