Bio3D 2.3.0 Released

Version 2.3.0 is a new minor release of the Bio3D package for structural bioinformatics.

This release, dated September 2016, provides a number of new features and enhancements including: new facilities for ensemble normal mode analysis (NMA) with all-atom elastic network model (ENM) and Gaussian network model (GNM), enhanced NMA calculations with the rotation-translation block (RTB) method, new “4-bead” coarse-grained ENM, more efficient reading of large PDB files using Rcpp, PDB annotation from the PFAM database, and more supported I/O file formats.

We have also updated online vignettes and other documentations. For a fine-grained list of changes, or to report a bug, please consult:

Version 2.3.0 will be available on CRAN shortly. For full install instructions see:

Major new/enhanced functions include:

  • aanma: All-atom ENM normal mode analysis (with RTB and 4-bead ENM supported)
  • aanma.pdbs: Ensemble NMA with all-atom ENM
  • gnm: Gaussian network model (GNM) calculations
  • gnm.pdbs: Ensemble NMA with GNM
  • dccm.gnm: Dynamical cross-correlation for GNM
  • pdbs2sse: Retrieve SSE from pdbs object with appropriate residue numbers for plotting
  • mask.dccm: Produce a new DCCM object with selected atoms masked
  • pdb.pfam: Function for PFAM annotation of PDB IDs
  • pymol.pdbs: Builds a pymol session from a ‘pdbs’ object
  • read.cif: Read a Protein Data Bank (mmCIF) coordinate file
  • read.dssp: For reading existing DSSP output files
  • read.stride: For reading existing STRIDE output files
  • read.crd: Read a CHARMM CARD (CRD) or AMBER coordinate file
  • read.prmtop: Read parameter and topology data from an AMBER PrmTop file
  • read.pdb: Use Rcpp to (more rapidly) read and parse PDB files
  • read.pdb2: Renamed old read.pdb function
  • plot.matrix.loadings: For plotting loadings obtained from pca.array()
  • community.aln: To align communities from two or more related networks
  • Supports ‘insert’ identifier
  • vmd.cna and vmd.cnapath: Renamed view.cna and view.cnapath
  • pymol.dccm, pymol.modes, pymol.nma, and pymol.pca: Renamed functions
  • plot.fasta: Improved plotting function for multiple sequence alignment
  • read.mol2, write.mol2,, trim, as.pdb: Read, write and manipulate mol2 files with functions

Happy Bio3Ding!

Your Scientific Writing

Here we collect advice, tips and wisdom to help you get your scientific papers written and published. A successful postdoc should aim for at least two or three high quality papers to be published each year. By high quality we mean good science in good journals. Every results chapter of a graduate students thesis should ideally be a published paper (typically ~4 papers).

The key to becoming a good efficient writer is to read extensively, practice writing frequently, and get feedback on your writing from others.

As a graduate student or postdoc you should always be writing something (e.g. drafting your manuscript, writing a review of some interesting and relevant research by others). By this I mean for you to get into the practice of writing and to get feedback on your writing from others.

Specific advice and tip:

Useful books:

How to Write a Lot: A Practical Guide to Productive Academic Writing” Paperback by Paul J. Silvia

Note. Barry has a range of books on scientific writing that you are welcome to borrow - just ask!

Science Writing Checklist

(modified from D. Zuckerman)

Your co-authors and research supervisor may not want to see your manuscript draft unless …


  • Have you followed the structure outlined and discussed here.
  • Does each paragraph focus on a single idea or point which is introduced/summarized in the paragraph’s first sentence? Remember that a paragraph is like a mini-essay - see this post on paragraph structure.
  • Is the flow of logic clear from paragraph to paragraph? From your draft, you should be able to (re)write the outline of the paper – in fact, just from the first sentences of the paragraphs. Check this.
  • Did you repeat key points in several sections to emphasize them?
  • Did you spend a lot more time on logic and clarity in addition to grammar and sentence structure?
  • Have you ruthlessly avoided complicated sentences? Remember that clarity will always trump elegance.


  • Does the abstract avoid distracting technical details?
  • Is it clear from the abstract why the work is new and worthy of publication?


  • Did you clearly explain the reason why the work was done – the existing problem?
  • Did you clearly and briefly explain what you did to make progress – what’s new?
  • Did you cite pertinent work done before? Even by people you may not like?
  • Did you read the introductions of several related papers to be sure you explained the ideas properly and cited the important work?


  • Did you remind your readers why a new/old method was used? You can write a mini-introduction for the Methods section.
  • Did you provide enough information so a reader could exactly reproduce your results? The whole procedure should be outlined, even if some details must be found in other work or Supplemental Information.


  • Did you make sure the main results are not buried? Again, use mini-introductions.
  • Did you save commentary and speculation for the Discussion section?


  • Did you clearly explain what’s new, as compared to previous work?
  • Did you avoid repeating information from the Results section?
  • Did you admit the limitations of your work?
  • Did you describe future applications, improvements, and generalizations?


  • Could a reader in a rush read just the Conclusions and learn just about everything (including acronyms)?
  • Did you avoid exaggeration and let the data speak for itself?
  • Did you acknowledge everyone who helped, including funding agencies?


  • Do figure titles describe the main point of each figure?
  • Have you put labels/arrows in the graphic to minimize effort for the reader?


