Semester: WT 2025/26
Jupyterhub API Server: https://tu-jupyter-t.ca.hrz.tu-darmstadt.de

Course Contents
The module provides an introduction to modeling and analysis approaches relevant to synthetic biology. It builds on the mathematical basis provided in the module “mathematical foundations of modeling and analysis”. Apart from short introductory lectures, practical programming of respective algorithms will be the main modality to learn the subject. The course covers purely data-driven methods from biostatistics and machine learning but also first-principle modeling approaches from biophysics and biochemistry. Concrete scientific problem statements will used to learn about the modeling and analysis algorithms.
[list]
[*]Introduction to scientific programming using Julia
[*]Introduction to biostatistics, bioinformatics and machine learning
[*]Deterministic and stochastic approaches for modeling reaction networks
[*]Thermodynamic analysis of reactions networks
[*]Principles of molecular dynamics, structure prediction
[*]Statistical methods for structure prediction
[*]Numerical solution and simulation methods
[/list]

Literature
[list]
[*]Neil Jones & Pavel Pevzner. An Introduction to bioinformatics algorithms, MIT Press, 2004
[*]Daniel Beard & Hing Qian. Chemical Biophysics, Cambridge University Press, 2010
[*]Darren Wilkinson. Stochastic modeling for systems biology, CRC Press, 2006
[*]Kevin P. Murphy. Machine Learning – A probabilistic perspective, MIT Press, 2012
[/list]

Preconditions
Passing of module “Basics in Synthetic Biology”

Semester: WT 2025/26
Jupyterhub API Server: https://tu-jupyter-t.ca.hrz.tu-darmstadt.de

Semester: WT 2025/26
Jupyterhub API Server: https://tu-jupyter-t.ca.hrz.tu-darmstadt.de

Course Contents
In small groups, students have the opportunity to participate in the everyday clinical practice of various medical disciplines and to experience the use of medical devices in daily use as well as to experience the possibilities and limitations of the device technologies. They participate in various everyday clinical situations in a hospital and learn the clinical communication channels, workflows and treatment strategies.

Preconditions
Recommended are „Medical Morphology, Terminology and Applied Anatomy“ and „Cell Biology and Physiology“ as well as being vaccinated against measles, mumps, varicella, tetanus and hepatitis B according to the recommendation of the Standing Committee on Vaccinations.

Semester: WT 2025/26
Jupyterhub API Server: https://tu-jupyter-t.ca.hrz.tu-darmstadt.de
Course Contents
In this course, the students produce scientific reports from changing subject areas. Each student has to explore a subject related to IT system development and produce a written report as well as a final talk with a presentation.  A list of the subjects of the current semester is available at [url]https://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/sst-s[/url].

Preconditions
Basic knowledge in software engineering and programming languages

Further Information
The number of participants is limited due to the capacity of the laboratory. A registration is obligatory.

Additional Information
[url]https://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/sst-s[/url]

Online Offerings
moodle

Semester: WT 2025/26
Jupyterhub API Server: https://tu-jupyter-t.ca.hrz.tu-darmstadt.de
Course Contents
The lecture gives an introduction to the broad discipline of software engineering. All major topics of the field - as entitled e.g. by the IEEE’s “Guide to the Software Engineering Body of Knowledge” - get addressed in the indicated depth. Main emphasis is laid upon requirements elicitation techniques (software analysis) and the design of software architectures (software design). Ethical issues are addressed using the “ACM/IEEE-CS Software Engineering Code of Ethics and Professional Practice”. UML (2.0) is introduced and used throughout the course as the favored modeling language. This requires the attendees to have a sound knowledge of at least one object-oriented programming language (preferably Java). During the lecture, running examples are utilized to explain and exercise the presented software engineering techniques.

Literature
[url]https://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/se-i-v[/url] and Moodle

Preconditions
Solid knowledge of an object-oriented programming language (preferably Java)

Online Offerings
moodle

Semester: WT 2025/26
Jupyterhub API Server: https://tu-jupyter-t.ca.hrz.tu-darmstadt.de