symbolic systems technology group - sri international · 2011-09-14 · – polymorphic network...
TRANSCRIPT
Symbolic Systems Technology Group
Using formal systems and logic to understand• How things work• Why things don’t work• ....
Team (alphabetical order)
• Grit Denker
• Daniel Elenius
• Rukman Senyake
• Ashish Gehani
• Steven Eker
• Ian Mason
• Anupama Panikkar (SV)
• Andy Poggio
• Malabika Sarker (Oklahoma)
• Mark Oliver Stehr
• Minyoung Kim
Projects (in no particular order)
• Maude Development
• Cyber-physical Systems (CPS)
• Circuit Analysis w Superresolved Infrared Optics (CASIO)
• Content Based Mobile Edge Networks (CBMEN)
• Biology
A smattering of each
Maude Development (Steven Eker)
w Meseguer@UIUC
Distributed/Concurrent Maude (NSF)
• many optimizations
• new stack based execution engine
• concurrent search
• concurrent objects (eventually)
Unification (NSA)
• Unification is the problem of finding a substitution such that
• t1 = u1 ... tn = un (in an equational theory)
• Free theory easy/finite
• Generally may be infinite, undecidable
• AC, ACU doable but challenging
• Key application narrowing
• rewriting with unification rather than matching
• backwards narrowing -- finds all initial states leading to target pattern
• Maude NPA (crypto protocol attacks)
• Document Logic (business process risks)
• Border Gateway Protocol Analysis (Anduo Wang, Boon Tau Loo, Andre Scedrov UPenn)
• Modeling Regulations for Clinical Trials (Andre Scedrov (UPenn), Vivek Nigam (Munich), ....
Some Maude Applications
FDA regulations Requirements implicit in trial protocolRuntime monitoringAdaptive plan generation Post trial inspection
Internet Routing Systems
R border gateway router
internal router
R1
AS1
R4
R5
B
AS3
eBGP
R2 R3
A
AS2
IGP: Interior Gateway Protocol. Example: OSPF, RIP
iBGP
IGP
table at R1: dest next hop
A R2 B R2
Share connectivity information across ASes
34
BGP: Border gateway protocol. iBGP within AS eBGP across AS
eBGP
Cyber-physical Systems ProjectsNCPS (NSF), Fractionated (ONR), CYPRESS (NSF) (PDL,MOS, MK, IAM, AP, GD, ...)
http://ncps.csl.sri.com
Challenges and Objective
• Networked Cyber-Physical Systems (NCPS) consist of networked hard- and software components embedded in the physical world and interacting with it through sensors and actuators.
• Increasingly large numbers of heterogeneous and potentially resource-constrained and unreliable components
• Need to work in challenging environments with unreliable/intermittent connectivity
• Need to operate in the entire spectrum between autonomy and cooperation
• Objective: General principles and tools for building robust, effective NCPS using individual cyber-physical devices as building blocks.
NCPS Approach and Contributions
• Partially ordered knowledge-sharing model for loosely coupled distributed computing
• Implemented in new application framework for NCPS
• Distributed logic for declarative control
• Simulation case study:
• distributed surveillance, collaborating team of mobile robots
Partially ordered knowledge sharing
• Builds on DTN work,,minimal assumptions on network connectivity (can be very unreliable)
• Locally each cyber-node uses an event-based model with local time and may have attached cyber-physical devices
• Communication via knowlegde sharing rather than message passing
• Partial order allows the network to replace obsolete or subsumed knowledge
• Global consistency is not enforced (impossible in disruptive environments)
• Avoids strong non-implementable primitives, e.g. transactions
Distributed Logic• Key Problem
• Traditional logics are not designed for distributed reasoning
• Logics are traditionally closed systems, i.e. not interactive
• Knowledge is transparently shared
• Knowledge = Facts + Goals
• Facts can represent observations
• Goals can represent control objectives
• Distributed logical framework
• Integrates forward and backward reasoning
• Partial order is essential part of the distributed logic
Distributed derivation
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Cyber-Application Framework Architecture• Cyber-framework implements partially ordered knowledge-sharing model
• Logical framework is implemented as a cyber-application
• Applications cannot distinguish between simulation and reality
Principles and Foundations for Fractionated NCPS
• Problem: Current models are too abstract by not taking into account fundamental physical limitations and hence are not efficiently implementable or scalable
• Once explicitly represented, physical limitations can be overcome to some degree (e.g. probabilistically)
• Diversification, redundancy, and randomization can offset many physical limitations
• Distribution is a source of redundancy and diversification and can be turned from an obstacle into an advantage
STEM: A Stochastic Task Execution Model for Fractionated Software
• Fractionated software system composed of nodes with partial connectivity (many, small components -- fracments).
