cobra phylogeny paper slides

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods

Dr. Nathan LewisBeng 212

Feb 17, 2015

Constraint-based modelingMetabolism: a network of chemical reactions…

… with extra complexities

Lewis, et al. Nat Rev Microb, 2012

Modeling cellular objectivesNatural selection…– selects traits that enhance growth, given the environment

Biomass objective Flux balance analysis– optimizing the objective


The growing toolbox of constraint-based methods for computational modeling

FBA: popular/biased

Unbiased Methods


Flux balance analysis and the addition of constraints

Optimization of a “biological objective”Many solutionsGeometric FBA

Lewis, et al. Nat RevMicrob, 2012

Constraints on fluxFBAwMC – constraints based on

enzyme crowdingpFBA – minimizes enzyme

catalyzed flux

Accounting for changes in media

DFBA

Exploring a variety of solutionsand coupled reactions

Flux variability analysisBayesian FBAFlux coupling finder


Simulating genetic perturbations

Metabolite essentialityMOMAROOM


Metabolite essentiality for drug discovery

Kim, et al. Mol Syst Bio 2011

Considerations in strain design

Coupling production to a cell objective or selective marker (growth? Enzymes?)

Is the perturbation realistic?


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

Constraining directionality with thermodynamic constraints

Network refinement

Filling in gaps and extending network

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

Gap filling

Reed, PNAS, 2006

Adding regulatory constraints

Different paradigms


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

Expression data as a constraint: Constraining flux

E-flux

Colijin, et al. Plos Comp Bio, 2009

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

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

Does GIMME work?

Pathways evolved on a new substrate

Lewis, et al., unpublished

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

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

Probabilistic Regulation of Metabolism (PROM)

Chandrasekaran and Price, PNAS, 2010

Model construction methodsIdentify 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

Machado and Herrgård, PLoS Comp Bio, 2014

Which to use?

http://journal.frontiersin.org/article/10.3389/fpls.2014.00491/full

APPLYING THE METHODS TO STUDYING CANCER METABOLISM

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

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

Needless to say, it is not always clear how variations in genomic sequence result in different phenotypes

What causes cancer?

Adding regulatory constraintsfor cancer-specific models

Lewis and Abdel-Haleem. Front. Phys., 2013

ZnPP is an inhibitor of Hmox1

Zn2+

Frezza, et al. Nature, 2012

HMOX and FH are synthetically lethal

Only killed cells missing FH(i.e., the cancer cells)

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)

COBRA in Community MetabolismDynamics 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.


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

cobra phylogeny paper slides

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