SEESO: A Semantically Enriched Environment for Simulation Optimization

Jun HanJohn A. Miller

Department of Computer ScienceUniversity of Georgia

Gregory A SilverCollege of Business, Anderson University

Introduction Simulation Optimization (SO) Using SO for Glycomics

◦ Overview of Glycomics◦ Glycan Quantification◦ Metabolic Pathways

Techniques for Simulation Optimization SESSO Framework Two Scenarios Conclusions

Conceptual model Domain Modeling Simulation Designing

and Execution Decision Parameter

Optimization

History of Simulation Optimization from 1987◦ 1987: “An art, not a science”◦ 1998: Systematic survey and introduction

◦ 2000: A sub-chapter in simulation textbooks◦ Numerous application and research on how to

integrate optimization and simulation◦ 2011: Regular track on Simulation Optimization in

WSC 2011

Decision parameter Discrete Continuous

Solution Random Search Gradient methods

Random search methods◦ Random walk, Simulated Annealing

Gradient based methods◦ Steepest descent, Conjugate gradient, BFGS

Heuristic methods◦ Genetic algorithm, Particle Swarm Optimization

Meta-modeling methods◦ Response surface methodology

Sample path optimization◦ Monte Carlo Simulation

Glycan◦ produced by linking saccharides and attached to

proteins and lipids Possible Applications

◦ Cell differentiation◦ Disease processes◦ Cancer Markers

Glycomics◦ “an integrated systems approach to structure-

function relationships of glycans” ◦ Identification◦ Quantification

Omics Overview.http://jdr.sagepub.com/citmgr?gca=spjdr;90/5/561 7

Experiments Analysis

Label-free methods Isotopic labeling

◦ Static IDAWG™◦ Dynamic IDAWG™

Mass Spectrometry

Modeling Simulation Optimization Statistics

9

MS Raw data

Raw data processing

Glycan Structures

Isotopic distribution calculation

Simulator OptimizerQuantification

and visualization

Yesoptimized?No

Pathway model

Mass Spectrum

model

Metabolism Biochemical reactions Metabolic Network

GalNAc (mucin-type) core synthesis/branchinghttp://www.ccrc.uga.edu/~moremen/glycomics/OglycanBranching/OglycanBranching/OglycanBranching.htm

SEESO: A Semantically Enriched Environment for Simulation Optimization

Bootstrapped by◦ JSIM: web-based simulation environment◦ ScalaTion: simulation environment using

domain-specific language (DSL)◦ DeMO: Discrete-event Modeling Ontology◦ SoPT: Simulation oPTimization ontology

SEESO

ScalaTion

JSIM

SoPT

DeMO

DomainModelerDomainModeler

Conceptual model

and Optimization

requirement

Optimization

result and

visualization

Problem Our Solution

Communication and sharing of domain model and optimization problem

Ontology

Transformation from domain Model to optimization algorithm

Domain Specific language (DSL)

Selection of proper optimization algorithms

Rule inferencing

Simulator, Optimizer and (possible) Cost Analyzer

Loosely Coupled Iterative approach

Simulator

CostAnalyzer

Optimizer

{x}0 {(x, Y=R(x))}i

{(x, Y=R(x), Z=c(Y))}i

(x*, Z*)

{x}i+1

Objective Function Steepest Descent, etc.

def solve (x0: VectorD): VectorD = {

var x = x0 // current point var xx: VectorD = null // next point var gr: VectorD = null // gradient

breakable { for (k <- 1 to MAX_ITER) { // determine direction search gr = if (usePartials) gradientD (df, x) // use functions for partials else gradient (fg, x)

xx = lineSearch (x, gr) if (abs (fg(xx) - fg(x)) < EPSILON) break x = xx}} // for

x} // solve

Establish connection between numerous real world problems and optimization algorithms

Top level classes:◦ Optimization Component◦ Optimization Problem◦ Optimization Method

Optimization Component

Optimization Problem

Optimization Method

has-componentcan-solve

Optimization Goal Constraint

Nonlinear Constraint

Quadratic Constraint

Linear Programming

is-a

Objective Function

Nonlinear Objective Function

Quadratic Objective Function

Linear Objective Function

is-a

is-a

is-a

Solution

Restriction

Binary Restriction

Integer Restriction

Real Restriction

Mixed Restriction

is-a

Solution Quality

Approximate Solution Exact Solution

Heuristic Solution

is-a

is-ais-a

is-a

is-a

is-ais-a

is-ais-a

Optimization component

is-ais-ais-ais-a is-a

Nonlinear Programming

Quadratic Programming

Linear Programming

is-a

is-a

Stochastic Programming

Optimization Problem

has-Constrainthas-Restriction

has-OptimizationGoal

Objective Function Constraint Optimization

GoalRestriction

has-ObjectiveFunction

Derivative Free

Optimization Method

Gradient based

Heuristic Method

Meta-Modeling Method

Random Search Method

Hooke and

Jeeves Direct Search

Simplex Method

Simplex Algorithm

Nelder Mead

Method

Conjugate Gradient Descent

Polak Ribiere

Conjugate Gradient

Interior Point

Method

Newton Method

Steepest Descent

Quasi Newton Method

BFGS Method

L-BFGS Method

Ant Colony Optimization

Genetic Algorithm

Bacterial Foraging

Optimization Algorithm

Particle Swarm

Optimization

Kriging RSM

Response Surface

Methodology

Quadratic Fit

Sample Path Optimization

Local Search

Simulated Annealing

Tabu Search

DFP Formula

A set of Rules Rule inferencing (Rete algorithm)

if (ObjectiveFunction is quadratic_objective_function) and (SolutionQuality is exact_solution) and (Constraint is none) and (Restriction is real_restriction) then (OptimizationAlgorithm is Steepest_Descent)

if (ObjectiveFunction is linear_objective_function) and (SolutionQuality is exact_solution) and (Constraint is integer_constraint) and (Restriction is integer_restriction) then (OptimizationAlgorithm is Simplex_Algorithm)

if (ObjectiveFunction is nonlinear_objective_function) and (SolutionQuality is heuristic_solution) and (Constraint is none) and (Restriction is real_restriction) then (OptimizationAlgorithm is Genetic_Algorithm)

Automatic Algorithm Configuration Algorithm execution using DSL

Rule Inference Engine

Optimization Algorithm Selection

Algorithm Configuration

Algorithm Execution

OptimizationOntology

RuleVisualization

Model definition using DeMO Code generation using ScalaTion DSL Optimization algorithm selection using SoPT Optimization execution using DSL

entry: Source

nurseQ:WaitQueuetoNurseQ

doctorQ:WaitQueue

toDoctorQ

nurse: Resource

doctor: Resource

door: Sink

toDoor

Gal1GalNAc1 Neu5Ac1Gal1GalNAc1 Neu5Ac2Gal1GalNAc1

β3 β3α3β3

α3

α6St6GalNAc2St6GalNAc1St3Gal1

CMP CMP CMP CMP

1 2

4 5

3

E + S ES E + Pkfkr

kcat

Gal1GalNAc1

St3Gal1

Neu5Ac1Gal1GalNAc1

St6GalNAc1

St6GalNAc2

Neu5Ac2Gal1GalNAc1

27

Quantitative glycomics needs simulation optimization

Integration of ontology and DSL can facilitate modeling, simulation and application of simulation optimization for domain modelers