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IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Metaheuristics for Search Problems inGenomics
— New Algorithms and Applications —
Stefano Benedettini1
DEIS, Alma Mater Studiorum Università di Bologna, Campus of Cesena, Italys.benedettini@unibo.it
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Motivations
Outstanding goals in modern engineering:Develop methodologies and tools to:
synthesise models of biological systemsanalyse real or artificial biological systems
Oftentimes, such systems are complexDivide et impera approach fails to capture importantrelationships
Our objective:Apply automatic procedures to the problem of model design
More precisely, we are interested in model instantiation
These procedures belong to the class of search methods
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Definitions
Model: the set of entities and relationships that:1 Can be used to explain a class of phenomena
or systems2 Can be expressed in a formal language
Model instance: application of aforementioned entities andconcepts to describe a specific system
Predator-Prey model
Model: a system of (parametric) differential equations,such as the Lotka-Volterra equations
Model instance: their applications to describe populationdynamics of rabbits and foxes in a forest
How to instantiate parameters?
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Our Proposed Methodology
Concepts involved:Employ metaheuristic techniques to instantiate models ingenomicsA solution is a model instanceThe search process manipulates (one or more) solutionsA merit factor (objective function) measures the “quality” ofa models/solution with respect to a set of desiderata
Applications:Resolution of biological problemsAutomatic synthesis of biological model instances(Ensemble Approach)
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Metaheuristics for Solving Biological Problems
Facts
Solutions to problems in genomics have to be “realistic”They have to make sense for biologists
Whatever model of a phenomenon is never 100% accurateby definition
A complete technique spends time to return a proof ofoptimality
Motivations
An optimal solution for an approximate model might not beuseful
Metaheuristics can easily incorporate different objectivefunction components
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Metaheuristics for Solving Biological Problems
Facts
Solutions to problems in genomics have to be “realistic”They have to make sense for biologists
Whatever model of a phenomenon is never 100% accurateby definition
A complete technique spends time to return a proof ofoptimality
Motivations
An optimal solution for an approximate model might not beuseful
Metaheuristics can easily incorporate different objectivefunction components
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
A Statistical Approach to Modeling
Ensemble Approach (EA)
Let’s define a feature space F
A generic system σ is identified by a coordinate vectorΦ(σ) = 〈f1, f2, . . . , fn〉 ∈ F
Is a model M an accurate description of a system σ?
The EA says that it is the case if point Φ(σ) is in the samecluster as (different realizations of) model M
Role of Metaheuristics
How to find model instances close to σ in F?
Define a suitable optimization problem and solve it
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
A Statistical Approach to Modeling
Ensemble Approach (EA)
Let’s define a feature space F
A generic system σ is identified by a coordinate vectorΦ(σ) = 〈f1, f2, . . . , fn〉 ∈ F
Is a model M an accurate description of a system σ?
The EA says that it is the case if point Φ(σ) is in the samecluster as (different realizations of) model M
Role of Metaheuristics
How to find model instances close to σ in F?
Define a suitable optimization problem and solve it
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
A Statistical Approach to Modeling
Ensemble Approach (EA)
Let’s define a feature space F
A generic system σ is identified by a coordinate vectorΦ(σ) = 〈f1, f2, . . . , fn〉 ∈ F
Is a model M an accurate description of a system σ?
The EA says that it is the case if point Φ(σ) is in the samecluster as (different realizations of) model M
Role of Metaheuristics
How to find model instances close to σ in F?
