Systems Biology: An simple Introduction

1
Systems Biology An (simple) Introduction

• Arthur Cheung

2
Systems Biology
P. Bork, Is there biological research beyond
Systems Biology? A comparative analysis of terms,
www.molecularsystemsbiology.com.
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 isnt made up of
  one single component but instead a multitude of
  genes, proteins and external influences.

4
What is systems biology?

• The idea a systems approach to biology first
  suggested by Norbert Weiner.
• Such approaches not feasible to recently.

5
Why systems biology?

• From the late 80s and throughout the 90s 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
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
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
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 wont 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
The -omics (cont.)

• Metabolomics studies the interactions within the
  cells much like the message passing between
  functions in a program.

10
When things go wrong with homeobox genes
11
Levels of abstraction

• Currently, the level of abstraction in systems
  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 -gt
  Molecular Systems Biology.

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
Modelling and Simulation

• A unified method for modelling will encourage
  interoperability between different biological
  systems with a view to understand the whole
  picture

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
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
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
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
SBML
Components in an SBML model (Tools for
Bioinformatics)
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
Short-comings of SBML
21
(No Transcript)
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
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
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
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.
• Klipp et al., Systems Biology in Practice
  (Wiley-VCH, Darmstadt, 2005).