What contribution can automated
reasoning make to e-Science?
Marta Kwiatkowska
School of Computer Science
www.cs.bham.ac.uk/~mzk
ARW’05 panel
Let’s begin with definitions
Science:
 a method of learning about the physical universe by applying
the principles of the scientific method, which includes making
empirical observations, proposing hypotheses to explain those
observations, and testing those hypotheses in valid and
reliable ways; also refers to the organized body of knowledge
that results from scientific study
e-Science
 computationally intensive science. It is also the type of
science that is carried out in highly distributed network
environments, or science that uses immense data sets that
require grid computing. Examples of this include social
simulations, particle physics, earth sciences and bioinformatics.
So it is about a transition from…
To this…
What enables e-Science?
 Data sharing: distribution, query searches, curation
– Distributed databases
– Web-services
– Ontologies
 Collaboration: coordination, concurrency control
– Collaborative protocols
– Workflow
– Web-service orchestration
 Compute power: scheduling, , load-balancing, performance
– Computer clusters
– Campus Grids
– Large-scale grids
The e-Scientist of the future
Will this work?
The Internet
Remote access to high-performance computers, via Internet
Remote access to visualisation facilities, via Internet
Computational steering from anywhere, via PDA
Visualisation, on your laptop
Fast, online, accurate, …
The pi-calculus example
 Biztalk
– Based on pi-calculus
 Verification of security policies
– Use ProVerif, pi-calculus tool
 Biological processes
– Stochastic pi-calulcus
 What is special about pi-calculus?
– About processes and dynamics
– Formal semantics, rigour
– Matches the software/systems trends: dynamic, mobile,
interacting, reconfigurable
– Automated tools!
Contribution from automated reasoning
 Computer systems-related
– More of what is already being done – databases, distributed
systems, collaborative environments – but larger scale!
– Distributed coordination, concurrency control, databases,
performance, etc
– New challenges
 Science-related
– Rigorous computational foundation for modelling process
dynamics, e.g. biological systems
– Study of fundamental principles
– Modelling – process calculi, automata, logics
– Analysis, simulation, model checking,
– Theory and tools
Modelling and Analysis
 Iterative cycle of
– Hypothesis forming, modelling, analysis
– Experimental validation, feedback
 Methods of analysis
–
–
–
–
Simulation
Automated verification, e.g. model checking
Probabilistic verification
Formal reasoning
 Key goals
– Realisation, when model consistently produces outputs that
cannot be falsified by biological experiment
– In-silico prediction of organism’s response
– Automation of the analysis process
What is computer science?
 Computer science
The systematic study of computing systems and
computation. The body of knowledge resulting from
this discipline contains theories for understanding
computing systems and methods; design
methodology, algorithms, and tools; methods for the
testing of concepts; methods of analysis and
verification; and knowledge representation and
implementation.
Computer Science contribution
 Formal languages and models
– Principles and interaction primitives specific for biological
processes
– Hybrid models for continuous and discrete dynamics
– Reasoning frameworks to establish important properties
– Control-theoretic techniques for robustness
 Automation of analysis: research leading to the tools
– Efficient algorithms, for bioinformatics, analysis
– Quantitative and qualitative model checking
– Grid computing, e-Science
 Scalability
– Model reductions, abstraction
– Hierachical decomposition
– Compositionality