my blog

Moral hazard in academic governance (the University of Bath, emerging protests and leading changes in society)

Moral hazard

Over the last decades we have seen everything about the good and the bad of free market economy; we have learnt a lot about the consequences of moral hazard. Moral hazard is the tendency for one person to engage in riskier behaviour when the consequences of this behaviour will be dealt by others. One example might be an insured shop owner, struggling, who will not have any incentive in investing in a new fire system as, after all, a fire would permit them to collect the premium of the insurance. The insurer does not have complete information on the behaviour of the insured or their intentions, therefore will incur a higher risk than the risk of fire in itself. However, this typical example does not adequately illustrate when moral hazard occurs at the level of management.

The 2006/7 financial crisis was an astounding example of moral hazard, where many groups of people accepted higher risk they should have done (banking management, intermediaries and the recipients of sub-prime mortgages). Let’s focus for a moment on the management within financial institutions as it is a more appropriate example of what I will have to say. Before the financial crisis unfolded, executives were either aware of the problem, and they defrauded millions of people, or they were not, they were just incompetent. Were they deserving their jobs and the substantial compensation schemes they had and still are receiving? Is the market setting this compensation packages? I will come back to this point later. Either way, the initial response of Governments was difficult: bailing-out private institutions, reinforcing the possibility for moral hazard to incur in the future, or let the system fall in an uncontrolled manner? Considering the situation, most Governments decided for supporting financial institutions, therefore reaffirming that management of financial institutions may have operated under moral hazard: as the fall-out of their riskier behaviour was, in very large part, handled by States, Governments and the People.

But this blog-post is not about the financial crisis.

The University of Bath ‘affair’

Over the last few weeks, national news in the UK reported on protests of employees and students at the University of Bath on regard of the high salary earned by Dame Prof Glynis Breakwell, their Vice-Chancellor (~£468,000 and benefits). Reports were initially accurate, but they got occasionally derailed by attempts from various academics to defend high salaries for academic governance in general or Prof Breakwell specifically. Let’s clarify immediately, I do not have an opinion about how much a Vice Chancellor should be paid. Half a million may be the right number, or it may be too low or too high, I just do not know enough. But a few things bothered me in this debate: responsibility, accountability, and gender equality.

The market sets compensation schemes, does it?

I have read the commentary written by Prof. David Blanchflower and published by The Guardian, entitled “University vice-chancellors deserve more pay, not less. Here’s why”. The reasons for high salaries described in this article are clear and even agreeable at first instance. Universities have to hire their governance in a global market with competitive salaries. I have nothing to debate about this truth, but I am afraid there is another truth, not too hidden, that went unnoticed here. The protests were not a generic complaint about the high salary of academic leadership, it was a specific complaint about an almost 4% salary increase for the highest paid Vice-Chancellor. Allegedly, compensation for the governance at the University of Bath was established with insufficient transparency, with an undeclared conflict of interests and a motion from the Court of the University was hindered by those that should have left the room during a vote because of conflict of interests. This is not my opinion, but the informed and competent judgment (I suppose) of HEFCE, the Higher Education Funding Council for England. If you are interested in this story, please read their report.

Is moral hazard or the market that set salaries?

I am no economist, but my naïve opinion is that this is a typical case of moral hazard in management. As discussed, moral hazard takes many shapes and forms, but it has been identified as a dominant reason for the uncontrollably increasing compensations for executives in Industry. It is true that Academia recruits on a global market, but Academia must shape the global market both training adequately future executives and leading by example.

There is a debate, until now unrelated to Academic governance as far as I know, about the compensation of executives that appears to have become untenable, influenced by executives themselves, paying marginal consequences when their actions damage the brand they represent.

Let’s say you have the right not just to ask a pay rise, but to vote in favour or against. Let’s say that you give yourself a million pounds per year, irrespective of outcomes, in addition to a complex package of benefits that may depend on the performance of your company. After one year of work, your company goes bust and you have lost the value of your shares and options. However, you have just earned 1 million in 1 year, that is an amount of money that corresponds to 37 years of median salary. So, corporate executives raise their pays (not all of them!), constantly, to capitalise on short-term investments, possibly (even unintentionally) misjudging the consequences of their decisions as their livelihood, and those of their families, contrary to the vast majority of people, do not really depends on their productivity. This is moral hazard.

