Congratulations to Matt Faria – winner of a Best Paper award at the CNBS annual meeting for his paper ‘Minimum Information Reporting in Bio–Nano Experimental Literature’ Nature Nanotechnology 13, 777–785 (2018)
Claire Miller’s new preprint is available now on arXiv.
In epithelial tissues such as skin, stem cells divide in order to replace cells that are lost at the surface. The maintenance of the stem cell niche is therefore an important component of any mathematical model of an epithelial tissue. In this paper we investigate how current modelling methods can result in erroneous loss of stem cells from the stem cell niche. Using established models of skin we find we are unable to maintain a stem cell population without including additional unbiological mechanisms. We suggest an alternative modelling methodology to maintain the stem cell niche in which a rotational force is applied to the two daughter cells during the mitotic phase of division to enforce a particular division direction. This methodology reflects the regulation of orientation of the mitotic spindle during the final phase of the cell cycle. We show using an agent-based multicellular model of human skin that this additional, biologically plausible mechanism is sufficient to maintain the stem cell niche.
Stuart Johnston’s new #arxiv preprint on calculating measures of spatial correlation for environments containing obstacles:
Reproducibility of scientific results, or lack thereof, has received increasing attention over recent years. Computational studies, by their nature, should be amongst the most reproducible. However it often proves to be a challenge to reproduce computational results, even when code is made available. The need to adopt standards for reproducibility of claims made based on computational results is now clear to researchers, however there is still a great deal of debate about where responsibility for checking reproducibility lies, and about appropriate tools and approaches to ensure reproducibility of a computational result.
Many technologies exist to support and promote reproduction of computational results: containerisation tools like Docker, literate programming approaches such as Sweave, knitr, iPython or cloud environments like Amazon Web Services. But these technologies are tied to specific programming languages (e.g. Sweave/knitr to R; iPython to Python) or to platforms (e.g. Docker for 64-bit Linux environments only). To date, no single approach is able to span the broad range of technologies and platforms represented in computational biology and biotechnology.
In our new preprint “Reference environments: A universal tool for reproducibility in computational biology”, now available on arXiv, we demonstrate an approach and provide a set of tools that is suitable for all computational work and is not tied to a particular programming language or platform. We illustrate this approach, which we call ‘Reference Environments’, using examples from a number of published papers in different areas of computational biology, spanning the major languages and technologies in the field (Python/R/MATLAB/Fortran/C/Java).
The Reference Environments approach provides a transparent and flexible process for replication and recomputation of results. Ultimately, the most valuable aspect of this approach is the decoupling of methods in computational biology from their implementation. Separating the ‘how’ (method) of a publication from the ‘where’ (implementation) promotes genuinely open science and benefits the scientific community as a whole.
Read it here:
Daniel G. Hurley, Joseph Cursons, Matthew Faria, David M. Budden, Vijay Rajagopal, Edmund J. Crampin
Reference environments: A universal tool for reproducibility in computational biology
A thermodynamic framework for modelling membrane transporters – published in Journal of Theoretical Biology
Membrane transporters are proteins which facilitate the entry and exit of molecules into cells. Transport processes often require a source of energy in order to move substances against unfavourable concentration gradients or, in the case of charged species, against electrochemical gradients. This places thermodynamic constraints on the function of transporter proteins.
In our new paper, published in the Journal of Theoretical Biology, we outline an energy-based modelling framework, using the bond graph approach, with which to model and understand transporters. We apply this modelling approach to several key transporters that occur in heart cells (the sodium pump, and the calcium transporter SERCA).
This work has significance for all cell models which involve transport process, as the vast majority of mathematical models of transporter proteins in the scientific literature are not thermodynamically consistent, and may therefore give misleading results.
The paper is available here:
M. Pan, P.J. Gawthrop, J. Cursons, K. Tran, E.J. Crampin (2018)
A thermodynamic framework for modelling membrane transporters
Journal of Theoretical Biology
Congratulations to Michael and coauthors on this work.
Minimum information reporting in bio-nano experimental literature – published in Nature Nanotechnology
Our new paper setting out minimum information criteria for bio-nano research has been published in Nature Nanotechnology.
Download the paper here.
Read the Nature Nanotechnology editorial about our work here.
This work, led by Matt Faria, has been a project of the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology.
An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose – published in Interface
Our new paper on quantifying the influence of the distribution of nanoparticle size (‘polydispersity’) on delivered cellular dose has just appeared in Journal of the Royal Society Interface.
S.T. Johnston, M. Faria, E.J. Crampin (2018)
An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose.
J. R. Soc. Interface 15: 20180364
See it here: http://dx.doi.org/10.1098/rsif.2018.0364
Congratulations Stuart and Matt.
This work was funded through the Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology @ARCCoEBionano