Open Access: is that… cheating?


Open Access is established to guarantee free access, redistribution and use of primary research. Open Access makes available to the public what has been funded by the public, therefore, democratizing access to knowledge.  I support these ideas so much that I believe Open Access, in its current form, is… cheating or – at least – an insult to the original spirit of the Open Access movement.

As I see it, Open Access does not provide free access to knowledge but provide access to knowledge after the taxpayers spent a huge amount of money to fund a publishing system that is obsolete and, perhaps, unnecessary.

What is the real cost of Open Access? Not only fees but the cost of yet another new section of administration in funding agencies and universities now dedicated to Open Access [1]. Is the taxpayer getting a good deal for their money? Why should not we publish free of charge on publicly funded repositories at a fraction of the costs that we are currently supporting?

Publishers run a business and they have to be financially viable. We could perhaps be astonished or outraged about the profit level of publishers of scholarly papers, but publishers are not guilty of anything. Of course, publishers are feeding on the flaws of the scientific community at the detriment of the common good. However, in contemporary societies, the public good seems not to be a responsibility of private business [2]. Therefore, this is not a rant about publishers, but a note for the policy maker and a critique to scientists, me included [3].

The large majority of scientists and engineers are overworked passionate people that dedicate their lives to the process of discovery and translation to practice with the implicit or explicit aim to improve our societies. However, scientists are also ultra-competitive people embedded in an over-competitive system of funding and rewards that damage effective and efficient collegial work creating a huge amount of waste in the process. Because of the ecosystem in which they are embedded, scientists as a community (I am one of those, just to be clear) are not capable to self-regulate in order to maximize benefit to society.

Open Access was the solution to an actual problem, solution then rigged to preserve a very expensive and inefficient system (see the debate on reproducibility of scientific results) in order to avoid changing the rules of the current ecosystem. Those scientists that are thriving in the current ecosystem are either not willing to change it to secure their leadership or too worried to lead the change that may damage the people they employ in the short term. Of course, there are many colleagues that would support these ideas, but with change not happening, I can only assume they are not a sufficiently willing or sufficiently powerful majority.

Advising funding agencies and publishers, we have saved our idol, the impact factor, we pretend that knowledge is now freely accessible. Relax (do not remove) competition, educate a new generation of scientists about the real value of their work and you will get real Open Access, with unrestricted access to literature at minimal costs. Keep the system as it is and we will continue to waste vast amounts of public money in fees, ever increasing administration, inefficient and costly peer-review, and irreproducibility of results.

Can I do something about it? Like many early or mid-career scientists, I feel trapped in this system. I do consider impact factors when I submit a paper, I do pay for Open Access, I do act as a referee (for free) and I am an academic editor (for free) of the Open Access PLoS ONE [4]. The alternative is permitting only who does not care about this issue to go ahead perpetuating the system forever or until it crashes. If I published with the modalities I wished, I would be soon purged from the scientific community. Therefore, what I can do is speaking about the issue, debating with colleagues and occasionally on social media and following the indication of the San Francisco Declaration on Research Assessment.

I can also try, here, to appeal to the policy maker and who amongst scientists advise the policy makers to change this vicious system. We are smart people after all and many of us have very strong values and dedication to the common good. It should not be difficult to envisage strategies to democratize science with a sustainable and efficient model of publishing; many have described possible solutions. Ideally, we would replace current incentives to full-blast competition with others rewarding collegiality of efforts for a common long-term good (not just in publishing). Is science like the financial sector pre- (well, even post-) crisis?

If we do not do it, it will be the public outrage that soon or late, will force change. And because public outrage is often followed and fostered by a selfish short-sighted populist politician, it is likely this will spell serious troubles for all of us.

Was I right to single-out Open Access in this post? I am not sure, but when good ideas, the ethical ways, are abused and spoilt, I get particularly annoyed.


[1] I have a very good opinion about the team at University of Cambridge dedicated to the administration of Open Access. My opinion is not against those that are, with conviction, trying to make Open Access working. My criticism is for those that are exploiting the system making it inefficient and wasteful in the broader sense.