Executives are entitled to ask for an increase of income, but the procedures for these extraordinary compensation packages (from the point of view of people paid ‘normal’ wages or unemployed) shall be transparent and not just justifiable but justified.

I do not believe that this is a common issue at Universities, but how the story unfolded at the University of Bath resemble one of these cases. Let me do an example.

Dame Prof Breakwell is paid a high salary because she has to look after public relationships and take important executive decisions for a large business and an important brand. True. Complaints about the pay rise should have been expected, so a high degree of scrutiny. The Council voted down a motion of the Court requesting more transparency. In authorising the pay rise in a period of protests about wages and trying to quench the formal protest at the University Council, some (let’s be clear not all) academics accepted the risk. This was a misjudgement, and now the University brand will pay the consequences. At the time I write, Dame Prof Breakwell will retire, after six months paid sabbatical and with a written-off loan: isn’t this moral hazard?

Are Vice Chancellors paid too much?

It is not that Vice Chancellors are paid a too high salary, but the problem is transparency, respect of the opinions of your employees and ‘customers’ (the students I suppose), respect for the brand one represents and, a better understanding of societal changes. I hope that other Vice-Chancellors will be proactive, not in cutting down their incomes (this is a different matter), but in understanding and shaping societal changes.

Universities have the moral duty to shape society not to be just the mirror of it. What some people might not appreciate is that the uncontrolled rise in executives compensation (in Industry) is causing damage to business three folds: by draining resources from investment to disproportionate personal wealth, by inducing short-sighted selfish behaviour with no incentive for long-term investment in assets and people, and bad publicity to industry creating a sense of complete disconnect, distrust and unethical behaviour that damage the brands they should promote. The latter point is often neglected, but it is very important. A chancing society and pressure groups, if neglected, can cause a lot of damage to a ‘brand’. This is not in Academia but in the world of Finance and Big Industries. Are we walking the same path now? Most people will say no, as compensation packages for academic governance are far from the excesses of USA corporations (true), but as I tried to say a few times by now, the issue is not necessarily the pay level, but the process and incentives in place.

Fully justify high salaries and adopt transparent decision processes, but also do not disregard the growing intolerance for inequality. Do not speak, in an abstract manner about the market, be specific. Moral hazard is part of the market, it is an unfruitful degeneration of it, which goes against the principles of free market economy. Most Academics and University employees dedicate their lives to improve society. Sometimes we are wrong, other times ineffective, but most of us, Vice-Chancellors included, are well-intentioned and passionate to make of the future UK, a competitive global and fair Nation. Therefore, do not let greed, or simply lack of due process, to damage our brand and collective efforts.

Was gender an issue?

There is another ‘inequality’ that bothers me: gender inequality. The top-level academic world is dominated by man. I am a white nearly middle-aged man, with the ambition to become a ‘fully-fledged’ academic. However, I cannot avoid noticing that women representation among academics is still low and it pains me that to get in the line-of-fire was a successful woman. I do not know statistics about Vice-Chancellors and gender-balance, but I did check the Russel Group. I count six female Vice-Chancellors and seventeen males, ~25% female representation, a figure we are too often familiar with. I am sure that all Universities are committed to improving, we feel very strongly about this at the University of Cambridge, but the historical heritage of male-dominated academia take a while to change.

The Guardian published another letter in support of Prof. Bakewell, this time signed by several female academics of the University of Bath, which start with “Being a successful woman seems to attract a disproportionate degree of negative criticism”. I am sure that this is true, but I should clarify that gender, for me, did not matter, and also for the articles and material I had the opportunity to read. The highest paid Vice-Chancellor in the UK was, by chance, a woman. HEFCE criticised procedures that were opaque, behaviours trenched in conflict of interest. When a woman raises at a position of power is, first and most importantly, laudable because being one of the best in their area irrespective of gender. Second, a woman in a position of power in a male-dominated environment is also laudable because of the many obstacles that women still experience in career progression, particularly at the top of the scale. However, people of power should accept the increased scrutiny and the responsibilities that come with the higher salaries they are paid, irrespective of gender and background.

Leadership for the future

My hope for the future is to see inequalities of all types to diminish and eventually disappear. I hope to see more women in academic governance. Irrespective of gender, I wish our leadership will keep fighting to improve the society we live in, fight against the profound inequalities we experience even in the United Kingdom, be this income or gender inequalities. I wished we trained the economists, the bankers and the lawyers that will reach the apex of various Industries to lead change. Because the only way we can defend a free market economy is to ensure that the right checks and balances are in place, monopolies are not created, packets of extraordinary wealth (corporate or individual) do not exist (otherwise they distort the dynamics of competition as much as a monopoly).

Let’s never say “it is the market that decides” because it is always people that decide, and it is ok if we follow due process.

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What is life?

Preamble

In this assay, I describe reflections on biological systems and the nature of life. If you do not know it, the title is an obvious reference to the famous Schrodinger’s assay that motivated many physicists to create the branch of science that is Biophysics. In its current stage, these words are not written with the intent to be precise or complete, but to guide my own thoughts in the understanding – however superficial – of which are the general principles, opposite to specific molecular mechanisms, that drive biological processes and that are more likely to help us in the understanding of human physiology and pathological states. I will often express trivial observations from which, perhaps, less trivial considerations may be built upon.

From disorder to self

A living organism is an active chemical system, one that is constituted by an identifiable ensemble of molecules that manifest cooperative behaviour. For life to be observed, it has to be identifiable. As trivial as this observation is, identity is a founding character of the chemical systems we call life.

Life, as we know it, is based on the basic chemical unit that we call ‘cell’. The boundary of the cell is defined by lipids, amphipathic molecules that are made of a portion that likes water and another portion that does not. A basic characteristic of amphipathic molecules is their capability of spontaneous self-assembly. The polar, water-liking, head of lipids will try to contact water molecules, whilst the water-repelling tails will associate with each other trying to exclude water molecules, like oil in a glass of water.

Local reduction of disorder (or entropy as often we call it) is a feature of living systems. Reduction of entropy can occur only as consequence of irreversible chemical reactions that convert energy to order the local environment, like a person burning calories to tidy up their home, that in the process generate waste that they dump into the external environment. However, self-assembling systems are spontaneous and reversible processes that increase order at no entropic cost to the environment. The best example for this are colloidal suspensions. If large spheres are mixed with smaller spheres, large spheres will start to organize in ordered structures. In fact, around the large sphere, there is a volume of solvent that is inaccessible to the small sphere (because of steric hindrance). The entropy of a system of a mixture of large and small sphere is higher when there is a level of organization in the large spheres. Another example of such process is the spontaneous ordering of polymers. Even without amphipathic properties, there are molecules such as polymers that can interfere with the internal bonding of water (water molecules like each other) and are therefore driven into ordered structures. The net effect of this process, driven by so called entropic forces, is to maximize the entropy overall, primarily increasing the entropy of the solvent by maximizing the number of states available to water molecules; at the same time, polymers or other macromolecules are driven into ordered, low entropy, structures.

Therefore, given the right conditions of solvent and molecules, a spontaneous process of self-organization will drive compartmentalization of chemical systems. It is very important to stress that one of the first fundamental steps that initiate life, i.e. establishing an identity through a boundary between the self and the environment, is a thermodynamically favoured process that occurs at no entropic cost to the environment. This step results in the maximization of the internal entropy but, counter-intuitively at first impression, generates order and define a self at the same time.

Such compartmentalized systems do give raise to special environments where chemical reactions can occur at high efficiencies. Furthermore, the constant supply of energy provided by the sun, can readily start catalysis in these compartmentalized systems and drive these systems far from the equilibrium. Of the many compartmentalized systems that can naturally and spontaneously occur, however, only those that will be stable in time will be able to evolve into nowadays ‘living systems’.

From transient to stable

What we call a living cell requires the preservation of its own identity for long enough to give birth to life as we recognize it—a living organism must exhibits mechanisms to ensure its own integrity over time: the integrity of its boundary and the integrity of an active internal chemical system. Both require the existence of favourable conditions such as suitable operational windows of temperature, pressure, pH, etc. All systems incapable of maintaining integrity within a range of conditions that may occur in time extinguish themselves as soon as environmental factors change even slightly. It appears, therefore, that the maintenance of integrity over time necessitate that a primordial biochemical system exhibits some sort of “thermodynamic resilience”, i.e. its chemistry can operate and its identity maintained in face of environmental challenges. We can easily postulate that any primordial biochemical system was simple in nature and manifested comparatively little resilience to environmental changes. Reversible processes of self-organization would be constantly counter-acted by more energetic stochastic events inducing a relentless process of creation and destruction of such thermodynamic systems. Natural selection forged life from the very beginning, enabling only those systems capable of increased “thermodynamic resilience” to survive.

