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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© 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?)