1. Systems Biology: An (simple) Introduction Arthur Cheung

2. Systems Biology: An Overview, Arthur Cheung 2006 2 Systems Biology P. Bork, Is there biological research beyond Systems Biology? A comparative analysis of terms, www.molecularsystemsbiology.com. www.molecularsystemsbiology.com

3. Systems Biology: An Overview, Arthur Cheung 2006 3 What is systems biology?  Systems biology is the study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life. (Institute of Systems Biology)  Instead of focusing on individual parts, the focus is on a complete system made up of different parts interacting with each other. Cf. Software systems made up of different modules interacting with each other.  Based on the philosophy that the whole is greater than the sum of the parts.  For example, the immune system isn’t made up of one single component but instead a multitude of genes, proteins and external influences.

4. Systems Biology: An Overview, Arthur Cheung 2006 4 What is systems biology?  The idea a systems approach to biology first suggested by Norbert Weiner.  Such approaches not feasible to recently.

5. Systems Biology: An Overview, Arthur Cheung 2006 5 Why systems biology?  From the late 80’s and throughout the 90’s a large influx of biological data largely driven by the human genome project  While significant, the human genome by itself does not tell us how the human body (or at least parts of it) behaves.  The need to interpret the human genome spawned or reinvigorated various directly and indirectly related fields including: Bioinformatics (Computational Biology), data mining, biotechnology, molecular biology… systems biology  Brings understanding of biology to a higher level.

6. Systems Biology: An Overview, Arthur Cheung 2006 6 Why systems biology?  Allows insight as to what each part plays in the whole system  Models from different species can be used to predict behaviour of similar systems in humans which in turn can be applied to develop new medical remedies.

7. Systems Biology: An Overview, Arthur Cheung 2006 7 The “-omics”  The lowest levels of a biological system: genome, transcriptome, proteome and metabolome.  Genomics: study of a whole genome.  Transcriptomics: study of the expression of genes at any given time.  Proteomics: study of proteins.  Metabolomics: study of metabolic interactions within a cell.

8. Systems Biology: An Overview, Arthur Cheung 2006 8 The “-omics” (cont.)  An explanation: At the lowest level, genes can be compared to that of a particular function in a programming language. The genome can be considered a large library of code where a large amount won’t be used and most likely be redundant, not unlike a library of code. At the transcriptomics level we try to explain the functions of the genes, like an API. There are special genes known as homeobox genes that code for proteins known as transcription factors. These controls what genes are coded into proteins, when and how. These are not unlike compilers The proteins can be seen as modular parts of a bigger program that is the cell.

9. Systems Biology: An Overview, Arthur Cheung 2006 9 The “-omics” (cont.) Metabolomics studies the interactions within the cells much like the message passing between functions in a program.

10. Systems Biology: An Overview, Arthur Cheung 2006 10 When things go wrong with homeobox genes

11. Systems Biology: An Overview, Arthur Cheung 2006 11 Levels of abstraction  Currently, the level of abstraction in system’s biology is not set in concrete and can range from the levels studied in the “omics” to the ends of the universe.  Trends are leading towards molecular approach -> Molecular Systems Biology.

12. Systems Biology: An Overview, Arthur Cheung 2006 12 Modelling and Simulation  Initiative directed towards modelling and simulation of biological processes.  Modelling focussed on increasing the depth of understanding.  Simulation focussed on predicting.  Development of tools to aid modelling can aid in understanding of processes.  Development of simulations can allow “dry experiments” to be used as a form of validation which can save time and resources.

13. Systems Biology: An Overview, Arthur Cheung 2006 13 Modelling and Simulation  A unified method for modelling will encourage interoperability between different biological systems with a view to “understand the whole picture”

14. Systems Biology: An Overview, Arthur Cheung 2006 14 Standards  No standards exist for developing models on biological systems.  Current models are developed according to individual tastes and trends within certain fields.  In general current existing models are specific with only their respective field in mind. Development of standards would need to be versatile enough to accommodate different fields.  Standards are required to integrate established existing models in order to develop larger more comprehensive models.

15. Systems Biology: An Overview, Arthur Cheung 2006 15 SBML and CellML  Systems Biology Markup Language and Cell Markup Language  A step towards standardising modelling.  Attempts to develop a method to share models between the multitude of modelling applications currently available.  Both are XML based.  Both are generally supported by most applications, but the purpose of a standardise language is defeated as most applications store important data in application specific annotations.

16. Systems Biology: An Overview, Arthur Cheung 2006 16 SBML  Appears to be favoured in community over CellML  Hierarchical structure as opposed to the modular structure of CellML. However, developments are underway to modularize the language in the next revision  SBML.org claims that over 110 software systems support SBML. These include BioUML, JDesigner and CellDesigner

17. Systems Biology: An Overview, Arthur Cheung 2006 17 Model Repositories  There are several repositories present that contain models of various formats including SBML and CellML. The most notable ones include: BioModels.net KEGG (Kyoto Encyclopedia on Genes and Genomes) CellML.org repository  While these databases are growing, many more systems remain to be indentified and modelled.

18. Systems Biology: An Overview, Arthur Cheung 2006 18 SBML Components in an SBML model (Tools for Bioinformatics)

19. Systems Biology: An Overview, Arthur Cheung 2006 19 Short-comings of SBML  Developers claim to have built SBML based on the principles of UML but it is really more a standard for data exchange rather than a modelling language.  Hierarchical approach is a step away from the modular approach required in systems biology  Too rigid, not flexible enough.  Effectively exchanging data between incompatible applications.

20. Systems Biology: An Overview, Arthur Cheung 2006 20 Short-comings of SBML

21. Systems Biology: An Overview, Arthur Cheung 2006 21

22. Systems Biology: An Overview, Arthur Cheung 2006 22 Adapting Business Modelling Techniques  The similarities in biological systems to those found in business solutions are too big to ignore.  Real modelling languages for biology might be able to be developed adapting the principles found existing business modelling languages such as UML and BPMN.  The i* framework with its agent based properties may have potential in aiding the development of simulation models.

23. Systems Biology: An Overview, Arthur Cheung 2006 23 Role of A.I.  A.I. can be applied especially in the development of simulations as we try to mimic how biological systems “think”.  The same problems found in reasoning about actions (I.e. Frame problem, qualification problem and ramification problem) can be applicable to systems biology.

24. Systems Biology: An Overview, Arthur Cheung 2006 24 Future  Still a maturing field, lots of potential.  While there had been an influx of data, most of that has been at the genomic level.  The field compliments the development of other fields in the lower levels such as the “omics” and molecular biology. As these fields grow so will this.

25. Systems Biology: An Overview, Arthur Cheung 2006 25 Further Reading  N. Weiner, Cybernetics or Control and Communication in the Animal and the Machine (MIT Press, Cambridge, Ma, 1948).  H. Kitano, Foundations of Systems Biology (MIT Press, Cambridge, MA, 2001).  H. Kitano, Systems Biology: A Brief Overview, Systems Biology Volume 295 page 1663-1664.  M.E. Csete and J.C. Doyle, Reverse Engineering of Biological Complexity, Systems Biology Volume 295 page 1664-1669.  P. Bork, Is there biological research beyond Systems Biology? A comparative analysis of terms, www.molecularsystemsbiology.com. www.molecularsystemsbiology.com  Klipp et al., Systems Biology in Practice (Wiley-VCH, Darmstadt, 2005).