Book Club: Jesus, Interrupted...

I find Bart Ehrman's books endlessly fascinating. For those unaware of his work, Ehrman is a New Testament scholar at the University of North Carolina, Chapel Hill who's written a series of books (both academic and popular) on the history of Christianity and the origins of the New Testament. He's probably most famous for his book Misquoting Jesus (2009; Harper One), which deals with the analysis of textual variants of the Bible - specifically how the text of the New Testament has been changed over the years due to copying errors, mistranslation, or to reflect specific theological messages.

Jesus Interrupted (JI; 2009; Harper Collins), can be thought of as a popular follow-up to Misquoting Jesus1 in that this book deals not so much with textual variants among copies of the New Testament, but rather with theological variations (ranging from minor to outright contradiction) within the New Testament itself.

JI begins by pointing out that among the few people who bother to read the Bible, most read it like a regular book. For example, you begin with the gospel of Matthew, then you read Mark, etc. Everyone recognizes that the gospels are not identical, but the stories told in each seem fairly similar. Therefore, most people assume that they're all reiterating the same message, perhaps emphasizing different aspects of Jesus' ministry, death, resurrection, etc.

Things change significantly if you line the four gospels up in columns, making sure that each story shared in common among two or more are adjacent, and read horizontally, comparing their accounts of the same stories to one another. It then becomes apparent that the books given different, contradictory accounts of particular situations (When was Jesus crucified? Why did Judas betray him? What happened during the Passion narrative? etc.) or that some seemingly significant events narrated in a particular gospel are completely absent in the others. Incidentally I find it mind boggling that Ehrman gets so much vitirol from Evangelical Christians about these contradictions given that he references all examples and you can just go read them yourself - they're there.

Ehrman argues that there are really only two ways to interpret these variations: 1) You do what most people throughout history have done and use some fancy leaps of logic to mash all of the narratives together (see for instance the Wikipedia article on The Sayings of Jesus on the Cross). Or 2) You assume that since the Gospels were written decades after Jesus' death, by people living in other countries2 who had never known him and who based all of their information on oral traditions and hearsay, they really are the product of very human foibles.

Furthermore once we accept that these differences exist, we can began to ask questions about the various theological messages that each of the books of the New Testament espouses. This is where it gets very interesting: On many significant issues, they're quite different. Compare the letters of Paul (especially that to the Galatians) to the Gospel according to Matthew: The paths to salvation outlined in each of these are very different, with the former requiring only belief in the resurrection, and the latter requiring adherence to Jewish Law. From a memetic perspective, it's not surprising that 'Pauline' Christianity won out - it's obviously much more attractive to gentile converts. There are many more such differences that Ehrman catalogs and discusses.

Though I won't go into it here, Ehrman spends considerable time discussing the possibility that the theological message associated with Christianity changed significantly, and fairly rapidly as what we now know as the New Testament coalesced. The various Biblical books can be dated roughly (at least in order to determine the order that they were written) and by doing so, we can ask how the earliest authors portray Jesus as compared to the latter ones. It's interesting to note that several critical Christian doctrines (e.g., the divinity of Jesus, Mary's virginity, the doctrine of the Trinity, etc.) change or appear only in later books. Ehrman's analysis (which is apparently quite mainstream in the field of Biblical textual criticism) makes a lot of sense via the principle of parsimony; but unfortunately it can never really be tested in a rigorous way: like many things involving Biblical knowledge - most variant analysis and alternative interpretations were destroyed as heresy over the long years.

The big thinking point that Jesus Interrupted generated for me really boils down to the reason I started questioning my own faith at the end of high school. Most people's specific religious beliefs and their relation to scripture, whether they choose to admit it to themselves or not, are post hoc in the sense that first they're instilled with a vague belief in God, and then they're exposed to particular doctrines of their respective faiths. In a weird way, the Bible really doesn't matter that much to most believers: they didn't base their beliefs on the Bible in the first place, and they interpret its text through the lens of their prior beliefs. My own crisis basically boiled down this: 1) If the Bible does matter, then why do we pick and choose what we want to follow (homosexuality is bad, but we can eat lobster, wear mixed fabrics, and we don't stone our disobedient children to death)? 2) Alternatively, if the Bible is irrelevant or perhaps metaphorical, then why are we believing what we believe? Why is our particular version of Christianity right as compared to countless variants or even other religions?

I know that for many people (most, I'd imagine), these aren't really 'issues' at all. Faith isn't about evidence. It's on this point that Ehrman ends his book - learning about discrepancies in passages and theological messages within the Bible shouldn't necessarily be taken as a direct attack on faith itself (if your faith is based on Biblical inerrancy, then there's a problem). However, what it may do is lead people to question the validity of particular literal interpretations of scripture (such as the idea that women should stay silent and remain subservient [1 Corinthians 14:34, see also 1 Timothy 2:12]). Hopefully faith is not solely compatible with hard line conservative rigidity, but rather can survive an honest analysis of the 'good book'.   

