IN SILICO METHOD FOR MODELING METABOLISM AND GENE PRODUCT EXPRESSION

AT GENOME SCALE

Lerman, Joshua A., Palsson, Bernhard O.

Nat Commun 2012/07/03

SO FAR – METABOLIC MODELS (M-MODELS)

Predict reaction flux Genes are either ON or OFF Special ‘tricks’ to incorporate GE (iMAT) ‘tricks’ are imprecise, more tricks needed

(MTA) Objective function debatable Usually very large solution space Flux loops are possible leading to unrealistic

solutions. No regulation incorporated

NEW – METABOLISM AND EXPRESSION(ME-MODELS)

Add transcription and translation Account for RNA generation and degradation Account for peptide creation and degradation Gene expression and gene products explicitly

modeled and predicted All M-model features included GE and proteomic data easily incorporated No regulation incorporated.

ME-MODEL: THE DETAILS

THE CREATURE

Model of the hyperthermophilic Thermotoga maritime (55-90 °C)

Compact 1.8-Mb genome Lots of proteome data Few transcription factors Few regulatory states…

ADDING TRANSCRIPTION AND TRANSLATION TO

MODEL

MODELING TRANSCRIPTION(DECAY AND DILUTION OF M/T/R-RNA)

Flux creating mRNA: (GE) Fluxes deleting mRNA:

(mRNA transferred to daughter cell) (NTPNMP)

Controlled by two coupling constants: (mRNA half life, from lab measurements) (lab measured or sampling)

Fluxes are coupled: Means 1 mRNA must be removed for every

times it is degraded Cell spends energy in rebuilding NMPNTP

MODELING TRANSLATION:MRNAENZYMES

Flux creating peptides: Translation limited by , upper bound on rate

of single mRNA translation, estimated from protein length, ribosome translation-frame and tRNA linking rate (global)

Fluxes are coupled:

Means 1 mRNA must be degraded every times it is translated

MODELING REACTION CATALYSIS (Michaelis-Menten kinetics)

is turnover number is complex concentration is substrate concentration is substrate-catalyst affinity

Assume

Means one complex must be removed for every times it catalyzes

Whole proteome synthesized for doubling Fast catalysis faster doubling (dilution)

BUILDING THE OPTIMIZATION FRAMEWORK

M-MODEL - REMINDER

Total Biomass Reaction: Experimentally measure lipid, nucleotide, AA,

growth and maintenance ATP Integrate with organism to define reaction

approximating dilution during cell formation Cellular composition known to vary with Cellular composition known to vary with

media LP used to find max growth subject to

(measured) uptake rates

ME-MODEL

Structural Biomass Reaction: Account only for “constant” cell structure

Cofactors like Coenzyme A DNA like dCTP, dGTP Cell wall lipids Energy necessary to create and maintain them

Model approximates a cell whose composition is a function of environment and growth rate

Cellular composition (mRNA, tRNA, ribosomes) taken into account as dynamic reactions

LP used to identify the minimum ribosome production rate required to support an experimentally determined growth rate

VALIDATION

RNA-TO-PROTEIN MASS RATIO RNA-to-protein mass ratio (r) observed to

increase as a function of growth rate (μ) Emulate range of growths in minimal medium Use FBA with LP to identify minimum

ribosome production rate required to support a given μ

Assumption: expect a successful organism to produce the minimal amount of ribosomes required to support expression of the proteome

Consistent with experimental observations, ME-Model simulated increase in r with increasing μ

COMPARISON TO M-MODEL

max biomass on minimal media, many solutions

Sample and approx. Gaussian, chance of finding solution as efficient as ME-model.

Can be found by minimizing total flux (many solutions stem from internal flux loops).

OPTIMAL PATHWAYS IN ME-MODEL

Produces small metabolites as by-products of GE

Accounts for material and energy turnover costs

Includes recycling S-adenosylhomocysteine, (by-product of rRNA and tRNA methylation) and guanine, (by-product of tRNA modification)

Frugal with central metabolic reactions, proposes glycolytic pathway during efficient growth

M-Model indicates that alternate pathways are as efficient

Blue – ME-model paths, Gray – M-model alternate paths

SYSTEM LEVEL MOLECULAR PHENOTYPES

Constrain model to μ during log-phase growth in maltose minimal medium at 80 °C

Compare model predictions to substrate consumption, product secretion, AA composition, transcriptome and proteome measurements.

Model accurately predicted maltose consumption and acetate and H2 secretion

Predicted AA incorporation was linearly correlated (significantly) with measured AA composition 

DRIVING DISCOVERY Compute GE profiles for growth on medium:

L-Arabinose/cellobiose as sole carbon source Identify conditionally expressed (CE) genes -

essential for growth with each carbon source In-vivo measurements corroborate genes found

in simulation – evidence of tanscript. regulation CE genes may be regulated by the same TF Scan promoter and upstream regions of CE

genes to identify potential TF-binding motifs Found high-scoring motif for L-Arab CE genes

and a high-scoring motif for cellobiose CE genes L-Arab motif similar to Bacillus subtilis AraR

motif  

SUMMARY

ADVANTAGES

Because ME-Models explicitly represent GE, directly investigating omics data in the context of the whole is now feasible

For example, a set of genes highly expressed in silico but not expressed in vivo may indicate the presence of transcriptional regulation

Discovery of new TF highlights how ME-Model simulations can guide discovery of new regulons

DOWNSIDES

ME-model is more intricate then M-model, more room for unknown/incomplete knowledge

May keep ME-model simulations far from reality on most organisms Lack of specific translation efficacy for each

protein Lack of specific degradation rates for each mRNA lack of signaling Lack of regulatory circuitry

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