Distribution of cell number and mass for different cell types in the human body (for a 70 kg adult man).

<p>The upper bar displays the number of cells, while the lower bar displays the contribution from each of the main cell types comprising the overall cellular body mass (not including extracellular mass that adds another ≈24 kg). For comparison, the contribution of bacteria is shown on the right, amounting to only 0.2 kg, which is about 0.3% of the body weight.</p

B/H ratio for different population.

<p>See Table B in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002533#pbio.1002533.s001" target="_blank">S1 Appendix</a> for full references.</p

The distribution of the number of human cells by cell type.

<p>Representation as a Voronoi tree map where polygon area is proportional to the number of cells. Visualization performed using the online tool at <a href="http://bionic-vis.biologie.uni-greifswald.de/" target="_blank">http://bionic-vis.biologie.uni-greifswald.de/</a>.</p

Revised Estimates for the Number of Human and Bacteria Cells in the Body

<div><p>Reported values in the literature on the number of cells in the body differ by orders of magnitude and are very seldom supported by any measurements or calculations. Here, we integrate the most up-to-date information on the number of human and bacterial cells in the body. We estimate the total number of bacteria in the 70 kg "reference man" to be 3.8·10¹³. For human cells, we identify the dominant role of the hematopoietic lineage to the total count (≈90%) and revise past estimates to 3.0·10¹³ human cells. Our analysis also updates the widely-cited 10:1 ratio, showing that the number of bacteria in the body is actually of the same order as the number of human cells, and their total mass is about 0.2 kg.</p></div

Bounds for bacteria number in different organs, derived from bacterial concentrations and volume.

<p>Bounds for bacteria number in different organs, derived from bacterial concentrations and volume.</p

Values of bacteria density in stool as reported in several past articles.

<p>Values of bacteria density in stool as reported in several past articles.</p

Back of the envelope estimate of the number of cells in an adult human body based on a characteristic volume and mass.

<p>Back of the envelope estimate of the number of cells in an adult human body based on a characteristic volume and mass.</p

A Minimalistic Resource Allocation Model to Explain Ubiquitous Increase in Protein Expression with Growth Rate

<div><p>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. We 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 <i>E.coli</i> as reflected in 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 may be partially driven by a global effect, not necessarily requiring specific cellular control mechanisms.</p></div

A strong positive Pearson correlation between the fraction out of the proteome and the growth rate is observed for a large number of proteins in two data sets.

<p>(A-B) Shown are histograms displaying the correlations of all proteins to growth rate in the data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref029" target="_blank">29</a>] (A) and [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref013" target="_blank">13</a>] (B). Functional protein groups are denoted by different colors. Thresholds defining high correlation are marked in dashed lines and further discussed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.s007" target="_blank">S4 Text</a>. (C) Shuffling the amounts of every protein across conditions for the data set from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref013" target="_blank">13</a>] reveals the bias towards positive correlation with growth rate is non-trivial.</p

Fraction of the proteome occupied by proteins that are strongly positively correlated with growth rate.

<p>The accumulated sum of the proteins that are strongly positively correlated with growth rate (defined as having a correlation above 0.5), as a fraction out of the proteome, with linear regression lines is shown. These proteins form a large fraction (≥ 50%) out of the proteome at higher growth rates. The accumulated fraction of the strongly correlated proteins doubles as the growth rate changes by about 5-fold. Assuming constant degradation rates, the trend lines correspond to protein half life times of ≈ 1.7 hours. Randomized data sets result in much fewer strongly positively correlated with growth rate proteins, implying a much smaller accumulated fraction (hollow circles).</p