• A goal is posted which is comprised of numerous subgoals
• Stochastically, a free node will take on a new subgoal (if capable)
• Correctness? Measure goal accomplishment quality,
• e.g. at least 90% subgoal coverage at 95% confidence level
CYPRESS Cyber-Physical RESilience and Sustainability
Dependability Techniques for Instrumented Cyber-Physical Spaces
Grit Denker, Nikil Dutt, Minyoung Kim, Sharad Mehrotra, Mark-Oliver Stehr, Carolyn Talcott, Nalini Venkatasubramanian
Leila Jalali, Zhijing Qin, Ronen Vaisenberg, Xiujuan Yi, Liyan Zhang
UCI SRI&
Goals & Approach
Instrumented UCI Campus
• Principles / techniques for infrastructure and data resilience
• Formal models and cross-layer tuning
• Middleware services
• Pervasive space testbed
CASIO: Circuit Analysis w Superresolved Infrared Optics(DARPA) ESD,PSD,CSL,AI, Princeton, Berkeley, ....
The CASIO challenge (DARPA hard^2)• Identify potential malicious regions in an ASIC
• Chip must work after analysis
• Aim for 45 nm resolution (currently 150ish)
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ENCODERS: Content Based Mobile Edge Network(DARPA) (MOS and many others, in negotiation)
© 2011 SRI International
CBMEN ENCODERS Edge Networking with Content-Oriented Declarative Enhanced Routing and Storage
1
• Declarative/Intentional Content-Based Networking: – Vision of network as a distributed cache of content:
users/applications state what content is needed, without constraining how to obtain it !
– Enable fine-grained expression of warfighters’ needs: transition from syntactic (name matching) to semantic networking (predicate resolution)
– Unified symmetric paradigm where requesters and originators of content express intent (i.e. query and scope)
– Multidimensional awareness: users, groups, location, mobility, organization, social network, content/interest
• Disruption-Tolerant and Attack-Resilient Foundation: – Push and pull dissemination for increased robustness
– Opportunistic caching and proactive replication
– Polymorphic network layer (morphs like a Chameleon)
– Spreading trust/responsibility through network coding
– Decentralized multiauthority security for high availability
– Fine-grained access control and metadata/query privacy
• Impact: Focus on the Mission not on the Network ! – Warfighter gets the right information at the right time – Eliminate content flooding and information overload
(for users and network) through fine-grained queries, efficient algorithms and context/interest modeling
