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    General info for January 2022

    At least for the first weeks, the course will be arranged online.

    Starting from lecture #8, lectures are organised on campus in lecture hall T2.

    An additional (non-obligatory) teaching session on will be organised on Monday, February 28th between 12:00-13:00, where you can ask questions and guidance for the assignment project. The meeting will be arranged via zoom (you can find the link from the lectures page).

    Lectures: 

    • On Tuesdays 12:15-14:00, and 
    • On Fridays 12:15-14:00 
    • First lecture: January 11, 2022
    • Link to the first lecture: https://aalto.zoom.us/j/61329728113
    • At least during the first weeks, the lectures will be arrange as live/online lectures. We will make video recordings of the live lectures available after the lectures (for course participants only). Some lectures may be available only as pre-recorded video lectures (for course participants only). In case of video lectures, we will have time for questions and discussion during the lecture time slot.
    • For online lectures, we will use zoom program. Link to a zoom meeting will be provided on the course web page a little before the beginning of the lecture. 

    Exercises: 

    • Wednesdays 14:15-18:00 
    • NOTE: There will be only one exercise session starting at 14:15 and continuing after 16:00 if needed
    • First exercise: January 12, 2022

    Lectures: 

    • Harri Lähdesmäki, email: harri.lahdesmaki@aalto.fi (office: B358)


    Exercises: 

    • Juho Timonen, email: juho.timonen@aalto.fi
    • Valerii Iakovlev, email: valerii.iakovlev@aalto.fi


    Project assignments: 

    • One assignment 


    Course description: 

    • Models of biological networks, covering molecular-level networks of transcriptional regulation, signaling, metabolism and epigenetics, with emphasis on modeling methods. Modeling formalisms include stochastic reaction networks, stochastic and ordinary differential equations, regression, Bayesian and Boolean networks, and dependency and correlation networks. Methods for inference of networks from experimental data, and prediction using the models. Mainly probabilistic and machine learning models.


    Notes: 

    • The course is also accepted as a postgraduate course


    Preliminary syllabus: 

    • Introduction and chemical reaction network models
    • Review of Markov processes in discrete and continuous time
    • Chemical and biochemical kinetics
    • Stochastic differential equations
    • Ordinary differential equation models for biological networks
    • Metabolic networks
    • Parameter inference for biological networks
    • Network structure selection for biological networks
    • Bayesian networks as biological networks
    • Boolean and dependency networks as biological networks
    • Undirected graphical models


    Prerequisites: 

    • Basic knowledge of probability, statistics, applied math and machine learning
    • Basic scientific programming skills


    Requirements: 

    • Exercises: Participation in the exercise sessions is not mandatory but you need to submit written reports (minimum of 15 points)
    • 1 assignment project. Assignment project can be done in pairs (two students). 
    • Final examination


    Grading: 

    • Exercises:  max. 30 points 
    • Assignment project: max. 30 points 
    • Exam: max. 30 points 
    • Total: max. 90 points
    • 45 points required to pass the course
    • Approx. 81-83 points will give you the maximum grade


    List of materials:

    • Darren J. Wilkinson, Stochastic Modelling for Systems Biology, Chapman & Hall/CRC, 2011
    • Ingalls BP, Mathematical Modeling in Systems Biology: An Introduction, MIT Press, 2013
    • Murphy KP, Machine Learning: A Probabilistic Perspective, MIT press, 2012
    • (tentative) Palsson B, Systems Biology: Properties of Reconstructed Networks, Cambridge University Press, 2006
    • Lecture notes
    • Note: The course covers only selected parts of the books. The books are available from our library as traditional printed books and as e-books.
    • The material may be supplemented by recent articles and possibly by other book chapters


    Exam: 

    • Exam on 24.2.2022 at 9:00 in T1 - C202 (in CS building). 
    • The exam covers the topics covered in the lectures. 
    • You are not allowed to use any material or equipments in the exam. 
    • Instead of long verbal explanations, the use of mathematical notation and equations is encouraged.