Inference based Hypothesis finding for Systems Biology

Transcription

1 Inference based Hypothesis finding for Systems Biology Katsumi Inoue National Institute of Informatics Tokyo, Japan Franco Japanese Workshop in the field of Informationand and Communication Science and Technologies Paris, November 2009

2 Automated hypothesis finding An emerging multi disciplinary research area that involves the use of high performance h computing to automate t scientific discovery King, R.D. et al., FunctionalGenomic Hypothesis Generation and Experimentation by a Robot Scientist, Nature, 427, King, R.D. et al., The Automation of Science, Science, 324, Muggleton, S.D., Exceeding Human Limits. Nature, 440, 2006.

3 Hypothesis finding in Systems Biology explanation and prediction of networks Maintypes ofcellularprocesses modeledinbiology: gene networks (transcriptome) protein signaltransduction (proteome) biochemical pathways (metabolome) Genomic sequences Cytosol Nucleus Intracellular signals TFs mrnas Proteins Cellular processes cis binding activities Expression profiles Elena A., Ben-Amor H., Glade N., Demongeot J.: Motifs in regulatory networks and their structural robustness, Proceedings of the 8th IEEE International Conference on Bioinformatics and BioEngineering, 2008, Athens, Grèce, 2008.

4 [ X ] ex ex = μ( GLC, ACE )[ X ] d ex d[ GLC ] = vpts [ X ] d[ G P] = vpts vpfk vg 6PDH μ[ G6P] d[ FDP] = vpfk valdo μ[ FDP] d [ GAP ] = 2vALDO vgapdh μ[ GAP] d[ PEP] = vgapdh + vpck vpts vpyk vppc μ PEP d [ PYR ] = vpyk vmez vpdh vpps μ[ PYR] d[ AcCoA] = vpdh vcs vpta μ[ AcCoA] d[ ICIT ] = vcs vicdh vicl μ [ ICIT ] d[ 2KG] = vicdh v2kgdh μ[ 2KG] d[ SUC ] = v 2 KGDH + v ICL v SDH μ [ SUC ] d[ FUM ] = vpts vpfk vg6pdh μ[ FUM ] d[ MAL] = vfum + vms vmdh vmez μ[ MAL] d[ OAA] = vmdh + vppc vcs vpck μ[ OAA] d[ GOX ] = vicl vms μ[ GOX ] d[ ACP] = vpta vack μ[ ACP] d[ ACE ] = vack vacs μ[ ACE ] Dynamic model of metabolic pathways 6 for glycolysis l and TCA cycle [ ]

5 Multi enzymes Mikulecky DC, Thellier M. Determining the transient kinetic behavior of complex multi enzyme systems by use of network thermodynamics. C R Acad Sci III. 316: (1993) Aix 09/07

6 Theoretical and scientific interests Development of a framework for knowledge discovery from biological databases using logic based AI. Clarification of the principles p of hypothesis formation and hypothesis evaluation and their efficient implementation. Modeling metabolic pathways, in particular prediction of steady states using flux dynamics. Two generic models: E. coli and Saccharomyces Cerevisiae. Explanation and prediction of states of reactions in biological pathways. In particular, identification of master reactions (elementary modes), which are involved in physiological states in the growth of microorganisms. Systems approach to cancer diagnosis and therapy.

7 Scientific knowledge development hypothesis prediction 2 3 Deduction Abduction knowledge Experiment anomaly Induction observation 1 4

8 G3PDH MurSynth TrpS yn th SerSynth Syn th1 M ets yn th, TrpSynth Synth2 RPPK Automated hypothesis finding in Systems Biology (Inoue et al., IJCAI 2009) discretization g1p PGM +fdp G1PAT mureine polysaccharide synthesis synthesis glycerol synthesis tryptophan synthesis serine synthesis GLUCOSE External PTS -g6p g6p PGI f6p +am p PFK +ad p fdp ALDO dhap TIS -nadp h G6PDH -6pg -pe p gap GA PDH pgp PGK 3pg PGluMu 2pg ENO 6pg TKb -atp -nadph PGDH ribu5p Ru5P R5PI xyl5 p rib 5p TKa sed7p gap TA e4p f6p DAHPS aromatic am ino acid synthesis nucleotide synthesis pep PEPC xylase +fdp c h o, m ur synthesis +am p +fdp PK -a tp oaa pyr PDH ile, ala, kival, dipim synthesis m et, trp synthesis accoa Background Knowledge Observations Pathway Model (Qualitative ti / Kinetic) Pathway Data (from lab + KEGG) 9.00E E E E E-01 Hypothesis Finder SOLAR hypothesis ranking Hypothesis Evaluator BDD-EM d d Hypotheses d [ G 6 P ] = v B H = O d [ FDP ] = v d [ GAP ] = 2v M d [ ACP ] = v d [ ACE ] = v Best Hypotheses [ X ] ex ex = μ ( GLC, ACE )[ X ] ex [ GLC ] = v [ X ] PTS PFK PTA PTS v ALDO ACK v v v PFK ALDO v ACK ACS v GAPDH G 6 PDH μ μ μ Pathway Analysis μ [ FDP ] μ [ ACP ] [ ACE ] [ GAP ] [ G 6 P ] -1.00E

9 Simplified with discrete time and constant wih time steps Discretization with continous HMMs Qualitative approximation to build a logic model for abduction

10 Abduction: logical framework Input: B : background theory G : observations Γ : possible causes (abducibles) Output: B H : hypothesis satisfying that B H G H B H is consistent H is a set of instances of literals from Γ. Abductive engine G InverseEntailment (IE) Computing a hypothesis H can be done deductively by: B G H We use a consequence finding technique for IE computation.