  • Did you go back to the ‘General’ section above and double-check those paragraphs and logic – even in the Results section?
  • Did you make several revisions of the entire manuscript (after completing a first draft)?
  • Did you check journal-specific formatting – section order; figures; references?
  • Did you put the date on your draft so that a reader with two versions can be sure of reviewing the latest one?
  • Did you include page numbers so reviewers can easily reference sections when discussing the draft with you?
  • If there is a deadline did you tell your co-authors about it?

Suggested Readings (Journals and Books)

Below you will find a subjective list of the top journals that I recommend you monitor, and of course aim to contribute to.

I suggest you subscribe to PubMed feed(s) from at least the vital journals in each topic area along with specific keywords relevant to your research. This should provide you with weekly updates that you can peruse for papers of interest.

If you are just starting out you can begin with monitoring the listed vital Science General journals (i.e., Science/Nature/Cell/PLoS Biology/PNAS, plus PLoS Computational Biology/Biophys J and Bioinformatics). If you see that you can handle the inflow of updates (and usefully read some papers) for about 2 months - then gradually increase the number of journals/keywords you follow until you have a good covering of the journals listed below.

Also consider setting up citation alerts for new papers that cite a particular key article you are interested in. There are a number of ways to do this. One is to find the paper in the ISI Web of Knowledge, and then create a ‘Citation alert’, which can be directed to your RSS.

Roughly one half of your time should be spent reading. Half of your reading should be immediately related to your current research; the other half should be potentially related to your current or future research. You should learn how to read journals to extract the most useful information in the least time.

Topic Areas:



  • Biochemistry
  • Biophys. Journal
  • Curr.Opin.Str.Biol.
  • Nature Str.Biol.
  • Structure
  • J.Mol.Biol.
  • PLoS journals, esp. PLoS Biology and PLoS Comp. Biol.
  • Protein Science
  • Proteins: Struct., Funct., & Bioinfo.
  • Ann.Rev.Biophys. & …
  • Ann.Rev.Biochem.


  • Bioinformatics
  • J.Biol.Chem.
  • BMC Biophysics
  • Biopolymers
  • Protein Eng.
  • Quart. Rev. Biophys.
  • Adv. Protein Chem.
  • Trends in Biochem.Sci.
  • Biophysical Chemistry
  • Curr. Opin. Chem. Biol.
  • Critical Revs.Biochem.Mol.Biol.
  • Eur. Biophys. J.
  • EMBO Journal
  • EMBO Reports
  • J.Struct.Biol.
  • Physical Biology
  • Progress in Biophysics and Molecular Biology
  • Systems Biology

Worth Considering

  • BMC Bioinformatics
  • Comp. Biol. & Chem.
  • Chem. & Biol.
  • Methods in Enzymol.
  • Nucleic Acid Research
  • Adv.Enzymol.



  • J.Amer.Chem.Soc.
  • Acc.Chem.Res.
  • Angewandte Chemie
  • Chem.Rev.


  • Chem.Rev.
  • Chem.Soc.Rev.
  • Chem.Eng.News
  • Adv.Phys.Org.Chem.

Worth Considering

  • Macromolecules



  • J.Chem.Phys.
  • Chem.Phys.Lett.
  • J.Phys.Chem. B
  • Ann.Rev.Phys.Chem.
  • J.Comp.Chem.
  • J. Chem. Theory and Computation


  • Chem. Phys. Chem.
  • Mol.Phys.
  • Adv.Chem.Phys.
  • Faraday Discussions
  • Physics Today
  • Chem.Eng.Sci.
  • Molec.Simul.
  • J. Chem. Inform. Model.
  • J.Comp.Aided.Mol.Design
  • Phys. Rev. Lett.
  • Phys.Chem.Chem.Phys.

Worth Considering

  • Langmuir
  • Rev.Mod.Phys.
  • Phys.Rev. E

Science General


  • Nature
  • Nature Reviews…
  • Science
  • Cell
  • PLoS Biology
  • Proc.Natl.Acad.Sci. USA
  • J.Med.Chem.


  • Sci. Amer.
  • ACS Chemical Biology
  • HFSP Journal
  • Chemical Biology and Drug Design
  • Computational Science and Discovery
  • Nature Chemical Biology
  • Molec. Cell
  • Neuron
  • Trends Pharmacol.Sci.
  • Trends Biochem. Sci.
  • Trends Cell Biol.
  • Comp. Sci. Eng.
  • Cold Spring Harbor Symp.Quant.Biol.
  • J. Struct. Biol.

Worth Considering

  • PLoS One
  • J. Mol. Graph. Model
  • Multiscale Modeling & Simulation (SIAM)

Suggested Molecular Biophysics Books


Arfken, G.B.; Weber, H.J. (2001): Mathematical Methods for Physicists, 5th ed. Academic Press, New York.

Berry, R.S.; Rice, S.A.; Ross, J. (2000): Physical Chemistry, 2nd ed. Oxford, New York.

Cantor, Charles R.; Schimmel, Paul R. (1980): Biophysical Chemistry. Vols. 1, 2, 3. W. H. Freeman and Company, San Francisco.

Fersht, A. (1977): Enzyme Structure and Mechanism. 2nd ed. W.H. Freeman, San Francisco. 371 pages.

Stryer, L. (1995): Biochemistry. 4th ed. W.H. Freeman, San Francisco.


Allen, M.P.; Tildesley, D.J. (1987): Computer Simulation of Liquids. Oxford University Press, New York. 385 pages.

Jackson, John David (1962): Classical Electrodynamics. John Wiley and Sons Inc., New York.

Levine, I.N. (2008): Quantum Chemistry. 6th. ed. Allyn and Bacon, Inc., Boston.

McCammon,J.A.; Harvey, S.C. (1987): Dynamics of Proteins and Nucleic Acids. Cambridge University Press, Cambridge.

McQuarrie, Donald A. (1976): Statistical Mechanics. Harper Collins Publishers, New York.

Pollard, T.D.; Earnshaw, W.C. (2002): Cell Biology. Saunders, Philadelphia.

Jackson, M.B (2006): Molecular and Cellular Biophysics. Cambridge University Press, Cambridge.

Alberts, B. et al. (2007) Molecular Biology of the Cell. Garland Science, UK.


Abramowitz, Milton; Stegun, Irene A. (1965): Handbook of Mathematical Functions. Dover Publishing Inc., New York. 1046 pages. Tables of nearly every function and transformation you’ll ever need.

Ben-Naim, Arieh (1992): Statistical Thermodynamics for Chemists and Biochemists. Plenum, New York.

Biophysics Textbook Online.

Brooks, C.L.,III; Karplus, M.; Pettitt,B.M. (1988): Proteins: A Theoretical Perspective of Dynamics, Structure, and Thermodynamics. Wiley Interscience, New York.

Creighton (1993): Proteins: Structures and Molecular Properties. W.H. Freeman and Company, New York. 507 pages.

Dill, K.A.; Bromberg, S. (2003): Molecular Driving Forces: Statistical Thermodynamics in Chemistry and Biology. Garland Science, New York.

Frenkel, D.; Smit, B. (2002: Understanding Molecular Simulation - From Algorithms to Applications, 2nd ed. Academic Press, San Diego.

Frohlich, Herbert (1958): Theory of Dielectrics; dielectric constant and dielectric loss. 2nd ed. Clarendon Press, Oxford. 192 pages.

Ghez, Richard (2001): Diffusion Phenomena: cases and studies. Kluwer, New York.

Haile, J.M. (1997): Molecular Dynamics Simulation: Elementary Methods. Wiley, New York.

Hansen, Jean Pierre; McDonald?, Ian R. (1986): Theory of Simple Liquids. Academic Press, London. 556 pages.

Isaacson, Eugene; Keller, Herbert Bishop (1994): Analysis of Numerical Methods. Dover Press.

Mathews, Jon; Walker, R.L. (1970): Mathematical Methods of Physics. 2nd ed. W.A. Benjamin Inc., New York.

Rice, Stephen A. (1985): Diffusion-Limited Reactions. In: Comprehensive Chemical Kinetics. Vol. 25. (Eds: Bamford,CH; Tipper,CFH; Compton,RG) Elsevier, Amsterdam, 1-401. (Y)

Schlick, T. (2002): Molecular Modeling and Simulation: An Interdisciplinary Guide. Springer, New York.

Warshel, Arieh (1991): Computer Modeling of Chemical Reactions in Enzymes and Solutions. Wiley, New York.

Writting Style

Make your points.

Decide what are the key conclusions of your work and state them clearly and succinctly in the Abstract and in the Discussion and/or Conclusions sections. Far too many papers say that “important insights were gained”, but do not explicitly state the insights. This leaves the reader suspicious that no insights were, in fact, gained.

Be mindful of the context of your work.

A research project takes place within a specific intellectual and social setting, and a sophisticated paper is written with this context in mind. The consequences include exploration, brief or lyrical, of potential implications of your results; acknowledgement, muted or forceful, of related controversies; and well-chosen terminology.

Don’t ramble.

Beware of writing long, boring descriptions of your results. Focus on the results that support your conclusions. The other details can be summarized in tables, figures, and/or supplementary materials. Paragraphs should almost always be less than 1 double-spaced page in length.

Be consistent.

Scientific papers are intrinsically hard to understand; consistency can make yours easier to read. For example: Use consistent terminology even at the risk of seeming repetitive. Clarity is more important than elegance. (Thanks to Barry Honig for this paraphrased advice.) The order of the rows of data tables should match the order of presentation in Results. Then your reader can follow the text by going down the rows one by one. The column headers of your tables should be consistent across tables. For example, if you use G for free energy in one table, use it in the other tables as well.

Use correct grammar.

Incorrect grammar is jarring to the reader; more importantly, it obscures your meaning. Do not expect your reader to finish reading a paper whose every sentence must be deciphered. If English is not your native language, don’t hesitate to get help! Many universities employ professional grant writers who can also help with your papers.

Be sparing with italics and bold font

Use italics or bold font only when powerful emphasis is needed. Overusing these font changes will make your text cluttered and sloppy and will also deprive them of their ability to focus the reader’s attention. Avoid the temptation to use italic or bold fonts to rescue unclear writing. It doesn’t work. Italics and bold font read as a raising of the voice and are similarly ineffective at generating clarity.

Make your figures clear and simple.

A figure is a way to help your reader to understand your data more easily, so strive for clarity and simplicity. Give the most significant or authoritative curve in a graph the most authorititive appearance; typically, this will be a heavy, dark-colored, solid line. The other lines can be made dotted, dashed, lighter in color, etc. The most significant or authoritative data might be from the most reliable experiments or the highest-quality calculations, or from a novel method with which other methods are being compared.

