As I get older, I am asked to sit on more senior grant panels. Larger projects generally mean more diverse research approaches. I am also offered to sit on multi-disciplinary panels, where the projects must feature different approaches, or even different fields. Finally, those panels are often made up of more “mature” scientists. As a result of all that:
1) Panel members have less expertise, if any, in some of the fields covered by the applications. Even in their own field, they mostly master only part of the landscape. They left university decades ago, and kept abreast of developments via reading scientific literature. Since there are only 24 h in a day, we tend to focus our reading largely on what directly impacts our own research. When it comes to techniques, most of senior panel members left actual experiments/programming/equations for quite a while, and although they have an “academic” knowledge of the methods, they might not know the state of the art, or master the subtleties and pitfalls of given techniques.
2) Panel members/reviewers have a shorter attention span. This is not only because of age (although let’s face it, there is some decrease in focus and stamina), but also because senior scientists have more commitments and are always running for deadlines (there is also the increasing number of projects to evaluate. In my latest panel, I was in charge of 25 projects). Finally, there might also be a bit of arrogance and “cannot be bothered” attitude.
Applicants to large grants should take these facts into consideration. One area where it is particularly important is Systems Biology. I am not going to dwell on what is Systems Biology, but a subset includes the development of mathematical models and numerical simulations to reproduce the behaviour of biological systems. This is an area where I encountered systematic strategic errors in the way grant applications are written, that decreased their chances of being successfully funded comparatively to projects in other areas. Below are a few advices that could allow senior panels to appreciate your projects better. This is only my opinion, and I might be wrong about the impact of those mistakes. Also, some of the advices seem obvious. But nevertheless, they are not systematically followed.
What is the question?
Building models is great. Models are integrators of knowledge, and building a model that can reproduce known behaviours of a system is the best way to see if we understand it. Models can also suggest new avenues of research or treatment. But models must fulfill a purpose. This purpose is not obligatory part of the project submitted itself, but it must be mentioned there. Why are you building the model? What questions do you want to answer with the model simulations and analysis? Note that this is not specific to computational modelling. I saw rejection of projects describing very complex experimental techniques which use was not justified. Technical activity must be commensurate with expected benefits.
Have a colleague to read your proposal, and ask them afterward “can you tell me what we are going to do with this model?” (Do not ask the question before, while handing them the application. Your colleague is clever and friendly. They will finds hints here and there, add their own ideas and put something together).
Clarity of the research plan
I cannot count the number of time where after a lengthy introduction, a detailed experiment part, projects end up with “and we will build a model”, or even worse, the dreadful “we will use a systems biology approach”. WTF is a “systems biology approach”? I have called myself a systems biologist for the best part of two decades and have not a clue. It does not mean anything, or it means too many things.
Explain what you are going to do and how. Which kind of modeling approach will you use? Why? Is this the best modeling approach considering the data you have and the questions you ask? If building a model, how will you build it? What will be the variables (e.g. which molecular species will be represented)? How will you relate them? Will you use numerical or logic approaches? Will you incorporate uncertainty? Will you study steady-states or kinetics? Will you use deterministic or stochastic simulations? Which software will you use? How are you going to analyse the data? How are you going to link the model to experimental evidence? Do you have a plan for parameterisation?
Don’t overdo it. One does not describe generic molecular biology kits or culture media in senior grant applications (except if this is at the core of the project), so we do not need to describe technical details that will not affect the results and their interpretation. But give enough details to convince the reviewers or panel members who might actually know what all that is about. An experimental plan that would not precise the organism or cell line to be used has almost no chance of getting through. Same for modelling! And actually, you also need to add enough explanation to allow for non-specialists to understand what you are going to do. For instance, many people are baffled by genome-scale constraint-based modelling of metabolism, confusing flux balance analysis and metabolic flux analysis, and therefore misunderstanding the (absent) role of metabolite concentrations. They also mistake them with ODE models, concluding that they are too big to be parameterised.
Have a colleague to read your proposal, and ask them afterward “Can you tell me which modelling method I will use and why it is the best for this project?”
Provide preliminary data
Almost any experimental project comes packed with preliminary data that shows 1) why is the investigated question interesting, 2) why is the workplan feasible. It is no different with modelling. Why would people believe you? Past track record on other projects is not sufficient. At best it can show that you can do modelling. Good. But this is not enough. Remember, this blog post is particularly focused on large projects, with multiple lines of investigations and requesting large amounts of money. Often these projects have been written over many months or even a year. I know a few such projects for which the production of preliminary data required another, smaller, dedicated funding. So there is no excuse not to spend a sufficient effort benchmarking the modelling approaches you will use, and getting preliminary results, hopefully exciting and justifying a scale-up.
Describe the validation steps
This is a very important part, even more important than for experimental projects. A modeller cannot say “let the data talk”. Any number of models can lead to reasonable simulation results. That does not mean the model is the correct one, or that the results mean anything. You must convince the panel that you have a plan to check that your results are valid and are not a bunch of random numbers or graphs. How will you validate the results of your simulations and analysis? How will this validation feedback in your model design? How precisely will your predictions lead to new experiments? Who will do the validation? Where? When?
Have a colleague to read your proposal, and ask them afterward “Can you draw me a workflow of the modelling part of this project, and identify the points of contact with the rest of the project?”
These were just some tips I came up with. Do you disagree with them? Would you add others?