Search

Antibiotics Resistance Evolution and Fitness Landscapes----------------------------------------------------------------------------EvoSysBio Landscapes of Incomplete Fitness Traits (LIFTs) are much easier to quantify accurately than full fitness landscapes because of the more limited nature of LIFTs, which only focus on Incomplete Fitness Traits (IFTs) in well-defined environments. In contrast, full fitness landscapes are extremely difficult or impossible to quantify using current methods, even in very simple systems.Here an example LIFT is shown. It focuses on the ability of microbes to continue to grow in the presence of a specific antibiotic due to their acquisition of resistance mutations. The more consequential IFT shown is the Minimum Inhibitory Concentration (MIC) of the antibiotic preventing cells from growing. The 5-dimensional genotype space is represented as a 1 dimensional mutational distance between the initial and final genotype.Acknowledgements--------------------------Thanks to Daniel Weinreich for providing data, helpful discussions; to Guillaume Achaz for permission to use output from the Magellan website as the basis for this figure; and to both for sharing these contributions under the CC-BY-4.0 license.

Antibiotics Resistance Evolution and Fitness Landscapes----------------------------------------------------------------------------Landscapes of Incomplete Fitness Traits (LIFTs) are much easier to quantify accurately than complete fitness landscapes because of the more limited nature of LIFTs, as they only focus on Incomplete Fitness Traits (IFTs) in well-defined environments. Here an example LIFT is shown that focuses on the ability of microbes to continue to grow in the presence of a specific antibiotic due to their acquisition of resistance mutations. The more consequential IFT shown is the Minimum Inhibitory Concentration (MIC) of the antibiotic that will prevent cells from growing. The 5-dimensional genotype space is represented as a 1 dimensional mutational distance between the initial and final genotype.Acknowledgements--------------------------Thanks to Daniel Weinreich for providing data and for helpful discussions, Guillaume Achaz for permission to use output from the Magellan website as the basis for this figure, and to both for sharing their content under CC-BY-4.0.

Evolutionary Systems Biology is a new and growing field that builds on the successes of evolutionary biology and systems biology in order to quantify evolution better. More thorough introductions can be found elsewhere (see refs on the last slide). The purpose of this presentation is to summarize an extraordinarily rich conceptual framework in a nutshell of 10 slides to provide a quick overview. The guiding assumption is that the complete quantification of full fitness landscapes can be seen as a keystone that rests on and requires most or all other results from biology. Since biology has quite some way to go before this goal comes into reach, EvoSysBio is defining a framework for Landscapes of Incomplete Fitness Traits (LIFTs) to accelerate progress towards understanding evolution better and predicting it more accurately. LIFTs are like the building blocks of full fitness landscapes, which can also be seen as full fitness causality networks. Since LIFTs are much easier to observe and simulate, it should become possible to develop standardized forms in which to collect LIFT observations in order to help combine them into more comprehensive fitness causality networks.EvoSysBio has a very long way to go before the ambitious goals presented here will come into reach for more complex systems, but we have to start somewhere. It took about 30 years from publishing faster sequencing methods to the era of genomics ...These slides update and clarify the conceptual framework for EvoSysBio as presented elsewhere (eg. Loewe 2009, see ref below, which still contains the most thorough review of work demonstrating the feasibility of computationally predicting all LIFT types; however, please use the improved nomenclature presented on the slides here). Many of the goals of EvoSysBio are too difficult to attain without an advanced programming language that supports biological concepts well. This realization has inspired the author to develop the new programming language Evolvix, which is being designed to enable EvoSysBio analyses and simultaneously make biological modelling easier in general (development of Evolvix is currently under way).Producing these slides helped to clarify the long-term goals and a wide net of biology-related requirements, which drive the practical work on developing Evolvix as supported by the National Science Foundation.

This is the final report on reproducibility@xsede, a one-day workshop held in conjunction with XSEDE14, the annual conference of the Extreme Science and Engineering Discovery Environment (XSEDE). The workshop's discussion-oriented agenda focused on reproducibility in large-scale computational research. Two important themes capture the spirit of the workshop submissions and discussions: (1) organizational stakeholders, especially supercomputer centers, are in a unique position to promote, enable, and support reproducible research; and (2) individual researchers should conduct each experiment as though someone will replicate that experiment. Participants documented numerous issues, questions, technologies, practices, and potentially promising initiatives emerging from the discussion, but also highlighted four areas of particular interest to XSEDE: (1) documentation and training that promotes reproducible research; (2) system-level tools that provide build- and run-time information at the level of the individual job; (3) the need to model best practices in research collaborations involving XSEDE staff; and (4) continued work on gateways and related technologies. In addition, an intriguing question emerged from the day's interactions: would there be value in establishing an annual award for excellence in reproducible research? Overview