There is experimental evidence that simple self-assembling systems such as lipid vesicles can grow by uptake of other lipids from the environment and trigger fission in smaller vesicles spontaneously. The propensity to fusion and fission events of the early proto-cell may have represented both a challenge and an opportunity to the evolution towards an early system. Fission and fusion can be seen as a challenge to thermodynamic resilience as the identity and composition of this proto-cell is extinguished when fusion and fission occurs unregulated. At the same time, simple mechanisms that would minimize fusion and regulate fission, for instance specific composition of proto-cellular membranes, under the empowering thrust of natural selection would lead to the emergence of a ‘replicator phenotype’. This replicator phenotype could be thermodynamically favoured in specific environmental conditions and, hypothetically, better supported by simple internal chemistry that would favour a stable process of fission.

During this phase, inheritance of characters could be only of one kind, structural inheritance. Composition of amphipathic chains of specific types may favour self-assembly with other chains of the same type and stochastically divide into multiple “daughter” entities that constituted by the same elements, different ever so slightly by chance, would be still favouring the same self-assembly processing to occur, but randomly accommodating variation in the composition of the boundary and inner content. At the same time, irreversible reactions stabilizing this process may emerge by increasing efficiency in the utilization of energy to maintain structural integrity.

Once that the cycle of relentless creation and destruction is replaced by a cycle of relentless replication, natural selection will favour the optimal “thermodynamic resilience” for a given environment.

From random to self-governed

In face of environmental changes a primordial system to survive into a life form will require adaptation of its active chemistry supporting different chemistry active on different operational windows. Gains in thermodynamic resilience may occur by: i) stabilizing thermodynamic variables (e.g., temperature, pressure, volume) within optimal windows (homeostasis), ii) migrating to a different environment (taxis) or iii) adapting the internal chemistry to the different conditions (through evolution or, on shorter time scales, by allostasis).

Homeostasis is defined as the capability of a system to maintain certain parameters nearly constant. For instance, the human body is kept at around 37°C where cell biochemistry operates optimally. Homeostasis is the incarnation of thermodynamic resilience, where this is ensured by an active process of self-government, another founding property of life. There are many different terms to define this property of biological systems (e.g., homeorhesis and allostasis), but homeostasis is the one encompassing all of them. For instance, allostasis is the process by which a system maintain its homeostasis through a change. Let’s consider a simple biochemical system where its internal chemistry depends on pH. In order to be thermodynamically resilient, this system will require the capability to buffer pH either by chemical composition or by utilizing regulated proton pumps that will ensure a stable pH. However, when these mechanisms are insufficient to guarantee pH stability, a system can shut down those internal machineries that generate variations of pH as a by-product. A partial loss of efficiency (read as fitness) in such a system will however guarantee maintenance of other fundamental reactions and, therefore, overall fitness.

Another possibility is to engage in taxis, the active movement towards a more permissive environment, for instance searching for those conditions of nutrients, temperature, pH, etcetera where a system operates optimally. Taxis is another property shared by all animate being. Either plants trough growth, or other organisms through migration, all organisms are capable to sense the environment and trigger movements to seek for their optimal environment where their metabolism will operate more efficiently. For instance, a plant will adapt its growth to seek for sunlight, bacteria sense gradients of chemicals to find food and higher organisms migrates to places where abundance of food and water, and environmental conditions are suitable for them.

Homeostasis and taxis are manifestations of self-government that can be established only once that a biochemical system acquires the capability to process information from its internal and the external environments to execute specific actions to ensure thermodynamic resilience.

Conclusive remarks

I am no expert in any of the topic I discussed here. However, I have the impression that thermodynamics aspects of life are often far too emphasized. The question is not if life contradicts the second law of thermodynamics (it does not), the question is how much the second law can teach us about living beings. Often, a connection with entropy, its evolution towards higher values, is seen as a necessary link to justify the spontaneous occurrence of life. Even if this was true, does it matter, if a much simpler justification is available in the process of natural selection as a fundamental law of Nature? And even if natural selection could be justified with a thermodynamics description, would this help us to understand life, or to resolve the many afflictions that living beings are cursed with?

