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![Approach Overview
#modeh inhib(+e, $m, $d).
#modeb nutr(+e, $m, ext).
#example not growth(2, 1).
modes
inhib(V1, 10, 1) :-
nutr(V1, "C08", ext),
nutr(V1, "C06", ext), exp (V1).
hypotheses
evidences
cytosol
C00013
C00000
C00006
C00005
C00119
2.4.2.18
C00254
5.4.99.5
C00251
C01302
4.2.1.10
1.3.1.13
2.5.1.54
C00011
C00108
4.1.1.48
C00001
4.2.1.11
C00279
C00631
I00279
C00279
1.1.1.25
C04302
5.3.1.24
C02637
C02652
?
C01269 C03175
4.2.3.5
C00009 2.5.1.19
C00074
C00944
C04691
I00631
C00631
start
4.2.3.4
C00493
2.7.1.71
C00008
C00002
react(5,
[C02], [C05]).
meta(C05).
obs_growth(2,1).
in_comp(Exp,
Meta,Comp,
Day) :-
s_comp(
Meta,Comp).
background
C00166
2.6.1.7
C00004
medium
I00079
I00082
C00078
C00079
C00082
end
C00003
4.2.1.51
C01179
4.1.3.27
C00025
C00014
C00064
C00022
C03506
4.2.1.20
C00463
C00065
C00026
C00078
C00661
C00079
C00082
I00078
XHAIL
14-17/09/2014 ILP '14, Nancy 5](https://image.slidesharecdn.com/ilp14-pdf-140916103526-phpapp01/85/ILP-2014-Nonmonotonic-Learning-in-Large-Biological-Network-5-320.jpg)
![Approach Overview
#modeh inhib(+e, $m, $d)
#modeb nutr(+e, $m, ext)
#example not growth(2, 1)
modes
new
XHAIL
react(5,
[C02], [C05]).
meta(C05).
obs_growth(2,1).
evidences
background
inhib(V1, 10, 1) :-
nutr(V1, "C08", ext),
nutr(V1, "C06", ext), exp (V1).
hypotheses
:1 =3.
:1 =2.
=4.
14-17/09/2014 ILP '14, Nancy 6](https://image.slidesharecdn.com/ilp14-pdf-140916103526-phpapp01/85/ILP-2014-Nonmonotonic-Learning-in-Large-Biological-Network-6-320.jpg)
Uploaded byStefano Bragaglia
ILP 2014 - Nonmonotonic Learning in Large Biological Network
The document discusses nonmonotonic learning applied to large biological networks, specifically detailing the XHAIL implementation and its scalability study. It explores various models and approaches to metabolic network revisions and introduces the application of logic programming in biological data analysis. The content emphasizes the tools used, such as ASP-based applications and specific examples of metabolic network hypotheses.
ILP 2014 - Nonmonotonic Learning in Large Biological Network
- 1. Nonmonotonic Learning inLarge Biological Networks Stefano Bragaglia, Oliver Ray stefano.bragaglia@bristol.ac.uk, oray@cs.bris.ac.uk Department of Computer Science University of Bristol 14-17/09/2014 ILP '14, Nancy 1
- 2. • New opensource XHAIL implementation • Study of scalability on Biological Networks • Found mistake in genome-scale network 14-17/09/2014 ILP '14, Nancy 2
- 3. XHAIL • NonmonotonicILP – normal (extended) logic programs – abductive, deductive, inductive • Prototype (2006) – ASP-based (lparse/smodels) – Prolog wrapper (SWI-Prolog) – Not defeasible – Not • Metabolic Network Revision (ILP 09) – AAA model (~30 reactions) – Pathway-specific model • Non – normal – Abductive • Current application – ASP-based (gringo/clasp) – Java wrapper (Java 8) – Defeasible (language bias) – Open source • Metabolic Network Revision – ABER model (1100+ reactions) – Whole-organism model 14-17/09/2014 ILP '14, Nancy 3
