I N SILICO METHOD FOR MODELING METABOLISM AND GENE PRODUCT EXPRESSION AT GENOME SCALE Lerman, Joshua...
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Transcript of I N SILICO METHOD FOR MODELING METABOLISM AND GENE PRODUCT EXPRESSION AT GENOME SCALE Lerman, Joshua...
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
THANK YOU