Zarecki, R., School of Computer Sciences, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
Oberhardt, M.A., School of Computer Sciences, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel, Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
Yizhak, K., School of Computer Sciences, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
Wagner, A., School of Computer Sciences, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
Segal, E.S., Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
Freilich, S., Newe ya'Ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel
Henry, C.S., Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, United States
Gophna, U., Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
Ruppin, E., School of Computer Sciences, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization). The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth. © 2014 Zarecki et al.