– Zero configuration: self-organizing combination of structured (distributed hashing) and unstructured (information diffusion) approaches
Tasks
1. Naming
2. Distribution
3. Managing
4. Securing
5. Assessment
6. Integration
Declarative Networking
Efficient Matching Technology
Disruption-Tolerant Networking
MANET and Mesh Routing
Cross-Layer Optimization
Decentralized Attribute-Based Encryption
Evaluation under Encryption
Campus-Wide Wireless Testbed
Policy-Based Networking
Information Management for Networking
Dynamic Traffic Shaping
Interest Modeling
Virtual Interest Groups
Traffic Monitoring
Agent Based Simulation
Systems Engineering
Content-Based Networking
Automatic Incremental Routing
Disruption Tolerant Networking
Network Coding for Caching
Content-Based Networking in Tactical MANETs
MANET and Inter-MANET Routing
Network Coding for Distribution
Situation-Aware Trust
Dynamic Attribute-Based Encryption
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Biology
•Pathway Logic•Combining Pathways and Cheminformatics •BioMarkers
Pathway Logic (PL)
http:pl.csl.sri.com
Executable models of cellular processes (Maude + Petri Nets)
• Queryable formal knowledge base (DKB) of experimental findings
• Executable network of reaction rules (inferred from DKB)
• Model ~ initial state (experimental setup) + relevant rules
• Analysis using the PL Assistant (PLA)
• Find subnets (proofs, executions)
• Find pathways (proofs, executions)
• In silico knocks
• Comparing networks
• Following connections
Signaling response to Egf (AMK)
Two ways to activate Erk
Datum query
RapA-inhib-CLc
81
MurAA-CLc
45
MurAA-degraded-CLc
CiaRH-CLc
142140
40
MecA-degraded-CLc
101
Mpr-gene-on-CLc
99
Bpr-gene-on-CLc
97
NprE-gene-on-CLc
181
SpoIVFB-inhib-CLm
RapA-CLc
80
BofA-CLm
103
NprB-gene-on-CLc
SpoIVFB-CLm
182180
proPhrA-XOut
112114
PhrA-XOut CSF-XOut
115
PhrG-CLc
120
CtsR-degraded-CLc
43
DegS-CLc
122
DegU-phos-CLcCtsR-CLc
68 52
50
51
FtsH-gene-downreg-CLc
RapE-inhib-CLc
PhrE-CLc
193
30
ClpE:ClpP-CLcRapF-inhib-CLc
PhrF-CLc
194
ClpE-CLc
64
RapA-gene-CLc
155
RapA-gene-downreg-CLc
31
ClpC:ClpP-CLc
RapG-CLc
121
33
109
Epr-gene-on-CLc
RapG-inhib-CLc
ClpP-gene-on-CLc
SpoJ-gene-on-CLc
SpaA-CLw
23 22
Heme:IsdG-CLc
18
113
PhrA-CLc
SigmaA-CLc
61 606291
58
SipV-gene-downreg-CLc
ClpC-gene-on-CLc
35394142 38 37189
44
47 48 49 46
17
Fe3+-CLc
124
SipT-gene-on-CLc
56
SipT-gene-downreg-CLc
Heme:IsdI-CLc
RapE-CLc
79
HtrA-gene-on-CLc
SipV-gene-CLc
57
SipS-gene-downreg-CLc
Abh-CLc
126
SipS-gene-on-CLc
Abh-gene-on-CLc
136
LytE-degraded-CLw
173
LytE-CLw
174 171
LytF-degraded-CLw
59
Lsp-gene-downreg-CLc pre-AmyQ-CLm
130129 128
FtsZ-CLc
