Table 1. Suggested basic core areas for Systems Biology education.
| Masters level programme in Systems Biology |
|---|
| Suggested basic core areas for Systems Biology Education |
| Mathematical and computational framework |
| Linear algebra—as relevant for stoichiometric modelling, genome-scale metabolic reconstructions and models; stability analysis—as relevant for stability of genetic circuits |
| Nonlinear dynamics—as relevant for signalling cascades, kinetic metabolic models, pattern formation, enzyme dynamics, cell differentiation and cellular decision-making |
| Stochastic modelling—as relevant for gene expression circuits, cell motility, ion channels, protein–protein interaction, diffusion and signal transduction pathways |
| Spatial modelling—as relevant for morphogenesis, cell communication, tissue formation, crowding, biofilms |
| Control theory—as relevant for design and analysis of metabolic pathways, gene expression circuits, pharmacokinetics and pharmacodynamics |
| Discrete and logic models—as relevant for genetic networks, signalling networks and cellular differentiation |
| Complex network analysis—as relevant for metabolic networks, protein–protein interaction networks, gene–disease networks |
| Optimisation—as relevant for metabolic engineering, genome-scale metabolic models, parameter estimation in signal transduction networks and reverse engineering |
| Networks and processes of life |
| Metabolic networks—as relevant for physiology, human diseases and metabolic engineering; fluxes, kinetics, rates and stoichiometry |
| Signalling networks—as relevant for information processing and engineering of cells and organisms; dynamics, feedbacks and adaptation |
| Gene regulation networks—as relevant for cellular decision making and differentiation, bi- and multistability phenomena as well as circuit design in Synthetic Biology |
| Cell and population networks—as relevant for development, pattern formation, disease (especially cancer), infection and ecology; cell variability phenomena |
| Genetic networks—as relevant for multifactorial traits and diseases, epistasis, as well as genome-wide association studies and meta-analysis thereof |
| Protein (and other types of) interaction networks—as relevant for complex inference and functional modules |
| Oscillatory processes—as relevant for cell cycle, circadian rhythms, metabolic oscillations and other processes where timing regulates states of functional activity/inactivity |
| Scientific programming |
| Programming: e.g., Matlab or Mathematica, Python, Perl, Java, R, C and C++ |
| Tools for genome-scale metabolic models, kinetic modelling (stochastic and deterministic), network analysis: e.g., Copasi, Cytoscape, OptFlux, XPP-Auto and COBRA |
| Standards: e.g., SBML, SBGN and MIRIAM |
| Methods and software tools for biological data visualisation |
| Bioinformatics and statistics |
| Fundamentals of DNA, RNA and protein sequence analysis |
| Integrative bioinformatics—interoperability, ontologies, semantics, databases and standards |
| Genomics of communities, meta-genomics of populations of cells and organisms |
| Molecular evolution, phylogeny and population genetics |
| Complex genotype–phenotype relationships—genome-wide association studies for human diseases and desirable traits in plants, animals and microbes |
| Data analysis—standard algorithms, basics of supervised and unsupervised statistical learning, data integration |
| Statistical inference—use of appropriate statistical tests, reverse engineering |
| Machine learning—clustering; neural networks, random forest |
| Experimental design, measurement, analysis, interpretation and knowledge generation |
| Quantitative imaging/microscopy—single-cell analysis using flow cytometry or microscopy or spectroscopy, image analysis and quantification, cell variability analysis |
| Global and high-throughput data—genetic, transcriptome, proteome and metabolome |
| Biochemical in vitro and in vivo assays for quantitative properties of proteins, reactions and interactions |
| Handling and culturing of cells and organisms |
| Quantitative and time-resolved experimental methods at low throughput—levels and modifications of biomolecules, especially proteins and RNAs |
| Principles of system perturbations—genetic, experimental, pharmacological perturbations to test, challenge and control systems |
| Principles of systems engineering—in vitro systems design, in vivo implementation employing genetic and biological engineering as well as evolutionary approaches, testing and optimisation of designed circuits |
(i) Mathematical and computational framework; (ii) networks and processes of life; (iii) scientific programming, (iv) bioinformatics and statistics; and (iv) experimental design, measurement, analysis, interpretation and knowledge generation. Each area is broken down into specific topics and the relevance in which it should be taught.