Unless you are skilled at embedding tables and figures in just the right places in the text, put them at the end of the paper, subsequent to the citations: tables first, and then figures. This will allow your reader to find them easily. For in-house drafts, it is helpful to place the table captions and figure legends directly below their respective tables and figures. However, you may need to group the legends in a separate section in the version that is submitted for publication.

Color figures are appealing but may be costly to print and they do not add genuine value to a figure if it can be rendered equally clearly with black and white. For example, three curves in a graph can be readily distinguished by making one solid, one dotted, and the third dashed. When you do use color, avoid gaudiness. The default colors provided by Excel are neither visually appealing nor particularly clear, so do not rely on them for presentation-quality graphics.

Cite prior work

For the sake of documentation, not to mention courtesy to your colleagues, almost every statement of scientific fact – or supposed fact – in a paper should be accompanied by a citation. Exceptions are typically made for time-honored textbook material, such as Coulomb’s law, or possibly other information that may be regarded as generally known; but when in doubt, cite.

Provide supplementary materials.

A paper is supposed to allow the reader to reproduce your work. This ideal is not always achievable, but providing supplementary files of data, structural information, methods, graphs, etc., can go a long way. It is also an act of good scientific citizenship.

Working Drafts

Put the date below the title and author list each draft of a work, so that a reader with two versions of it can be sure of reviewing the latest one. Include page numbers so colleagues and referees can easily reference sections of section when discussing the paper with you. Make sure Figure and Table number matches the text correctly, even in a rough draft, so that your colleagues do not waste time struggling to determine which figure or table you are describing in the text.

Use fonts and line spacing that are easy on the eyes. These are simple things that take only a few extra minutes when preparing your manuscript, but they can make a big difference to the experience of reading it. Remember: the idea is to make the editor’s (and reviewers’) life easier, not harder.

Structuring Your Scientific Paper

Publishing papers is a key step in successful research. Progress in science depends on sharing ideas, technological developments and scientific discoveries, all of which depend on communication through publication. On a personal level it is essential for recognition, obtaining funding, promotion and your next job.

Often the logical order presented in a paper is defined after the data have been collated. The gaps are filled in during the final stages of writing or even after reviewer’s comments.

Accuracy and precision, concise, unambiguous and impersonal presentation clarity of style and consistency of format are essential. The greatest failings of the inexperienced are to meander in a disorganized pattern, to use far to many words and to write in a way which may be aesthetically pleasing but is open to misinterpretation.

Less is best, do not be repetitive let the data speak for themselves - do not labor the point. Tell your story in clear, simple language and keep in mind the importance of the ‘big picture’.

“Good writing on a subject is always shorter than bad writing on the same subject”

Planning and preparation

Start of simply with something that you can get down on paper, such as a list of what you have (perhaps then you will see what is missing). Get all the data together, write table and figure legends (even if some of them will get dumped later). Make a summary of what you have found. Then, most importantly make a plan.

Your plan should give you structure and a goal a basic scaffold that you will continue to modify and add detail to as your writing progresses.

The first stage of a plan may seem obvious but is often forgotten – be sure of what you are writing and who will read it. In other words what journal, what format, what length and detail will be aiming for? This is where you may find you are missing important pieces of information.

Discuss your plan with someone experienced – your supervisor. Put the plan away for a few days then come back to it. Does it still look as good as it did when you first wrote it?

Choose the easy part of the plan to write first – the methods section is an obvious starting point.

It can be helpful to write a summary at the beginning. It will inevitably need to be revised and improved later, but this (like the plan) helps to crystallize thoughts.

Help the reader with ‘sign posts’ When you are writing think of the reader. She/he wants to get the message simply and quickly. Help them through the document with clear headings and regular references to figures, objective rather than subjective discussion and clear ‘sign posts’.

Sign posts are often headings, but can also be brief statements at the beginning and end of sections summarizing and leading on to the next part.

The saying ‘say what you are going to say, say it, then say what you have said,’ is useful advice – provided the first and last are brief and informative, and not simply repetitive.

Anatomy of a Scientific Paper


Don’t blow it with a boring title! As an author, this is also your chance to draw your readers in, to entice them to read on. If the title (and abstract) are comprehensible to only a handful of people directly in your field, you have greatly narrowed the potential readership of your paper.

Titles like “Studies of X and Y…” or “Characterization of A and B” cab make eyes glaze over. They tell you nothing and don’t offer much hope for the rest of the paper. The title should highlight the main point of the paper.

Author List

Obviously the people who contributed to the work you are writting about.


The Abstract is a very short summary of the paper which must stand on its own because many journals and databases provide the abstract without the rest of the paper. The reader then uses the abstract to help decide whether or not it is worth the effort of looking at the main part of the paper.The Abstract should state the field of the work, the topic of the study, the methodologic approach, the main results, and the main implications. Avoid unspecific summaries, such as “important insights were gained”; or if this seems unavoidable, try at least to squeeze in a sample insight. Similarly, instead of merely saying a quantity was found to be “large”, give the actual value or a range of values. It is usually easiest to write the Abstract after the rest of the paper is done.

