Osion kuvaus

  • Yleinen


    General info for January 2019

    Lectures: 

    • On Tuesdays 12:15-14:00 in T3, and 
    • On Fridays 12:15-14:00 in A136 
    • First lecture: January 8, 2018, starting at 14:15.
    • Lecture on Friday (January 11) has been moved to Thursday (January 10) 8:15-10:00 in T3.

    Exercises: 

    • On Wednesdays 14:15-16:00+ in Y342a, Otakaari 1 
    • First exercise: January 9, 2018

    Lectures:

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

    Exercises (and lectures):

    • Henrik Mannerström, email: henrik.mannerstrom@aalto.fi, office: A357
    • Juho Timonen, email: juho.timonen@aalto.fi, office: A357

    Assignments: 

    • Thanh Vo, email: thanh.vo@aalto.fi, office: 

    Description:

    Models of biological networks, with emphasis on molecular-level networks of transcriptional regulation, signaling, metabolism, and epigenetics. 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
    • 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
    • Biological networks as random graphs

    Prerequisites: 

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

    Requirements:

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

    Grading:

    • Exercises: 6 * 5 points, max. 30 points
    • Assignment projects: 2 * 10 points, max. 20 points
    • Exam: max. 30 points
    • Total: max. 80 points
    • 40 points required to pass the course
    • Approx. 65-70 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
    • (tentative) Junker BH, Schreiber F, Analysis of Biological Networks, Wiley, 2008 or Eric D. Kolaczyk, Statistical Analysis of Network Data: Methods and Models, Springer, 2009
    • Note: The course covers only selected parts of the books. The books are available from our library as traditional printed books and as ebooks
    • Lecture notes
    • The material will be supplemented by recent articles and possibly by other book chapters