I firmly believe that research in thermodynamics of self-assembly systems and the role of entropic forces in biology are essential to a better understanding of life. However, the question about why life has evolved and if this conflicts with thermodynamics has been already addressed. It seems, sometimes, there is a conflict between the laws of physics and biological mechanisms, but – of course – there is none. Life is a complex phenomenon, the emergent property of a highly compartmentalized ensemble of chemically active molecules that abide (again, obviously) the basic laws of physics, but which description as a system, may not be properly described by thermodynamics. This is why systems biology is branch of biophysics that was born for this purpose exactly.

The most elemental aspects of life are identity as an active biochemical system (i.e., its compartmentalized metabolism), its capability to maintain integrity (i.e., its homeostasis, its capability to replicate) in face of environmental pressures and to be autonomous (i.e., the capability to process information and undertake decision).

For those of us working on a disease such as cancer, it is thus unsurprising that cancer is intimately linked to deregulation of all of these fundamental characteristics of life.

References

I’ve written this assay in different periods reading literature beyond the scope of my own research. Therefore, I cannot reference my text properly, but these are the material I’ve read and may be of interest to you.

Devies et al. (2013) “Self-organization and entropy reduction in a living cell” Biosystems

Spanner (1953) “Biological systems and the principle of minimum entropy production” Nature

Prigogine (1971) “Biological order, structure and instabilities” Quarterly Reviews of Biophysics

England (2015) “Dissipative adaptation in driven self-assembly” Nature Nanotechnology

Frenkel (2014) “Order through entropy” Nature Materials

Schrodinger (1944) “What is life?

Yodh et al. (2001) “Entropically driven self-assembly and interaction in suspension” Phil. Trans. R. Soc. Lond. A

Bray (1990) “lntracellular Signalling as a Parallel Distributed Process” J Theor Biol

Bray (1995) “Protein molecules as computational elements in living cells” Nature

Bray (2003) “Molecular Networks: The Top-Down View” Science

Mc Ewen and Wingfield (2003) “The concept of allostasis in biology and biomedicine” Hormones and Behaviour

Ray and Phoha “Homeostasis and Homeorhesis: Sustaining Order and Normalcy in Human-engineered Complex Systems

Sterling “Principles of allostasis: optimal design, predictive regulation, pathophysiology and rational therapeutics” in “Allostasis, Homeostasis, and the Costs of Adaptation” by J. Schulkin

Berclaz et al. (2001) “Growth and Transformation of Vesicles Studied by Ferritin Labeling and Cryotransmission Electron Microscopy” J Phys Chem B

Markvoort et al. (2007) “Lipid-Based Mechanisms for Vesicle Fission” J Phys Chem B

Mostafavi et al. (2016) “Entropic forces drive self-organization and membrane fusion by SNARE proteins” PNAS

Stachoviak et al. (2013) “A cost–benefit analysis of the physical mechanisms of membrane curvature” Nature Cell Biology

Although I never found the time to read it, the following book seems to cover exactly the topics I discussed. Having browsed through its pages now and then, it is likely I have been influenced by it:
Radu Popa “Between necessity and probability: searching for the definition and origin of life

 

The ‘no asshole’ rule

Do you know when you are at the airport with some time to kill, and you go to a bookstore? Do you know that revolving shelf with business-oriented books suggesting how you will have a great career if you read them, yes, the one just near the other shelf with books on mindfulness, homoeopathy and wonder diets? Well, bear with me a moment, and I tell you about when I dared to buy one of those books (no, not on diets, silly!).

In these days, we are recruiting students, and I had the occasion to host several candidates in the labs for a non-formal chat. Unplanned but unavoidably, I find myself discussing how I have a culture of team-work and how I would not tolerate any behaviour that would undermine a good work environment, team-oriented. As rewards in academic biomedical research are rather individualistic (no one is rewarded for being a good team player, sadly), I also have to reassure people that when papers are published, I prefer to have few good papers with several people sharing authorships rather than having one person with a paper and around them… devastation. I try to discourage self-centred disruptive personalities, however bright, to work with me.

Why do I do that? I do that because the cost of handling the consequences of working with ‘assholes’ (yes, coming to that…) is far superior to the advantage of hiring sociopathic alpha females and males even when they fully deliver on the set goals, perhaps seemingly faster and effectively.

I do have witnessed the effect of various sort of bullying in the work environment, most times as a bystander. Not frequently, but I had the opportunity to see how dramatic can be the fall-out of such events on the psychology of those involved and how these behaviours can undermine efficiency and productivity of individuals, and their future careers*.

So, back to the airport. A couple of times, before a long trip and having exhausted all other possibilities, I dwelled in front of business books. A scientist can learn important things from business, but choosing a book that is not the equivalent of drinking-urine-cures-all-diseases or a specialist treaty on the economy is not trivial. I think I eventually landed on ‘the no asshole rule’ by Robert Sutton (I promise I’ll review this bit at the earliest convenience) and I try to adhere to this rule as much as I can!

First of all, I do agree with the ‘asshole’ definition, because broader than that of a ‘bully’. Sometimes people may not be recognised as a bully, but they are clearly disruptive assholes. Other times, people may seem bullies, but they are actually good people trying to foster discussion. The no asshole rules should be institutionalized so to make sure that disruptive behaviour is not rewarded, but first discouraged and eventually punished.

In the absence of a consensus on the ‘no asshole rule’, I invite you to adhere to this principle, or any other more or less colourful flagship rule, aimed to  create and support  good, efficient and productive work environments.


* I am rather happy about the team I lead, the group within which I work and the Department where I work. Now and then, here or elsewhere, however, I did see wrong behaviours. But, most importantly, even when we create good environments, we are constantly interacting with others (peers, journals, founders, etcetera). There are far too many that do consider science a tough business and therefore accept various shades of bullying as the acceptable norm. I’ll speak about this on other occasions, but here I wished to say just something about recruiting.

 

It is Crunch Time for Early Career Scientists.

In mid-2016, I read this Nature News about the payment of overtime for USA post-doctoral scientists and the possibility that this would lead to fewer positions available. This article inspired a post I published on LinkedIn, that I would like to repropose in a radically updated form. In the original post, the premise was overtime payments for post-docs in USA.

IDENTITY AND RESPECT | I am not very sensitive to titles and I am a very pragmatic person, therefore, I was never sensitive about being called a post-doc, but should you? Let me tell you a personal story.

A few years ago, I had the honour to work with Dr Virgilio Lew [1] at the University of Cambridge. Virgilio is a great scientist and a wonderful person who gave me the opportunity to taste how we should work in science, the love for the scientific question, the stories around small and big scientific discoveries, the humanity around individual researchers, the excitement of the scientific debate – even antagonistic at times –  but the preservation of the joy of science despite all the politics and over-competition of modern Academia.

I do have one regret, that I could not fully enjoy that period of my life. Back then, I was an anxious and moaning chap, distressed by the instability of our jobs, the constant mobility and the difficulties to synchronize careers with a partner, the self-inflicted hard days/nights/weekends work paid, at the time, below average UK income. In other terms, I was a post-doc, experiencing a rather normal life crisis, thrust by a very successful PhD into the space of wild competition for a PI position that I actually never cared to seek before.

When Virgilio would introduce me to colleagues, he would always say something along the line: “this is Alessandro, a [replace with a positive adjective of your choice] biophysicist who joined me and Teresa to work on the homeostasis of red blood cells infected with Plasmodia”. Or, “this is my colleague Alessandro…”  I never heard Virgilio calling me a post-doc. I think that elegance and class are in the spontaneous attention to detail. Did it matter? Well, the fact I took notice of it probably means it did at least at an unconscious level. I assume it inspired a sense of community, a sense of belonging to the academic world, irrespective of seniority. Many friends left science for many reasons, but one recurrent theme is not feeling to belong to a community.

EMPLOYEE LOYALTY AND EFFICIENCY | Of course, eventually, the word post-doc is not the issue [2]. It is very important to ensure our colleagues feel they belong to the organization they work with and the wider scientific community. As far as I understand, in Industry this simple concept is called employee loyalty and it is considered to be an important factor in boosting efficiency. Most members of staff working at Universities, at least within my limited experience, have open-ended contracts that allow them to identify with their own institution; similarly, academic staff can be loyal to their University and look after their good homes. Most junior researchers, however, are that ocean of colleagues between their PhD and a PI position – or a career change – that experience short-term contracts, high mobility, compounded with the uncertainties inherent in the scientific research. Often, this overlaps with a period of their lives when people start to build a family as well. Can we ask employee loyalty from junior scientists? Can we reach the level of efficiency that employee loyalty can offer? What about the experience we lose every time a junior scientist leave and the time we have to reinvest in training? And which are the mental health implications of the current system, asking our brightest and youngest minds to work under this level of pressure and insecurity?

