bedtime reading

After our lab move we’re setting up from scratch. I was asked to provide a list of books that would be useful for new people to read in order to get an idea of modelling and systems biology. I thought I would put the list on here. I attempted to include a recommended levels of maths knowledge with each of the books. Beginner means you know basic high school maths. Intermediate means collage maths (A-Level), and advanced maths mean you like to self harm with numbers.

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SYSTEMS BIOLOGY
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An Introduction to Systems Biology: Design Principles of Biological Circuits – Uri Alon.

Very good book for all things systems and synthetic biology. Modelling genetic interactions, enzyme kinetics, metabolic networks etc. The maths in the examples is very difficult for non-mathematicians and there are no answers (I believe there is an answers book in preparation). Intermediate – advanced maths required.
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Systems Biology in Practice: Concepts, Implementation and Application – Edda Klipp

Written by mathematicians. This book covers all kinds of areas leading students as well as advanced researchers from different disciplines to an understanding of current ideas in the complex field of comprehensive experimental investigation of biological objects, analysis of data, development of models, simulation, and hypothesis generation. Advanced maths and some experience of systems biology literature required.
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Mathematical Models in Biology (Classics in Applied Mathematics) – Leah Edelstein-Kreshet

Very nice general book on modelling. Illustrated, well written, extensive examples. General biology (not exclusively molecular / microbiology). Written for mathematicians applying themselves to biology. Advanced maths required. A “nice to have” book.
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Systems Biology: Properties of Reconstructed Networks – Bernhard Palson.

Bernhard Palsson is a pioneer of systems biology with Hans Westerhoff. Very well written book. The book focuses on network interactions. Written for mathematicians and computer scientists who are modelling metabolic networks. Intermediate – Advanced maths required.
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Stochastic Modelling for Systems Biology – Darren James Wilkinson

This is the best book for stochastic modelling in systems biology. The book has a very good introduction but the chapters after are very complex for non-mathematicians. Concise and well written. Superhuman maths and statistics required.
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Simulating, Analysing, and Animating Dynamical Systems. A guide to XPPAUT for Researchers and Students. Bard Ermentrout.

This book describes how to set up and use the graphical modelling tool XPPAUT. AUT can be quite a tricky thing to use and this book attempts to teach you how to do it with simple explanation and worked examples. The book covers several concepts from the field of dynamical systems, including chaos theory, how systems depend on parameters, and how simple physical systems can lead to complicated behaviour.

XPPAUT is about 15 years old but is still more capable than many modern software packages like matlab and mathematica, and fills in gaps in the likes of Copasi with bifurcation analysis using AUTO.

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GENERAL MATHS
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Basic Mathematics for Biochemists – Athel Cornish Bowden.

Introductory text for biochemists who want to revise algebra, plotting, solving equations, differentiation and integration. Primer for undergrad biochemistry. Basic – intermediate maths required. Recommended text for biologists on the Manchester DTC systems biology program.
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Engineering Mathematics – K.A. Stroud

Excellent comprehensive maths book. Starts at basic working knowledge of maths and goes right up to M.Sc level.
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Choosing and Using Statistics: A biologists Guide. – Calvin Dytham.

The best introduction to statistics I’ve found. Written for students. Worked examples in minitab, excel and spss. No equations. Practical applications and worked examples. Good reference text.
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Lab Math: A Handbook of Measurements, Calculations and Other Quantitative Skills for Use at the Bench – Dany Spencer Adams.

Useful book for all the lab calculations that you should know… but don’t!

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That’s my list. If anybody has any other recommendations, let me know.

SB.OS

I was recently looking at combining some sbml model files together. You can do this in any text editor as sbml is just an xml flat file format (if that’s the correct terminology). Therefore, you could load each file into a text editor and cut and paste the components of the model. SBML however isn’t meant to be directly coded, and is more a method of transferring models between software tools. SBMLeditor however is a useful customized text editor for sbml files, and presents the file in a very visually accessible format. I haven’t got a great deal of experience of SBML however and no free time to go about editing big model files. I had a look on sbml.org and found semanticSBML; academic software designed for manipulating sbml models in a variety of ways. There’s a web based version as well as code that can be compiled locally on Windows and Linux. I attempted to install it on both operating systems but this was a bit of a nightmare. SymantecSBML also downloads a local copy of many databases such as chebi and kegg which makes the installation process very long and uses huge volumes of storage, and then ended with many errors for me. There’s a web based version using Java but this seems to not be running at the moment(?). I was about to take my usual approach to academic software and remove it when I happened upon SB.OS (“systems biology operational software”). SB.OS is a Ubuntu livecd containing many pre-installed systems biology tools, including symantecSBML. It’s a handy tool-kit to keep in your CD wallet. You can boot it up on any computer without installing anything, use the tools which are all pre-loaded, and not effect your primary operating system, making it excellent for the non-computer scientists like me. I fired up semanticSBML and had my models merged in a couple of minutes. SB.OS contains lots of tools from sbml.org including the systems biology workbench, SBMLeditor mentioned above, Taverna, Cytoscape and some simulators such as Cell Designer, Copasi, and XPPAUT. Hopefully it will continue to develop and update in the future.