[2] I believe in a responsible free market, where private companies should serve the public good. But, I leave this opinion out of my judgement of publishers as business, nowadays, operates under different rules. 

[3] Publishing is e necessity for a scientist as we need to create new knowledge and this is recognizable only when is made public. However, many of us recognize several unhealthy attitudes and practices in scientific publishing,  particularly in biomedical research. I am no better than anyone else, I feel forced to play a game, which I try to play with integrity like the large majority of my colleagues. I try, at least, to foster debate on how we could improve the system.

[4] PLoS ONE at least addresses the issue of fairness during the peer-review process; this is why I fully endorse this initiative, at least for the time being.

[4] This post was originally published on my LinkedIn page in March 2016, but edited in its current form, as I believe it is still current.

This is my opinion and does not necessarily correspond to an institutional position of the University of Cambridge, the MRC CU or anyone working with me. My critique of certain aspects of contemporary science is not based on specific experiences with current or former employers or colleagues, but the overall experience as a scientist and the numerous passionate discussions I have with colleagues, friends and peers. In purpose, I do not cite sources because I simply wished to share my opinion on this subject; clearly, it is not an analytical study of the problem and I am not an expert on this specific topic

Which is the best model system for biomedical research? None, all model systems are wrong.

Which is the best model system for biomedical research? None, all model systems are wrong, but before I explain myself, let me tell you a story. One day I attended a retreat of the Molecular Physiology of the Brain Centre in Goettingen, and I genuinely had fun. Two things will remain in my memory.

First, Prof. Tom Jovin – one of the top scientists in the area I was working on – asked one of his most senior associates to show if the model I was using could provide representative results. I was studying the interaction of alpha-synuclein, a small protein involved in Parkinson Disease, with another protein, Tau, involved in neurodegeneration as well. Using molecular simulations, they demonstrated that the dynamic folding of alpha-synuclein is radically altered when alpha-synuclein is fused to a bulky fluorescent protein, a label I needed to quantify protein-protein interactions. As a PhD student, I was proud to deserve the scrutiny of Jovin’s group, a discussion that was based on reciprocal respect and motivated by the pursuit of the scientific truth. I aimed to compare differences between different mutants of alpha-synuclein and tau. I was using cell lines just as a test tube and to examine differences, obtaining the significant advantage of using a living cell to test these differences, but with the disadvantage of the requirement for a bulky label.

Second, there were several talks that day. I believe we started discussing NMR experiments on aqueous solutions of alpha-synuclein aimed to study its structure, then moving on work carried out in cell culture, fruit flies, mice, up to experiments done with primates. Once that people noticed the progression, the next scientist started to joke about the limitations of the model used by the previous colleague. To tell the truth, I do not remember if those were light-hearted comments or harsh criticisms, but I remember I came out of the meeting having had fun following the science and the debate, but also with a sense of uneasiness. Is there really a ‘best model system’ in biomedical research or all systems can be informative?

Let’s do another step back, away from this question.

Sometimes I like to say that ‘all biological model systems are wrong’ just for the fun of seeing the distorted faces of my interlocutor, probably caused by a wave of instinctive and unexpressed disdain or rage, before I explain myself. I assume I got (unconsciously and unwillingly) fond of this sentence, repeating a similar provocation expressed for mathematical models.

A mathematical model is ‘always wrong’ as it can never capture all the complex features of reality. Models are based on a few parameters that aid in reproducing and understanding a phenomenon providing as accurate as possible predictions. Models always lack some granularity in the description of reality and, therefore, they are always wrong. This idea is the exact opposite of what I was thought during my undergrad studies as a physicist. As far as a model has some predictive power, a model is correct, but some models are better than others in predicting a phenomenon. As it happens, these two contradictory statements mean the same thing. All models are wrong, as they will always be incomplete, but at the same time, correct, as they permit to predict – with varying degree of confidence – phenomena they represent. The most compelling example is the progression of models on relativity from Galileo Galilei, Isaac Newton to Albert Einstein, all great models that served humanity greatly.

Is this true also for biological model systems? Personally, I do not see why there should be any difference.

Let’s take the fruit fly for example. When a scientist is actually interested in understanding how a fruit fly works, a specific strain of fruit fly grown and examined in specific conditions will become the model system for all fruit flies. This is the closest that a biological model system can be to the system we intend to study (a fruit fly for fruit flies, C. elegans for nematodes, a C57BL/6 mouse strain for mice). An experimenter will be able to identify general principles, for instance, that certain genes or classes of genes are necessary during development to develop morphological or functional features like the wings and the capability to fly, the eyes and vision, colour and shape of structures etcetera. Scientists will be able to investigate also specific mechanisms, for instance, that a specific protein-protein interaction mediates the processing of information from receptors eventually resulting in the capability of the fly to find food. Researchers will be then capable of generalising their specific observations of a laboratory strain to the genetically identical but wild fly (I’ll discuss this more commenting on mice models), then to all fruit flies, and perhaps other living beings.

Of course for most scientists fruit flies are not model systems of fruit flies or other insects. Because of the genetic tools available, the fruit fly has been a fantastic model system to understand genetics and to explore the role of specific genes, gene interaction mechanisms, gene regulation during development and the role of genes in development. As pea plants permitted Mendel to formulate the first basic observations that led to the foundation of human genetics, the fruit fly expanded our knowledge of genetics (and much more) permitting us to understand better how genes work in humans. Are peas and flies the right models for human genetics or to study human physiology and disease? There is no right or wrong, beans and flies were good models for human genetics insofar they provided sufficient predictive power about humans, after which more accurate models could and will always be available.

A decade later my meeting on alpha-synuclein, I am often confronted with this type of questions. Sometimes, this is caused by a daunting sense of impending doom realising to have invested years of work in studying the ‘wrong’ model, simply because of choices that were taken at a different time or because of the (always) limited resources I had or have. Other times, I am confronted by the different views of colleagues, more often anonymous referees, depicting a model system as inadequate.

Let me briefly describe a few actual examples before a few closing remarks.

One of the earliest critical comments I heard on model systems after I moved into cancer research was during a lab meeting at the Venkitaraman’s lab. Although I do not recall the details, a colleague must have presented work on DT40 cells, a lymphoblast chicken cell line. Once again, I do not remember the tone of the conversation, but I do recall the comment that was shot at the speaker: ‘are we trying to cure chicken cancer?’ The DT40 cell line is made of floating immature avian white cells, which are certainly not the right model for human solid tumours we try to understand. However, DT40 cells exhibit a high capacity for gene recombination, permitting to modify their genetic background in a very efficient way and, therefore, DT40 cells have been successfully used to study the roles of several genes, BRCA2 in the laboratory where I work. Here, a well-resourced lab can carry experiments including in vitro assays, passing from DT40 and human cell lines, up to mice models and arriving at the study of human clinical samples.

In vitro experiments, where individual constituents of a biochemical reaction or a molecular machinery are reconstituted and studied, represent the most simple of the model systems (not necessarily the most straightforward experiments though). For instance, scientists can see kinesin molecules walking on microtubules. Among other functions, kinesins deliver cargos to and from peripheral cellular regions of the cell when or where diffusion of molecules would be an inefficient process to deliver specific cellular constituents. A kinesin molecule has two ‘legs’ that sequentially interact with microtubules, the (cyto)skeleton of the cell which is used by kinesin-like motorways. Kinesin utilizes ATP, one of the molecules used by nature to store energy, to propel itself forward. Without in vitro experiments, the molecular understanding of molecular motors would be unlikely. Work in cell lines was necessary to fine-tune our understanding of the system, but I do not think any colleague would feel the need for an unlikely/impossible in vivo human experiment attempting to falsify the model of kinesin motor. Does kinesin walk differently over synthetic microtubules on a coverslip or in their cellular context? I am no expert in this area, by I assume that while there would be substantial differences, the molecular principles of the kinesin stride, in this case, are safe. Cell culture work refines and improves these models and cell culture experiments done within a three-dimensional sample, where tissue-like forces are appropriately set will provide even a better picture of kinesin, but the basic in vitro work is and has been essential.