I would like to work with deserving colleagues for ten or twenty years, if they do not care about changing their jobs, but we rarely can because the job of a post-doc is unsecured. Therefore, I agree with those proposing to establish more permanent posts for researchers and to establish also in Academia the role of professional scientists, a rare position. Currently, we seek efficiency through fierce competition, with post-doc ‘killing machines that will select the best’, at the same time distracting them from their primary work (doing science), giving incentives to cut corners and behave very individualistically. However, we can achieve efficiency by building employee loyalty, building teams of scientists that can dedicate themselves to the big picture, and not ‘just’ to how to solve a career problem.

One of the issues we have in Academia is that there is too much emphasis on leadership and too little on team-working and community effort. We are rewarded only if we are the first author on a paper, the principal investigator on a grant, the group leader in the lab, and this eventually leads far too many people to aspire to a leadership position and neglect the importance of working together. This is combined with the fact that to remain in Academia, we have to become leaders, otherwise, we are out. How many PIs were good scientists, but were not selected on the basis of their capabilities to manage people to then… manage people?

A NEW CAREER STRUCTURE | There will be always plenty of people that will aspire to become a leader or to climb the ladders of management, either in Academia or Industry. There are also plenty of people that are more than happy to do science at the bench and, as I have already mentioned, creating good and stable employment opportunities as professional scientists might improve efficiency and life of early and mid-career researchers. However, even if you agreed with me, there are significant barriers to establishing such a system.

First of all, while progressing in their careers, scientists become financially unsustainable certainly in comparison to a PhD student but also compared to more junior colleagues. Then, it does not matter if you wished to retain a co-worker because of their experience and expertise, you have to move on to who you can afford to employ. If we shifted our scientist base to more senior people, on average, there will be unavoidable cost implications that can be addressed only with higher budgets or fewer appointments.

Second, there are only limited positions available as post-doctoral scientists; if you are lucky and love this job, you can get a 5 years continuous appointment. To have this job for life, we could probably appoint 90% fewer post-docs, while waiting for one retiring.

Therefore, increasing the number of professional scientists in Academia will require a drastic reorganization of the sector and probably higher budgets to maintain a similar level of researchers. This will require an enlightened and competent central government working together with a united academic governance both agreeing that change is needed. I am pessimistic for this to happen, but if we cared about productivity in science (not measured by impact factors, but by discoveries that will impact society) and the mental health of our scientists, perhaps we should consider change.

FEWER AND BETTER PhDs | Another change that may be required is how we train PhD students. At least in the UK, we are under pressure to get students finishing their PhDs in 3 years and consider this just as part of a broader training. Personally, I wish a PhD student in scientific topics to be an inexperienced but bright scientist, an expert in their field by the end of their PhD. Training students to do experiments should be the duty of undergrad courses, but here I am very biased by my background in physics. In physics, at least at the University of Genova, we got trained in physics of course, but also on how to build equipment, to develop software, to interface the two, to execute experiments, to analyse data, all of this with practical courses after passing examinations in the lab, written and oral. I guess I am an old-fashion one by now, but I wished that with the completion of pre-doctoral studies all students should be capable to do experiments, including coding and hardware interfacing. Also, I wish that students were also ready for industry. The PhD, in my opinion, should be Academic-oriented (wait a a sentence or so…), taking between 4 to 5 years giving them enough time to lead to substantial discoveries. On the way, some students (or their supervisors) will notice that Academia is not what they want to pursue and they could opt-out of their doctoral studies for a lower degree, if deserving. Both students completing their PhDs or lower degrees should be offered business-oriented experiences, in order to offer a wide range of choices and facilitating the transitioning to Industry or to non-academic posts within Universities. Far too often, the transition to non-academic jobs is lived or considered a failure, where this should be embraced as one of the likely, actually the most likely, outcome after studying at University instead. Therefore, we should have srudents ready for Industry irrespective of docoral studies and there should be planned paths from doctoral studies to Industry. There is too little awareness, during doctoral studies, about the need to plan a career outside Academia and about the odds to stay in Academia. If we could train better our students at undergrad and graduate levels, avoiding to rush them on the job market, but getting them on the right track as soon as they know what is best for them, Industry will benefit from more qualified junior scientists and Academia from a more moderate pressure of intake of junior scientists towards unsecured jobs.

