Preliminaries: 

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Introduction:

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  1. Computational Approaches for Analyzing Information Flow in Biological Networks - Kholodenko, Yaffe, Kolch

  2. Physiochemical Modelling of Cell Signaling Pathways - Aldridge, Burke, Lauffenburger, Sorger

  3. Exploring Complex Networks - Stogatz

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Data Driven:

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  1. How to infer gene network from expression profiles - Bansai, Belcastro, Ambesi, di Bernardo

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Logic Modelling (Topology Driven):

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  1. Intro: Quantitative and Logic Modeling of Molecular and Gene Networks - Le Novere

  2. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction - Saez-Rodriguez, Alexopoulos et al

  3. Identifying Drug Effects via Pathway Alterations using an ILP Optimization Formulation on Phosphoproteomic Data - Mitsos et al

  4. NLP Formulation for Quantitative Modeling of Protein Signal Transduction Pathways - Mitsos et al

  5. Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using ILP on Interaction Graphs - Melas, Samaga, Alexopoulos, Klamt

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Data Analysis - Machine Learning:

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(Introduction with MATLAB)

  1. A review of feature selection techniques in bioinformatics - Saeys, Inza, Larranaga

  2. Machine Learning in bioinformatics

  3. Deep Learning - LeCun, Bengio, Hinton