Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1203.4721

Growth efficiency as a cellular objective in Eschericia coli

Tommi Aho·Juha Kesseli·Olli Yli-Harja·Stuart A. Kauffman
View original source ↗Read source document ↗Export metadata ↓

Abstract

The identification of cellular objectives is one of the central topics in the research of microbial metabolic networks. In particular, the information about a cellular objective is needed in flux balance analysis which is a commonly used constrained-based metabolic network analysis method for the prediction of cellular phenotypes. The cellular objective may vary depending on the organism and its growth conditions. It is probable that nutritionally scarce conditions are very common in the nature and, in order to survive in those conditions, cells exhibit various highly efficient nutrient processing systems like enzymes. In this study, we explore the efficiency of a metabolic network in transformation of substrates to new biomass, and we introduce a new objective function simulating growth efficiency. We examined the properties of growth efficiency using a metabolic model for Eschericia coli. We found that the maximal growth efficiency is obtained at a finite nutrient uptake rate. The rate is substrate-dependent and it typically does not exceed 20 mmol/h/gDW. We further examined whether the maximal growth efficiency could serve as a cellular objective function in metabolic network analysis, and found that cellular growth in batch cultivation can be predicted reasonably well under this assumption. The fit to experimental data was found slightly better than with the commonly used objective function of maximal growth rate. Based on our results, we suggest that the maximal growth efficiency can be considered as a plausible optimization criterion in metabolic modeling for E. coli. In the future, it would be interesting to study growth efficiency as a cellular objective also in other cellular systems and under different cultivation conditions.

Explore related subjects

q-bio.MN

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tommi Aho, Juha Kesseli, Olli Yli-Harja, Stuart A. Kauffman. 2012-03-21. Growth efficiency as a cellular objective in Eschericia coli. https://arxiv.org/abs/1203.4721

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Minimality in Reflexive and Stoichiometric Autocatalysis

Autocatalysis, the ability of a chemical subsystem to sustain its own constituents when supplied with sufficient food molecules, has been closely related to the origin of life on Earth. Emerging from Wilhelm Ostwald's considerations about an explicit autocatalytic reaction, different notions of autocatalysis have been developed over the years. The two most prominent are reflexively autocatalytic F-generated sets (RAFs) and stoichiometric autocatalysis. After having shown that each RAFs is, under reasonable conditions, in general stoichiometrically autocatalytic, we examine here the relationship between the two notions of minimality: irreducible RAFs and autocatalytic cores. To this end, we overcome the obstacle that RAFs and stoichiometric autocatalysis have been formalized in distinct systems of chemical reactions, i.e., catalytic reaction systems (CRS) and chemical reaction networks (CRNs), respectively. We show that reactions in a CRS constitute equivalence classes of reactions of the corresponding CRNs w.r.t. their specific catalyzations. Using the fact that CRN and CRS can be canonically identified whenever each CRS reaction is associated with a single catalyzation, we demonstrate that the Kőnig graph of a monocatalyzed, irreducible RAF is composed of strong blocks devoid of food and waste species that are separated by reaction vertices, each of which contains an autocatalytic core. In fact, a single irreducible RAF can, in general, contain exponentially many autocatalytic cores.

q-bio.MN↗
arXiv2026

Implementation of Linear Regression and Linear Interpolation using Reaction Networks

Statistical inference is a fundamental component of data science. In this work, we focus on two classical inference techniques: regression and interpolation. We propose a reaction-network-based framework for implementing linear regression, including both univariate and multivariate settings, as well as linear interpolation. Our approach encodes the outputs of these inference techniques in the steady-state concentrations of species within the reaction network. A key ingredient of the construction is a novel generalized division module capable of handling division involving negative numbers. We validate the proposed framework through in silico implementations on standard synthetic datasets and obtain the expected regression and interpolation outputs.

q-bio.MN↗
arXiv2026

Mapping disease-regulatory flux through eQTL-based causal gene networks: a complex-trait framework applied to coronary artery disease

A substantial proportion of inherited susceptibility to common diseases is mediated through tissue-specific regulatory variation. The omnigenic model hypothesizes that much of this risk arises from distant (trans) regulatory effects that are propagated via gene-regulatory networks (GRNs) from numerous regulatory genes onto a relatively small set of core genes. Two major challenges have hindered an empirical assessment of this model. First, transcriptome-wide association studies primarily assess gene expression that is regulated locally (in cis), and thus miss trans-acting effects. Second, trans regulation has so far been examined only in aggregate, without identifying which regulatory genes transmit disease-associated signals to which target genes. Here, we present a framework that decomposes each gene's disease association into a local cis component and a set of trans components attributable to its upstream regulators, propagated along a directed causal GRN. For each gene, we estimate sparse Bayesian expression models and we assess model performance using out-of-sample prediction. Propagating trans signals through the inferred network yields a disease-regulatory flux map: a directed, signed representation that quantifies the contribution of each regulator to the disease association of each target gene. Applying this framework to seven tissues relevant to coronary artery disease, we demonstrate how genetic variation flows through the GRN to affect disease risk. Outgoing regulatory influences from individual genes are directionally heterogeneous, whereas disease-associated genes tend to integrate convergent input from multiple regulators; these convergent targets are enriched for cardiovascular-related biological processes. Consequently, each gene's disease association can be reinterpreted as a detailed allocation of the disease signal among its contributing regulators.

q-bio.MN↗