transcriptional regulation in constraints-based metabolic models of e. coli published by markus...

Transcriptional Regulation in Constraints-based metabolic Models of E. coli

Published by Markus Covert and Bernhard Palsson, 2002

Outline

• Background– Metabolic Network Modeling– What is FBA model?– Application and Challenge of FBA– How to integrate FBA with regulatory constrains

• Method• Results

Metabolic network modeling(MNM)

Terminology I• Flux: the rate of flow of metabolites along a metabolic

pathway.

• Stoichiometric matrix

Terminology II

• Mass balance

• Constrains– Invariant constrains

• Physico-chemical

– Variant constrains• Environmental constraints• Regulatory constrains

FBA model

Summary of FBA model

Resource and tools for FBA

Application of FBA model

Challenges of FBA

• Reconstruction problem– Rely fundamentally on the availability of genome

sequences and annotations • Incomplete annotation – Some path and enzyme may be missing

• Select objective function

rFBA• Add regulatory constrains to FBA

– Boolean formalism ：AND , OR, NOT

– ON and OFF• If OFF, flux 0• If ON, the flux is calculated by FBA model

Trans= IF (G) AND NOT (B)

Rxn= IF (A) AND (E)

Metabolic and Regulatory Network Reconstruction

The metabolic network was reconstructed by identifying a set of biochemical reactions in the central E. coli metabolism, taken from the annotated genome sequence as well as from biochemical and physiological literature.

The regulatory network was derived from the literature data and represented as a set of regulatory rules following established procedures. These rules werebased on external conditions and/or internal conditions of the system.

Regulatory constraints were described using a Boolean formalism in which gene products are either available (ON) or unavailable (OFF) to the cell.

Method

Regulatory network: 149 genes 16 regulatory proteins and 73 enzymes 113 reactions

Transcriptional Regulation and the Calculation of Steady-state Metabolic Flux Distributions

FBA was used to determine an optimal metabolic flux distribution for the given conditions

For the purposes of these simulations, capacity constraints included maximum uptakerates of oxygen as well as substrates such as glucose, acetate, and lactose, as determined from growth experiments found in the literature.

The production of growth precursors in certain ratios was used here as an approximation. LINDO was used to calculate the optimal flux distributions.

Method

Changing Environments and Time-dependent Cell Behavior

The time constants that describe metabolic transients are fast (on the order of milliseconds to tens of seconds) as compared with the time constants associated with transcriptional regulation (generally on the order of a few minutes or slower) or cell growth (on the order of hours to days).

Therefore, dynamic simulations may be performed by considering the behavior inside the cell to be in a quasi-steady state during short time intervals relative to the environment.

Time-dependent Cell behavior Beginning at T0, all the condition as initial

Generate regulatory rules based on current environmental and internal conditions

Determined which genes are up-regulated

Set reactions related to up-regulated genes as unconstrained, otherwise set to 0

Run FBA to calculate the flux distribution

Terminated in 3sec, calculate environmental and internal conditions

Time D

elay

rFBA

Combined regulatory/metabolic network for central metabolism in E. coli

Mutant Study

Mutant Study

rFBA FBA

Correct prediction

106/116 97/116

Dynamic Growth Simulation

• Case 1: Aerobic growth of E. coli on acetate with glucose reutilization» When glucose is deleted from the environment, the acetate is then

reutilized as a substrate

• Case 2: Anaerobic Growth on glucose

• Case 3:Aerobic growth on glucose and lactose

Dynamic Growth Simulation

• Case 1: Aerobic growth of E. coli on acetate with minimal glucose» When glucose is deleted from the environment, the acetate is then

reutilized as a substrate

• Case 2: Anaerobic Growth on glucose

• Case 3:Aerobic growth on glucose and lactose

Aerobic growth on acetate with glucose reutilization

Aerobic growth on acetate with glucose reutilization

Dynamic Growth Simulation

• Case 1: Aerobic growth of E. coli on acetate with glucose reutilization» When glucose is deleted from the environment, the acetate is then

reutilized as a substrate

• Case 2: Anaerobic Growth on glucose

• Case 3:Aerobic growth on glucose and lactose

Anaerobic Growth on glucose

Anaerobic Growth on glucose

Dynamic Growth Simulation

• Case 1: Aerobic growth of E. coli on acetate with glucose reutilization» When glucose is deleted from the environment, the acetate is then

reutilized as a substrate

• Case 2: Anaerobic Growth on glucose

• Case 3:Aerobic growth on glucose and lactose

Aerobic growth on glucose and lactose

Aerobic growth on glucose and lactose

Reference• Transcriptional Regulation in Constraints-based Metabolic Models of

Escherichia coli, Markus Covert and Bernhard Palsson, doi: 10.1074/jbc.M201691200

• Markus W Covert, Christophe H. Schilling and Bernhard PalssonRegulation of Gene Expression in Flux Balance Models of Metabolism, J Theor Biol. 2001 Nov 7;213(1):73-88.

• Flux balance analysis of biological systems: applications and challenges, karthik Raman and Nagasuma Chandra, Brief Bioinform (2009) 10 (4): 435-449. doi: 10.1093/bib/bbp011

• Genome-scale metabolic networks, Marco Terzer Nathaniel D. Maynard Markus W. Covert and J org Stelling, DOI: 10.1002/wsbm.037

• Cellular Metabolic Network Modeling, Eivind Almaas, NetSci Conference 2007

• http://en.wikipedia.org/wiki/Main_Page