158160 163 161 159 162
LytF-CLw
172
Lsp-gene-CLc
167
LonB-gene-on-CLc
SigmaB-inhib-CLcFtsZ-inhib-CLc
165
SigmaH-degraded-CLc
29
ClpP:ClpX-CLc
36
164
ClpX-CLc
54
EzrA-CLm
157
LonA-CLc
166
SigmaG-inhib-CLc
93
Spo0A-CLc
SigmaB-CLc
63 67 176175178179
89
Spo0B-phos-CLc
Spo0F-CLc
90
Spo0B-CLcSpo0A-phos-CLc
ClpX-gene-downreg-CLc
55
73
SpoIIAA-gene-on-CLc
ClpX-gene-CLc
SpaA-anchored-CLw
24
SrtF-CLm
EzrA-degraded-CLm
SigmaW-CLc
2527 32
FosB-gene-on-CLcFtsH-gene-on-CLc
SecY-CLm
71
MecA-CLcSecY-degraded-CLm
pre-PrsA-CLm
131
ComA-CLc
146
75
Spo0A-gene-on-CLc
PrsA-CLm
138
156
CodW:CodX-CLc
Spo0E-degraded-CLc
SrfA-gene-on-CLc
AmyQ-XOut
Lsp-CLm
105
Vpr-gene-on-CLc
SigmaH-CLc
pbpE-gene-on-CLc
107
WprA-gene-on-CLc
SigmaH-act-CLc
CodW-CLcCodX-CLc
72
SpoIIE-gene-on-CLc
186
SpoIIAB-gene-on-CLc
190
SpoIIAA-CLc
SpoIIAA-phos-CLc
183
SpoIVFA-cleaved-CLm
SpoIVB-CLc
Vpr-XOut
116
Vpr-gene-CLc
147
SrfA-gene-CLc
SrfA-CLc
106
145
143
SpoJ-gene-CLcSpoJ-CLc
108
SigmaK-CLc
pro-SigmaK-CLm
Heme:IsdA-anchored-CLw
13
DegU-degraded-CLc
YwbN-gene-CLc
133
134
YwbN-CLc
KinE-CLc
88
28
PBP4-CLmWprA-gene-CLc
KipI-inhib-CLc
WprA-CLw
Spo0F-phos-CLc
YwbN-gene-on-CLc
84
KipA-CLc
Lipid-II-bound-CLw
Lipid-II-CLm
21
SigmaG-CLc
pbpE-gene-CLc
70
9
Heme:IsdC-anchored-CLw
IsdH-anchored-CLw
ComK-degraded-CLc
ComS-CLc
IsdA-anchored-CLw
10
IsdB-anchored-CLw
11
7
ComS-degraded-CLc
AbrB-gene-downreg-CLc
111 95 96
135
AbrB-gene-CLc
AbrB-CLc
119
AprE-gene-downreg-CLc ClpC-gene-downreg-CLc ClpP-gene-downreg-CLc
McsA-CLc
53
Spx-CLc
McsB-CLc
Spx-degraded-CLc
SigmaX-CLc
132
195
SinR-CLc
118123
AprE-gene-on-CLc
proCSF-XOut
Parks-nucleotide-CLc
19
Undecaprenyl-phosphate-CLm
Lipid-I-CLm
20
MraY-CLm
SpaB-CLwSpaC-CLw
SpaABC-pilus-Sig
Hpr-CLc
117
RapF-CLc
SpoIVFA-CLm
184
151
ComK-gene-on-CLc
CtpB-CLc
153
DegU-CLc
150
ComK-gene-downreg-CLc
110
Epr-XOut Epr-gene-CLc
26
FosB-CLcFosB-gene-CLc
69
FtsH-CLm FtsH-gene-CLc
94
ClpC-gene-CLc
66
ClpC-CLc
65
ClpP-CLcClpP-gene-CLc
152
ComK-CLc
ComK-gene-CLc
100
Bpr-XOutBpr-gene-CLc
77
AprE-XOut AprE-gene-CLc
Abh-gene-CLc
125 127
SipS-gene-CLc SipS-CLmSipT-CLmSipT-gene-CLc
SpeB-gene-on-CLc
170
proSpeB-XOutSpeB-gene-CLc
76
137169
144
RapD-gene-CLcRapD-CLc
154
SigmaF-CLc
188
SigmaF-gene-on-CLc
192
SigmaF-gene-CLc
SigmaF-act-CLc
191
Sporulation-Sig
104
NprB-gene-CLc NprB-XOut
102
Mpr-XOutMpr-gene-CLc
RapD-gene-on-CLc
148 149
NprE-gene-CLc
98
NprE-XOut
LonB-CLc
168
HtrA-CLm
139
141
HtrA-gene-CLc
LonB-gene-CLc
12
Heme:IsdE-CLm
1615
IsdI-CLcIsdG-CLc
IsdH-CLw
3
814
IsdC-anchored-CLw
SigmaW-act-CLc RsiW-degraded-CLm
KinA-inhib-CLc
KipI-CLc
83
KinA-CLc
82
IsdD-CLm IsdF-CLm
