Posts Tagged Systems
Nobel laureate Sir Paul Nurse tells the story of another great idea in biology – genes as the basis of heredity – in a lecture at the Royal Institution in London. It all started with the gardening monk Gregor Mendel and his peas in the 19th century and reached a key milestone with the unravelling of the molecule of heredity, the DNA double helix, by James Watson and Francis Crick in 1953
The great ideas of biology covered are
- the cell
- the gene
- natural selection
- Life as chemistry
- Biology as an organized system.
Similarly to “A Brief Introduction to Genetics” David Murawsky (as mentioned around here before, but hey, they repeat stuff on TV all the time, and not only the goodies) put another impressive clip out there: “18 Things You Should Know About Genetics“. Enjoy!
Just recently, I found this REAL bug sitting on the edge of my screen while coding – the (admittedly quite nerdy) irony of it is hard to miss. Rest assured, I ‘guided’ it away from ‘the system’ to the outside as gently as possible, resisting any impulse to to squash it using the keyboard on the spot. You know the rule, “Never touch a running system”, and unfortunately double-clicking and pressing <DEL> didn’t seem to work here.
A more funny (and nerdy) take on debugging code is this video by Atlassian called “Software Bugs” that made my morning:
“All bugs welcome! … create some buzz, … and when the spider gets here, I guess we can start talking web development”
Some more in-depth understanding of the issues involved is provided in this talk by Prof. Stephen Freund on “Stopping the Software Bug Epidemic” – he also touches on the halting problem, memory leaks and parallel code execution.
Although the talk is very informative throughout while presenting the basic issues in an entertaining way, I wonder why he didn’t mention the “Dining Philosophers Problem” – I guess it’s hard to trace deadlocks by automated checkers? In addition, he only refers to the (ancient) waterfall-modell of software engineering. Some comments on how more modern development philosophies (eXtreme programming, agile etc.) fit into the picture would have been nice. Anway, Happy deBugging!
A collection of free science books is available (in .pdf format) at INTECHopen – among them are the following ones on experimental / computational aspects of systems biology and on HMMs which might be of interest:
During my studies, I made a habit of visiting the Cambridge University Press bookstore at least once a month. As a kind of wishlist, I noted potentially interesting new books – something I might continue in an open format here. Whenever the occasion and funding presented itself, I could draw from that list and get what seems most relevant and helpful. So far, reading on a tablet or screen does not quite have the same sensual appeal as a book for me quite yet – call me hopelessly old-fashioned. But of course, this being the 21st century, the advantage of carrying an entire electronic library with you in a tin-box that weighs less than your average textbook is a point that’s hard to argue about, especially in combination with advanced search and analysis tools. Nevertheless, here are some recent publications available in classical dead-tree format:
Systems Biology: Simulation of Dynamic Network States
Bernhard Ø. Palsson, University of California, San Diego
EMBOSS User’s Guide
by Peter M. Rice, European Bioinformatics Institute, Hinxton
Alan J. Bleasby, European Bioinformatics Institute, Hinxton
Jon C. Ison, European Bioinformatics Institute, Hinxton
The European Molecular Biology Open Software Suite (EMBOSS) is a well established, high quality package of open source software tools for molecular biology. It includes over 200 applications for molecular sequence analysis and general bioinformatics including sequence alignment, rapid database searching and sequence retrieval, motif identification and pattern analysis and much more.
The entire list of CUP titles in the section “Genomics, bioinformatics and systems biology” is here.
The International Supercomputing Conference (ISC’11) in Hamburg just ended yesterday, and there’s plenty in terms of a video blog, social media feed, live-stream etc. to check out. There is quite some stuff happening in terms of hardware-developments in HighPerformanceComputing (HPC), also with respect to applications in the Life Sciences. Probably the main headline is that there is a new Nr.1: The new japanese supercomputer K (@ Riken) now packs more of a punch than the next five systems on the top500-list combined, displacing the Tianhe-1A, who took pole position last October.
However, some things have not changed:
* Linux is still the dominant OS
* Big Blue (IBM) still dominates the market, followed by HP and Cray
* The trend towards GPU acceleration continues (although the “K” doesn’t use them)
* massively parallel processing (MPP) systems continue to increase their share
for more in-depth-info, see http://www.hpcwire.com/
To continue with examples in communicating “what is it all good for”: the University of British Columbia’s former “iCapture Centre” (now James Hogg Research Centre) has done a great piece on linking Asthma treatment with the underlying genetic and molecular mechanisms. (Here is Part1 and Part2)
They did some impressive work for nature on RNAi:
This series of three animations was created for Nature Reviews Genetics in 2004, built originally to live on Nature’s site temporarily, perhaps a month or so.
Seven years later, with millions of hits, it has proven to be one of the most popular things Nature has ever had on its site.
Plans are in the works to revisit the piece, since much has been learned about RNAi in the interrim. Look for the new RNAi animation by the end of 2011.
You’ve heard it before.
You’ve experienced it.
The data deluge is not really helping to generate knowledge, let alone novel insights,
or is it?
The mantra of data-mining is: just somewhere in the down there in the mountains of biological data the jewels are waiting to be found, you just have to keep digging. For myself, it never works quite that way.
I’m not getting into a discussion about hypothesis-driven vs. data driven science here and now, but this one is a breeze of different thinking. Although I don’t agree to it all, it’s worth watching: