Biology subjects

Ron Milo

Publications and source records attributed to Ron Milo.

3 recordsLinked to original sources

bioRxiv2016

Design principles of autocatalytic cycles constrain enzyme kinetics and force over-expression at flux branch points

A set of chemical reactions that require a metabolite to synthesize more of that metabolite is an autocatalytic cycle. Here we show that most of the reactions in the core of central carbon metabolism are part of compact autocatalytic cycles. Such metabolic designs must meet specific conditions to support stable fluxes, hence avoiding depletion of intermediate metabolites. As such, they are subjected to constraints that may seem counter-intuitive: the enzymes of branch reactions out of the cycle must be overexpressed and the affinity of these enzymes to their substrates must be relatively weak. We use recent quantitative proteomics and fluxomics measurements to show that the above conditions hold for functioning cycles in central carbon metabolism of E.coli. This work demonstrates that the topology of a metabolic network can shape kinetic parameters of enzymes and lead to seemingly wasteful enzyme usage.

Systems Biology↗

bioRxiv2016

Revised estimates for the number of human and bacteria cells in the body

We critically revisit the \"common knowledge\" that bacteria outnumber human cells by a ratio of at least 10:1 in the human body. We found the total number of bacteria in the \"reference man\" to be 3.9{middle dot}1013, with an uncertainty (SEM) of 25%, and a variation over the population (CV) of 52%. For human cells we identify the dominant role of the hematopoietic lineage to the total count of body cells ({approx}90%), and revise past estimates to reach a total of 3.0{middle dot}1013 human cells in the 70 kg \"reference man\" with 2% uncertainty and 14% CV. Our analysis updates the widely-cited 10:1 ratio, showing that the number of bacteria in our bodies is actually of the same order as the number of human cells. Indeed, the numbers are similar enough that each defecation event may flip the ratio to favor human cells over bacteria.

Systems Biology↗

bioRxiv2015

A minimalistic resource allocation model to explain ubiquitous increase in protein expression with growth rate

Most proteins show changes in level across growth conditions. Many of these changes seem to be coordinated with the specific growth rate rather than the growth environment or the protein function. Although cellular growth rates, gene expression levels and gene regulation have been at the center of biological research for decades, there are only a few models giving a base line prediction of the dependence of the proteome fraction occupied by a gene with the specific growth rate.\n\nWe present a simple model that predicts a widely coordinated increase in the fraction of many proteins out of the proteome, proportionally with the growth rate. The model reveals how passive redistribution of resources, due to active regulation of only a few proteins, can have proteome wide effects that are quantitatively predictable. Our model provides a potential explanation for why and how such a coordinated response of a large fraction of the proteome to the specific growth rate arises under different environmental conditions. The simplicity of our model can also be useful by serving as a baseline null hypothesis in the search for active regulation. We exemplify the usage of the model by analyzing the relationship between growth rate and proteome composition for the model microorganism E.coli as reflected in two recent proteomics data sets spanning various growth conditions. We find that the fraction out of the proteome of a large number of proteins, and from different cellular processes, increases proportionally with the growth rate. Notably, ribosomal proteins, which have been previously reported to increase in fraction with growth rate, are only a small part of this group of proteins. We suggest that, although the fractions of many proteins change with the growth rate, such changes could be part of a global effect, not requiring specific cellular control mechanisms.

Systems Biology↗