Here I touched the topic of three-dimensional cultures. Organisms are three dimensional, but the vast majority of experiments are performed with cell lines growing on surfaces. There is no doubt that 2D biology differs from 3D biology, as the topological and mechanical properties of the 2D or 3D structures will definitely alter the biological processes we study. Recently, a colleague of mine told me of a very peculiar comment they received during the refereeing of one of their manuscripts. In a very brief report, an anonymous colleague stated that “cancer occurs in vivo and not in a petri dish”, concluding that not having in vivo relevance that research was not worth publishing. How to rebuttal such a true statement? Perhaps, with a better understanding of what a model and a model system are? Two-dimensional culturing methods have provided us with such a wealth of information on how life works and cell biology will be not the great discipline is today without these model systems.

In the early nineteen-hundreds, Theodor Boveri inferred essential aspects of the process of oncogenesis studying, as a zoologist, cell division in the fertilised egg of the sea urchin. His experimental observations were as crucial as distant from the ‘right model system for cancer’ with the sea urchin egg being a one-dimensional culture system of an organism so remotely related to humans. During the first half of the last century, after studying oxygen consumption in fertilized sea urchin eggs, Otto Warburg revealed metabolic changes in cancer by placing tumours into a petri dish and analysing their metabolic action, what we would call today an organotypic culture. However, Otto Warburg later published a paper (‘On the origin of cancer cells’) commenting work performed on cell lines ‘What was formerly only qualitative has now become quantitative. What was formerly only probable has now become certain. The era in which fermentation of the cancer cells or its importance could be disputed is over, and no one today can doubt we understand the origin of cancer cells if we know how their large fermentation originates, or, to express it more fully, if we know how the damaged respiration and the excessive fermentation of the cancer cells originate’. At the best of my knowledge, Warburg’s work was controversial at the time, as much as Boveri’s, but only because of the hypotheses they brought forward, not because of their model systems. Although the fine details of their discoveries may be more or less accurate (or popular) with the judgement of state-of-the-art observations, Boveri’s and Warburg’s contributions to the understanding of the origin of cancer is invaluable and was largely based on very simple, very wrong, and yet so very correct model systems.

On a much more personal and less grandiose note, recently, colleagues and I were criticised for using HeLa cells for our studies. HeLa cells had been controversially (not at the time) derived from a cervical cancer of a non-consenting patient. HeLa cells grow in culture since 1951; they are hypertriploid cells, i.e. HeLa cells carry ~80 chromosomes rather than the normal set of 46 and ~25 are abnormal chromosomes. The genome of HeLa cells is otherwise considered stable. In a timeline article in Nature Reviews Cancer, John Masters describes ‘the good, the bad and the ugly’ of HeLa cells and he states: ‘Our knowledge of every fundamental process that occurs in human cells – whether normal or abnormal – has depended to a large extent on using HeLa and other cell lines as a model system. Much of what we know today, and much of what we do tomorrow, depends on the supply of HeLa and other cell lines.’