1I say 'popular' because the vast majority of the material in Jesus Interrupted is presented in Ehrman's academic books, such as Lost Christianities (2003; Oxford University Press).

2The Bible was originally written in Ancient Greek, while Jesus and his followers spoke Aramaic. In addition, some of his followers are said by the Bible itself to have been illiterate, and it's not difficult to imagine that all of Jesus' apostles were. Finally, some of the specific sayings quoted in the Bible only work in Greek (they involve double-entendres of Greek words that don't work in Aramaic), and thus it's impossible that they're litteral quotations. 

Computational Biology 'Tips'...

I've come to the conclusion that like me, many scientists are finding that their datasets are becoming too large to manage without at least a bit of computational 'know how'. This typically involves learning how to use some version of Linux, the R statistical language, and in some cases, the dreaded, simple programming language known as PERL.

Most of us are 'cast to the wolves', so to speak, and have had to cobble together our limited knowledge of such things from countless books, websites, and user forums. I've also found that many of us keep little notes on how to use some of our most frequently employed little commands. The official manuals are often written at a level that's too complex for the uninitiated, leading to much trial and error (it's often the case that you'll never use many of the more esoteric functions of certain programs too1).

Thus, based on the suggestion of a few of my friends in 'the business', I've begun putting my little help files online, cleaning them up where necessary and adding direct links to appropriate additional resources when possible. I have to admit that I've delayed doing this for a long time because it seemed like a lot of effort for very little payoff, but I've come to think that this opinion is incorrect. I'm putting together these notes for myself anyways, and if I put new notes on the web directly as I learn more about how to use this stuff, it'll be made available immediately to anyone else who can benefit from it.

So, you can see what I've put up by following the 'Comp. Biology Notes' tab at the top of the page. There's not much there yet, but it's a start, and I'll slowly add the rest of what I've got as time permits! Please feel free to email suggestions or comments!

1Actually, one of the things I've been finding is that as you get more familiar with certain software and/or programming languages, the esoteric details can become massive time-savers!

The Hammer Falls...

To anyone outside of the Ivory Tower many of the posts on my blog probably make it seem as though academia is a complete mess. Unfortunately, this is at least partially true - standards of achievement vary wildly among institutes, departments, and even individuals. Ask any number of professors what you should be striving to accomplish as a wannabe professor and they're likely to give you a substantial number of different responses (some aspects, e.g., work hard, publish good papers, etc. tend to be invariant).

A good example of this is the very act of postdoc-ing: What should an interested, newly minted Ph.D. be looking for in a potential postdoc? What should a new postdoc be trying to accomplish during their tenure? On these subjects, as above, I've gotten a few different responses.

One popular answer to the former question is that a postdoc is a time when you should be 'consolidating' your interests into a viable research program. Basically, you should be focusing on publishing some big papers in your field of interest that will get potential employers interested in hiring you to pursue your work with a lab of your own. I've been told that in order to accomplish this goal, you shouldn't enter a postdoc that involves too radical a shift away from the skills you learned during your Ph.D. - that is to say, don't start doing something completely new1.

On the other hand, there have been a few folks who opined that a postdoc is an excellent time to diversify your skills. Also withing the confines of this view, I was told that the postdoc is the best time to be a scientist: You're much more independent (and well-trained) than most graduate students, but you're still free to pursue your own research, free from the later demands of writing grants and running a lab.

While I see some of the wisdom in the second perspective (I have certainly learned a ton of stuff in the ~16 months of postdoc-ing I've done), I have to admit that 'mine' own experiences are probably more in line with the first one. I'm way too busy as a postdoc to spend any significant amount of time improving my skills in areas that are not directly related to my research. I've always got at least 3 projects on the go involving significant time investments, and almost everything I've been doing has clearly defined deadlines - Our lab is involved in the Drosophila ModENCODE Project, which involves a large consortium of labs and thus deadlines are inevitable. When the hammer falls and deadlines loom, the practice of science changes radically: No longer is it the purely intellectual journey of discovery, but rather the focus shifts to determining 'deliverables' and getting s$%t done, ASAP. I'm in one of those periods at the moment, unfortunately.

The funny thing is that, while I'm learning a ton of stuff during my postdoc, I tend to feel that I have less time to reflect and absorb said knowledge than I did during my Ph.D. For instance, during grad school, I began creating text files, wherein I would type summaries of new computational techniques I'd learned that would be useful in subsequent analyses (I had a file on Linux commands, a file on R, a file on PERL, etc.). While I still refer to these files daily, I find it difficult to expand them with the new information I've gained. I'm typically running through analyses so quickly that I don't feel like I have time to explore all of the details of some of the software I use - much to my chagrin2. I've kind-of resolved myself to take a little time here and there to beef up my text files and if I can get them all nice enough, I may even post them here on my site.

Oh well, what else is new, right? I began this post by remarking on how opinions about what I should be doing vary, however, it's pretty clear that the current consensus is somewhere along the lines of 'work your ass off, publish as much as possible, make sure people know who you are.' I'm sure that other people's experiences are different though. If so, I'd love to hear about them!

P.S.: I would like to give a shout out to looniechemist, who runs the blog 'An American postdoc in Canada' and who wrote an interesting post about her early experiences as a postdoc that got me thinking about writing this post.

1For instance, my Ph.D. was a pretty radical departure from the sorts of skills I learned during my undergrad and Masters (I switched from Biochemistry to Evolutionary Biology). This led to me essentially 'burning' a year in order to bring myself up to speed in the field - something I don't regret in the slightest. On the other hand, burning a year as a postdoc is potentially devastating. 

2I think that this has serious implications for the quality of science produced using certain types of software, and I'll expand on this in a future post.

Interview with Rosie Redfield.

Just wanted to point everyone to an interview with the always enthusiastic Dr. Rosie Redfield, a professor in the Zoology Dept. at UBC in the new issue of Current Biology (you'll probably need academic access to follow the link). Rosie runs the RRResearch blog, which recently generated a lot of buzz over her recent dissection of the arsenic loving bacteria paper/controversy and esuing comments. Rosie also supports an open-access model of research and result-sharing, which is both interesting and a little terrifying for someone in my position! I encourage people to read about it.

The Myriad Variety of Writing Styles...

I'm often driven to ponder how I'd handle the opportunity to teach or mentor students of my own someday. As many people realize (typically at some point during graduate school), professors are usually never formally taught  how to teach or mentor. I say 'formally taught' in the sense that there are no specific 'classes' that teach effective mentoring skills. In my experience, a large part of learning in science is accomplished via personal observation (e.g., how you or others were mentored) as well as the 'learn-by-doing' philosophy1.

I've been exposed to four different research labs in varying capacities, and one particular aspect of mentoring that has varied considerably for me is how different PIs expect you to write a scientific paper (something that I'm in the process of doing, so it's weighing on my mind). Now, to anyone 'in the know' this isn't particularly surprising - it seems that everyone I talk to has different opinions on which section should be written first, or how it should be laid out, etc. What I've found can be challenging is that I have a particular style that works properly for me, but not necessarily for everyone else.

It's probably because it was my first opportunity to write an actual manuscript, but I've subscribed pretty strongly to the writing philosophy of my M.Sc. supervisor: With the exception of the abstract, a scientific paper should be written in the order that it is presented. So a typical paper is laid out as follows:

ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
MATERIALS AND METHODS2
REFERENCES

As my M.Sc. supervisor argued, by providing background as to what research has come before, the introduction necessarily establishes how the results should be presented (what should be emphasized? What are the novel contributions that this paper is making?). Similarly, the discussion requires the context of the introduction to interpret the results.

I know that this is not necessarily a popular method of writing. Much more common in my experience is the idea that you should write the results first, then worry about the other sections later. Another variant of this that may be popular with people who come from a genetics background is that you generate the figures before anything else, and then write the entire manuscript around those3. My work is typically fairly computational and there are often many different ways to anazlyze the same data. One of the great 'travesties' of exploratory science is that too often we try to extract all kinds of meaning out of complex data, whereas proper experiments should be designed to test specific questions. Of course, some data are arguably generated in a question-agnostic manner (e.g., large scale gene expression analysis), but the way that you analyze the data is nevertheless contingent on the questions you want to answer. My opinion is that knowing what questions to ask comes from the information contained in the introduction. 

Anyways, in an era of big collaborative manuscripts, writing styles seem destined to clash. Either we get along and modify our approaches, or some privileged few 'pull rank' and lay out the program. And thus we return to the question of mentoring: Is it better to 'teach', and potentially enforce, a particular style, or is it better to encourage a student/postdoc to develop those skills that seem to work for them? Probably a tougher question than I realize, but regardless, I figure it's best to get them while they're young. It's notoriously difficult to teach an old dog new tricks ;-)

1For instance, during my M.Sc. I had a class on effective scientific communication that essentially boiled down to choosing a paper and being marked on your ability to present it. Many students complained that the class didn't include any aspect of actually being taught how to effectively communicate science. Your 'lesson' also happened to be your exam.

2Yes, the position of the M&M section can vary, and it can be written last regardless.

3In certain fields, the figures are the results, but such is not always the case.