Introduction to Systems Biology
, INTRODUCTION TO SYSTEMS BIOLOGY
INHOUDSOPGAVE
LECTURES 3
Week 1 Introduction to Systems Biology – Introduction 3
Practical 3
Week 2 3
Practical Top-down Systems Biology 3
Lecture Big Data 5
Week 3 + 4 6
Practical Cellular composition and time scales 6
Lecture Molecular processing in cells 8
Week 5 + 6 9
Practical Kinetics of biochemical reactions 9
Lecture State analysis 10
Week 7 13
Practical Chapter 4: Signal Transduction 13
Lecture Signalling 15
Exercise class quiz content 17
, Lectures + Chapters + Papers
Week 1 Introduction to Systems Biology – Introduction
Practical
Dynamics: set of elements actions/interactions that give change or movement to a system.
Dynamics: change with respect to something.
So a dynamical model is a model that represents these interactions.
Network structure: explanation of which exact molecules have what kind of interactions. Their
number and nature also give extra information.
Parameter: a property of a certain component of a system. So for an enzyme a parameter is
molecular rate for example. E.g. the initial concentration of glucose is the parameter. The
concentration at a certain timepoint is a variable
Variable: something you can measure and change (like cell size, volume etc.)
Metabolic network: network of interconversions between different molecules.
What problems can systems biology solve? Systems approaches allows you to solve more than only
the specific problem you work on.
Week 2
Practical Top-down Systems Biology
Principle component analysis: calculating interesting projections based on variance maximization in
a system of perpendicular axes. Start with a first principal component, an axis along which the data
has the highest variance, then go on to as many axis (perpendicular to the previous one) as variables.
t-SNE is a non-deterministic alternative for PCA. This method derives distance from the distribution
of the data.
Statistical learning
Supervised learning: learning from data by using additional knowledge about the data, with the aim
of making predictions about samples collected in the future having unknown properties. A second
goal is finding these variables either alone or in combination.
Common techniques are: principal component analysis, hierarchical- and k-means clustering.
Un-supervised learning: learning from data without using any additional knowledge about the
samples to which the data apply.
There are different ‘distances’ used in clustering or finding structures.
- Euclidian distance: the distance measured along a straight line between objects.
In 2D the Euclidian distance 𝐷𝑒 (𝑥, 𝑦) between two points 𝑥 = (𝑥1 , 𝑥2 ) and 𝑦 = (𝑦1 , 𝑦2 ) is
calculated as: 𝐷𝑒 (𝑥, 𝑦) = √(𝑥1 − 𝑦1 )2 + (𝑥2 − 𝑦2 )2
- Base pair difference: the number of differences in homologous sequences of DNA.
Hamming distance: number of differences in strings of characters in general.
Jukes-Cantor distance: a metric to quantify DNA differences.
- Distance between communities of species: often used when interested in causes and effects
of differences in distribution of species in an ecosystem. Two ways to calculate the distance
between distributions:
, INTRODUCTION TO SYSTEMS BIOLOGY
INHOUDSOPGAVE
LECTURES 3
Week 1 Introduction to Systems Biology – Introduction 3
Practical 3
Week 2 3
Practical Top-down Systems Biology 3
Lecture Big Data 5
Week 3 + 4 6
Practical Cellular composition and time scales 6
Lecture Molecular processing in cells 8
Week 5 + 6 9
Practical Kinetics of biochemical reactions 9
Lecture State analysis 10
Week 7 13
Practical Chapter 4: Signal Transduction 13
Lecture Signalling 15
Exercise class quiz content 17
, Lectures + Chapters + Papers
Week 1 Introduction to Systems Biology – Introduction
Practical
Dynamics: set of elements actions/interactions that give change or movement to a system.
Dynamics: change with respect to something.
So a dynamical model is a model that represents these interactions.
Network structure: explanation of which exact molecules have what kind of interactions. Their
number and nature also give extra information.
Parameter: a property of a certain component of a system. So for an enzyme a parameter is
molecular rate for example. E.g. the initial concentration of glucose is the parameter. The
concentration at a certain timepoint is a variable
Variable: something you can measure and change (like cell size, volume etc.)
Metabolic network: network of interconversions between different molecules.
What problems can systems biology solve? Systems approaches allows you to solve more than only
the specific problem you work on.
Week 2
Practical Top-down Systems Biology
Principle component analysis: calculating interesting projections based on variance maximization in
a system of perpendicular axes. Start with a first principal component, an axis along which the data
has the highest variance, then go on to as many axis (perpendicular to the previous one) as variables.
t-SNE is a non-deterministic alternative for PCA. This method derives distance from the distribution
of the data.
Statistical learning
Supervised learning: learning from data by using additional knowledge about the data, with the aim
of making predictions about samples collected in the future having unknown properties. A second
goal is finding these variables either alone or in combination.
Common techniques are: principal component analysis, hierarchical- and k-means clustering.
Un-supervised learning: learning from data without using any additional knowledge about the
samples to which the data apply.
There are different ‘distances’ used in clustering or finding structures.
- Euclidian distance: the distance measured along a straight line between objects.
In 2D the Euclidian distance 𝐷𝑒 (𝑥, 𝑦) between two points 𝑥 = (𝑥1 , 𝑥2 ) and 𝑦 = (𝑦1 , 𝑦2 ) is
calculated as: 𝐷𝑒 (𝑥, 𝑦) = √(𝑥1 − 𝑦1 )2 + (𝑥2 − 𝑦2 )2
- Base pair difference: the number of differences in homologous sequences of DNA.
Hamming distance: number of differences in strings of characters in general.
Jukes-Cantor distance: a metric to quantify DNA differences.
- Distance between communities of species: often used when interested in causes and effects
of differences in distribution of species in an ecosystem. Two ways to calculate the distance
between distributions:
