I left off right before Popper’s falsificationism and Kuhn’s paradigms came into view. Each of them has their own problems. Popper, for instance, introduced the falsificationist concept with simplistic examples that the actual scientist rarely encouters. Nevertheless, Popper’s Logic of Scientific Discovery does seem to reflect some of the approach that the typical scientist has been taught to apply in formulating testable hypotheses. As a result, sophisticated falsificationism takes a somewhat defendable position by reiterating falsificationism in strongly qualified statements.

Thomas Kuhn then introduced scientific revolutions as “paradigm shifts”, exposing the hard truth that science is normative. No argument there. But the problem lies in the logical conclusion that many people draw from the realization that science is normative: science is therefore more subjective and more falliable than we originally may have supposed, and pseudoscience might find comfort in the doubt sowed in science therein. Kuhn simply could not reconcile his normative description of science with what is obvious to any empirical scientist, which is that many scientific theories can explain wide ranges of natural phenomena with a high degree of precision. In other words, though science may be normative in practice, it is also grounded in high-level approximations of reality, and basic facts exist which can be said to be objective.

As a result, I characterize Kuhnsian paradigms as not a philosophy of science, but a sociology of science. That view has gotten me in some strongly-worded discussions with other scientists, but it’s a position that I stick to. It is very clear that some theories are better than other, and that science does indeed represent progress. One needs only to look to the offspring of science, technology. Advancements in biomedical, mechanical, electrical, and chemical technology are not mere paradigms.

Enter the Bayesian theorem of science and the New Experimentalism.

Thomas Bayes, an 18th-century mathematician, established a theorem that has a great deal of bearing for philosophy of science. Bayes’ theorem is about conditional probabilities, which prescribes how probabilities of truth statements are to be changed in the light of new evidence. Chalmers describes, on page 175:

In the context of science the issue is how to ascribe probabilities to theories or hypotheses in the light of evidence. Let P(h/e) denote the probability of a hypothesis h in the light of evidence e, P(e/h) denote the probability to be ascribed to the evidence e on the assumption that the hypothesis h is correct, P(h) the probability ascribed to h in the absense of knowledge of e, and P(e) the probability ascribed to e in the absense of any assumption about the truth of h. Then Bayes’ theorem can be written:

P(h/e) = P(h) x P(e/h)/P(e)

P(h) is referred to as the prior probability, since it is the probability ascribed to the hypothesis prior to consideration of the evidence, e, and P(h/e) is referred to as the posterior probability, the probability after the evidence, e, is taken into account.

So the formula tells us how to change the probability of a hypothesis to some new, revised probability in the light of some specified evidence.

This symbolic calculus serves to illustrate that any disagreements in science between proponents of rival research paradigms or programs must have their source in the prior probabilities held by those scientists, since the evidence is taken as given and the inference considered to be objective. But the prior probabilities are themselves totally subjective and not subject to a critical analysis.

Consequently, those who raise questions about the relative merits of competing theories and about the sense in which science can be said to progress will not have their questions answered by the Bayesian. Bayes’ theorem of science does, however, reflect the importance of the relevance of new data. That is, empirical evidence is not all considered equal - some evidence is strongly weighted as far as importance goes, whilst other evidence is considered irrelevant.

The New Experimentalism is an intriguing contrast. Chalmers starts off with an example (an experiment by Michael Faraday on electromagnetism) and then asks (page 195), “Is it useful or appropriate to regard this accomplishment of Faraday’s as theory-dependent and falliable?” Without question we can say that, at best, one can only refute the extreme empiricist position that facts must be established directly by the entry of sensory data into a mind that otherwise knows nothing, and that the recognition of a new experimental effect cannot be said to be falliable in any sense.

Thus, the production of controlled experimental effects can be accomplished and appreciated independently of high-level theory. Molecular biology is replete with examples of experimental observations that are tightly controlled, and the results derived therein can be considered objective. Extrapolating from those observations to theoretical implications is not always straightforward, to be sure, but possible if the experiment itself has relevance to aspects of those theories which are in contention among scientists.

Deborah Mayo offers the best articulation of the New Experimentalism in her 1996 book, Error and the Growth of Experimental Knowledge. She sides with Kuhn’s notion of normal science, reformulating it in such a way that reflects the ability for scientists to make factual statements independent of theory, even though they remain subjective and fallible to a degree.

So I found myself nodding very much through reading about Deborah Mayo and the New Experimentalism. I am surprised that I hadn’t read much about this area of the philosophy of science before.

Overall though, I think it also helpful to note that each of the major philosophers of science tackle a separate aspect of science - how hypotheses are made; how science is normative; the role of inductive and deductive logic; how experiments are formulated; how facts and theories are inter-dependent; etc. Each of them has a point, but none of them can be extrapolated to science as a whole.

But focusing on results per se is not a good way to work because, as a scientist, you can’t “get” results. You can’t “make” them happen.

Essentially in every experiment you are asking a question of a biological system, and the answer, the result, you obtain will depend on the biological system itself as much as your skill as a scientist.
If whatever you are asking of the system fundamentally does not work or can’t happen, then there is no way you can “get” the result, no matter what you do.

So a better way to work is to focus on how you ask the question.

Your job as a scientist is to ensure that you are asking the question in the right way with a properly researched, well designed and carefully executed experiment. So if you don’t get the result you are looking for then you just have to think about how you asked the question.

Could you have done something better?
Could you have designed the experiment differently?
Is there something you are missing?

If the question can be asked in a better way then a new experiment should be performed and the cycle repeated until you are convinced that the experiment is watertight.

At that point, if you are focused on how you ask the question rather than getting a result, it is a lot easier to accept the negative result while giving yourself credit for the good work you have done and move onto the next question with your confidence, sanity and faith in your scientific method in tact.

And that’s more likely to bring you results in the long run.

23andMe’s Advanced Global Similarity Tool
Dienekes takes a critical look at 23andMe’s new “global similarity” tool, which allows you to visually place your own genetic data in the context of genome-wide SNP data from over 1,000 individuals from around the world.

Industry vs. Academic Research
There has been some tendency in some corners of academia to knock industry-sponsored or -executed biomedical research as tainted. Is this attitude changing?

Getting the Roles of Blogs and Journals Straight
“[Blogs] certainly don’t replace the journals, but add a way to inject the results into the public sphere, where they can be part of a popular conversation.”

Micropipettes and the Shakes
“Something that we often don’t think about in science education but is critically important when you’re actually in a lab: having good hands.”

100 Years of Genetic Research and Science Journalists are Still Confused
Alex comments on a NY Times article on “The Gene” and has a few interesting quibbles to discuss with you.

The Mason’s Apprentice
PZ Myers has an outstanding article on the evolutionary origins of multi-cellularity.

Embryonic Stem Cells Form Functional Brain Tissue
Mo comments on a fascinating new paper out on a political hot-topic in Cell Stem Cell.

- Thaw the competent cells on ice
- Add DNA
- Electroporate (or incubate then heat shock for chemically competent cells)
- Add rich medium (LB or SOC)
- Incubate at 37°C (or appropriate temperature) for 30-60 minutes
- Spread onto antibiotic plates

That 30-60 minute incubation can be pretty annoying, especially if you are performing the transformation late in the day. The good news is that if you are using ampicillin as your selection antibiotic, you can miss this step out without too much problem.

The reason is that ampicillin acts differently from the other antibiotics commonly used for selection of E.coli in molecular biology (for a great summary, see this article).

Antibiotics like chloramphenicol and kanamycin act by binding to the bacterial ribosome and inhibiting translation. This means that if you plated your transformation straight onto either of these antibiotics, translation of the antibiotic resistance enzyme would be suppressed, which would prevent the cells from acquiring resistance to the antibiotic.

With these antibiotics, the 30-60 minute incubation allows the cells time to synthesise the antibiotic resistance enzyme so that they can survive when plated on the antibiotic.

But ampicillin does not work in this way. Instead it prevents newly divided cells from synthesising the cell wall that they need for survival. Looking at it another way, this means that ampicillin does not kill cells unless they are dividing.

So newly transformed cells plated straight onto ampicillin can synthesise the beta-lactamase enzyme required for resistance without any problem, thereby acquiring the means to inhibit ampicillin activity that will allow them to synthesise their cell wall normally when they divide.

Ampicillin is not the greatest selection antibiotic in the world since it can allow satellite colony formation and plasmid loss (see this article) but this feature makes it a bit more convenient than others for transformations.

Having said this, leaving the incubation step out might not be a great idea if you are performing a transformation that requires particularly high efficiency, for example if you are building a library or doing a particularly tricky ligation, because efficiency could be somewhat reduced.

For example, individual cells that were close to dividing at the time of transformation might not have time to synthesise the beta-lactamase before the divide, so would not survive. This will only happen with a small portion of the cell population so will not be a problem for the majority of applications.

What are your favorite shortcuts in the lab?

My experience in the process of hiring gave me a new perspective on all the reasons why people fail in interviews, or even to get them at all, and what is the recipe for a successful job application.

I thought I would share with you some of these new insights and maybe I can spare you from making some of the common mistakes people made in applying for my job.

Here’s my advice:

1. Read the job description and write a cover letter that is specific to that job

Common sense, right? Not as far as I’ve seen.

Many applicants sent us an apparently generic cover letter that was presumably used to blast numerous hiring managers. Often, nothing in the cover letter mentioned any of the key words from our ad.

And even worse, many applicants had absolutely no experience all in the area of research we asked for. It was obvious they never visited our web page or even read the ad.

The best cover letter tells why they are the best candidate based on the specific qualifications – make sure yours fits the bill.

See our article on killer cover letters for more tips.

2. Visit the website before applying and make sure you have an understanding of what the company does.

If you don’t thoroughly read the website, you won’t really understand the job you are applying for and, worst of all, you demonstrate a lack of interest in the company.

This lack of knowledge will be apparent in your application, or glaringly obvious in the interview if you are called for one and kill your chances of getting the job.

Make sure you read the web page, understand the company; what it does, where it came from and where it’s going. Don’t just scan it, get as much information as you can and make sure you can impress the hiring manager every step of the way.

3. Know why you want to work for that company

One question we always ask is: why do you want to work for us?

Is it because you need something local? Is it because you want to get the hell out of wherever you are now? Is it because you love our products and love our culture?

You need to have an answer to this question and the answer needs to be positive and needs to tell the hiring managers that you believe in our mission and want to be a part of it.

Surprisingly, some people could not answer this question, or answered it in a way that made us feel that we were something to hold them over until a better position came along.

I’ll let you guess whether or not they got the job.

4. Make it clear: “I want to work for you”

What the hiring people and your new colleagues want is someone that really wants to be part of the team. They want someone who is as passionate about the same things they are.

Are you a person who will fit in and get along with others? You need to show it.

The key is that if you think the job you are interviewing for is the perfect position for you, then say so, and say why.

Make it clear that you really want this job above all others you have applied to and want to be on this team.

5. Be positive about yourself and your abilities

No candidate actually matches every qualification on the list - employers don’t expect you to. If they list 10 “nice-to-have” qualities and you have 5 of them, apply.

For example, if you have experience searching for and writing patents but no experience in presenting at conferences, let them know your strengths. Do not focus on the qualifications you don’t have or any other weaknesses for that matter.

In fact you should never mention your weaknesses unless and until they are specifically brought up in the interview. If you have a specific weakness that you know may be a problem for the job, come to the interview armed with good suggestions on how you could overcome it and examples of how you have overcome similar (or other) hurdles in the past.

This is the attitude that a hiring manager wants to see: someone who is not afraid to try new things and willing to learn and push themselves past their comfort zone.

We covered an interesting approach to being positive in job interviews in this article.

6. Be friendly and energetic… and make eye contact

Especially in small research groups or companies a major emphasis is (and should) be placed on how well any new employee would fit with the personalities and culture in the group.

It is impossible to predict exactly what different groups are looking for in this regard as all are different but something virtually everyone will be attracted to is a friendly and energetic approach.

So if you get an interview, be friendly; look people in the eye, smile where appropriate and show that you have an enthusiasm for life that you will bring to the job.

Of course you are likely to be nervous so you should give yourself a pep-talk before you go to the interview to get yourself in the right frame of mind and having something as simple as being friendly to focus on can help you get over your nerves.

7. Salary negotiation- a tricky area

It was shocking to me the amount of salary some post-docs wanted for their first job. Salary can range depending on the size of the company and the position. Beginning PhD scientist positions with no industry experience will range between $65,000-85,000 US. [Nick's note: it's way less in the UK - you can probably expect something like 20-25K, depending on subject and geographical area]

For a small company with less than 100 people, it will be on the low end and for a large company with >1000 people, it will likely be on the high end. Some start-ups with venture capital can also offer attractive salaries.

Your salary will depend on your degree, experience, and whether you have specific expertise in a technique or area that is highly valued, and if you have previous industry experience, you can negotiate a higher salary.

A person coming from academia to industry has a big adjustment to make in their mindset toward their research so hiring company knows there is going to be more training around a company mindset and a slower learning curve.

The company is taking a big risk in hiring someone with no experience in an industrial lab just as you are taking a risk in making the jump out of academia in exchange for the big salary.

When asked for your salary requirements, say instead “the going rate” and then see what is proposed to you.  It is ok to counter-offer if you amount is below your expectation but keep it within $5000-$10,000.

Asking for too much more gives the impression that you will not be happy with your salary and will consider leaving the first chance you get.

Summary

The job interview process is a stressful experience. It’s normal to not have much practice at it since you want to stay as long as possible at any position you have. The most important thing to remember is to be confident in yourself. You would not be applying if you didn’t feel you could handle the job and that is what the interviewers want to see. A person who is a team player, a fast learner, and a hard worker can get almost any position they set their sights on.

A grad student, a post-doc, and a professor are walking through a city park and they find an antique oil lamp. They rub it and a Genie comes out in a puff of smoke.

The Genie says, “I usually only grant three wishes, so I’ll give each of you just one.”

“Me first! Me first!” says the grad student. “I want to be in the Bahamas, driving a speedboat with a gorgeous woman who sunbathes topless.” Poof! He’s gone.

“Me next! Me next!” says the post-doc. “I want to be in Hawaii, relaxing on the beach with a professional hula dancer on one side and a Mai Tai on the other.” Poof! He’s gone.

“You’re next,” the Genie says to the professor.

The professor says, “I want those guys back in the lab after lunch.”

There’s some reality behind that humor, albeit less extreme than the joke of course. This is a fairly common mindset in academia. However, industry has it’s own boss-related challenges. You still have a boss who wants to keep your productivity high, although you still look forward to those vacations.

But I’m finding that working in industry is growing on me. I get to leave my work at work, and just forget about it. And I have a reliable income, not having to live from grant to grant.

It’s also sort of what I was forced into. Living in a small Mediterranean country (Cyprus) with a small research infrastructure, I consistently got the response from heads of laboratories that “I’d love to hire you, but I don’t have enough funding.” Getting involved as a microbiological analyst at a pharmaceutical factory has been the best opportunity to a solid career, and the chance to support a family.

What do you think about the challenges of working in academia vs industry?

Back from the dead. Sandra Porter at Discovering Biology in a Digital World covers the recent cloning of some long frozen mice by a Japanese group, which could take us one step closer to being able to clone woolly mammoths and such like.

Bursting at the seams. At Biocurious, PhilipJ’s Molecular of the Month is mechanosensitive channels, which help to control osmotic balance in bacteria.

Is there no end to DNA’s talents? Yun Xie at Nobel Intent reports on a nanotech application that uses DNA as the basis for nanowires that can convey information through a clever use of  FRET.

Algae-munching bacteria. Merry at Small Things Considered covers Algicidal bacteria, the big game hunters of the marine bacterial world.