- 4. XHAIL • NonmonotonicILP – normal (extended) logic programs – abductive, deductive, inductive • Prototype (2006) – ASP-based (lparse/smodels) – Prolog wrapper (SWI-Prolog) – Not defeasible – Not • Metabolic Network Revision (ILP 09) – AAA model (~30 reactions) – Pathway-specific model • Non – normal – Abductive • Current application – ASP-based (gringo/clasp) – Java wrapper (Java 8) – Defeasible (language bias) – Open source http://github.com/cathexis-bris-ac-uk/XHAIL • Metabolic Network Revision – ABER model (1100+ reactions) – Whole-organism model 14-17/09/2014 ILP '14, Nancy 4
- 5. Approach Overview #modehinhib(+e, $m, $d). #modeb nutr(+e, $m, ext). #example not growth(2, 1). modes inhib(V1, 10, 1) :- nutr(V1, "C08", ext), nutr(V1, "C06", ext), exp (V1). hypotheses evidences cytosol C00013 C00000 C00006 C00005 C00119 2.4.2.18 C00254 5.4.99.5 C00251 C01302 4.2.1.10 1.3.1.13 2.5.1.54 C00011 C00108 4.1.1.48 C00001 4.2.1.11 C00279 C00631 I00279 C00279 1.1.1.25 C04302 5.3.1.24 C02637 C02652 ? C01269 C03175 4.2.3.5 C00009 2.5.1.19 C00074 C00944 C04691 I00631 C00631 start 4.2.3.4 C00493 2.7.1.71 C00008 C00002 react(5, [C02], [C05]). meta(C05). obs_growth(2,1). in_comp(Exp, Meta,Comp, Day) :- s_comp( Meta,Comp). background C00166 2.6.1.7 C00004 medium I00079 I00082 C00078 C00079 C00082 end C00003 4.2.1.51 C01179 4.1.3.27 C00025 C00014 C00064 C00022 C03506 4.2.1.20 C00463 C00065 C00026 C00078 C00661 C00079 C00082 I00078 XHAIL 14-17/09/2014 ILP '14, Nancy 5
- 6. Approach Overview #modehinhib(+e, $m, $d) #modeb nutr(+e, $m, ext) #example not growth(2, 1) modes new XHAIL react(5, [C02], [C05]). meta(C05). obs_growth(2,1). evidences background inhib(V1, 10, 1) :- nutr(V1, "C08", ext), nutr(V1, "C06", ext), exp (V1). hypotheses :1 =3. :1 =2. =4. 14-17/09/2014 ILP '14, Nancy 6
- 7. Model Revision -- D-ERYTHROSE-4- PHOSPHATE GLYCERATE-2- PHOSPHATE C00279 C00631 C00005 C00006 YDR127W | 4.2.1.11 C00074 | -- -- -- -- | YBR249C YDR035W 2.5.1.54 C04691 C00009 4.2.3.4 C00944 4.2.1.10 C02637 | YDR127W YDR127W -- YDR127W 2.5.1.19 C03175 -- YGR254W YHR174W YMR323W C00008 YDR127W 2.7.1.71 C00002 C00009 C01269 C00009 1.1.1.25 C00493 | -- | C00025 YHR137W YGL202W 2.6.1.1 C00026 % Task A: YER090W as enzyme complex in 4.1.3.27 knockout(1, "YER090W"). #modeh component($orf, $enzID) :1 =3. #example not predicted_growth(1, 1) =4. Anthranilate C00108 -- YDR354W C04302 C00064 C00022 C00025 YDR007W C00119 2.4.2.18 C00013 5.3.1.24 C01302 YKL211C 4.1.1.48 C03506 -- | | C00009 C00251 YPR060C 5.4. 99.5 C00254 -- C00006 YBR166C 1.3. 1.13 -- YER090W YKL211C + YER090W YNL316C 4.2. 1.51 -- 4.1.3.27 C00011 C00011 C00166 -- C00065 C00118 C00011 YGL026C 4.2.1.20 C00078 C00005 C01179 C00082 C00025 YHR137W YGL202W 2.6.1.7 C00079 C00026 TYROSINE PHENYLALANINE Indole C00463 C00065 C00001 TRYPTOPHAN knockout(2, "YER090W"). additional_nutrient(2, "C00108", medium). #example predicted_growth(2, 1) =4. H: component("YER090W", 54). | | -- | YGL148W 4.2 .3.5 -- 14-17/09/2014 ILP '14, Nancy 7
