Modeling Life

Course goals

Learning goals
The course aims to provide you with an introductory understanding of computational modeling to study living systems, and their large range of applications.
After this course the student can:

• Explain how biological systems can be studied with computational models,
• Translate a biological system into a working computational model using python,
• Use algorithmic thinking to break down problems into programmable steps,
• Identify the underlying assumptions and limitations of computational models,
• Identify the modeling approach & formalism best suited for a research question.

Skills

• Formulating a computational model to address a research question,
• Use modern tools like AI assisted coding to program & debug these models,
• Using experimental observations to build a computational model,
• Hands on experience on the computational research cycle
• Knowledge about career opportunities in computational modeling
• Work in teams to answer biological problems through an interdisciplinary approach.

 

Skills	Part of the course?	Explicitly examined?
Writing
Presenting	x	x
Data handling
Practical research skills	x	x
General research skills	x	x
Co-operation	x
Critical thinking	x	x
Career orientation	x
Interdisciplinarity	x

 

Content

Entry requirements
To successfully follow this course on Modeling Life, you do not need to be a math or programming wizard. You do need to have some basic knowledge of how to read a differential equation and analyze its behavior, as well as some limited experience with programming in either R or python. For Biology students this is part of “Biologische Modellen en Statistiek (former ‘Kwantitatieve Biologie)”, for MBLS students this requires having followed the courses “Mathematics & Programming” and “Bioinformatics and Dynamical modelling”. For Biology students we highly recommend “Biologische Modellering” (background mathematical modeling) and “Data Science en Biologie” (programming background).

Study path
Modeling Life is part of the study path Theoretische Biologie & Bioinformatica, and is recommended for Ontwikkelingsbiologie, Plantenbiologie, Evolutie & Biodiversiteit, and Ecologie en Natuurbeheer. The course is an (optional) part of the Bachelor’s program MBLS. The course prepares for the Masters programs Bioinformatics and Biocomplexity and Molecular and Cellular Life Sciences.

Language
All components of this course are in English.

Content description
Have you ever wondered how cells “know” which genes to express at a given time and place, or how a plant seed “decides” when is the best time to germinate? Or would you like to know what the stripes of a zebra, the fingers on your hand, and the patterning of vegetation in dry ecosystems have in common? The aim of this course is to learn how computational models can be used to help address these questions.

Living systems like those mentioned above are made up of many different interacting elements, from genes, to cells, tissues, organs, organisms, and ecosystems. In this course on Modeling Life you will learn to use computational models to understand how these interactions together produce self-organizing mechanisms, from the gene regulatory networks processing information and making cell fate decisions, to how cell-to-cell communication enables the regular distribution of tissues (i.e., fingers) and organs (e.g. lateral roots in plants), how mechanical processes shape tissues in morphogenesis, and finally, how evolution shapes these and many other processes. Additionally, you will learn how to create models, which model type to pick and how to judge the quality of the model.

This course will consist of lectures (HC) covering various modelling approaches to study life at different levels accompanied by computer practicals (WC) where students will get hands-on experience on running computational models of the development or the evolution of animals, plants, and microbial systems. The students will learn how to formulate a computational model to address specific questions (algorithmic thinking), how to analyze the output of the model, and how to question a model based on the underlying assumptions. The students will have the opportunity to learn to use modern tools like AI assisted programming to develop their models during the mini project.

Educational forms
Lectures, computer practicals, and a mini project (groups of 3-4 students). Poster presentation of the mini projects at a Modeling Life mini symposium.

Grading & Assessment
To pass this course, a minimum of 5,5 is mandatory. Your grade is calculated by the following components:

• Exam:                    65%
• Mini project:            35%

Active participation in the computer practicals is mandatory. If more than 20% of the computer practicals is missed, you cannot pass this course. For each of the two components you must get a grade of at least 5,50.

Study material
Mandatory:

• Personal laptop (tablets, Chromebooks and similar, are not suited),
• Study material available on Brightspace:

• Computer practical’s materials: Python code and manual
• Lecture slides.