Success in academic–industry collaboration could be improved by selection of appropriate Collaboration Interface Participants (CIPs) based on inter-individual variability in expression of the putative collaboration gene, clb.
The world of academic – industry collaborations
Collaborations play a vital role in innovation and in the pursuit of new translatable knowledge. These can range from very informal interactions through to the creation of start-up companies with unlimited opportunities for generation of
Figure 1. The Interaction Continuum from informal networking through shared students and postdocs to small business spin offs and start-ups (image by Ruth Roberts).
societal and commercial value (Figure 1). Very early informal interactions play a vital role in providing informal peer review and challenge that can be used both in affirmation of ideas and strategies as well as in ‘unsticking stuckness’ (when problems that can seem insurmountable are easily resolved by a different perspective). Further along the continuum, resources can be pooled to catalyse conceptsa and move pilot projects forward to grant applications and subsequent publication1,2.
As well as studentships, there could be postdoctoral fellows, fee for service contracts and generally ‘bigger things’ at this contractual entry level in the continuum. This step also provides opportunities for cementing relationships via tangible output such as the organisation of scientific sessions and coauthored publications, all of which are positive indicators for further grant funding. Finally, collaborations can lead to commercial opportunities ranging from patents to the creation of startups companies that may ultimately by floated or sold hopefully with significant gain for those who backed the right risk.
The Fourth Dimension of the Industry-Academia Collaboration Continuum
Collaborations are often thought of in 3 dimensions; money, time and geography. From the industry side, the money aspect includes budgetary constraints that must be balanced with perceptions of cost of the collaboration versus perceived value to the sponsoring organisation. From the academic side, scientists are often competing for limited resources within the institution or with external funding bodies. Fortunately for the UK academic environment, studentships are relatively plentiful since cost is shared between the host institute and funding bodies such as the BBSRC3 and the MRC4.
Time is another key element both in terms of the duration of the collaboration but also since trends and priorities ebb and flow with economic cycles. During times of plenty, organisations are much more likely to support projects that pursue knowledge for knowledge’s sake; during harder times funding streams may be only for applied work where the potential for commercial impact is more obvious. For some organisations funding may dry up altogether in leaner times. Geography is also key: despite predictions that the electronic era would overcome geographical barriers, collaboration distances have not increased over time and regional collaboration clearly predominates3.
Although money, time and geography are key parameters in both the initiation of and the success of collaborations, there is a fourth dimension often overlooked: people (Figure 2). The majority of collaborations have been initiated, cemented and progressed when like-minded scientists from different organisations discover a common goal, a shared curiosity or a pet hypothesis to be addressed. Often these scientists may already know one another – an informal survey of a couple of large UK-based companies revealed that a disproportionate number of the MRC and BBSRC studentships were with the industry sponsors’ previous PhD supervisorb. Success at this early stage depends on the willingness of scientists to engage and to take the time and energy to share their thinking informally, to listen carefully to the challenges others are facing and to incorporate this thinking into their own ideas.
Figure 2. The Four Dimensions of the Evaluation of the Evolution of Industry-Academic Collaboration (image by Ruth Roberts).
The Collaboration Phenotype
Most academic and industrial organisations fully recognise that collaboration between academia and industry is key to creating and driving forward innovation in the biosciences, particularly in the search for new medicines. As a consequence of this, most organisations invest significant effort into seeking, organising, maintaining and publicising collaborations. Organisations often have full or part time roles aligned to these tasks, given titles such as industrial liaison manager, head of academic outreach or externalisation director. But in my experiencec and that reported by colleagues, some of the most potentially complex collaborations run smoothly with some institutions whereas even the most simple of studentships can hit numerous inexplicable problems with others. In some organisations, it would seem that collaboration comes naturally and intuitively whereas elsewhere it has to be forced via top-down instruction, perhaps based on targets or process. This appears to be as effective as planning to be spontaneous.
So what can be done to rectify this? As highlighted earlier, people are the key fourth dimension in the likely success of industry-academic collaboration. So, institutions need to think carefully about the selection of the their Collaboration Interface Participants (CIPs) since these are the individuals that can make a success of almost any project and equally well can kill a great idea before it can be explored. CIP phenotype must be considered when selecting individuals for formalised collaboration roles (industrial liaison manager, head of academic outreach, externalisation director, etc) but also in informal interfaces such as meeting potential collaborators and attending networking events such as conferences and discussion groups. Certain informal tests can be applied to shortlist these individuals based on behavioural phenotyped but also on motivation where primary positive indicators could include a genuine enthusiasm and excitement for the topic whereas primary negative indicators could include process (numerical personal and/or institutional targets), competitive behaviours and/or self-promotion. CIPs with the right collaborative phenotype will dramatically enhance success in innovation and delivery. In contrast CIPs with the wrong phenotype will default to process, generating innumerable barriers to innovation and progresse killing any potential collaboration before it really started.
