The topic of our research deals with proposing a solution to the current outbreak of the Oak Processionary Caterpillar (OPC). Aided by the implicit effects of climate change, the species has spread far from its native borders in southern Europe and has reached the northern countries as well as areas in the Middle East, where a lack of natural predators has escalated the expansion and transformed the species’ classification into that of a pest (Forest Research, 2020). Given the negative effects, both to the fauna and flora, and the health risks brought by the presence of the OPC in these areas, it is an understatement to emphasize the importance of finding a solution to the current, unconstrained expansion of the species. Nonetheless, finding the right approach, one that not only provides an efficient method of population control, but that also improves on the current measures and, most importantly, helps preserve the biodiversity of the ecosystem where the OPC has established itself, is a non-trivial task that requires first and foremost, an analysis of the variables that affect the population distribution of the species.

Furthermore, taking an active role in controlling the population distribution of a species and finding a way of maintaining the balance and biodiversity of ecosystems is a mission that must be taken on responsibly; any measure chosen must have appropriate foundations and reasoning that thoroughly support the specific course of action. Thus, exhaustive analyses must be done even previous to the experimental phase of the research. In order to understand the effects caused by implementing certain approaches, our team has adapted and developed a model that allowed us to study the population changes of the OPC.

It is important to emphasize that this approach is not exclusive to an outbreak of the OPC species, any research that tackles insect outbreaks and aims to develop measures to deal with the abundant spread of a specific species must first analyse and model the effect of interconnected variables in population changes. Therefore, we hereby provide an in-depth explanation, as well as instructions, of the mathematical model used by our team in the development of our population control product for the OPC, a model whose qualitative parameters have been quantified to facilitate its adaptation to other species and thus, its use by other research teams.

Dr. Ludwig’s famous model Qualitative Analysis of Insect Outbreak Systems: The Spruce Budworm and Forest explains how insect populations change over time, as well as how outbreaks can suddenly occur from one year to the next. Nonetheless, this model uses multiple abstract measures, like “carrying capacity” and “intrinsic growth rate”, that are not only difficult to determine and quantify, but that also change over time.

A dynamical systems model representing the population changes within the OPC ecosystem. With full dots representing stable fixed points and the hollow dots representing unstable fixed points.

Figure 1.
(a) Non-parameterized model. (b) Full formula used to find stable points.

Figure 2.
rs - Average branch growth rate (m^2/year)
Ke - Upper limit of energy storage (J)
Ks - Upper limit of branch surface (m^2)

Figure 3.
re - Intrinsic energy expansion per year (J/year) which can be quantified by the amount of sunlight the system receives in joules.
Ke - Maximum energy storage within the system (J) which can be quantified as the maximum amount of plant matter within the system times the average density of the plant of choice, in our case it was oak wood.
p - Energy loss caused by the insect of choice (J) which can be quantified as consumption rate of energy per m^2 branch.

The fast slow and very slow factor formula's to both quantitively and qualitatively determine the population changes of insects within the ecosystem. With parameter explanation and units.

In most literature, the model is used in its non-parameterized form as shown here (Figure 1).

However, this form only provides a qualitative analysis and no quantifiable results. In order to obtain the desired results, it was needed to expand on the parameters of carrying capacity (K) and predation rate (α), since these terms are time dependent and initially difficult to quantify. First, the abstract carrying capacity (K) was split into the measurable maximum insect density per square meter (K’) and the surface area of the branches in square meters (S) as follows:

K = K’S

Subsequently, the predation rate (α) was split into the half maximum density of predation (α’ , units: caterpillars/km^2/year) and the parameter S from above so that,

α = α’S

These parameters are all quantifiable now, however the parameter S is also dynamic and changes over time thus, it needs its own quantification with identification of stable points. This is given by the following formula, which represents the branch surface area change within the system (Figure 2).

This leaves the final parameter (E) to represent the energy in the system in joules. Once again, this parameter is difficult to measure and quantify and thus, it needs its own quantification with identification of stable points. We determined that the following formula provides the best approximation of the energy changes within the system (Figure 3).

We not only expanded upon the base framework by quantifying these parameters, but also took into account how these variables change over time within the system. This development changed the model from just an explanation of ‘why’ the outbreaks occur, to a functional, ‘quantifiable tool’ that can accurately model how the population changes based on quantifiable parameters. The resulting values provide us with a selection of the factors that are the biggest contributors to the population growth of the insect species.

