Max-Planck-Institute Magdeburg

1. MAX-PLANCK-INSTITUT DYNAMIK KOMPLEXER TECHNISCHER SYSTEME MAGDEBURG St. Julians, 26. November 2007 Systems biology – an engineering perspective Andreas Kremling Max-Planck-Institute Magdeburg, Germany Max-Planck-Institute Magdeburg

2. What is `Systems Biology'? • I.Systematic approach, focused not on "individual genes and individual proteins, systems biology, on the other hand, is interested in analyzing whole systems of genes or proteins. What this means is that we use tools for capturing information from many different elements of the overall system. " (L. Hood) • Integration of information from DNA, RNA and protein level

3. What is `Systems Biology'? • II. "Systems biologyis the application of systems and control theory to investigate the organisation and dynamics of genetic pathways." (O. Wolkenhauer) • Analysis of closed control loops on genetic and metabolic level based on a mathematical description

4. What is `Systems Biology'? State-of-the-art Parts list available (genes → mRNA → proteins) Characterized by similarities Characterized by interactions (protein-DNA, protein-protein) → Networks are know → Functions of circuits are often unknown !

5. Outline NetworksSignal transductionControl & Dynamics

6. Networks High number of interacting components Tools and methods from graph theory Connectivity: Distribution of number of links on the nodes of the graph

7. Networks Biochemical networks Power-law distribution: Many nodes with few connections Few nodes with many conncetions → Hubs Jeong et al, Nature, 2000 Barabasi & Albert, Science, 1999

8. Networks Hierarchical structures in transcriptional networks - RNA polymerase is a hub - Effect of retroactiviy

9. Networks Hierarchical structures in transcriptional networks - How is the interaction of various TF organized?

10. Hierarchical control RNA polymerase - ca 5000 molecules - ca 400 binding sites Crp transcription factor - ca 500 molecules - ca 80 binding sites LacI repressor - ca 20 molecules - ca 2-3 binding sites

11. Hierarchical control Defining levels for each TF Transcription efficiency Ψ = Ψ(RNA-P) Ψ ≡ fraction of occupied promoters Transcription efficiency Ψ‘ = Ψ‘(Ψ,Crp,cAMP) Transcription efficiency Ψ‘‘ = Ψ‘‘(Ψ‘,LacI,Allol)

12. Hierarchical control Ψ1 Ψ1 Ψ2 Ψ2 cAMP

13. RNA polymerase Application to carbohydrate uptake in E. coli

14. Computer based modeling with ProMoT

15. Signal transduction Response to external stimuli i. Conversion of a stimulus in an intracellular signal ii. Signal processing iii. Response: synthesis of new proteins

16. output Signal transduction Two-component signal transdcution Active sensor hand on phosphoryl groups Transcription factors binds to DNA and activates protein synthesis Description: Input/output characteristic curve input

17. Signal transduction Functionality of signaling circuits Signal amplification Switch on/off Transient dynamics Oscillating circuits Irreversible decision Crosstalk

18. on off Signal transduction Functionality of signaling circuits Signal amplification Switch on/off Transient dynamics Oscilating circuits Irreversible decision Crosstalk

19. Signal transduction Functionality of signaling circuits Signal amplification Switch on/off Transient dynamics Oscilating circuits Irreversible decision Crosstalk

20. Signal transduction Realization of logic operations (AND, OR, etc) like computational elements

21. Signal transduction Realization of logic operations (AND, OR, etc) like computational elements

22. Signal transduction Continuous response vs. binary logic E. coli measures glycolytic flux in a continuous way

23. Sensor maps the specific growth rate (physiological parameter) directly to the activity of a EIIAP (component of the sensor PTS) PTS 2 operational mode: Non-PTS

24. Signal transduction Continuous response vs. binary logic E. coli measures glycolytic flux in a continuous way

25. Signal transduction Continuous response vs. binary logic E. coli measures glycolytic flux in a continuous way

26. Equation EIIAP µ • Formalization • Flux distribution • Measurement Equation • Operator dµ

27. Measurement device • Robustness EIIAP µ

28. rate pyk uptake rate/ µ PEP FBP uptake rate/ µ Simple model explains the relationship between uptake rates and transcription factor activity!! Feedforward loop guarantees robustness

29. Simple model explains the relationship between uptake rates and transcription factor activity!! EIIAP over growth rate

30. Control & dynamics Feedback leeds to robust behavior !

31. Adaptation precision is very robust !

32. Translation into control circuit reveals integral feedback loop signal ≡ disturbance controller total Xm set point controlled system k1 R Signal is • Differentiated • Filtered

33. Translation into control circuit reveals integral feedback loop • System is controlled in a redundant way • Integral control is frequently applied in technical systems

34. Control & dynamics Graph theory and closed loop systems: application of „monotony“ to cellular systems Check elements of the Jacobian Check graph X time

35. Control & dynamics Graph theory and closed loop systems: application of „monotony“ to cellular systems Open loop has a defined input/ output relationship S(u) X time g-1 Y U

36. Control & dynamics Graph theory and closed loop systems: application of „monotony“ to cellular systems output input

37. Control & dynamics Control engineers expect interesting problems from systems biology: -Strategies for closed loop systems Performance, robustness, hierarchies, etc. - What should be measured ? - In which way we should stimulate ?

38. Summary NetworksSignal transductionControl & Dynamics

39. Cooperation partners MPI: K. Bettenbrock & M. GinkelK. Jahreis & J.W. Lengeler (Osnabrück)K. Jung (Munich)