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1. Basic experimental techniques in systems biology
1.1. Genomics, transcriptomics and proteomics

1.1.1 Sequencing Methods

1.1.2 Chip technology (transcriptome, proteome)

1.1.3 Reporter genes

1.1.4 Quantitative immunoblotting

1.1.5 2D gel electrophoreses (DIGE)

1.1.6 Real-time polymerase chain reaction (RT-PCR)

1.2. Metabolomics, fluxomics and reaction kinetics

1.2.1 Quantitative mass spectroscopy

1.2.2 Quantitative nuclear magnetic resonance (NMR) spectroscopy

1.2.3 Quantitative chromatography methods (LC, GC, CE)

1.2.4 Metabolic isotopes

1.3. Single cell measurements

1.3.1 Fluorescence microscopy/-spectrocopy

1.3.2 Live cell imaging, software tools

1.3.3 Spectroscopic methods

1.3.4 Fluorescent markers

1.4. Cell culturing and automation

1.4.1 Standardisation, protocols, SOPs

1.4.2 Micro titre plates, shaking flasks, bioreactors

1.4.3 Sample drawing und cell disruption

1.4.4 Flowcytometry

1.4.5 Microfluidics

1.4.6 Laboratory robotics

1.4.7 Quality control, reproducibility, error models
2. Theoretical and methodological basis of systems theory
2.1. Modelling and simulation of biochemical networks

2.1.1 Fundamental concepts: Dynamics, modelling, simulation, analysis, induction, prediction)

2.1.2 Particle based models (Brownian dynamics, cell automata)

2.1.3 Stochastic models (Markov processes, Gillespie algorithm

2.1.4 Spatially distributed deterministic models (partial differential equations, PDEs)

2.1.5 Mechanistica network models (reaction kinetic, stationary and trasient systems)

2.1.6 Structural and stoichiometric network analysis (accessibility, realtionship, cycles, elementary mode

2.1.7 Isotope marker networks (isotopomers, 13C stock flow analysis)

2.1.8 Network thermodynamics (NET formalism, valid reaction directions, concentration spaces.

2.1.9 Software tools, databases and standards (markup languages, model databases)

2.2. System analysis

2.2.1 Structural network analysis (graph theoretic algorithms)

2.2.2 Linear dynamical systems (eigenvalues, solution, stability)

2.2.3 Nonlinear dynamic systems (linearization, stability, oscillation, chaos, identifiability)

2.2.4 Bifurcation alalysis (continuity method, classification of bifurcations)

2.2.5 Sensitivity analysis (sensitivity measure, metabolic control theory, robustness)

2.2.6 Stochastic processes (ergodicity, stationarity, approximation)

2.2.7 Optimization problems for biological networkd (LP, ILP, nonlinear problems)

2.2.8 Application for the listed types of models.
3. Systems biology of cellular systems (exemplary studies)
3.1. Metabolic networks

3.1.1 Forms of metabolic regulatory circuits

3.1.2 Energy metabolism (glycolysis, citrate cycle), ATP-homeostasis

3.1.3 Diverse biosynthesis pathways and secondary metabolism

3.1.4 Energetic view of metabolic pathways

3.1.5 Network reconstruction

3.1.6 Adaptations of metabolism by gene regulation

3.1.7 Evolution of metabolic networks

3.2. Cellular transport processes

3.2.1 Diffusion processes in the intracellular matrix (cytosol, molecular crowding, cytoskelett)

3.2.2 Wave propagation (calcium dynamics)

3.2.3 Trasnport via membranes (mechanisms, PTS system)

3.2.4 Bullous transport (endocytosis, exocytosis, protein maturation

3.2.5 Molecular motors

3.3. Signal trasduction

3.3.1 Dynamics of procaryotic signalling networks

3.3.2 Dynamics of eucaryotic signalling networks

3.4. Gene expression and protein turnover

3.4.1 Various types of metabolic regulatory networks

3.4.2 Stochastic dynamics of trascription and translation

3.4.3 Gene regulatory networks

3.4.4 Determination of interaction networks

3.4.5 Protein degradation

3.5. Cell growth and cell division

3.5.1 Procaryotic cell division

3.5.2 Eucaryotic cell cycle, phenomenological models, structured population models

3.5.3 Mechanistic cell cycle models

3.5.4 Regulation of the cell cycle by signalling pathways

3.6. Cytomechanics

3.6.1 Membrane dynamics, cell adhesion

3.6.2 Cytosceleton, actin networks, cell migration

3.7. Cell-cell communication

3.7.1 Intercellular synchronisation mechanisms

3.7.2 Autocrine and paracrine dynamics of growth factors
4. Bioinformatics
4.1. Databases

4.1.1 DNA-, RNA-, Protein sequences (e.g. Ensemble, Genbank, DDBJ, UniProt, Swiss-Prot, MIPS, etc.)

4.1.2 Biological experiments (Arrays, Gels, etc.)

4.1.3 Literature (e.g. PubMed)

4.1.4 Models (e.g. JWS Online, BioModels)

4.1.5 Pathways (e.g. KEGG, Transpath, MetaCyc)

4.1.6 Structures (e.g. PDB, CATH, SCOP, etc.)

4.1.7 Protein-Protein interactions (e.g. BIND, Reactome)

4.1.8 Enzymes and reactions (e.g. BRENDA)

4.2. Multivariate statistical methods

4.2.1 Learning (supervised, unsupervised methods, neural networks, SOMs)

4.2.2 Clustering (hierarchical, k-means)

4.2.3 Classification methods and dimensions reduction (discriminant analysis, PCA)

4.2.4 Regression analysis (error models, parameter estimation, experiment design, model selection)

4.2.5 Significance testing

4.2.6 Random numbers and Monte Carlo methods

4.2.7 Bayesian methods and Bayesian networks

4.2.8 Time series analysis

4.3. Machine learning

4.3.1 Supervised and unsupervised learning

4.3.2 Artificial neural networks

4.3.3 Support vector machines

4.3.4 Self organizing maps

4.3.5 Nearest neighbour methods

4.3.6 Inductive logic programming

4.3.7 Genetic algorithms, Evolutioanry computing, Genetic programming

4.3.8 Decision trees

4.4. Image processing and visualisation of data

4.4.1 Interaction graphs (e.g. Cytoscape, BioUML, BioTapestry, etc.)

4.4.2 Pathways (e.g. CellDesigner, Molecular Interaction Maps)

4.4.3 Numeric data (e.g. scatter plots, histograms, contour plots, Heatmaps, etc.)

4.4.4 Software tools

4.5. Programming

4.5.1 (Bio-)Perl

4.5.2 Python

4.5.3 MySQL

4.5.4 R, Bioconductor

4.5.5 Markup languages

4.5.6 XML based standards (e.g. BioPAX, CellML, SBML, MATHML, MIRIAM, PSI-MI etc.)

4.5.7 Data mining tools, data warehousing

4.5.8 Workflow management tools

4.6. Sequence analysis

4.6.1 Alignment (local, global), dot plots

4.6.2 Dynamic programming (-Waterman Smithalgorithm)

4.6.3 Markov chains, Hidden Markov models

4.6.4 BLAST, ClustalW

4.6.5 File formats

4.6.6 Phylogenetic analysis

4.6.7 Structure and function prediction

4.6.8 Ontologies

4.6.9 Algorithms and software tools
5. Synthetic biology

1.2.1 Quantitative mass spectroscopy

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( Rating 3 from 2 votes )

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Module description: Quantitative mass spectroscopy (MS) is a technology that allows to quantitate the number of specific protein molecules in studied samples.

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