1. Modeling and Static Analysis of
Complex Biological Systems
Débora Schuch da Rosa
University of Trento

2. Context
• 20th century:
– century of gene - starting with the rediscovery of
Mendel's laws on inheritance, it ended with the
sequencing of the human genome.
• 21st century:
– century of information society
– major challenge: novel computing paradigms for
improved processing of human and biological data

3. Modeling biological systems
• is a challenge for computer science.
• complexity exceeds that of computer systems by
orders of magnitude.
• models of dynamics needed to organize the huge
amount of data available in the post-genomic era.
• mapping structure to function

4. Problem: state space explosion
• huge size of the representation
• investigation of properties of interest grows
exponentially in the size of the program
Solution: static analysis
• classical alternative to dynamic analysis

5. Collaborations and references
• Magali Roux-Rouquié •Control flow analysis in
CNRS,Université Marie BioAmbients, Proceedings
Curie, Laboratoire BioConcur 2003
d’Informatique de Paris 6
•Static analysis for systems
• Corrado Priami
biology, Proceedings WISICT
Microsoft-Research- 2004
University of Trento
Centre for Computational •Ten top reasons for systems
and Systems Biology biology to get into model-
• F.&H.Nielson, DTU, driven engineering,
Copenhagen Proceedings GaMMa2006

6. Outline
1. Static Analysis and the Succinct Solver
2. Language Definition: Star Ambients
3. Methodology
4. Application in Systems Biology
5. Model-Driven Engineering
6. Automatic Translation:
diagrams to formal language
1. Related work
2. Conclusions and Future work

7. 1. Static analysis
• static extraction of complex information about the
dynamic behavior of programs by:
• systematic inspecting the program text
• instead of program execution/ simulation
• origin:
• compiler optimization, to handle large programs
• validation of safety and security properties of
programs and system

8. Benefits & drawbacks
• The information • For most interesting
extracted from a properties it is
program is impossible to obtain
guaranteed to be a exact information
correct description of • thus static analysis is
the behavior of the typically
program. approximative.

9. Approximations
universe
exact answer static analysis res
The exact word Over-approximation
Under-approximation Unacceptable situation

10. Under-approximation
When we have an under-approximation to the exact behavior of a program
we can guarantee the certain events will indeed happen
– namely those included in the analysis result.
exact answer
universe
under-approx.

11. Over-approximation
universe
exact answer
static analysis
result
When we have an over-approximation to the exact behavior of a program
we can guarantee the certain events will never happen
– namely those not included in the analysis result.

12. Succinct Solver
• implemented in SML thus formally featured with
modular structures, continuation and
memoryzations.
• Control Flow Analysis – polynomial time

13. 2. Star Ambients: motivation
• problems in static analysis in BioAmbients:
• kill capability
• acid capability
• duplicate capability
• divide capability
• difficulties in creating a quantitative version of the calculus
• not present in Star Ambients.

14. Star ambients: characteristics
• free domain formal language for global
computing
• messages are signals
• ambients are processes,
• ambients move using special movement capabilities
• operators easily capture dynamics
• coding methodology
• check properties of complex systems
• static analysis via succinct solver

15. Star Ambients: syntax

16. Closure conditions

17. Reduction rule
Reduction rule:
[enter n.P | Q] | [accept n.R | S]  [[P | Q] | R | S] ] Red In
µ µ
µ2
µ1 µ 2
µ1
enter µ’.P | Q accept µ’.R | S  P|Q |R|S
I (*, enter  ' )
  1 ,  2 ,  : I (,  1 )  I ( ,  2 ) 
I (  1 , enter  ' )  I (  2 , accept  ' )
 I ( 2 ,  1 ) 
D (  1 , enter  ' )  D (  2 , accept  ' )

18. Why approximative results?
• We have studied the two basic capabilities of
the calculus – communication and movement
• We have detected when the Succinct Solver
loses control of the flow of the information