This model can be used by future iGEM teams that aim to tackle an insect outbreak as well as to determine, for example, which factor to choose in order to tackle the insect of choice in the most efficient manner; future research teams just have to collect the data from our parameter list provided below and input it into the model.



Very early in our project process, we set ourselves the goal to create a Proceedings Journal compiling all the projects of the 2020 iGEM competition. For this, we decided to collect research papers from all the iGEM teams willing to participate. This Journal works exactly like a regular scientific journal, including peer review. This will be a great opportunity for every participating team to train their academic writing and actually get their work published in a student journal! Every article was peer-reviewed by other teams that sent in an article as well. Therefore, all teams are not only the author of a peer-reviewed article but peer-reviewers as well.


Peer-reviews are used as a method for quality assurance in the field of scientific publishing. This is achieved by having reviewers, with expertise in the same field (peers), evaluate a work. This provides a form of accountability and assures a high standard for publications.
We developed a guide that aims to help peers in writing their peer-review. It will guide them through each step in order to assess the quality of the manuscript and give the authors the best feedback to improve it.


The peer-review model used by us was modelled after the peer-review guidelines of several big scientific Journals, like Wiley, Reed Elsevier and PLOS. These Journals provide the peer reviewers with certain guidelines and we used those guidelines and made a step by step review guide/framework out of them. The highly accredited Publisher Wiley suggests a two step peer review process, with a first step skimming phase and a second step deep reading process, in which the peer-reviewer answers a specific set of questions that are predetermined by the publisher. The Netherlands-based information and analytics company Reed Elsevier, specializing in scientific journals such as The Lancet and Cell, and the ScienceDirect collection of electronic journals, also has specific peer-reviewing guidelines and we adapted some of their main features like the flagging of plagiarism and fraud and the determination of potential ethical issues.
Lastly, we had a talk with Demitra Ellina, the Editorial Community Manager at F1000Research, about the peer-reviewing process of the F1000Research Journal. Based on that interview we modified our peer review process in such a way that the peer review process was conducted in an ‘open system’. This means the reviewer and authors are not anonymous and the revisions and comments will be openly accessible. This way acknowledgements can be given to the input and hard work of the reviewers. An open process also ensures that everyone is doing their very best and treatment will be fair and respectful. In addition to that, it creates a better learning experience for everyone that took part in this collaboration. The full Peer Review form can be found below. Once as a PDF and another one as a word document, so that future iGEMers can just download the Framework and use it immediately in the word document.


This Peer review guide developed by us can be used by other teams in the future if they want to create a student-led journal themselves, or just to double-check their own articles. Or in their normal scientific career, when they peer-review their colleges. The use of this Framework is versatile and can aid other teams in training their academic writing and evaluation skills.


This year conditions were exceptional due to the global pandemic and how it impacted our social contacts and approaches to reach any audience. We soon realised the importance of being able to communicate and share what we were doing, without which the iGem whole concept loses its sense. Quickly, we understood that videos are a powerful medium in communication. Sharing educational content on social media and YouTube channels, investing effort in iGem project promotion or in any other type of deliverables, whatsoever; our team definitely needed to be able to produce videos. Even though none of us had a concrete deep experience in editing, we dedicated time and efforts to learn it and to enter the wonderful world of video creators. Therefore, we decided we needed to share what we learned, in the idea that some team could one day face a similar situation as we did, and in the hope this material would make their lives easier, their progress faster, and their result greater.

Here you will thus find a framework of the basic tools required in the process of video editing using Davinci Resolve, a free software from Black Magic Design. You will also find some notes and pieces of advice of our own. We tried to introduce basics for playing with green screen, smooth transitions, audio adjustments and many other things. We tried to focus on the small discoveries we made progressively (sometimes very late) that then highly helped us and that we wish we would have known sooner.

We did not want to give an exhaustive blueprint because it would have probably made it unnecessary long and obscure, but also because trying to find its own ways and look for specific results with different approaches are parts of the game and contribute to make a video unique. Therefore, we wish to spark interest and curiosity in the editing world but hope that this framework mostly encourages others to save some time on the first steps and reinvest it in more elaborate techniques.

Finally, we hope that this small help will allow you to skip the laborious parts that new skills acquisition requires and to enjoy it as soon as possible! Editing takes some time, but the result is often worth it. Have fun!


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