Constraint Programming in Community-based Gene Regulatory Network - PowerPoint PPT Presentation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming in Community-based Gene Regulatory Network Inference Ferdinando Fioretto Enrico Pontelli Dept. Computer Science, New Mexico State University Sept. 24, 2013

Background Constraint Programing in Community Networks Experiments and Results Conclusions Talk Outline Background 1 Constraint Programing in Community Networks 2 Experiments and Results 3 Conclusions 4

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Networks A cell contains different entities (including pro- teins, RNA) which interact and perform specific functions.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Networks A cell contains different entities (including pro- teins, RNA) which interact and perform specific functions. DNA transcription

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Networks A cell contains different entities (including pro- teins, RNA) which interact and perform specific functions. DNA transcription mRNA translation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Networks Some proteins (Transcriptor Factors (TF)) can regulate the production of other proteins. Done by enhancing or inhibiting DNA transcription or mRNA translation. The unit of encapsulation of these interactions are the coding regions of the DNA: the genes. A Gene Regulatory Network is the set of the interactions among genes.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Networks Modeling A GRN is described by a weighted directed graph G = ( V , E ) . V is the set of genes of the network. E ⊆ V × V × [ 0 , 1 ] is the set of the regulatory interactions. Each regulatory interaction s → t is associated with a confidence value ω s → t ∈ [ 0 , 1 ] . Example G 1 regulates G 2. G 2 regulates G 5. G 3 is regulated by G 4. G 4 regulates G 2 and is regulated by G 5.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Network Inference GRN inference from high-throughput data Motivation: Key to understand important genetic diseases, such as cancer. Crucial to devise effective medical interventions.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Network Inference Current Methods and Challenges Methods proposed: Correlation-based. Bayesian Networks. Information-theoretic based. Regression-based. Boolean Networks. Stochastics. Based on different assumptions. Exhibits peculiar limitations.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Network Inference Current Methods and Challenges Methods proposed: Correlation-based. Bayesian Networks. Information-theoretic based. Regression-based. Boolean Networks. Stochastics. Based on different assumptions. Exhibits peculiar limitations. Solutions proposed: Integrating heterogeneous data into the inference model. Meta-approaches using multiple inference models (Community Networks (CN)).

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Network Inference Community Networks G J G 2 community network G 1 edge ranking Borda voting score: ω j → t : the ranked interaction s → t |G| s # → t = 1 � ω j ω by the j -th method in G . # |G| # s → t s j = 1 D. Marbach et al. “Wisdom of crowds for robust gene network inference”. Nature Methods, 9(8):796–804, Aug. 2012.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Network Inference Our Approach CN approach for an “initial analysis” of the GRN. Community prediction collective agreements. Integrate additional biological knowledge (when available). Leverage specific GRN properties.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Network Inference Our Approach CN approach for an “initial analysis” of the GRN. Community prediction collective agreements. Integrate additional biological knowledge (when available). Leverage specific GRN properties. Why CP ?

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constraint Programming Constraint Satisfaction Problem (CSP) Variables X : x i = position of the queen in the i th column. Domains D : D x i = { 1 , . . . , n } . Constraints C : ∀ i , ∀ j with i < j : x i � = x j x i + i � = x j + j x i − j � = x j − j Search = Labeling + Constraint Propagation Solution = assignment for X satisfying all c ∈ C

Background Constraint Programing in Community Networks Experiments and Results Conclusions Gene Regulatory Network Inference Our Approach CN approach for an “initial analysis” of the GRN. Community prediction collective agreements. Integrate additional biological knowledge (when available). Leverage specific GRN properties. Why CP ? Separation between prediction methods and model. Declaratively. Constraint expressions allow incremental model refinement.

Background Constraint Programing in Community Networks Experiments and Results Conclusions Constrained Community Networks CSP Modeling GRN inference (GRNi) problem: Given a set of n genes, a GRNi is a CSP �X , D , C� X = � x 1 , . . . , x n 2 − n � (regulatory relations, exuding self regulations). D = � D 1 , . . . , D n 2 − n � , with each D k = { 0 , . . . , 100 } (possible confidence values). C is a list of constraints expressing properties of the GRNs. Notation: x s → t : “s regulates t” and D s → t its domain. d ( x s → t ) : the value assigned to x s → t .