Google will save the world. Alan Cann at Microbiology Bytes has an excellent post about Google’s Predict and Prevent initiative, which combined digital, genomic and IT technology to identify “hot spots” of emerging threats, such as pandemics and environmental catastrophes and provide early warning before they become global crises.

…and finally…

How do you tissue culture? The Black Knight and post commenters at Life of a Lab Rat highlight one of the many little-known trials and tribulations faced by scientists; the logistics of wearing an IPod while doing tissue culture.

Molecules is an application that allows you to view PDB structure files, yes it’s the 3 dimensional protein structure just like on software like rasmol, deepview etc. on your computer. I’ve tried out the app and its nifty, of course you wouldn’t want load big molecules which take up too much processing power to render on the screen and then becomes a pain! The app also allows you to browse through the PDB database to retrieve any molecule of interest. You can find more information on Molecules at Sunset lake software

Solutions is a very handy app that i already use on my mac dashboard as a widget. It makes the job of preparing solutions easy by helping you deal with the molarity calculations.

In fact the ipod/iphone app is now enhanced to allow one to retrieve information regarding an entered chemical from two databases (pubchem & chebi), the entries are then stored in the recently accessed section of the app, making it easier for later calculations. I am fan of the authors, Mekentosj, who by also make the fabulous Papers, 4peaks and more, which we covered in “Free Mac Software for Molecular Biologists”.

But although it is a very handy app it doesn’t come free and i feel is a bit overpriced. I may be feeling this because i use the same (or similar) app on my mac for free! Regardless, the app which targets students mostly should surely be priced may be no more than a dollar or so.

Pubsearch Plus is a tool that allows you to download Open access articles and also subscription only access by personal authentication or via an institutional (EZ) proxy. Handy features are being added to it everynow and then. It also allows you to easily email an article that you’ve accessed to your friends or colleagues. This again is not a free app, but a free, feature limited version is available. More information about Pubsearch is available from deathraypizza (nice name!).

The Chemical touch is a iphone/ipod version of the periodic table of chemical elements but thrown along is an Amino acid companion that is just so handy when you need to quickly look up say the 3 letter/1 letter code for an amino acid. The layout it like the table that you can find inany standard biochemistry text, so it’s nice and easy to use. You can find what else the chemical touch can you help you with at the developer’s page.

I would also recommend the Bio Courseware page of the University of Nottingham which opens up some cool applications to play with. I will try to update this post every now and then when i come across apps worth mentioning, meanwhile if you think that I’ve missed something please do let me know by posting them in the discussions.

Update–The fifth app: I just noticed that the fabulous PhD Comics is available on the iphone/ipod as an app, it is definitely worth checking out.

Yet, researchers seeking funded face ever greater competition for limited funds - funds that are overly wedded to safe, unadventurous research. This, in effect, ostracizes off-the-wall ideas, which often cross disciplinary boundaries and would have potentially big payoffs should they work. Researchers long ago learned that the last people they should tell about their big ideas are their sources of financial support.

There are exceptions and a limited number of researchers succeed in injecting revolutionary ideas into mainstream science. And some degree of “safe, unadventurous research” is going on and is much needed. But in the face of calls to revise the NIH grant review process, perhaps one solution is to expand high risk efforts.

Efforts like the US National Institutes of Health (NIH), which has systematically promoted risky research through several initiatives. Or military imperatives for innovation through the US Defense Advanced Research Projects Agency (DARPA).

Or efforts like that of the Bill & Melinda Gates Foundation:

In a similar vein, frustrated with repeatedly encountering the same faces from the same countries, and a narrow range of ideas, the Bill & Melinda Gates Foundation decided to cast the net wider in the search for new people and ideas. Last week, it announced the 104 winners of the first round of its five-year, US$100-million ‘Grand Challenges Explorations’ programme. This solicited unconventional ideas for protecting against infectious diseases, limiting drug resistance and exploring latent tuberculosis. Future rounds will include ideas for vaccines for killer diseases and tools to help eradicate malaria.

