I N SILICO METHOD FOR MODELING METABOLISM AND GENE PRODUCT EXPRESSION AT GENOME SCALE Lerman, Joshua A., Palsson, Bernhard O. Nat Commun 2012/07/03

S O FAR – M ETABOLIC M ODELS (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

N EW – M ETABOLISM AND E XPRESSION (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

T HE C REATURE Model of the hyperthermophilic Thermotoga maritime (55-90 °C) Compact 1.8-Mb genome Lots of proteome data Few transcription factors Few regulatory states…

A DDING T RANSCRIPTION AND T RANSLATION TO M ODEL

M ODELING T RANSCRIPTION (D ECAY AND D ILUTION OF M / T / R -RNA)

M ODELING T RANSLATION : M RNA  E NZYMES

M ODELING R EACTION C ATALYSIS

B UILDING THE O PTIMIZATION F RAMEWORK

M- MODEL - REMINDER

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

V ALIDATION

RNA- TO -P ROTEIN M ASS R ATIO 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

C OMPARISON TO M- MODEL

O PTIMAL 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

S YSTEM L EVEL M OLECULAR P HENOTYPES 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 H 2 secretion Predicted AA incorporation was linearly correlated (significantly) with measured AA composition

D RIVING D ISCOVERY 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

S UMMARY

A DVANTAGES 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

D OWNSIDES 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

T HANK Y OU