PrimerPy

I recently designed primers for my impending qPCR work. qPCR primers are typically shorter than normal PCR primers, with a higher melt temp and produce short 150-200bp amplicons. This requires some slightly different design than normal. There are infinite resources of information on designing primers for qPCR but I found PrimerPy a useful bit of software for mine. It’s free for use and open source, built using the Python programming language.

The current version uses Primer3 to do the calculations, optimized for SyBr green qPCR. I sent off my primers a couple of weeks ago and ran than them last week on the RotorGene and got a signal so they seem to be ok. Should be ready to go once the yeast get going.

PrimerPy is available to download here: http://code.google.com/p/oligobench/wiki/PrimerPy

stuck in a bad project

God bless the Zheng Lab and their cheery lab monkeys. As I stare into the growing abyss each day this reminds me that dozens of other poor b******s do the same. God bless our PI’s.

parallel computing – biology style

I’ve been reading Regot et al’s Nature paper, out last November which is very similar to my project. It’s very interesting if anybody is into building biological analogues of electrical circuits. The work by Regot, led by Ricard Solé and Francesc Posas at University Pompeu Fabra in Barcelona has used a distributed computing approach and built logic processing circuits as an emergent property of many cells working together with their own individual simpler circuits. I find this quite interesting as, as the author states, building complex circuits in single cell lines can be a complicated task. Many components must interact predictably and robustly within a complex matrix of pre-existing interactions that are poorly understood. Ron Weiss of MIT presented a workshop at FEBS in Alpbach 2 years ago and explained how he could build and model interactions between a promoter and a reporter gene, but going beyond 2 promoters the behaviour becomes almost completely unpredictable. There’s just too much stochastic noise and underlying lack of understanding of the cellular chassis (hence the push for Craig Venter’s synthetic cell) to build predictable and reliable behaviours that can be thoroughly characterised and remain stable over a prolonged time period. Using very simple interactions in individual cells is simple to build (theoretically!), and provides a library of simple constructs. These libraries can then be hot swapped (to use a computer geek term) with each other to make much more complex behaviours from the culture as a whole. This is a true combination of systems and synthetic biology. The individual circuits remain robust as they provide a lighter load on the cell line to maintain, and the emergent property subsequently remains robust (for >9h in the paper). The combinations can also be simulated and optimum mixtures designed for the desired circuit behaviour.

In systems as complex as yeast (compared with E. coli) this is a much more desirable framework to work, in my humble experience anyway. Libraries of strains can be collected and combined to explore complex behaviours or engineer interacting microbiomes with useful engineering applications like biosensing and biological computing. Previous papers in Nature to do with bio-computing have utilized RNA aptamers (e.g Win and Smolke, 2008) to enable switching on and off gene expression to replicate boolean logic, however RNA secondary structure can be difficult to predict, and can form many different conformations under different physiological conditions. Designing RNA aptamers can also be challenging, compared with cloning. One could imagine this leads to RNA aptamer structures being unstable or the results difficult to replicate across laboratories without highly skilled expertise. It is interesting to compare this kind of approach with using multi-cellular systems for biological computing.

I wonder if the type of systems being built by Ragot et al could combine eukaryotic and prokaryotic systems and exploit features of each, such as the fast growth of E. coli with the signal processing abilities of yeast. One could exploit the environmental robustness of Saccharomyces or Candida with motile prokaryotes etc. I imagine the growth cultures and the potential for each to attempt to out compete the other would be somewhat complex!

pressure

Still no data from the lab, and many broken plasmids to sort out. Just ordered some additional components that will need to be investigated. They should arrive next week.

There is less than 10 months remaining to submission and many of my colleagues are sorting out their final experiments before beginning to write up. There’s no denying this project has been a disaster from start to finish. The months of lost time during the lab move compounded the problem, but I’m hoping to get something out of the new qPCR machine, couple it with some westerns, throw it in a model and get the hell out of dodge. I’m not sure if any Ph.D student has come this close to the wire, but it’s certainly an exercise in mental stability. If I can generate data by end of February then it should be a good stress reliever. Game on, I guess….