It is the nature of the call for proposals, and their peer review, that is intriguing. The grant proposal is one that many researchers can only dream of — a two-page explanation of the idea, with no supporting data required. To emphasize that it’s the idea that matters, reviewers were blinded to the name, profession and nationality of the applicant.

The reviewers themselves were atypical. Instead of consensus review by experts in the field — as is the norm — the 4,000-odd proposals received were sent to individuals, not just in science but also in engineering, business and beyond — people the foundation considers to be ‘champions’ with strong track records in high-risk research.

[...]The first-round grants, at $100,000, are small. Dishing out large sums of money on far-fetched ideas would be foolhardy, given that as many as nine out of ten of these projects are expected to fail. But those that show signs of success will be eligible for further funds of $1 million, or much more.

Why wouldn’t this be a better model for the grant review process? Make small amounts of funding relatively easy to get for important ideas, streamline the evaluation of those grant applications, and provide large benefits to those few ideas that manage to succeed.

For another great article on research funding, check out Undergraduate Research: Genomics Education Partnership - an effort to promote undergraduate research experiences in genomics.

Is the position you are in at the moment helping you to reach that goal, or are you stagnating?

These are questions I think everyone should be asking themselves at least a couple of times a year.

Career Growth During Study

If you are studying for an undergrad degree or a PhD, this is quite simple to work out. If you started your course of study with a fairly clear idea about what you want to do when you get the degree, and chose your course accordingly, then you are moving in the right direction since you are growing your skills and knowledge every day and moving along the career path toward the next point you have planned.

If you have not planned in this way, it might be a good idea to do some serious thinking. Doing a degree or a PhD just because it’s the easiest option available - the line of least resistance - is not a good way to do things. Sure, if you put the effort in you will come out with a set of skills and shiny certificate to say how clever you are.

But if those skills don’t allow you to do what you really want to do, then it’s a waste of your time.

If you are in this position, you should think about making some adjustments to either

1. Your studies, by taking modules or additional courses that will give you the skills you need to do what you want.
2. Your career goals, by looking at the skills you will have at the end of your degree and working out whether there is a way to modify your career goals to fit those
3. Your course. Maybe your course is just taking you in completely the wrong direction, and if so the best option might be to cut your losses and move to a completely different course (or job) that is more in line with your goals.

Option 3 especially may seem very harsh but it’s just a case of being realistic about your goals and planning ahead. If you are not realistic then you will be faced with a worse option than quitting your course - the prospect of a career that you do not really want to do when your course is finished.

Career Growth While Working

Judging whether your career is moving in the right direction when you are working in a job is a bit more complicated than while studying, but it basically boils down to the same questions. Were do you want to be, and is this job helping you get it?

A real difficulty is that you can be lulled into a false sense of growth by measuring yourself by the standards of others, rather than your own career goals.

For example, your boss may be delighted in your progress if you are working diligently in the lab becoming better and better at a specific set of techniques each year. In this situation you are certainly growing in some ways since you are becoming better at your job, and so delivering more value to the company or group you are working for.

But are you growing in terms of your career? It depends on what your goals are, and on what else, other than lab techniques, you are learning.

If your career goal is to stay in that, or a similar, job for the rest of your working life and just get better and better at it, then you are doing just fine. You are growing in the direction of your goal.

But more likely, you will be looking to move toward a career that requires additional skills such as different lab techniques, management, presenting, writing, negotiating or whatever.

The question is, are you growing in these areas in your current job? If not, is there a way for you do so, like taking on extra responsibilities, training or a new post?

If the answer is “no” then it is time to consider moving on, especially if you feel like you have plateaued in the skills that you actually are learning.

Like quitting studies that are not right for you, this can be a big decision, but if you postpone making the decision for too long you are only wasting your own time, so it is definitely best to be brave and ruthless.

Moving the goalposts

One final thing to consider is that your career goals should evolve as you move through your career. You can only really plan a certain amount of time ahead, maybe 3-5 years, in my opinion, so continuous re-evaluation of your goals is essential.

So how about you, are you growing in your career?
(note: use this temporary link to join the discussion - the link below is broken but we will fix it soon!)