SYSTEMS BIOLOGY COURSE

 

Lecturers and organizers
Ricard V. Solé
, Berta Alsina Cristina Pujades Marta Cascante Gustavo Deco

The course on Systems Biology, whose contents appear detailed below is a self-contained module within the MSc on Bioinformatics for Health Sciences . In this coures we intend to provide a complete overview of the different problems, approaches, methods and simulation models used in this field. Using specific examples from developmental biology, cancer, matabolomics and proteomics (among other areas) we will explore how simple models can be useful in understanding key questions and providing good answers to them.


1. Systems Biology:

Models, experiments and simulation. The system’s concept. Levels of organization and description. Reductionism and complexity. Complex diseases. Evolution of complex systems: selection, tinkering and constraints.

Theory 1h, Ricard Solé
Video: “Antichaos” (1h,) Ricard Solé

2. Dynamical systems I:

Linear versus nonlinear models. Continuous versus discrete models. Attractors, trajectories and stability (definitions). Examples from biology. Noise and determinism. Models of random events. Redundancy in cellular functions.

Theory 2h, Ricard Solé
Practical 6h: 4h BASIC programming, 2h simulation of continuous/discrete system. Ricard Solé

3. Dynamical systems II:

Continuous, one-dimensional models. Stability and instability. Mean-field approaches. Numerical simulation. Applications to RNA viruses, cancer, epidemiology, neurons and basic gene regulation.

Theory 2h Ricard Solé
Practical 4h (1h+3h): Gene regulation in cancer (1h intro-Cristina Pujades), Gene regulation in cancer (1.5h), Growth network (p53, MDM2) (1.5h) Ricard Solé.

4. Dynamical systems III:

Cellular automata (CA) as models of complexity. Automata classes, spatial pattern formation and measures of complexity. Stochastic CA: definitions and computer modelling. Self-organization and order out of chaos. Applications to tumor growth and development. Experimental examples of self-organization will be presented (the Belousov-Zhabotinsky reaction and Benard convection).

Theory 2h Ricard Solé
Practical 3h (1h+2h): Differential Cell Adhesion Biological Problem (1h Berta Alsina), Simulation (2h Ricard Solé)
Project: Modelling tumor growth evolution

5. Dynamical systems IV:

Boolean models of regulatory interactions. Boolean gates and their molecular counterparts. How molecular systems perform computations. Sigmoidal response functions, neural and genetic networks. Application to regulatory systems: cell types as attractors. Order and chaos in regulatory systems. Robustness of the system.

Theory 3h Ricard Solé
Practical 3h (1h+2h): Gene combinatory in establishing cell identity (1h Cristina Pujades)
practical examples to work on lambda phage and LacZ operon regulation (2h Ricard Solé)
Project: Modelling gene networks with Boolean circuits

6. Network biology I:

Why complexity is made of networks. Types of networks. Deterministic and random. Measuring networks: Paths, cycles and modules. Small world structures: social interactions and epidemics. Scale-free graphs. Importance of scaling. Language networks and universality. Protein folding as networks. Protein interaction maps: definitions, importance and modelling.

Theory 3h: Baldo Oliva, Ricard Solé
Practical 4h (2h+2h): Baldo Oliva, Ricard Solé
Project: Small world patterns in protein contact maps
Project: Reconstructing protein interaction networks and their evolution
Project: Cancer cell networks

7. Network Biology II:

Dynamics on networks. Simple non-linear dynamics on graphs: motifs and modules. Signal propagation on small world graphs. Cell signalling networks: modelling MAPK cascades. The problem of noise. Neural networks and computation within cells. Numerical simulation using Dynetica.

Theory 3h (2h+1h): Cristina Pujades, Ricard Solé
Practical 2h: MAPK cascades modelling, Ricard Solé
Project: Modelling signalling cascades

8. Network Biology III:

Metabolic networks. Modularity and scaling in metabolic pathways. Flows, stability and oscillations. Case studies. Cancer and metabolic phenotypes.

Theory 2h: Marta Cascante
Practical 4h: Marta Cascante
Project: Modelling metabolic networks and paths
Project: Metabolic cascades in complex diseases

9. Development and evolution: Pattern formation.

Cell-cell communicationand symmetry breaking. The notch-delta system. Turing mechanisms. Cellular automata and reaction-diffusion models. Meinhardt equations. Applications to early morphogenesis. Artificial life and emergent morphogenesis.

Theory 4 h (2h+2h): Berta Alsina, Ricard Solé
Practical 4h (3h+1h): Pattern formation, Morphogen gradients (3h Ricard Solé),
Project: Evolution of pattern formation in early development

10. Computational Neuroscience:

Theory (3h): Gustavo Deco
Practical: (2h): Gustavo Deco