Översikt

  • Allmänt


    Lectures: 

    • On Tuesdays 12:15-14:00, and 
    • On Fridays 12:15-14:00 
    • First lecture: January 10, 2023


    Exercises: 

    • Wednesdays 14:15-18:00 
    • Location: Y342a (Linux) - Y342a, Kandidaattikeskus
    • NOTE: There will be only one exercise session starting at 14:15 and continuing after 16:00 if needed
    • First exercise: January 11, 2023


    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 or two assignment(s)


    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
    • Markov processes in (discrete and) continuous time and Poisson process
    • 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, math (differential calculus and linear algebra) 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
    • Approx. 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 XX.2.2022 at XX:00 in XX. 
    • 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.