1. What contribution can automated reasoning make to e-Science?
Marta Kwiatkowska
School of Computer Science
ARW’05 panel

2. 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 bio-informatics.

3. So it is about a transition from…

4. To this…

5. 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

6. 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, …
WiFi sales grow exponentially
Bluetooth new technology
Intereference claimed in
But not in
Use the same frequency range, but b uses channelisation of range (sends large data packets)
BlueTooth sends small packets and uses frequency hopping (more frequent than WiFi), hence BlueTooth can react
and bring WiFi down
2.4Gz range used is unregulated, microwaves can emit noise in that frequency

7. 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!

8. 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

9. 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

10. What is 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.

11. 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