- 8. Model Revision -- D-ERYTHROSE-4- PHOSPHATE GLYCERATE-2- PHOSPHATE C00279 C00631 C00005 C00006 YDR127W | 4.2.1.11 C00074 | -- -- -- -- | YBR249C YDR035W 2.5.1.54 C04691 C00009 4.2.3.4 C00944 4.2.1.10 C02637 | YDR127W YDR127W -- YDR127W 2.5.1.19 C03175 -- YGR254W YHR174W YMR323W C00008 YDR127W 2.7.1.71 C00002 C00009 C01269 C00009 1.1.1.25 C00493 | -- | C00025 YHR137W YGL202W 2.6.1.1 C00026 % Task B: C00082 inhibits YBR249C in 2.5.1.54 knockout(3, "YDR035W"). additional_nutrient(3, "C00082", medium). #example not predicted_growth(3, 1) =4. #modeh inhibitor($enzID, $meta, cytosol) :1 =2. Anthranilate C00108 -- YDR354W C04302 C00064 C00022 C00025 YDR007W C00119 2.4.2.18 C00013 5.3.1.24 C01302 YKL211C 4.1.1.48 C03506 -- | | C00009 C00251 YPR060C 5.4. 99.5 C00254 -- C00006 YBR166C 1.3. 1.13 -- YER090W YKL211C + YER090W YNL316C 4.2. 1.51 -- 4.1.3.27 C00011 C00011 C00166 -- C00065 C00118 C00011 YGL026C 4.2.1.20 C00078 C00005 C01179 C00082 C00025 YHR137W YGL202W 2.6.1.7 C00079 C00026 TYROSINE PHENYLALANINE Indole C00463 C00065 C00001 TRYPTOPHAN experiment(4). additional_nutrient(4, "C00082", medium). #example predicted_growth(4, 1) =4. H: inhibitor(25, "C00082", cytosol). | | -- | YGL148W 4.2 .3.5 -- 14-17/09/2014 ILP '14, Nancy 8
- 9. Model Revision -- D-ERYTHROSE-4- PHOSPHATE GLYCERATE-2- PHOSPHATE C00279 C00631 C00005 C00006 YDR127W | -- -- | YBR249C YDR035W 2.5.1.54 C04691 C00009 4.2.3.4 C00944 4.2.1.10 C02637 | YDR127W YDR127W -- YGR254W YHR174W YMR323W YDR127W 2.5.1.19 C00008 C00074 C03175 YDR127W 2.7.1.71 C00002 C00009 1.1.1.25 % Task C: C00463 contamination in 4.2.1.20 knockout(5, "YKL211C"). additional_nutrient(5, "C00463", medium). #modeh include($reaction) :1 =3. #example predicted_growth(5, 1) =4. C01269 -- | | -- C00064 C00022 C00025 | | | C00009 C00251 YPR060C 5.4. 99.5 C00254 -- C00006 YBR166C 1.3. 1.13 -- YER090W YKL211C + YER090W YNL316C 4.2. 1.51 -- 4.1.3.27 C00011 C00011 C00166 -- 4.2.1.11 | -- -- YGL148W 4.2 .3.5 C00005 C01179 -- -- C00009 C00493 | | YHR137W YGL202W 2.6.1.1 C00082 C00025 C00026 C00025 YHR137W YGL202W 2.6.1.7 C00079 C00026 TYROSINE PHENYLALANINE knockout(6, "YGl026C"). additional_nutrient(6, "C00463", medium). #modeh catalyst($reaction, $enzID) :1 =3. #example not predicted_growth(6, 1) =4. Anthranilate C00108 -- YDR354W C04302 YDR007W C00119 2.4.2.18 C00013 5.3.1.24 C01302 YKL211C 4.1.1.48 C03506 -- C00065 C00118 C00011 YGL026C 4.2.1.20 C00078 Indole C00463 C00065 C00001 TRYPTOPHAN knockout(7, "YKL211C"). observed_growth(false, 7, 1). #example not predicted_growth(7, 1) =4. H: catalyst(10910, 43). include(10910). 14-17/09/2014 ILP '14, Nancy 9