Figure 3. Family tree showing inheritance of clb genotypes. Individuals carrying two copies are natural and compulsive collaborators (image by Ruth Roberts).
So what explains these differences in behaviours? We propose these are driven by differences in expression of the collaboration gene, clb (Figure 3). Homozygous individuals (clb+/+) are driven to collaborate and have a very open and encouraging attitude to new ideas, especially those coming from others. They are willing to run with concepts and take risks. In contrast individuals homozygous for the recessive mutant (clb-/-) appear unable to exhibit collaborative behaviours and are intrinsically suspicious of new ideas especially those proposed by scientists from another organisation. Generally, the clb-/- genotype will seek to solve issues without consultation, exhibiting the so called have-all-the-answers (HATA) phenotype. When placed into a collaboration interface the clb-/- genotype will often create complex processes, structures and metrics in place of judgement and intuition. In contrast, individuals that are hemizygous for the collaboration gene (clb+/-) are highly variable in behaviour and appear to be directly influenced by their environment. In a negative collaboration environment (Table 1) these individuals may behave largely as the clb-/- genotype, resorting to HATA mode especially when challenged. However, when placed in a dynamic, collaboration positive environment or team, collaboration behaviours are switched on creating the induced collaborator (IC) phenotype. These observations provide tentative evidence for environmental regulation of the clb gene, although the mechanisms of such an induction remain to be elucidated.
Table 1. Impact of a negative (-ve) or a positive (+ve) collaborative environment on behaviours in individuals negative, heterozygous or positive for the putative collaboration gene. HATA: have-all-the-answers; IC: induced collaborator; Coll: collaborator (table by Ruth Roberts).
Humans’ ability to collaborate to obtain otherwise inaccessible goals may be one main cause for our success as a species6; thus it is not surprising that similar behaviours are central to higher order functions such as success in scientific endeavour. For mutually beneficial collaboration, individuals need cognitive mechanisms to coordinate actions and methods to disseminate benefits in a way that incentivizes partners to continue collaborating6. It is tempting to speculate that the clb gene plays some role in controlling these mechanisms. Additionally, we highlighted earlier that homozygous individuals (clb+/+) have a very open and encouraging attitude to new ideas, and are willing to run with concepts and take risks. Recent data have correlated risk taking behaviour to variations in local brain structure7 suggesting there may be a structural basis for the differences in collaborative behaviour associated with clb genotype. In summary, it’s vital that forward-looking organisations consider CIP phenotypes alongside money, time and geography as a key parameter that will dictate the likely success of their collaborative efforts.
1. A. C. Bayly, N. J. French, C. Dive and R. A. Roberts, Non-genotoxic hepatocarcinogenesis in vitro: the FaO hepatoma line responds to peroxisome proliferators and retains the ability to undergo apoptosis, J. Cell Sci., 1993, 104, 307-315.
2. A. C. Bayly, R. A. Roberts and C. Dive, Suppression of liver cell apoptosis in vitro by the non-genotoxic hepatocarcinogen and peroxisome proliferator, nafenopin. J. Cell Biol., 1994, 125, 197-203.
3. Biotechnology and Biological Sciences Research Council (BBSRC) 2015. http://www.bbsrc.ac.uk/funding/studentships/
4. Medical Research Council (MRC) 2015. http://www.mrc.ac.uk/skills-careers/studentships/
5. S. Von Proff and A. Dettmann, Inventor Collaboration Over Distance: A Comparison of Academic and Corporate Patents, Scientometrics, 2013, 94, 1217-1238.
6. A. P. Melis, The evolutionary roots of human collaboration: coordination and sharing of resources, Ann. N. Y. Acad. Sci., 2013, 1299, 68-76.
7. Z. Nasiriavanaki, M. ArianNik, A. Abbassian, E. Mahmoudi, N. Roufigari, S. Shahzadi, M. Nasiriavanaki, B. Bahrami, Prediction of individual differences in risky behaviours in young adults via variations in local brain structure. Front. Neurosci., 2015, 9, 359-345.
a The Catalyst Concept was well illustrated one Friday evening in 1996 when Caroline Dive brought some H33256 (a DNA stain) to my lab on the off-chance that suppression of apoptosis could explain why cultured rat hepatocytes survived indefinitely in the presence of peroxisome proliferators. These data provided the pilot work for a successful BBSRC grant application and subsequent publications that helped move forward the field.
b This can be very productive but a future hypotheses to be tested proposes that exciting and fruitful collaborations are more likely to arise from new relationships and new ideas.
c Chair of the AstraZeneca Global Safety Assessment (GSA) External Collaborations Group (ESG) 2007-2012.
d The poster session test: positive indicators include full participation and engagement; negative indicators include disappearing to ‘catch up with a few emails’.
e Confidentiality agreements, intellectual property rights, legal contracts, key performance indicator (KPI) metrics, etc.
Any views or opinions presented in this post are solely those of the author and may not represent those of The Royal Society of Chemistry.