Make use of effective redundancy: conclusions in the abstract Abstract contains a forward (the before: motivation) & summary (the after: outcome)


  • The context: global view of why the need (see below) is so pressing
  • The need: why something needed to be done (motivation)
  • The task: what was undertaken to address this need
  • The object: “this paper presents/explains/describes/discusses”. Use present tense

Afterward (outcome):

  • The findings:
  • The conclusions: what the findings mean for the audience
  • The perspective: what the future holds, beyond this work

“Prose is architecture not interior decoration” - Hemingway


Make the introduction short and concise. Remember, you are not writing an Annual Review of XYZ. You need to tell the reader only what he or she needs to know to understand this piece of work. Provide just enough background so that the reader can understand how the question(s) you are asking fills a gap in the knowledge of the field. When the prior literature is extensive, citing recent review papers can be a good way to keep your introduction from growing too long. Key papers that focus on issues addressed by your paper should be noted specifically, however. Then explain briefly the approach your study takes to the problem at hand, and close with a short paragraph stating what the paper shows highlighting the structure of your paper.

You should cite all the relevant references - remember, editors and reviewers use PubMed too!

A note on paragraph structure

Each paragraph in your paper should start with an introductory sentence that explanes what the paragraph is about and how it is structured. Paragraphs should be able to stand alone and deliver a clear message. Think of the first sentence as the key message (i.e. the title of a slide) Link sentences by common content within a paragraph by referring to, or following on from, the subject of the previous sentence

The first sentence of every paragraph should say what you want your audience to remember!

Try to make each of your paragraphs have one key message.


The Methods section is a technical description of the methods used in your study. Ideally, it is complete enough to allow another skilled scientist to replicate your work. If the methods are complex, including some material in Supplementary Material can be a good way to be complete without making the print version of the paper overly long. Try to start the Methods section with a paragraph explaining the overall strategy of the methods. Then provide a series of subsections that describe each method in detail. Each subsection should provide a very brief, intuitive explanation of the method, followed by the details. This structure allows the reader quickly to get a sense for what you did. He or she can read the details later if needed. To quote Ben Schneiderman, “Overview first, then details on demand.” Occasionally, a few results may be put into the Methods section; for example, the results of a study establishing the parameters of a method. When in doubt, however, keep all results in the Results section.


The Results section should crisply present your new data, and normally contains most or all of the paper’s tables and figures. It can include a very small amount of interpretation and reasoning. For example, Results can say, “Good agreement was obtained, as shown in Table x, which presents…”; or: “In order to evaluate the sensitivity of the results to variable y, the calculations were repeated with two additional values of this parameter, as shown in Figure z.” However, more extended analysis belongs in the Discussion section. Avoid the common mistake of allowing methodological details to infiltrate the Results section. This happens most commonly when the Results section becomes a narrative which describes the need for studies beyond those originally envisioned and detailed in Methods, and proceeds to describe additional methods and the results of their application. This makes for a sloppy and confusing paper, so if you realize this has happened, move the additional methods back to the Methods section. The Results section can still explain – briefly – the reason for the additional studies.


The Discussion section explains what you have concluded from your results and why, and explains the implications for the field. It should not present further data, but it can include a figure or two to help explain a new model based on the data, for example, and it can reference additional papers as it puts your work into perspective. The Introduction and Discussion are the two most flexible parts of the paper, and the Discussion is more flexible than the Introduction. Once you are comfortable with the basics of paper-writing, you can start working on writing lyrical and thought-provoking Introduction and Discussion sections. Keep in mind, though, that your comments should be connected with the data you presented, and should be well-reasoned.


A Conclusions section can be used to briefly summarize the main points of a complicated paper. However, this section is optional and should be omitted from a straightforward paper whose main points are already apparent.


A place to thank the individuals and organizations that played a supporting role in the research and paper-writing. Check whether your grantor requires a specific format or disclaimer; the NIH does.

Scientific English

A note on tense

Use what was done (past), what is still true of the data (present) and don’t mess around with others - i.e. be consistent.

In this work the extent of residue-residue interaction was assessed by calculating the number of contacts that every residue makes with all other residues in each of the available structures. Two residues were assumed to be in contact if any two heavy atoms of these residues are closer than 5.0Å.

Published data are normally described in the present tense (i.e. as a fact) while your data are normally past tense e.g.

‘Loop12 is highly flexible in the absence of ligand (Jones et al.)’ but ‘the the results presented here show that Loop12 is stabilized by both ligand and effector binding’

Accuracy and precision

Try to be concise, unambiguous and impersonal. Clarity of style and consistency of format are essential. The greatest failings of the inexperienced are to meander in a disorganized pattern, to use far to many words and to write in a way which may be aesthetically pleasing but is open to misinterpretation.

‘Jones et al determined the effect of X on Y and showed an increase in the growth rate of Y in response to X’

Should read

‘X increased the growth rate of Y (Jones et al.)

Less is best, do not be repetitive let the data speak for themselves - do not labor the point.

Be quantitative and precise

Avoid terms such as ‘most, many sometimes, occasionally’ unless you give a value to it e.g.

‘most (%70) of cells responded’

### These data not this data Data are plural ‘these data’

Common mistakes by students

Here are some common mistakes I see in student drafts (not including the common mistakes covered in The Elements of Style):

  • Incorrect punctuation of et al. (it is short for et alia, which is Latin for “and others,” so there is a period after “al.” but not after “et”).
  • In common U.S. usage, commas and periods often go before closing quotation marks. (Note that this is different from British English punctuation, and probably most other languages.)
  • Single quotes should only be used to quote something within double quotes, e.g. “Bob said, `Me too!’ “ (Again, this is U.S. usage.) If you want to draw attention to or define a non-standard word usage, double quotes should be used.
  • Capitalized Names. The word Boolean should always be capitalized (in honor of George Boole). Same with Brownian, Eigen etc.
  • Citations at the end of a sentence should be placed before the period McMains, 2005.
  • Incorrectly using “which” instead of “that” as the relative pronoun for a restrictive clause.