Define a suitable optimization problem and solve it
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Biological Definition
Entities involvedDiploid organism
Haplotype
Genotype
Issues in DNA Sequencing
Haplotype collection is very expensive
On the contrary, genotype collection is not
Haplotype Inference
Obtain haplotype information explaining genotype data
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Biological Definition
Entities involvedDiploid organism
Haplotype
Genotype
Issues in DNA Sequencing
Haplotype collection is very expensive
On the contrary, genotype collection is not
Haplotype Inference
Obtain haplotype information explaining genotype data
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Biological Definition
Entities involvedDiploid organism
Haplotype
Genotype
Issues in DNA Sequencing
Haplotype collection is very expensive
On the contrary, genotype collection is not
Haplotype Inference
Obtain haplotype information explaining genotype data
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Mathematical Definition
Definition (Genotype)
A genotype g is a vector in {0, 1, 2}m
Definition (Haplotype)
An haplotype h is a vector {0, 1}m
Definition (Genotype Resolution)
We say that the haplotypes h, l resolve genotype g, and we write 〈h, l〉⊲g, if and only if:
g[i ] = 0 ⇒ h[i ] = l [i ] = 0g[i ] = 1 ⇒ h[i ] = l [i ] = 1g[i ] = 2 ⇒ h[i ] 6= l [i ]
Definition (Haplotype Inference by Maximum Parsimony)
Let G be a set of genotype; find the minimal set of haplotypes H so that∀g ∈ G, ∃ h, l ∈ H | 〈h, l〉 ⊲ g
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Mathematical Definition
Definition (Genotype)
A genotype g is a vector in {0, 1, 2}m
Definition (Haplotype)
An haplotype h is a vector {0, 1}m
Definition (Genotype Resolution)
We say that the haplotypes h, l resolve genotype g, and we write 〈h, l〉⊲g, if and only if:
g[i ] = 0 ⇒ h[i ] = l [i ] = 0g[i ] = 1 ⇒ h[i ] = l [i ] = 1g[i ] = 2 ⇒ h[i ] 6= l [i ]
Definition (Haplotype Inference by Maximum Parsimony)
Let G be a set of genotype; find the minimal set of haplotypes H so that∀g ∈ G, ∃ h, l ∈ H | 〈h, l〉 ⊲ g
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Mathematical Definition
Definition (Genotype)
A genotype g is a vector in {0, 1, 2}m
Definition (Haplotype)
An haplotype h is a vector {0, 1}m
Definition (Genotype Resolution)
We say that the haplotypes h, l resolve genotype g, and we write 〈h, l〉⊲g, if and only if:
g[i ] = 0 ⇒ h[i ] = l [i ] = 0g[i ] = 1 ⇒ h[i ] = l [i ] = 1g[i ] = 2 ⇒ h[i ] 6= l [i ]
Definition (Haplotype Inference by Maximum Parsimony)
Let G be a set of genotype; find the minimal set of haplotypes H so that∀g ∈ G, ∃ h, l ∈ H | 〈h, l〉 ⊲ g
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
State of the Art
Integer Linear Programming formulationBranch-and-Bound, Branch-and-Cut, . . .
Incomplete techniquesGenetic AlgorithmsTabu Search
Pseudo-Boolean Optimizationrpoly
Complete techniques don’t scale well for large-sizedinstances
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Contributions
Hybrid Ant Colony Optimization algorithmFlexible:
Can accommodate different genetic models by changingthe objective functionCan integrate different resolution criteria, e.g., statisticaltechniques
Effective:Comparable performance to state of the art exact solverrpoly
Scalable:Can cope with large instance sizeSuperior to rpoly in this cases
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Experimental Results
40 60 80 100 120 140 160 18040
60
80
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140
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180
10 20 30 40 50 60 700
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80 100 120 140 160 18080
100
120
140
160
180
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Biological DefinitionPurpose
Study the evolutionary history of a population of humanDNA sequencesHelps biologist to discover genetic bases of complexdiseases
Genetic Basis
A population is evolved from a relatively small number ofhaplotype foundersCrossover breaks and shuffles fragments of sequences
Goal
Find a set of founders which can reconstruct the sequences inthe population with the least number of crossovers
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
An Example of a “Mosaic”
0 1 1 0 1 0 0
1 1 0 1 1 1 1
1 0 1 0 0 0 1
1 1 0 1 1 0 1
1 0 1 0 0 0 1
0 1 1 1 1 1 1
0 1 1 0 1 0 0
1 1 0 0 0 1 1
Breakpoints correspond tocrossover eventsSolution value: 4Mosaic is not uniqueGiven a founder matrix, thecomputation of the optimalmosaic is polynomial
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