11 Input: Consequence Finding The main inference task B : first order (clausal) theory C : new clausal theory P : language restriction ( production field ) ) Output: S : the (subsumption minimal) minimal) new consequences satisfyingthat that B C S B /= S B S belongs to P. SOL resolution (Inoue, ) C Conseq. Finder SOLAR (Nabeshima, Iwanuma & Inoue, ) Applicable to many AI problems, e.g., Theorem Proving, Query Answering, Abduction, Induction, Default Reasoning, Multi Agent Systems, Diagnosis S

12 Abduction and discovery Application of abduction to scientific discovery (Zupan et al., Bioinformatics 2003), (King et al., Nature 2004; Science 2009), (Muggleton, Nature 2006), etc. Knowledge is structured as a network Knowledge and data bases are incomplete Constraints are often very weak, so there exist a large number of logically possible hypotheses Hypotheses are composed for multiple observations 20 metabolites, 10 explanations for each Hypothesis evaluation is indispensable.

13 Hypothesis evaluation Given a numerous number of hypotheses, Which hypotheses are most likely? Statistical hypothesis selection Learning L i probabilities bili i of a model dldescribed dby a Boolean formula of propositions and their probabilities from the observations. Evaluation by the BDD EM algorithm The EM algorithm: maximum likelihood lih estimation Binary decision diagrams (BDDs): compact expression of Boolean formulas.

14 Prediction of inhibitory effects of a toxin (Tamaddoni Nezda d et al., Machine Learning 2006) Goal: Find inhibitions in a metabolic tbli pathway Approach: Abduction (Inverse Entailment by SOLAR) Background Theory B : Causal rules (4) and Integrity constraints (4) Chemical reactions (76) in a metabolic network from KEGG Observations (Input) E : Changes (up/down) of metabolites concentrations (20*5=100) Hypothesis (Output) H : A set (conjunction) of literals l whose predicate is inhibited

15 Metabolic pathway representation Enzyme Reactions: 76 (from KEGG) Metabolites: 30 Extracellular: l 20 Intracellular: 10

16 An output of SOLAR Observation conc. up: conc. down: Hypothesis inhibition acceleration

17 Ranking of hypotheses SOLAR found 66 minimal explanations for 20 observations in Time = 8 hrs (and 5,145 minimal explanations in Time = 96 hrs). The top 7 in Time = 8 hrs satisfy two desirable properties suggested by biologists, and the worst 22 do not satisfy them. 9.00E E E E E E

18 JST CNRS Strategic International Cooperative Program : Knowledge based Discovery in Systems Biology Katsumi Inoue (NII) [Organization] Inference, Representation, Theory Taisuke Sato (Tokyo Inst. Tech. ) Probabilistic Reasoning/Modeling Yoshitaka Kameya (Tokyo Inst. Tech.) Probabilistic Modeling, Data Mining Koji Iwanuma (Univ. Yamanashi) Inference, Data Mining, Theory Hidetomo Nabeshima (Univ. Yamanashi) Inference, Constraint Programming Andrei Doncescu (LAAS CNRS) [Organization] Systems Biology, Informatics Louis Travé Massuyès (LAAS CNRS) Model based Reasoning Luis Fariñas del Cerro (IRIT / UMR CNRS) Theoretical Informatics, Inference Jacques Demongeot (TIMC IMAG / UMR CNRS) Genetics Pierre Siegel (Univ. Provence) Inference, Constraint Programming

19 Franco Japanese collaboration in Knowledge based Discovery in Systems Biology Goals Modeling biological systems (qualitative / kinetic) Inference based hypothesis finding for Systems Biology Japan Team develops inference engines (SOLAR, CF Induction, etc) develops probabilisticmodelingtools tools (PRISM, BDD EM) France Team provides bio data (S. Cerevisiae, E. Coli, breast cancer) validation of hypotheses by experiments

20 Ongoing g( (and future) collaboration Collaboration with the group of Philippe Dague (LRI) Common inference techniques (consequence finding, SAT) Both applicable to network based data and knowledge Complementary techniques non distributed / distributed propositional / first order Need methods to handle large, heterogeneous knowledge bases 1 st Franco Japanese Meeting at LRI in September, 2009 Our techniques Inference (deduction, abduction, induction) Constraint programming (CSP, SAT, ASP) Probabilistic reasoning/modeling Multi agent learning (Gauvain Bourgne, in connection with LIP6, LAMSADE) Members from: NII, Yamanashi, Kobe, Kyushu, Tokyo Inst. Tech.,

21 Memory Of Understandings for research cooperation betweennii and French institutes LINA: Laboratoire a o d'informatiquede Nantes Atlantique, a que,université éde Nantes INRIA: Institut National de Recherche en Informatique et en Automatique INPG: Institut National Polytechnique de Grenoble Universite Joseph Fourier Grenoble 1 LIP6: Laboratory of Computer Sciences, Pierre and Marie Curie University (UPMC) Paris6 Institute National Polytechnique de Toulouse Université Paul Sabatier (Université de Toulouse III) CNRS: National Center for Scientific Research (as of October, 2009) MOU grant NII International Internship Program JFLI: Japanese French Laboratory for Informatics