Here is a summary of the correct usage adapted partly from “Side by Side Spanish and English Grammar” by Farrell and Farrell:

Relative pronouns begin relative clauses. If the relative clause is a “restrictive clause” defining the noun that precedes it, the clause is not set off by commas and “that” is used: “The one that you want is over there” (in this example, “that you want” is the relative clause). Restrictive clauses are essential definitions; often if you were to omit the relative clause the sentence would make no sense. Sometimes you can cross out a restrictive clause and still end up with a reasonable sounding sentence (as for “The book that you want is over there”), but the meaning is not clear without the definition provided by the restrictive clause.

A non-restrictive relative clause, on the other hand, is not essential to the meaning of the sentence. It gives additional information, but the meaning of the rest of the sentence wouldn’t change if it were to be omitted. It is typically set off from the rest of the sentence by commas: “My favorite book, which I re-read regularly, is Strunk’s `Elements of Style.’” If you can put the relative clause in parentheses without changing the basic meaning of the sentence, it is a non-restrictive clause. Non-restrictive clauses begin with “which” (when they refer to things, that is – if the relative clause refers to people, there are different relative pronouns to worry about, but I won’t get into that). Note that a comma should precede the use of “which.”

Personally I think that when to use which is the most subtle rule of English grammar, and as such I may not always catch a misused “which” when I check your papers. But for readers who have internalized the rule, misuse diminishes the clarity of your writing. So if you haven’t internalized the rule, make a special effort to consciously double-check that every “which” in your paper is really introducing a non-essential description, not an essential definition.

Spelling out numbers

Dave Patterson also has great advice on not spelling out numbers less than ten if the reader will be doing arithmetic with them, as well as other useful tips on his page on common errors in grad student writing.

“The general rule of thumb is to spell out one to ten and use numbers for numbers for 11 and up. However, I find its much better to consistently use numbers when the reader might naturally compare or do arithmetic with the numbers with a sentence or a paragraph. For example, “ The 8-processor case (model 370) needs only 4 computers to hold 32 processors. “ Blindly following the rule of thumb would change the sentence to “ The eight-processor case (model 370) needs only four computers to hold 32 processors. “ Its easier to read and understand we use numbers (84=32) instead of words (eightfour=32)”.

Confusing Words (Affect vs Effect)

Affect means to produce a change in something Effect is defined as a result of something or the ability to bring about a result


These compounds affect microtubule stimulated ADP release Multiple simulations were used determine the allosteric effect of inhibitor binding

In summary:

  • affect - to influence, to pretend (verbs); feeling (noun)
  • effect - a result; being in operation (nouns); to make happen (verb)

More examples:
Self-concept affects learning. She affected intellectualism by wearing glasses and using long words Her affect is always sour in the morning. One effect of lunar gravity is tides. The new state income tax was in effect last fall. The president effected a new policy on international trade.

Note: Most often affect is used as a verb and effect is used as a noun. Something that affects you will have an effect on you.

If in doubt use effect!

However, to make these words even more confusing than they already are, both can be used as either a noun or a verb.

Let’s start with the verbs. Affect means to influence something or someone; effect means to accomplish something.

“Your job was affected by the organizational restructuring” but “These changes will be effected on Monday.”

As a noun, an effect is the result of something: “The sunny weather had a huge effect on sales.” It’s almost always the right choice because the noun affect refers to an emotional state and is rarely used outside of psychological circles: “The patient’s affect was flat.”

Summery: When using as a noun, if in doubt use effect!

Also for commonly [miss-spelled]]( and miss-used words see

Conferences and Meetings

Once established in the lab I encourage everyone to attend at least one meeting a year. Don’t pass up the opportunity to give a talk, but go even if you are not presenting a talk. Once you are at a meeting be as active as you can about learning the field and meeting people. Poster sessions are the best places to meet and interact with other scientists. Go to as many posters as you can. Ask questions. Take addresses. Always try to present a poster if you can (even if you are giving a talk).

Below is a list of some recommended meetings. If there are other meetings you would like to attend, and preferably present at, please just ask and we will try to facilitate your attendance.

Meeting Rating Website
Biophys ****
ACS **
Gordon Conferences ***
Telluride workshops ***
Keystone Symposia *

Many conferences have travel funds available through inquiring with organizers as well as through travel grant applications. Getting these funds can be an excellent line on your CV.


Getting orientated - the Grant lab README!

Welcome to your lab! We hope you find it an exciting, enjoyable and stimulating working environment. The information below, elsewhere on this site and our lab wiki, is designed to help you navigate the lab. If you find that something is missing or inaccurate then please let us know (or just edit as you see fit - we will teach you how to do this with markdown and git). Hopefully, the information here will help you learn a little about how your new laboratory beats and hums.

General lab organization and procedures

The Grant lab is part of the Department of Computational Medicine and Bioinformatics (DCM&B) located in both Palmer Commons and the North Campus Research Complex (NCRC). If you have not already met them I encourage you to introduce yourself to other members of the department at your earliest opportunity. The department is a resource that can provide you with ideas, equipment and connections. Please use the department and don’t just hide away in the lab. Your dealing with members of the department can greatly influence the happiness and productivity of your lab life.