State of the Art
Complete algorithms:Dynamic ProgrammingTree search enhanced with pseudo Branch-and-Boundpruning: RECBLOCK
Incomplete techniques:Greedy HeuristicTabu Search
Complete techniques don’t scale well for large-sizedinstances
Nevertheless, running times for Tabu Search are quite longand still performances are not impressive
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
State of the Art
Complete algorithms:Dynamic ProgrammingTree search enhanced with pseudo Branch-and-Boundpruning: RECBLOCK
Incomplete techniques:Greedy HeuristicTabu Search
Complete techniques don’t scale well for large-sizedinstances
Nevertheless, running times for Tabu Search are quite longand still performances are not impressive
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
State of the Art
Complete algorithms:Dynamic ProgrammingTree search enhanced with pseudo Branch-and-Boundpruning: RECBLOCK
Incomplete techniques:Greedy HeuristicTabu Search
Complete techniques don’t scale well for large-sizedinstances
Nevertheless, running times for Tabu Search are quite longand still performances are not impressive
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
State of the Art
Complete algorithms:Dynamic ProgrammingTree search enhanced with pseudo Branch-and-Boundpruning: RECBLOCK
Incomplete techniques:Greedy HeuristicTabu Search
Complete techniques don’t scale well for large-sizedinstances
Nevertheless, running times for Tabu Search are quite longand still performances are not impressive
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Contributions
Randomized Iterated GreedyFast incomplete algorithmBetter than former Tabu Search algorithmProvides the initial solution to our LNS
Large Neighborhood Search (LNS)Integrates and boosts RECBLOCK
Anytime solverBut eventually reaches the optimum if given enough timeCurrent state of the art method for FSRP
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Comparison against RECBLOCK
LNS−1−caching Reckblock−heur
0.00
0.05
0.10
0.15
evo instances
Sol
utio
n va
lue
rela
tive
diffe
renc
e
LNS−1−caching Reckblock−heur
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
ms instances
Sol
utio
n va
lue
rela
tive
diffe
renc
e
LNS−1−caching Reckblock−heur
0.00
0.01
0.02
0.03
0.04
rnd instances
Sol
utio
n va
lue
rela
tive
diffe
renc
e
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Comparison against Iterated Greedy
LNS−1−caching Iterated Greedy
0.00
0.02
0.04
0.06
0.08
0.10
0.12
evo instances
Sol
utio
n va
lue
rela
tive
diffe
renc
e
LNS−1−caching Iterated Greedy
0.00
0.02
0.04
0.06
0.08
ms instances
Sol
utio
n va
lue
rela
tive
diffe
renc
e
LNS−1−caching Iterated Greedy
0.00
0.01
0.02
0.03
0.04
0.05
rnd instances
Sol
utio
n va
lue
rela
tive
diffe
renc
e
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Outline
1 Introduction
2 The Haplotype Inference Problem
3 The Founder Sequence Reconstruction Problem
4 Boolean Network Design
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Scope of the Research
Boolean networks (BNs) are complex dynamical systems(and models of complex systems)
Recent research, mainly in biology, needs to find/designmodels satisfying given requirements
Hot topic in complex system biology
Biological Standpoint
Employ Boolean networks as a modeling tool
Ensemble Approach
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Boolean Networks
Introduced by Stuart Kauffman as a models of geneticregulatory networks (GRNs)
Discrete-time/discrete-state dynamical system
Non trivial (complex) dynamics
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Boolean Networks
Structure
Directed graph of N nodesNode i :
- Boolean value xi
- Boolean function fi
Boolean function arguments are variables associated toinput nodes of i
Node state (i.e., Boolean variable) updated as a function offi
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Boolean Networks
Dynamics
System state at time t : s(t) = (x1(t), . . . , xN(t))
Dynamics controls node update
Deterministic synchronous update
Every state has an unique successor
Variants
Several variants exist:BNs with asynchronous dynamicsBoolean threshold networksProbabilistic Boolean networksGlass networks
We focus on the most studied model
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Dynamical Features
Trajectories
TransientAttractor
Attractors
FixpointsCycles
Basin of Attraction (of attractor A)
Set of states belonging to the trajectories ending at attractor A
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Problem Difficulties
How do we obtain networks that match a set ofdesiderata?