- 10. Model Revision -- D-ERYTHROSE-4- PHOSPHATE GLYCERATE-2- PHOSPHATE C00279 C00631 C00005 C00006 YDR127W | -- -- | YBR249C YDR035W 2.5.1.54 C04691 C00009 4.2.3.4 C00944 4.2.1.10 C02637 | YDR127W YDR127W -- YGR254W YHR174W YMR323W YDR127W 2.5.1.19 C00008 C00074 C03175 YDR127W 2.7.1.71 C00002 C00009 1.1.1.25 % Task D: slow import of C00166, C01179 knockout(8, "YBR166C"). additional_nutrient(8, "C01179", medium). #example not predicted_growth(8, 1) =4. C01269 -- | | -- C00064 C00022 C00025 | | | C00009 C00251 YPR060C 5.4. 99.5 C00254 -- C00006 YBR166C 1.3. 1.13 -- YER090W YKL211C + YER090W YNL316C 4.2. 1.51 -- 4.1.3.27 C00011 C00011 C00166 -- 4.2.1.11 | -- -- YGL148W 4.2 .3.5 C00005 C01179 -- -- C00009 C00493 | | YHR137W YGL202W 2.6.1.1 C00082 C00025 C00026 C00025 YHR137W YGL202W 2.6.1.7 C00079 C00026 TYROSINE PHENYLALANINE knockout(9, "YNL316C"). additional_nutrient(9, "C00166", medium). #example not predicted_growth(9, 1) =4. Anthranilate C00108 -- YDR354W C04302 YDR007W C00119 2.4.2.18 C00013 5.3.1.24 C01302 YKL211C 4.1.1.48 C03506 -- C00065 C00118 C00011 YGL026C 4.2.1.20 C00078 Indole C00463 C00065 C00001 TRYPTOPHAN #example not predicted_growth(10, 1) =4. #modeh inhibited(+ex, $enzID, $day) :2 =2. H: inhibited(V1, 53, 1) :- experiment(V1). inhibited(V1, 67, 1) :- experiment(V1). 14-17/09/2014 ILP '14, Nancy 10
- 11. Model Revision -- D-ERYTHROSE-4- PHOSPHATE GLYCERATE-2- PHOSPHATE C00279 C00631 C00005 C00006 YDR127W | 4.2.1.11 C00074 -- -- -- -- | YBR249C YDR035W 2.5.1.54 C04691 C00009 4.2.3.4 C00944 4.2.1.10 C02637 | YDR127W YDR127W -- YDR127W 2.5.1.19 C03175 -- YGR254W YHR174W YMR323W C00008 YDR127W 2.7.1.71 C00002 C00009 1.1.1.25 C00493 % Task E: Defeasible example #example not predicted_growth(11, 1) =4. H: - C: 0 example/s out of 1 C01269 -- | | -- Anthranilate C00108 -- YDR354W C04302 C00064 C00022 C00025 YDR007W C00119 2.4.2.18 C00013 5.3.1.24 C01302 YKL211C 4.1.1.48 C03506 -- | | | C00009 C00251 YPR060C 5.4. 99.5 C00254 -- C00006 YBR166C 1.3. 1.13 -- YER090W YKL211C + YER090W YNL316C 4.2. 1.51 -- 4.1.3.27 C00011 C00011 C00166 -- | YGL148W 4.2 .3.5 C00005 C01179 -- C00065 C00118 C00011 YGL026C 4.2.1.20 C00078 C00009 | | YHR137W YGL202W 2.6.1.1 C00082 C00025 C00026 C00025 YHR137W YGL202W 2.6.1.7 C00079 C00026 TYROSINE PHENYLALANINE Indole C00463 C00065 C00001 TRYPTOPHAN 14-17/09/2014 ILP '14, Nancy 11
- 12. Scalability Analysis onValidation Experiments standard expressions 14-17/09/2014 ILP '14, Nancy 12 biased expressions # reactions hypotheses time (s) - means "out of memory"
- 13. Scalability Analysis ondata provided by Robot Scientist 30% faster! C01269 -- | | -- -- D-ERYTHROSE-4- PHOSPHATE GLYCERATE-2- PHOSPHATE C00279 C00631 C00005 C00006 YDR127W | Anthranilate C00108 -- YDR354W 362 C04302 C00064 C00022 C00025 YDR007W C00119 2.4.2.18 C00013 5.3.1.24 361 C01302 YKL211C 4.1.1.48 360 C03506 -- | | 363 | 370 C00009 C00251 YPR060C 5.4. 99.5 369 C00254 -- C00006 YBR166C 1.3. 1.13 -- YER090W YKL211C + YER090W YNL316C 4.2. 1.51 -- 4.1.3.27 C00011 C00011 366 368 C00166 -- 4.2.1.11 823 C00074 | -- -- -- -- | YBR249C YDR035W 2.5.1.54 376 C04691 C00009 4.2.3.4 375 C00944 4.2.1.10 374 C02637 662 | YGL148W 4.2 .3.5 C00005 C01179 -- YDR127W YDR127W -- YDR127W 2.5.1.19 660 C00003 C03175 -- YGR254W YHR174W YMR323W C00008 YDR127W 2.7.1.71 C00002 C00009 C00065 C00118 C00011 YGL026C 4.2.1.20 359 C00078 C00009 1.1.1.25 661 U52_ 1.3. 1.12 C00004 C00011 C00493 | | YHR137W YGL202W 2.6.1.1 365, 357 367 C00082 364 C00025 C00026 C00025 YHR137W YGL202W 2.6.1.7 C00079 C00026 TYROSINE PHENYLALANINE Indole C00463 C00065 C00001 TRYPTOPHAN 14-17/09/2014 ILP '14, Nancy 13
