http://elifesciences.org/content/3/e02935
Young Seok Ju, Ludmil B Alexandrov, Moritz Gerstung et al.
Mitochondrial DNA (mtDNA) mutations are often observed in cancer, but their causal significance in tumorigenesis has been called into question. This study extensively explores somatic mtDNA mutation in 1675 tumours, and compare their findings to a null model of random genetic drift. They find that silent/missense mutations occur at a rate predicted by simulations of random genetic drift, due to endogenous copying errors during mtDNA replication. However, severe mutations which result in protein truncation (such as frame shifts) are actually negatively selected for - meaning that tumours are observed to have fewer such mutations than expected by chance. Although low heteroplasmy levels of such mutations are tolerable, cells which cannot generate sufficient energy output have a lower fitness and are selected against.
Friday, 5 December 2014
Wednesday, 3 December 2014
Explicit Tracking of Uncertainty Increases the Power of Quantitative Rule-of-Thumb Reasoning in Cell Biology
http://www.cell.com/biophysj/abstract/S0006-3495%2814%2901124-2
Biological quantities often come with substantial associated uncertainty, either because experimental measurements have errors, or biological systems are naturally variable (or both). This uncertainty sometimes makes it hard to fully interpret rough calculations in biology, because uncertainties in quantities can combine in non-obvious ways. This (awesome :-) ) paper introduces an approach and web tool designed to perform calculations while explicitly tracking uncertainty, so that a calculation doesn't result in a single number but rather a descriptive interpretable distribution. The web tool is linked to the BioNumbers database of experimental measurements in biology, facilitating quick and easy "back-of-the-envelope" calculations in biology.
As a mitochondrial example, here's a toy calculation of the number of protons in a mitochondrion. BioNumber #100438 gives us a guess at the volume of a mitochondrion; BioNumber #105939 tells us the internal pH (each quantity has an uncertainty). We can use the two values to estimate the number of protons in a mitochondrion:
http://www.caladis.org/compute/?q=10^%28-%24105939%29*6e23*%24100438*1e-15&v=105939%3Alogn%2C7.98%2C0.07%3B100438%3Alogn%2C0.5%2C0.25&x=off&n=m&h=fd&a=rad
Biological quantities often come with substantial associated uncertainty, either because experimental measurements have errors, or biological systems are naturally variable (or both). This uncertainty sometimes makes it hard to fully interpret rough calculations in biology, because uncertainties in quantities can combine in non-obvious ways. This (awesome :-) ) paper introduces an approach and web tool designed to perform calculations while explicitly tracking uncertainty, so that a calculation doesn't result in a single number but rather a descriptive interpretable distribution. The web tool is linked to the BioNumbers database of experimental measurements in biology, facilitating quick and easy "back-of-the-envelope" calculations in biology.
As a mitochondrial example, here's a toy calculation of the number of protons in a mitochondrion. BioNumber #100438 gives us a guess at the volume of a mitochondrion; BioNumber #105939 tells us the internal pH (each quantity has an uncertainty). We can use the two values to estimate the number of protons in a mitochondrion:
http://www.caladis.org/compute/?q=10^%28-%24105939%29*6e23*%24100438*1e-15&v=105939%3Alogn%2C7.98%2C0.07%3B100438%3Alogn%2C0.5%2C0.25&x=off&n=m&h=fd&a=rad
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