Network space is enormous so random generation is not anoption
How do we effectively explore such space?
How do we guide such search process?
How do we evaluate the “quality” of a network?
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Our Approach to Automatic Design
We cast this problem into the framework of combinatorialoptimization
1 A Boolean network is a point in the search space2 A suitable objective function (OF) is defined3 Search space is equipped with a notion of neighborhood
(topology)4 We choose and apply a (meta)heuristic search strategy5 OF evaluation can be performed by sampling the network
state space
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Iterated Local Search Framework
Generic Algorithm Template
1: INPUT: a local search2: s ← generateInitialSolution()3: s∗ ← localSearch(s) {Stochastic Descent in our
experiments}4: while termination conditions not met do5: s′ ← perturbation(sbest)6: s′
ls ← localSearch(s′)7: s∗ ← acceptanceCriterion(s∗
, s′ls)
8: end while9: return s∗
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
The Boolean Network Toolkit
OF evaluation is the most computationally intensive task
A fast and flexible simulator is required
BnToolkit
Efficient library BN simulation and analysis
Written in C++
Open Source project
Available athttp://booleannetwork.sourceforge.net
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
The Boolean Network Toolkit
OF evaluation is the most computationally intensive task
A fast and flexible simulator is required
BnToolkit
Efficient library BN simulation and analysis
Written in C++
Open Source project
Available athttp://booleannetwork.sourceforge.net
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Applications
Our design methodology successfully applied to threeproblems in biological modelingBNs with maximally distant attractors
We can study properties of more biologically plausiblenetworks
Boolean networks as classifiersInvestigate an important topic in artificial learning systems
BNs as models of cellular differentiationIt helps to validate the model against real dataWe aim to generate networks that predict behaviour ofexisting cell types
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
BNs with Maximally Distant Attractors
Limitations of Classic BNs
Attractors in BNs can be interpreted as cell typesThe attractor set in classic BNs is very similar attractors(they differ for just a few values)They are no longer distinguishable if a different updatescheme is usedSynchronous deterministic update could generate spuriousattractors
Overcoming the Limitations
We aim at designing synchronous deterministic BNs inwhich attractors be as much different as possible
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
BNs with Maximally Distant Attractors
Limitations of Classic BNs
Attractors in BNs can be interpreted as cell typesThe attractor set in classic BNs is very similar attractors(they differ for just a few values)They are no longer distinguishable if a different updatescheme is usedSynchronous deterministic update could generate spuriousattractors
Overcoming the Limitations
We aim at designing synchronous deterministic BNs inwhich attractors be as much different as possible
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Boolean Networks as Classifiers
Biological motivations
Cell behaviour can change in response to differentconditions in the environmentFrom an abstract standpoint, a cell is able to solve aclassification problem
Environmental conditions are examples to classifyCell dynamics, represented by attractor states, areresponses
GoalDesign BNs which are able to solve the DensityClassification Problem (DCP)
Determine if a binary string contains more 0s than 1s
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Boolean Networks as Classifiers
Biological motivations
Cell behaviour can change in response to differentconditions in the environmentFrom an abstract standpoint, a cell is able to solve aclassification problem
Environmental conditions are examples to classifyCell dynamics, represented by attractor states, areresponses