However, HeLa cells have shown a significant adaptability to different culturing conditions and, therefore, HeLa cell lines may behave differently in different laboratory, but eventually, this does not depend on the cell line, but on careful experimental practice, which is true for all model systems. More worrying is the issue of cross-contamination that again is true for any work and, incidentally, it is more likely to affect the work done on cell lines other than HeLa (contaminated by HeLa cells) rather than HeLa cells themselves. Not only we understood so much of how HeLa cells work, not a very interesting topic, but we were able to port much of this knowledge on other model systems and humans as well. Are HeLa cells the right model system for human physiology? Certainly not. Are HeLa cells the right model system to study molecular machineries acting in human cells? No, at the extent that all model systems are wrong, but yes as plenty of models derived from HeLa cells had predictive power, and we could infer that plenty that did not may have been because of lack of good scientific practice in general rather than HeLa cells themselves. John Masters, in his review, cites the opinion of Stan Gartler who revealed cross-contamination issues already in the sixties: ‘If the investigator’s requirement was for any human cell line, whether or not it was HeLa or another cell line does not seem important. However, in those cases in which the investigator has assumed a specific tissue origin of the cell line, the work is of dubious value’. Of course, it is of critical importance to accurately report on material used and to interpret data within the assumptions and limitations inherent in a specific model, but there is no reason to stigmatize HeLa cells, instead we should stigmatize poor practice in science, so widespread in the laboratory and the peer-review process, sometimes for ignorance, self-interest or – more often – because of the limited resources (time and money) and the high pressure scientists have to work with.

But let’s move on from two-dimensional culturing systems and arrive at the opposite, mouse model systems. Once again, mice models are very precious and had provided invaluable understanding on how life works and, how human physiology and pathology work. Are mice models the right model for human physiology and disease? Well, you got my opinion by now. No, they are the wrong model, but yes they are the correct model. It has been reported that observations carried out on mice cannot be reproduced in humans and not too infrequently even across laboratories. However, it may be argued that most of this lack of predictive power and reproducibility is again about scientific practice. Mice are not unfrequently of the wrong reported genetic background, laboratory conditions are too different from ‘real life conditions’ so that a laboratory imposed diet, physical and social activities will influence the outcome of the experiments, and the statistics used often lack the required rigour.

Again, the anonymous referee’s comment is sometimes revealing. I heard of colleagues being asked to attempt the falsification of their hypotheses by experiments done with transgenic mice. So often, that when we submit a paper, jokingly we predict we will be asked to do some animal experimentation. Often but not always, this may be a very considerate request, as to test the physiological relevance of observations done in vitro, in cell culture, or in insects, is certainly of fundamental importance. However, animal experimentation is performed within specific ethical guidelines and we, scientists, are asked to minimise the amounts of animals we use for research. Therefore, the choice to perform experiments in animals should be taken only when these experiments are necessary (and there are plenty of such cases). This choice should not be biased by the perception that some models are the perfect models for human physiology or disease while other are imperfect models (and there are plenty of such cases).

Then, which system is the best model system? The answer is obvious to anyone: it depends on the question. Every model system provides important information on a phenomenon within the limitations of the specific model. Or, in other words, any model system is ‘wrong’ because they are not the real thing. Contrary to physics, where we often study the real objects we want to study, in biomedical research, we cannot do experimentation on human beings, and we have to resort to model systems. It is always important to remember assumptions and limitations of a specific one.

Let me finish with another consideration, as I have mentioned the peer-reviewing process rather frequently in this assay. We, scientists, agree with each other a lot, but we equally disagree, and often passionately. Personally, I cannot understand why we may disagree on the fact that all model systems are valuable (or alternatively incorrect) or why colleagues will frequently ask to repeat experiments in yet another model system of the referee’s. Science is based on the process of falsification through experimental observation. This process cannot (and should not) be performed by a single scientist, group or consortium. Not only individuals rarely have the resources to perform experiments with a multitude of models and techniques; even if they do, experiments have to be performed by different experimenters, with different models and methods, in different places, in any case. All models are ‘wrong’, all techniques are limited, all experiments are somehow biased, but collectively they can inform us on the general principles and molecular mechanisms of human physiology and pathological states. Therefore, when arguing about the superiority of one model system compared to another one, let’s be passionate about it, let’s disagree, but if you are still arguing, let’s keep in mind that you are using the ‘wrong’ model system—like anyone else.


Otto Warburg “On the Origin of Cancer Cells” Science, Vol. 123, No. 3191 (1956)

John Masters “HeLa cells 50 years on: the good, the bad and the ugly” Nature Reviews Cancer, Vol. 2, 315-319 (2002)