Biological Physics

Course goals

By the end of this course, you should be able to:
 

1. Express physical quantities with the associated uncertainty, either calculated directly or indirectly, according to the correct scientific notation
2. Apply dimensional analysis to quantitatively reason about (bio)physical systems and their units
3. Interpret scaling laws on a bi-logarithmic plot
4. Solve quantitative (bio)physical problems by abstracting them into equations, in the context of:

• Classical mechanics, including kinematics and dynamics of a point particle
• Harmonic oscillations and oscillating biological systems
• Waves, with particular focus on electromagnetic waves in optics
• States of matter, with particular focus on biomaterials and gases
• Electromagnetism, with particular focus on bioelectricity

5. Solve numerically quantitative biophysical problems using Python
6. Present a scientific argument in front of peers, using the appropriate interdisciplinary terminology and evidence-based reasoning from physics, biology and computer science

Content

In this course students develop a physical understanding of biological phenomena and learn to apply the fundamental laws of physics to biological problems.

Introduction:
The structure and function of all living cells are governed by fundamental laws of physics and chemistry. Therefore, to understanding biological systems, it is critical to develop a physical understanding of natural phenomena. Physical laws, usually expressed by mathematical equations, are scientific generalizations that represent a summary description of nature. In doing so, this conceptual framework facilitates organization of observations and other data in a meaningful way and helps to identify the most critical parameters of a given process, as well as their interplay. Furthermore, it can be used to predict future outcomes when system-dependent conditions are specified. Because of the increasing use of sophisticated technology in biological experiments, it is now possible to acquire large amounts of quantitative data and to perform precise quantitative perturbation experiments. Therefore, it is necessary for all researchers in the Life Sciences to have a quantitative intuition about living systems in order to interpret data, to identify the most important aspects, and to plan critical experiments. In this course we will lay the foundation to develop such skills.

Set up of this course:
This concept-context course is structured around two weekly plenary lectures and two tutorials during which relevant biological case studies and examples are used to introduce the fundamental physical concepts essential in the study of biological phenomena. For instance, classical mechanics is applied to investigate oscillations important for the perception of sound, continuum mechanics to describe the biomechanical properties of tissues, and statistical physics to investigate random motion of molecules. Although the language of physics is mathematics, the emphasis of the course is on physics, not mathematics. Students are introduced to the fundamental tools for quantitative descriptions, study different branches of physics, and learn to apply them to biological problems. Throughout the course, students engage with the material in a diverse set of assignments and computational exercises. This learning-by-doing strategy teaches students how to use considerations based on fundamental physical principles to develop a quantitative intuition about biological systems.

Relation to other courses:
This course builds on the knowledge and skills students acquired in the course “Mathematics and Programming” (MBLS-102), expands this knowledge with an understanding of the physical laws underpinning all biological processes, and applies this to the wider context of living systems. This course provides the fundament for more advanced biological physics courses. 

Teaching format course (estimation): 
Lectures = 25%
Tutorials = 15% (ratio max 30/group)
Practicals (programming) = 10%
Presenting = 5%
Self study = 45%

This course is set up according to a Concept-Context teaching method. Concept-context rich education is a form of education in which subject matter (“concepts”) is offered to students in various “contexts” in order to deepen subject knowledge, enhance transfer of knowledge and skills, and building connections among subjects. Moreover, an authentic and relevant context has been shown to increase students’ motivation to learn. Lectures are alternated with tutorials.

Tutorials are led by the teaching assistants, who will show how to approach and solve quantitative problems in an interactive way. The tutorials serve to clarify any remaining questions and to initiate a curiosity-driven exploration of quantitative models.
Grading (see course manual for details):

1. First digital exam (45%)
2. Second digital exam (40%)
3. Group presentation of programming exercises (15%)

A minimum of grade of 5.0 must be obtained for both digital exams. The final grade (weighted average of the assessments) should be at least 5.5 in order to pass the course. In case only one of the two digital exams is insufficient, a re-take for that part is possible, otherwise a final comprehensive exam determines the final grade.
Given the nature of the programming presentation, there is no minimum grade for it, but also no re-take. Unless in exceptional cases, absence in the programming presentation will be registered as a 1.