Scientific “ecosystem” paper now published in F1000 Research

Biosystems Analytics

I’ve previously blogged about our PeerJ Pre-print on moving away from the dominant metaphor of the scientific enterprise as “pipeline” leading to professorial positions in universities, towards a metaphor of diverse “ecosystem”. The paper has now been published in F1000 Research and has already garnered one peer review:

Lancaster AK, Thessen AE and Virapongse A. A new paradigm for the scientific enterprise: nurturing the ecosystem [version 1; referees: 1 approved]. F1000Research 2018, 7:803
(doi: 10.12688/f1000research.15078.1)

One the major points of the paper is that we need to move away from the currently closed system that emphasizes artificial scarcity (e.g. in journal spots), towards a system that emphasizes abundance, and we feel that publishing in journals that use post–publication and transparent peer review (like F1000 Research) helps us “walk-the-walk” as we build those new ecosystems.

Table 1 from the paper reinforces this point: illustrating the contrasting language between…

View original post 6 more words


The limitations of Big Data in life science R&D

Biosystems Analytics

Big Data has become an increasingly large presence in the life science R&D world, but as I have blogged about previously, increasingly larger datasets and better machine algorithms alone, will not leverage that data into bankable knowledge and can lead to erroneous inferences.  My Amber Biology colleague, Gordon Webster has a great post over on LinkedIn leavening the hype around Big Data, pointing out that analytics and visualizations alone are insufficient for making progress in extracting knowledge from biological datasets:

Applying the standard pantheon of data analytics and data visualization techniques to large biological datasets, and expecting to draw some meaningful biological insight from this approach, is like expecting to learn about the life of an Egyptian pharaoh by excavating his tomb with a bulldozer

“-omics” such as those produced by transcriptomic and proteomic analyses are ultimately generated by dynamic processes consisting of individual genes, proteins and other molecules…

View original post 130 more words

Where is this cancer moonshot aimed?

Biosystems Analytics

Much has been made of the recent announcement of VP Biden’s cancer moonshot program.  In these days of ever tightening research funding, every little bit helps, and the research community is obviously grateful for any infusion of funds.   However, large-scale approaches to tackling cancer have been a staple of funding ever since Nixon announced his “War on Cancer” back in the 1970s, and any new approaches must grapple with the often complicated history of research funding in this area.  Ronin Institute Research Scholar, Curt Balch, has a interesting post over on LinkedIn breaking down some of these issues.

What seems relatively new in this iteration of the “war”, however, is a greater awareness of the lack of communication between different approaches to those working on cancer.  Biden has specifically mentioned this need and has pledged to “break down silos and bring all cancer fighters together”.  This…

View original post 536 more words

Life scientists: what are you looking to code?

Biosystems Analytics

My Amber Biology colleague, Gordon Webster, and I are working on an accessible introduction for biologists interested in getting into programming.  Python for the Life Scientists will cover an array of topics to introduce Python and also serve as inspiration for your own research projects.

But we’d also like to hear from you.

What are the life science research problems that you would tackle computationally, if you were able to use code?

You can contact us here in the comments, on  or on the more detailed post:

“Are you still using calculators and spreadsheets for research projects that would be much better tackled with computer code?” on the Digital Biologist.

View original post

Connecting the cognitive dots in systems biology modeling

Biosystems Analytics

Building computational models in any discipline has many challenges starting at inclusion (what goes in, what’s left out), through to representation (are we keeping track of aggregate numbers, or actual individuals), implementation (efficiency, cost) and finally  verification and validation (is it correct?).  Creating entire modeling softwareplatforms intended for end-user scientists within a discipline brings an entirely new level of challenge.  Cognitive issues of representation within the modeling platform – always present when trying to communicate the content of a model to others – become one of the most central challenges.  To create modeling platforms that, say, a biologist might want to use, requires paying close attention to the idioms and metaphors used at the most granular level of biology: at the whiteboard, the bench, or even in the field.

Constructing such software with appropriate metaphors, visual or otherwise, requires close collaboration with working scientists at every…

View original post 402 more words

Quantifying cost-effectiveness of scientific cloud computing in genomics and beyond

Biosystems Analytics

On-demand computing, often known as “cloud computing” provides access to the computing power of a large data center without having to maintain an in-house high performance computing (HPC) cluster, with attendent management and maintenance costs.  As even the most casual observers of the tech world will know, cloud computing is growing in any many sectors of the economy, including scientific research.  Cheap “computing as a utility” has the potential to bring many large-scale analyses within reach of smaller organizations that may lack the means or infrastructure to run a traditional HPC.  These organizations or individuals could include smaller clinics, hospitals, colleges, non-profit organizations and even individual independent researchers or groups of researchers.  But beyond the industry enthusiasm, how much can cloud computing really help enable low-cost scientific analyses?

There is now a veritable smorgasbord of offerings from many different vendors, but the big players are Amazon…

View original post 1,638 more words

Biologist Mickey von Dassow on collaboration, citizen science and ctenophores

Biosystems Analytics

Mickey von Dassow is a biologist who is interested in exploring how physics contributes to environmental effects on development. He created the website Independent Generation of Research (IGoR) to provide a platform to allow professional scientists, other scientists, non-scientists or anyone to collaborate and pursue any scientific project that they are curious about. I talked to him recently about his new site, citizen science and the future of scientific research and scholarship.

Mickey_headshot Mickey von Dassow

Can you describe your background?

My background is in biomechanics and developmental biology. My Ph.D. asked how feedback between form and function shapes marine invertebrate colonies. During my postdoc I worked on the physics of morphogenesis in vertebrate embryos, specifically focusing on trying to understand how the embryo tolerates inherent and environmentally driven mechanical variability. Since then I have been independently investigating interactions among ecology, biomechanics, and development of marine invertebrate embryos, as…

View original post 2,009 more words