Production Management:
New Lessons from Biology
James E. Metherall, Ph.D., M.B.A.
Associate Professor of Human
Genetics
University of Utah
Major Points
1) Metabolic Regulation is an Extremely Complex
Operations Management Problem
1) Data Collection is NOT the Problem
1) Data Analysis is the Problem: Attempting to
Understand Metabolic Regulation Through
Simulation
1) Sharing Tools and Concepts: Beginning the
Discussion
Enzymatic Transformations
Substrate
Product
Enzyme: Glucokinase (2.7.1.1)
Simplifi ed View of Metabolic Pathways
Sigma-Aldrich
Simplified View of Metabolic Pathways
Sigma-Aldrich
Glossary of Terms
Production
Metabolism
Transformation Process
Reaction
Raw Material
Precursor/Substrate
Inline Inventory
Intermediate
Product
Product
Workstation
Enzyme
Tool
Cofactor
Workstation Blueprint
Gene
Workgroup
Multienzyme Complex
Organizational Layout
Subcellular Compartmentation
Transport
Transport
Outsourcing
Dining
Operations Management
Metabolic Regulation
Simplified View of Metabolic Pathways
Sigma-Aldrich
Major Points
1) Metabolic Regulation is an Extremely Complex
Operations Management Problem
1) Data Collection is NOT the Problem
1) Data Analysis is the Problem: Attempting to
Understand Metabolic Regulation Through
Simulation
1) Sharing Tools and Concepts: Beginning the
Discussion
Gene Expression: Making Enzymes
On
Transcription
Gene
DNA
mRNA
Off
Transcription
Gene
DNA
Translation
Enzyme
mRNA
Hybridize
Gene Expression
Microarrays
Synthetic
Probe
Annealed
Extend
* *
Flourescent
Nucleotides
*
*
* * **
* *
* * * **
*
*
Hybridize
** ** ** **** ** *
Microchip
** ** ** **** ** *
Destroy mRNA
** ** ** **** ** *
Polymerase
Photolithography
www.affymetrix.com
Affymetrix Chips
www.affymetrix.com
Affymetrix Gene Chip System
www.affymetrix.com
Affymetrix
Results
www.affymetrix.com
Gene Expression: Making Enzymes
On
Transcription
Gene
DNA
mRNA
Off
Transcription
Gene
DNA
Translation
Enzyme
Simplified View of Metabolic Pathways
Sigma-Aldrich
Major Points
1) Metabolic Regulation is an Extremely Complex
Operations Management Problem
1) Data Collection is NOT the Problem
1) Data Analysis is the Problem: Attempting to
Understand Metabolic Regulation Through
Simulation
1) Sharing Tools and Concepts: Beginning the
Discussion
Hierarchical
Clustering
Regulation of Cellular Cholesterol Metabolism
Acetyl CoA + Acetoacetyl CoA
HMG CoA
HMG CoA
Reductase
Mevalonate
7-DHC
LDL
LDL
Receptor
Cholesterol
Regulation of Cellular Cholesterol Metabolism
Acetyl CoA + Acetoacetyl CoA
HMG CoA
HMG CoA
Reductase
Mevalonate
7-DHC
LDL
LDL
Receptor
Transcriptional
Cholesterol
Sterol-Regulated Genes
Rab GGP Transferase
HMG CoA Synthase
Cyclin B1
Proteosome Subunit X
Ribosomal Subunit S19
b-Amyloid Precursor
NADP Transhydrogenase
COX-2
Acetyl CoA Carboxylase
LDL Receptor
Cdc2-related Protein
Leguain
Ribosomal Subunit L18A
Ferritin Heavy Chain
Disinigrin
FPP Synthase
Stearoyl Desaturase
Ribosomal Subunit L37
G3P Acyl-transferase
MLN64 (STar-related)
Calmodulin
Dermatan
Cullin 3
HMG CoA Reductase
Mer
F1 ATPase Subunit 6
Ribosomal Subunit S24
7-DHC Reductase
F1 ATPase Subunit d
IPP Isomerase
Squalene Synthase
Ubiquinone Oxidoreductase
Lysosomal Lipase
Electron Transfer Flavoprotein
Fatty Acid Synthase
Nonsense-mediated Decay Protein
Activin Type B
Cox-3
Squalene Epoxidase
Stearoyl Desaturase
Cathepsin L
Regulation of Cellular Cholesterol Metabolism
Rate Limiting
Multivalent Control
Acetyl CoA + Acetoacetyl CoA
Acetyl CoA + Acetoacetyl CoA
HMG CoA
HMG CoA
HMG CoA
Reductase
LDL
LDL
Receptor
HMG CoA
Reductase
Mevalonate
Mevalonate
7-DHC
7-DHC
Cholesterol
LDL
LDL
Receptor
Cholesterol
Simulating Cholesterol Homeostasis
L
EL
N
A
E1
I
EN
C
Si
aa
nuc
Sa
mRNA
Ex
Simulating Homeostasis
40000
+ LDL
[L]
30000
20000
10000
- LDL
0
0
400
800
1200
Time (sec)
1600
Multivalent Control Maintains
Intermediate Concentrations
Model
[I3]ss
- LDL + LDL
[I10]ss
- LDL + LDL
steady state concentration (arbitrary units)
Rate limiting
Multivalent
124
989
0
999
183
800
0
929
Regulatory Oscillations
A. Regulatory Oscillations
10000
6
[C] ss
7
5000
h=8
0
0
25
50
Time
75
100
Metabolic Simulation: GUI
Metabolic Simulation: Toolbox
Metabolic Simulation: GUI
Metabolic Simulation: Architecture
1) Relational Database
– All Model and Simulation Parameters
– All Result Data
2) Computational Strategy
– Multithreaded
• Reactions Calculate their own Propensities and “Fire” Themselves
• Metabolites Inform Dependent Reactions of Changes
– Distributed (Screen Saver)
• Look Up Simulation To Run from Database
• Run Simulation
• Return Data to Database
3) Hardware
– Processors
• Dual Core CPU: 2 Cores, 4 Threads
• GPU: 240 Cores; >1000 Threads
– GPU Development Tools - NVIDIA Cuda
• Windows, Mac, Linux
• Direct Calls - Visual Basic, C, C#, C++, Java
• Free, Open Source Development
– GPU Servers
• Desktop Monitors Flicker
• 960 Cores, 4000 Threads per 1U Unit
Major Points
1) Metabolic Regulation is an Extremely Complex
Operations Management Problem
1) Data Collection is NOT the Problem
1) Data Analysis is the Problem: Attempting to
Understand Metabolic Regulation Through
Simulation
1) Sharing Tools and Concepts: Beginning the
Discussion
Summary
1)
Production Management and Metabolism Share the
Same Goal
•
•
•
2)
Satisfy Demand
Efficiently Convert Raw Materials into Products
Series of Transformations
Both Processes Must be Efficient: They are Subject to
the Law of Survival-of-the-Fittest
•
3)
Efficiencies are Necessary to Ensure Survival of the
Organization and Organism, Respectively
While Operation Managers Strive to Create Efficient
Processes, Biologists Strive to Understand Efficient
Processes
•
•
Share Tools and Concepts
Can Metabolic Regulation Instruct Production Management
Practice?
•
Why not emulate what evolution has perfected?
•
Why not emulate what God has created?