YwlE-CLc
ClpC-inhib-CLc YpbH-CLc
UDP-GlcNAc-CLc
1
SrtA-CLm
2
MurG-CLm
IsdB-CLw
IsdC-CLw
4
SrtB-CLm IsdE-CLm
SpoIIAB-degraded-CLc
YjbH-CLc
74
SpoIIE-CLmSpoIIE-gene-CLc
187
SpoIIAB-gene-CLc SpoIIAB-CLc
185
SpoIIAA-gene-CLc
RsiW-CLm
RsbW:SigmaB-CLc
RsiW:SigmaW-CLc
92
Spo0E-gene-CLcSpo0E-CLc
Spo0E-gene-on-CLc
Spo0A-gene-CLc
KinB-CLm
85
KinC-CLc
86
78
87
KinD-CLc
PrsW-CLmSigmaF:SpoIIAB-CLc
SepF-CLc
FtsZ:SepF-CLc
RsbU-CLc
SigmaB-act-CLc
RsbT-CLc RsbW-CLc
177
RsbW:RsbV-CLc
RsbV-CLc
YlmF-CLc
FtsZ:SpoIIE-CLm FtsZ:ZapA-CLc
ZapA-CLc
FtsZ:YlmF-CLcDegU-inhib-CLc
RapD-gene-downreg-CLc
RsbS-CLc
FtsZ:FtsA-CLc
RghR-CLcRopB-CLc
pbpE-gene-downreg-CLc
FtsL-degraded-CLm
FtsL-CLm
34
RasP-CLm
FtsA-CLc
StreptolysinS-inhib-XOut
6
Heme:IsdH-anchored-CLw
StreptolysinS-XOut RapC-gene-CLc
RapF-gene-downreg-CLc
RapF-gene-CLc
RapC-gene-downreg-CLcSrfA-gene-downreg-CLc
IsdA-CLw
IlvB-degraded-CLc
IlvB-CLc
SpeB-XOut
GlmS-CLc
GlmS-degraded-CLcPurF-degraded-CLc
PurF-CLc PyrB-CLc
PyrB-degraded-CLc
RapB-CLc
5
Heme:IsdB-anchored-CLw
Heme-XOut
initial states
possible reachable states
transition/rules
consumed/produced
enzymatic relation
106
FtsH-CLm
Spo0A-gene-on-CLc
75
93
Spo0E-degraded-CLc
90
Spo0B-CLc
Spo0B-phos-CLc
Spo0E-CLc
76
Spo0A-CLc
Spo0A-gene-CLc
Vpr-XOut
Vpr-gene-CLc
Vpr-gene-on-CLc
103 97
Epr-gene-on-CLc
99
Epr-XOut
110
Epr-gene-CLc
Spo0A-phos-CLc
NprE-gene-CLcNprE-
XOut
109
NprE-gene-on-CLc
104
NprB-gene-CLc
Bpr-gene-CLc
100
Bpr-gene-on-CLc
Bpr-XOut
98
Mpr-gene-on-CLc
101105
Mpr-gene-CLc
102
NprB-gene-on-CLc
NprB-XOut Mpr-
XOut
Protease interaction network (Gram Positive Bacteria) (Anupama) What regulates a protease,
what does it regulate
What happens if you knockout FtsH?
Multiple synthesis routes
TB-mycolic-acid synthesis (Malabika)
Combining Cheminformatics Methods and Pathway AnalysisNIH STTR with Collaborative Drug Design (CDD)
• Looking for TB drug candidates
• Phase I: Predicted 30 candidate drugs, tested 20, 3 had activity
• Phase II submitted.
Point‐Of‐Care Biological Assays for Determining Absorbed Ionizing Radiation Dose (Biodosimetry)(BARDA) David Cooper PI -- PSD,BSD,ESD,CSL ....
• Objective: Biomarkers of irradiation for triage
• Metric: find panel of proteins that can distinguish >=6gy from <6gy in mouse
• Experiments:
• Mouse subjected to irradiation at different levels
• Human (cancer patients undergoing radiotherapy)
• Data: Mass Spectra, Elisa of plasma samples
• Computational analysis:
• Univariate statistics, multivariate linear classifiers, SVM
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The end! (The beginning?)