19. Example Movement capability

20. 3. Methodology

21. Mechanism for Safe Movement

22. Static Analysis (Star Ambients
+ mechanisms)
a) b)
over-approx. over-approx.
Over-approximation (inexact answers)
c) d) The Universe
=
over-approx. exact answers
The exact world to the problem

23. A language and a
tool for diverse analysis
• Pathway and reachability
» 6 mechanisms
• Heredity
» 12 mechanisms
• Inverse heredity
» 12 mechanisms
• Learning In total, we offered 50
» 12 mechanisms mechanisms, that would
be added automatically for
the Star Ambients codes

24. 4. Applications in Systems Biology
We covered a wide range of biomolecular mechanisms:
• covalent binding • transport
• proteolytic cleavage • state combination connectors
• stoichiometric conversion • degradation
• stimulation • non-covalent binding
• transcriptional activation • inhibition

25. Covalent modification
Cleavage of covalent bound
Enzimatic stimulation of a reaction

26. Proteolitic cleavage
Stoichiometric conversion

27. General symbol for stimulation
Transcriptional activation

28. Transport
State-combination connectors

29. Degradation products (garbage collection!)
Non-covalent binding

30. Asymetric binding

31. Multimolecular complex

32. Homodimer formation

33. 5. Model driven engineering
• conceptual convergence:
– towards a system view
– complexity of design
– context awareness
– star-abilities
– modeling at the heart
– computational evaluation
– models integration
– domain specific modeling language
– biological systems as engineering systems

34. 6. Automatic translation
• hide formal details to the designer
• extraction of process algebra specifications from
UML diagrams
• step towards the current use of formal methods
in the practice of software development.

35. Knowledge hierarchies
• towards extraction of biological information
registered from public databases and their
integration into SB-UML framework notably in
terms of hierarchies.
• UML class hierarchies:
• aggregation
• composition
• outputs of SB-UML automatic code generator as
input for static analysis based on Star Ambients.

36. 7. Related work: differences
Nielson & Nielson & Pillegard: Our work:
• implementation of the • keep the use of the
succinct solver, aims: simplest implementation
• in order to get more of the succinct solver
powerful analysis for • created a specific
BioAmbients codes, language for running on
• towards the precision in the the succinct solver,
analysis results. • aiming also to obtain
precise analysis results.

37. Advantages of our approach
• Star Ambients, is a free-domain formal
language
• delivers a methodology of programming
• precise analysis of models at minimal
computational cost

38. 8 Conlusion: inovative
aspects 6) first approach based on model-
driven engineering using
metamodeling in Systems
1) first approach: the use of static Biology
analysis and Systems Biology
7) automatic translation of class
2) Star Ambients formal language hierarchies in Star Ambients
This translation is XMI based,
3) methodology for programming in following the Object
Star Ambients intrinsically related Management Group (OMG)
to static analysis outcomes standards
4) suggested implementations for 8) new programming paradigm -
discovering knowledge in an data and programs are not
optimized way and for facilitating disjoined: data carrying
the sharing of static analysis executable codes
results through the web
9) precision in the analysis results
5) two Star Ambients mechanisms without increasing the time
had their origin in the complexity of the tool created
understanding of principals of to this end.
modeling protein interaction

39. Future work:
1) Use of the metamodel- 2) Improvement of the
framework for confirming a automatic translation: state
method that: diagrams to Star Ambients
• describes incrementally any 3) Association of quantitative
biological system at different analysis to Star Ambients
levels of abstraction,
4) Application of Star
• formalizes the experimental Ambients for other domain
observations and knowledge, specific problems
and considered intractable
• transforms models into coded
representation which will lead
to model-based testing with
formal tools.

40. Before Star Ambients
• no adequate formalism for complex problems
• such that functional compositional constructions
of systems
• could be dynamically evaluated in polynomial
time.

41. Thank you!
Questions?