- 14. NONMONOTONIC LEARNING INLARGE BIOLOGICAL NETWORKS Stefano Bragaglia, Oliver Ray stefano.bragaglia@bristol.ac.uk, oray@cs.bris.ac.uk • Thanks for your attention • Any questions? 14-17/09/2014 ILP '14, Nancy 14
Editor's Notes
- #4 Requirements Engineering (FLTL, ILP’06) Deals with negation Completion Ross King Fluent Linear Temporal Logic
- #5 Requirements Engineering (FLTL, ILP’06) Deals with negation Completion Ross King Fluent Linear Temporal Logic
- #7 Help the user to explore the part of the search space they want to focus on
- #8 We prepared a first set of tests to validate and revise the portion of ABER relating to AAA. These tests are divided into tasks. Task A consists of two experiments that allow XHAIL to infer that YER090W is required in all enzyme complexes catalysing reaction 4.1.3.27. Experiment 1 simply knocks out YER090W. ABER observes growth as it thinks YKL211C can catalyse 4.1.3.27 alone.
- #9 We prepared a first set of tests to validate and revise the portion of ABER relating to AAA. These tests are divided into tasks. Task A consists of two experiments that allow XHAIL to infer that YER090W is required in all enzyme complexes catalysing reaction 4.1.3.27. Experiment 1 simply knocks out YER090W. ABER observes growth as it thinks YKL211C can catalyse 4.1.3.27 alone.
- #10 We prepared a first set of tests to validate and revise the portion of ABER relating to AAA. These tests are divided into tasks. Task A consists of two experiments that allow XHAIL to infer that YER090W is required in all enzyme complexes catalysing reaction 4.1.3.27. Experiment 1 simply knocks out YER090W. ABER observes growth as it thinks YKL211C can catalyse 4.1.3.27 alone.
- #11 We prepared a first set of tests to validate and revise the portion of ABER relating to AAA. These tests are divided into tasks. Task A consists of two experiments that allow XHAIL to infer that YER090W is required in all enzyme complexes catalysing reaction 4.1.3.27. Experiment 1 simply knocks out YER090W. ABER observes growth as it thinks YKL211C can catalyse 4.1.3.27 alone.
- #12 We prepared a first set of tests to validate and revise the portion of ABER relating to AAA. These tests are divided into tasks. Task A consists of two experiments that allow XHAIL to infer that YER090W is required in all enzyme complexes catalysing reaction 4.1.3.27. Experiment 1 simply knocks out YER090W. ABER observes growth as it thinks YKL211C can catalyse 4.1.3.27 alone.
- #14 We have also prepared a second batch of 40 experiments using real data provided by a robot scientist. The conventions are the same as before but we only compare the results in the case of the hardest problems. We observe a gain in performance of about 30%.