Our postal address

The Grant Lab
Department of Computational Medicine and Bioinformatics
Room 2017, Palmer Commons
100 Washtenaw Avenue
Ann Arbor, MI 48109-2218

Phone: 734-615-5510 (main office)

Geography and finding coffee

Our main lab space in Palmer Commons is room 2055 and across the hall in room 2044. The main DCM&B administrative area is in room 2017. Coffee, Tea, Bottled water (both hot and cold) and the refrigerator are located in the kitchen area of room 2017. We also have a small fridge and microwave in 2055 that you are always welcome to use.

Small group meetings can also be held in the conference room in 2017. Lager meetings are frequently held in room 2036 (e.g. regular group meetings) or in one of the fith floor board rooms.

Getting help

For computer help Ken and Jonathan are located in room 2044. You can also send an email to or directly to and Any office supplies such as notebooks, pens, or even ibuprofen can be obtained from Awura or Venece in room 2017. Awura can also help you out with any administrative questions.

Getting started

Once you have your university ID and “unique-name” talk to Awura in room 2017 to get it setup for afters hours building access. Awura can help you get a lab key. Note that your key may unlock all the doors including the kitchen and office area. Thefts have occurred, so please keep unoccupied rooms closed/locked at all times.

Please have Xin-Qiu or Barry add you to the current group contacts wiki page and group mailing list.

Lab setup and equipment

You can get an overview of major lab hardware and software on the wiki Computing page.

There are a range of HowTo guides and protocols for common experiments and computations on the wiki that you should familiarize yourself with as needed.

If you are new to Unix computing, please start with the following UNIX guides before delving into the various simulation and bioinformatics guides.

You may find the material from my Introduction to Biocomputing course particulary useful.

Learning Unix, R, Python and Git are essential for our day to day work in the lab. If there is anything missing from our HowTo guides then please mention it at the next lab meeting!

Laboratory meetings

Lab meetings are held to discuss the current research of individual lab members, the current research of the field (since recent journal articles are often discussed, these are known as journal clubs), and organizational problems.

Current lab meeting details can be found here.

Please try to attend all lab meetings. Unless you have a desperately pressing experiment, arrange your time so that you can go to all journal clubs and research presentations. Content aside (and you will probably learn a lot), your attendance shows support for your coworkers and is important for lab and departmental cohesiveness

Literature and other resources

The lab has a reference site called CiteUlike. Group members add manuscripts and several other items for group consumption there. The group maintains several mailing lists in addition to departmental and seminar lists - ask Barry how to be added to these lists. Please make sure you are signed up for our main group mailing list ( thegrantlab ). Again please ask Barry or send a mail to the mailing list for help with this.

Open Door Policy

The lab operates an open door policy - if an office door is open then all lab members are encouraged to stop by whenever they feel the need to meet and ask questions, discuss suggestions, and address problems or concerns. If a door is closed it usually means that I am on the phone or in a meeting or just not there. If you are finding it hard to find me then please send an email to find a good time to meet.

Basic survival through common sense and courtesy

Our lab has a dynamic that is fairly unique, in that people tend to work both collaboratively and independently. Practically, this means that everyone is equal, and should be treated as such.

Happy Researching!

How to fail a Ph.D.

Here I reflect on some of the bar­ri­ers to PhD com­ple­tion that I’ve seen. I have structured these as six steps to failure. Please do not follow them!

1. Wait for your super­vi­sor to tell you what to do

A good super­vi­sor will not tell you what to do. PhD stu­dents are not meant to be research assis­tants, and a PhD is not an extended under­grad­u­ate assign­ment. So wait­ing to be told what to do next will usu­ally get you nowhere.

By the time you grad­u­ate with a PhD, you are sup­posed to be an inde­pen­dent researcher. That means hav­ing your own ideas, set­ting your own research direc­tions, and choos­ing what to do your­self. In prac­tice, your super­vi­sor will usu­ally need to tell you what to do for the first year, but even­tu­ally you need to set the research agenda your­self. By the third year you should cer­tainly know more about your topic than your super­vi­sor, and so are in a bet­ter posi­tion to know what to do next.

2. Wait for inspiration

Sit­ting around wait­ing for great ideas to pop into your ahead is unlikely to work. Most of my best ideas come after a lot of work try­ing dif­fer­ent things and becom­ing totally immersed in the problem.

A good way to start is often to try to repli­cate some­one else’s research, or apply someone’s method on a dif­fer­ent data set. In the process you might notice some­thing that doesn’t quite work, or you might think of a bet­ter way to do it. At the very least you will have a deeper under­stand­ing of what they have done than you will get by sim­ply read­ing their paper.

Research often involves dead-​​ends, wrong turns, and fail­ures. It’s a lit­tle like explor­ing a pre­vi­ously unmapped part of the world. You have no idea what you’ll find there, but unless you start wan­der­ing around you’ll never dis­cover anything.

3. Aim for perfection

Per­fec­tion takes for­ever, and so stu­dents who are aim­ing for per­fec­tion never fin­ish. Instead they spend years try­ing to make the thesis that lit­tle bit bet­ter, pol­ish­ing every sen­tence until it gleams. Every researcher needs to accept that research involves mak­ing mis­takes, often pub­licly. That’s the nature of the activity.