CAREER TALKS AND MANAGING EXPECTATIONS | In any management course, you will be taught that one of your duties is to manage expectation of those working with you. Then, often, we operate giving the illusion to junior scientists that they will become known professors, you just need to do that big discovery published in Nature, creating lots of press releases. In career talks, people provide examples of success stories – and rightly so – but rarely depicts, in my opinion, the reality that a junior scientist faces, with plenty of exceptions of course.

Once I attended a meeting where the work of mathematicians employed at post-doctoral level was instrumental in advanced magnetic resonance imaging with important implications for basic science and translational medicine. At the end of the meeting, a simple question was shot: “you discussed all these great advancements, but what career opportunities those mathematicians have?”. The Academic in charge, honestly, candidly and with clear expressions of sympathy and compassion (my interpretation) replied: “None. Unfortunately, we do not have the right career structures to support them.” A colleague, trying to rescue the situation added: “but all these people have successful careers within Industry”. Let’s be clear, sometimes you will have no prospect in Academia, not because you are not excellent, not because you are not appreciated, but simply because there is not an adequate career structure for you, in the specific case, the possibility to promote mathematicians adequately within a medical department. If this is the case, plan your career path away from Academia soon enough. But how much waste of talent from Academia which, hopefully, is compensated by big gains in Industry. But this exchange of talent might result in a net loss of efficiency in Academic research.

Another time, during a leadership course, a colleague asked: “my husband is a PI here in Cambridge, but I got a good offer in another University. We have a young child and a difficult choice to do because perspectives for post-docs in Cambridge are rather bleak”. Answer: “I understand is tough, but sometimes we need to decide which are our priorities, family or career”.

Then here it comes a career talk speaking about how difficult but rewarding it was to become a Principal Investigator at the University. Question: “if someone does not want to become a PI, but loves to do science at the bench in the academic environment, which options have?”. Answer: “I advice my people to embrace research assistant positions”

Not all of us will agree, by a system ran by smart people, often of very liberal background, often committed to social advancement, Academia, should not systematically put people in condition to decide between family and work (often penalizing women in STEM), not have the right career structures, or operate on a competition model without adequate incentives for employee loyalty.

CONCLUDING | I am not an expert in this area, but I wished to provide my view on how we should change career structures in Academia.

  1. Work upstream and invest more on under-grad and grad students at the same time awarding fewer PhDs. Fewer and better junior scientists in Academia will reduce pressure on mid-career scientists. Better undergrad students with more practical experience will help Industry as well.
  2. Value training and career options. A PhD is not a piece of paper to stay a while in Academia and then get higher pays in Industry. Other careers are great alternatives, they are respectable choices of great value for individuals and society, including being a scientist without being a leader.
  3. Work downstream, increasing investments in stable posts that do not depend on grants for senior scientists adding this as a valuable career option currently reserved to a very few. Not everyone wants to or should be a PI or a Professor. Loyal and less stressed employees will work more efficiently.
  4. Identify the appropriate career structures suitable for the age we live in, including flexible working for who has family commitments and for scientists moving across disciplines.

Failing to do this, we can simply advise our students and junior colleagues to identify not only the best possible path to a possible leadership position in Academia, if this is what they want, but also the best possible path to the best job they could aspire outside Academia. The transition to Industry should not be considered a lesser alternative choice – after some ‘postdoc-ing’ [2] – but an equally valuable choice. And, we shall always be clear about the chances anyone will have, even if good, to obtain one of those precious posts at University.

Notes

[1] Let me acknowledge the great work we have done as a team, Dr Virgilio Lew, Dr Teresa Tiffert, Prof. Clemens Kaminski and Dr Jakob Mauritz. Here, I speak only about Virgilio as I report only a personal account of our conversations.

[2] There are plenty of terms I dislike. For instance, the term ‘permadoc‘ to define lifetime postdoctoral appointments made me shiver. True, do not let terminology to bother you too much, but also do not let other to push you down by (inadvertently I am sure) defining you with inadequate terminology. If you are a post-doc, you are a junior scientist or junior researcher, you are not ‘post-doc-ing’ (for the goodness sake!) and you do not aspire to become a permadoc. You are a scientist, growing experience and maturity, looking for a stable work in academia or, of course, for a career change.

Open Access: is that… cheating?

800px-Open_Access_PLoS.svg

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.

Notes

[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)