GoalDesign BNs which are able to solve the DensityClassification Problem (DCP)
Determine if a binary string contains more 0s than 1s
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
A Model of Cellular Differentiation
Biological motivations
Overcoming the limitation of attractors/cell typescorrespondenceCell types are Threshold Ergodic Sets:
Sets of attractors. . .Stable under certain level of noiseNoise models external environmental conditions
Can describes differentiation trees of pluripotent cells
Goal
Impose constraints on:Attractors landscapesDifferentiation tree shapes
Ongoing researchStefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
A Model of Cellular Differentiation
Biological motivations
Overcoming the limitation of attractors/cell typescorrespondenceCell types are Threshold Ergodic Sets:
Sets of attractors. . .Stable under certain level of noiseNoise models external environmental conditions
Can describes differentiation trees of pluripotent cells
Goal
Impose constraints on:Attractors landscapesDifferentiation tree shapes
Ongoing researchStefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
A Model of Cellular Differentiation
Biological motivations
Overcoming the limitation of attractors/cell typescorrespondenceCell types are Threshold Ergodic Sets:
Sets of attractors. . .Stable under certain level of noiseNoise models external environmental conditions
Can describes differentiation trees of pluripotent cells
Goal
Impose constraints on:Attractors landscapesDifferentiation tree shapes
Ongoing researchStefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Summary
Competitive hybrid metaheuristic algorithm for HaplotypeInference by Parsimony
State-of-the-art metaheuristic algorithm for the FounderSequence Reconstruction Problem
Automatic design methodology of BNs successfully appliedto three modeling problems
Flexible and efficient Boolean network simulator software
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Activity I
Journal papers:Battarra, M., Benedettini, S., and Roli, A. (2011).Leveraging saving-based algorithms by master-slavegenetic algorithms. Engineering Applications of ArtificialIntelligence, 24:555–566Roli, A., Benedettini, S., Stützle, T., and Blum, C. (2012).Large neighbourhood search algorithms for the foundersequence reconstruction problem. Computers & OperationsResearch, 39(2):213–224Benedettini, S., Manfroni, M., Villani, M., Serra, R.,Gagliardi, A., Pinciroli, C., Birattari, M., and Roli, A.(accepted with minor revision). Learning Boolean networks:an approach with metaheuristics. Neurocomputing
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Activity II
Collaborations:Prof. Christian Blum, UPC, Barcelona, from sep. 2009 tomar. 2010Prof. Thomas Stützle, IRIDIA, ULB, Brussels, from sep.2010 to dec. 2010Prof. Roberto Serra and Marco Villani, University ofModena and Reggio Emilia, 2009-ongoing
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
Metaheuristics for Search Problems inGenomics
— New Algorithms and Applications —
Stefano Benedettini1
DEIS, Alma Mater Studiorum Università di Bologna, Campus of Cesena, Italys.benedettini@unibo.it
Stefano Benedettini Metaheuristics for Search Problems in Genomics
IntroductionThe Haplotype Inference Problem
The Founder Sequence Reconstruction ProblemBoolean Network Design
References I
[1] Battarra, M., Benedettini, S., and Roli, A. (2011).Leveraging saving-based algorithms by master-slave geneticalgorithms. Engineering Applications of Artificial Intelligence,24:555–566.
[2] Benedettini, S., Manfroni, M., Villani, M., Serra, R.,Gagliardi, A., Pinciroli, C., Birattari, M., and Roli, A.(accepted with minor revision). Learning Boolean networks:an approach with metaheuristics. Neurocomputing.
[3] Roli, A., Benedettini, S., Stützle, T., and Blum, C. (2012).Large neighbourhood search algorithms for the foundersequence reconstruction problem. Computers & OperationsResearch, 39(2):213–224.
Stefano Benedettini Metaheuristics for Search Problems in Genomics
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