https://studiegids.vu.nl/en/courses/2026-2027/XB_0142How does the behavior of complex systems emerge from their simple components? How can we use available large datasets to discover new biology? The course Basics of Bioinformatics and Systems Biology provides an introduction to how we can address these questions. Systems Biology studies the principles of how the properties of cells and higher levels of organisation emerge from molecular interactions using quantitative experiments, computer modelling, and theory. We will study a number of theoretical findings of systems biology about the dynamics and regulation of molecular networks inside living cells using small "toy" mathematical models and basic principles of biochemistry and physics. Bioinformatics uses computational techniques to extract biological meaning and insights on function from DNA/RNA/protein sequences. In practical sessions, we will work on a bioinformatics benchmark, gaining hands-on experience with algorithms and uncovering areas for further advancement in this rapidly evolving research field. The course follows a two-part structure: Systems Biology in the first half of the course and Bioinformatics in the second. Each part comes with its own learning goals that will be assessed in individual exams. Learning goals By the end of the course, the students will be able to: Systems Biology:Understand what differential equations are and how they can be used to describe biological systemsAnalyze biological systems (molecular networks, two-species ecosystems, epidemiological models) and formulate differential equations to describe themPredict a system’s behavior from its structure by analyzing differential equations to identify dynamics and steady-states, using graphical and algebraic methodsDefine and identify key properties of biological systems, such as equilibrium, steady state, bistability, and perfect adaptationGraph and interpret important functions that appear frequently in biological systems (linear, saturating, switch-like)Bioinformatics:Recognise the main data types and databases used in bioinformatics (Knowledge)Explain important statistical concepts used in bioinformatics data analysis (Knowledge, Comprehension)Summarise basic concepts of supervised and unsupervised machine learning in the context of bioinformatics application (Knowledge, Comprehension)Explain the steps of sequence and transcriptomics data retrieval and analysis (Knowledge, Comprehension)Apply chosen steps of bioinformatics analysis to real-world data such as sequence search, transcriptomics data quality check, differential gene expression, and functional enrichment (Application) and interpret the results of bioinformatics data analysis (Analysis)In the Systems Biology part, we will coverbasics of differential equationsbalances of system variablesmass-action kinetics and basic enzyme kinetics (Michaelis-Menten)thermodynamic equilibrium and steady statedynamics of simple systems: protein complex formation, small signaling circuits, role of positive and negative feedback, and moresimple ecological systems (Lotka-Volterra model)basic epidemiological models (SIR model)The Bioinformatics lectures will cover:Evolutionary profilesData resources in the Life Sciences, including the Gene Ontology Database (GO)Homology Search (BLAST/PSI-BLAST)Next-generation sequencing BenchmarkingComputational Analysis of Genome Sequencing (Genome Assembly)Differential gene expressionClustering, machine learning, and pattern recognitionThe course consists of15 lectures (2 lectures per week, 2 hours per lecture) covering Systems Biology (7 lectures) and Bioinformatics (8 lectures)14 group practical sessions (2 sessions per week, 2 hours per session), partially supervised: Systems Biology (7 practical sessions) and Bioinformatics (7 practical sessions). Feedback on assignments will only be given during the practical sessions.Two group Bioinformatics assignments on Homology search and Differential gene expression[10%] Bioinformatics group assignments [35%] Partial exam to assess Bioinformatics lecture topics [10%] Systems Biology midterm assignment [35%] Partial exam to assess Systems Biology lecture topics [10%] Canvas quizzes To pass the course, an average of at least 5.5 across the exams must be reached AND the final average grade across all assignments, quizzes, and exams must be at least 5.5.Course material (reader, slides, scientific papers) on Canvas.The course is mandatory for 2MNW students and available for students in the minor 'Bioinformatics and Systems Biology'. Third-year Bachelor students in any beta or life science discipline (including Medicine) are also allowed to enter.Note on AI use: Use of generative AI is permitted, with explanation and reflection (level 3) In this course, generative AI (GenAI), such as ChatGPT or similar tools, may be used as part of the working process. The student must explicitly explain how GenAI was used and critically reflect on the quality, usefulness, and limitations of the output. Assessment also focuses on how GenAI has been used consciously, critically, and responsibly. Incorrect use of GenAI, including failure to comply with the requirements for explanation and reflection, is considered fraud. If established, the submitted work or assessment result will be declared invalid, and the Examination Board may impose further measures.An interest in programming (mostly Python) and solving biological problems. Basic knowledge of mathematics (e.g., statistics, algebra), (bio-)chemistry, and cell biology. Computer scientists or mathematicians can follow the course, but will need to plan time early on to quickly catch up with the biology (links to material will be provided)