Don’t wait until your paper or the­sis is per­fect. Work through a few drafts, and then stop, rec­og­niz­ing that there are prob­a­bly still some errors remaining.

4. Aim too high

Many stu­dents imag­ine they will write a the­sis that will rev­o­lu­tionise the field and lead to wide acclaim and a bril­liant aca­d­e­mic career. Occa­sion­ally that does hap­pen, but extremely rarely. A PhD is an appren­tice­ship in research, and like all appren­tice­ships, you are learn­ing the craft, mak­ing mis­takes, and you are unlikely to pro­duce your best work at such an early stage in your research career.

It really doesn’t mat­ter what your topic is pro­vided you find it inter­est­ing and that you find some­thing to say about it. Your PhD is a demon­stra­tion that you know how to do research, but your most impor­tant and high impact research will prob­a­bly come later.

5. Aim too low

My rule-​​of-​​thumb for a PhD is about three to six pieces of pub­lish­able work. Not all of these need to be actu­ally pub­lished, but the exam­in­ers like to see at least three publications plus enough mate­r­ial to make up another few papers that would be accept­able in a rep­utable jour­nal. Just writ­ing 200 pages is not enough if the mate­r­ial is not suf­fi­ciently orig­i­nal or inno­v­a­tive to be pub­lish­able in a jour­nal. Point­ing out errors in every­one else’s work is usu­ally not enough either, as most jour­nals will expect you to have some­thing to say your­self in addi­tion to what­ever cri­tiques you make of pre­vi­ous work.

6. Leave all the thesis writ­ing to the end

In some fields it seems to be stan­dard prac­tice to have a “writ­ing up” phase after doing the research. Per­haps that works in some fields, but it certainly not the best way to proceed in ours. You haven’t a hope of remem­ber­ing all the good ideas you had in first and sec­ond year if you don’t attempt to write them down until near the end of your third year.

I encour­age all stu­dents to start writ­ing from the first week. In the first year, write a series of notes sum­ma­riz­ing what you’ve learned and what research ideas you’ve had. It can be help­ful to use these notes to show your super­vi­sor what you’ve been up to each time you meet. In the sec­ond year, you should have fig­ured out your spe­cific topic and have a rough idea of the plan of a couple of chapters and their table of con­tents. So start writ­ing the parts you can. You should be able to turn some of your first-​​year notes into sec­tions of the rel­e­vant chap­ters. By the third year you are fill­ing in the gaps, adding sim­u­la­tion results, tidy­ing up proofs, etc.

Open Positions

There are lots of opportunities to get involved in our work at the ground level!

In addition to postdoctoral openings, we are very interested in hearing from energetic University of Michigan graduate students and undergraduate students who would like to learn more about our new research group. Several rotation positions will be available for 2012/2013.

Interested UM freshman and sophomores should apply to work in the lab via the UROP program.

Prospective graduate students and postdocs should refer to the Jobs page for further details or contact Professor Grant directly (

Grant Lab Alumni


Andrew Kalenkiewicz,
Cellular & Molecular Biology, University of Michigan, Ann Arbor
Undergraduate Student


Ali Alhilal
Medicinal Chemstry
University of MIchigan, Dearborn
Undergraduate Student


Rubben Torella,
Graduate student in Chemistry, University of Cambridge, UK
Visiting Fellow


Ivan Ma
Department of Computer Engineering, University of Michigan, Ann Arbor
UROP Undergraduate Student

Join Our Team - Open Positions

There are lots of opportunities to get involved in our work at the ground level!

In addition to postdoctoral openings, we are very interested in hearing from energetic University of Michigan graduate students and undergraduate students who would like to learn more about our new research group. Several rotation positions will be available for 2012/2013.

Interested UM freshman and sophomores should apply to work in the lab via the UROP program.

Prospective graduate students and postdocs should refer to the Jobs page for further details or contact Professor Grant directly (

Software Licensing

The site can help you quickly chose a license for your digital content. For more detailed information the following article provides a good overview of licensing and licensing options from the perspective of scientists who write code:

Morin, A., Urban, J., and Sliz, P. “A Quick Guide to Software Licensing for the Scientist-Programmer” PLoS Computational Biology 8(7) (2012): e1002598.

Fundamentally what matters is that there is a clear statement as to what the license is, and that the license is one already vetted and approved by the Open Source Initiative.

An important point to consider is that a license is best chosen from the very start of a project - even if for a repository that is not public. Pushing off the decision only makes it more complicated later, because each time a new collaborator starts contributing, they, too, hold copyright and will thus need to be asked for approval once a license is chosen.

These few licenses are by far the most popular:

The GNU-GPL is different from most other open source licenses in that it is “infective”: anyone who distributes a modified version of the code, or anything that includes GPL’ed code, must make their code freely available as well.

Creative works

Manuals, reports, manuscripts and other creative works are eligible for intellectual property protection and are hence automatically protected by copyright, just as software source code. Creative Commons has prepared a set of licenses using combinations of four basic restrictions:

  • Attribution: derived works must give the original author credit for their work.
  • No Derivatives: people may copy the work, but must pass it along unchanged.
  • Share Alike: derivative works must license their work under the same terms as the original.
  • Noncommercial: free use is allowed, but commercial use is not.

Only the Attribution (CC-BY) and Share-Alike (CC-BY-SA) licenses are considered “Open”.