This document summarizes various constraint-based modeling methods for metabolic networks. It describes flux balance analysis and its extensions, which optimize an objective function like biomass production while incorporating additional constraints. Methods are presented that integrate gene expression data, thermodynamic constraints, and regulatory information to construct condition-specific metabolic models. Applications to cancer metabolism are discussed, such as identifying synthetic lethal drug targets. Community modeling examples are also provided that simulate microbial community dynamics and host-pathogen interactions.

1. Constraining the metabolic genotype-
phenotype relationship using a phylogeny
of in silico methods
Dr. Nathan Lewis
Beng 212
Feb 17, 2015

2. Constraint-based modeling
Metabolism: a network of chemical reactions…
… with extra complexities
Lewis, et al. Nat Rev Microb, 2012

3. Modeling cellular objectives
Natural selection…
– selects traits that enhance growth, given the environment
Biomass objective Flux balance analysis
– optimizing the objective
Lewis, et al. Nat Rev Microb, 2012

4. The growing
toolbox of
constraint-based
methods for
computational
modeling
FBA:
popular/biased
Unbiased
Methods
Lewis, et al. Nat
Rev Microb, 2012

5. Flux balance analysis and the
addition of constraints
Optimization of a “biological
objective”
Many solutions
Geometric FBA
Lewis, et al. Nat Rev
Microb, 2012

6. Constraints on flux
FBAwMC – constraints based on
enzyme crowding
pFBA – minimizes enzyme
catalyzed flux

7. Accounting for changes in
media
DFBA

8. Exploring a variety of solutions
and coupled reactions
Flux variability analysis
Bayesian FBA
Flux coupling finder
Lewis, et al. Nat Rev
Microb, 2012

9. Simulating genetic
perturbations
Metabolite essentiality
MOMA
ROOM
Lewis, et al. Nat Rev
Microb, 2012

10. Metabolite essentiality for drug discovery
Kim, et al. Mol Syst Bio 2011

11. Considerations in strain design
Coupling production to a cell objective or selective
marker (growth? Enzymes?)
Is the perturbation realistic?
Lewis, et al. Nat Rev
Microb, 2012

12. Adding reactions for strain
design
OptStrain
– Test to see if a product can be made
using a universal reaction database
and host reactions
– Minimize the number of reactions
you must add from a universal
reaction database
– Growth couple the product by
reaction removal, if possible

13. Constraining directionality
with thermodynamic
constraints
Network refinement
Filling in gaps and
extending network

14. Thermodynamic constraints
Based on metabolite
http://www.ncbi.nlm.nih.gov/pubmed/21281568
Based on network topology
http://en.wikipedia.org/wiki/Group_contribution_method

15. Gap filling
Reed, PNAS, 2006

16. Adding regulatory constraints
Different paradigms
Lewis, et al. Nat Rev
Microb, 2012

17. Expression data as a constraint: Constraining flux
E-flux
Colijin, et al. Plos Comp Bio, 2009
+ Uses continuous
values for
expression levels
- Requires arbitrary
function mapping
expression to
upper bound of
reaction flux

18. Expression data as a constraint: Constraining flux
E-flux
Colijin, et al. Plos Comp Bio, 2009

19. Expression data as a constraint:
Context-specific model construction
Objectives:
* Flux objective function
(e.g. biomass)
– GIMME
– GIM3E
Add reactions with an
expression-based
penalty
* Minimize addition of low
expression reactions
– iMAT
* Maximize model
consistency with data
– MBA
– mCADRE
* Pathway addition from
differential expression
– MADE

20. GIMME

21. http://journal.frontiersin.org/article/10.3389/fphys.2012.00299/full

22. Does GIMME work?

23. Pathways evolved on a new substrate
Lewis, et al., unpublished

24. iMAT
MILP framework generates a context-
specific model
No biomass objective function needed
Maximizes the number of highly
expressed reactions that are active
and the number of lowly expressed
reactions that are inactive
Shlomi, et al., Nat Biotech, 2009

25. Metabolic Adjustment by
Differential Expression (MADE)
Adds/removes pathways based
on differential expression
Gives a view on how
metabolism changes
between states
Jensen and Papin, Bioinformatics, 2011

26. Probabilistic Regulation of
Metabolism (PROM)
Chandrasekaran and Price, PNAS, 2010

27. Model construction methods
Identify high
expression/confidence “core”
reactions
Ensure that all “core” reactions
are active
Eliminate as many others as
possible
http://journal.frontiersin.org/article/10.3389/fpls.2014.00491/full

28. Machado and Herrgård, PLoS Comp Bio, 2014

29. Which to use?
http://journal.frontiersin.org/article/10.3389/fpls.2014.00491/full

30. APPLYING THE
METHODS TO
STUDYING
CANCER
METABOLISM

31. Deregulated growth in cancer results from a
myriad of molecular changes
SNPs, indels, translocations, chromosomal aberrations
Aberrant post-translational modifications
Changes in DNA and histone modification
Altered xenobiotic metabolism
Variations in glycans
Metabolic rewiring
Oncometabolites

32. Contributions of
metabolism to cancer
Kroemer and Pouyssegur, Cancer Cell, 2008
Many mutations and changes are
connected to metabolism
Metabolic alterations are associated
with the hallmarks of cancer
Lewis and Abdel-Haleem. Front. Phys., 2013

34. Needless to say, it is not always clear how variations
in genomic sequence result in different phenotypes
What causes cancer?

35. Adding regulatory constraints
for cancer-specific models
Lewis and Abdel-Haleem. Front. Phys., 2013

36. ZnPP is an inhibitor of Hmox1
Zn2+
Frezza, et al. Nature, 2012

37. HMOX and FH are synthetically lethal
Only killed cells missing FH
(i.e., the cancer cells)

38. Omic analysis: improved resolution of your data
Essential knowledge understand causation in biology
– Physical laws (mass balance and thermodynamics)
– Interactions (genome-scale metabolic pathways)
– Components (-omes)

39. COBRA in Community Metabolism
Dynamics of
competition and
community
composition
modeled between
Geobacter
sulfurreducens and
Rhodoferax
ferrireducens.
Under low acetate
flux, Rhodoferax
dominates when
sufficient ammonia
is available.
Synthetic mutualism modeled with auxotrophic E. coli mutants.
The benefit of symbiosis is contrasted with the cost of sharing.
Evolution in
community modeled
by simulating genome
reduction from E. coli
to Buchnera aphidicola
in its aphid host.
Minimal gene set was
enriched in genome,
and simulated gene
loss order correlated
with phylogenically
reconstructed gene
loss order
Host-pathogen interaction modeled with M. tuberculosis.
Internalized Mtb biomass inferred by transcriptomic data and simulation.
Simulations showed a decreased glycolytic flux and increase glyoxylate shunt.
Lewis, et al. Nat Rev
Microb, 2012

40. Shameless plug for my website
There are ~200 COBRA methods out there now…
http://cobramethods.wikidot.com/