Team:ETH Zurich/human/essay/influence


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Influence on our scientific project

As soon as we agreed on a subject for our iGEM project, we started to observe patterns and complexity all among us. Our awareness of structures and compositions, as well as exchange and interaction between subunits, continuously increased. It quickly became a running gag among us to say: "Anyway, it is too complex".

Here we aim at describing the influence human practice had on our scientific project. Our human practice part is intricately linked to our scientific project, since we investigated in a philosophical and sociological way the concepts we tried to reproduce biologically: The emergence of a complex pattern from simple rules. By analyzing complexity and pattern formation in depth, we learned new strategies to approach them. Our human practice project allowed us to consider our scientific project from different points of view. 

Wet Lab

In our human practice project we interviewed experts of different fields, conducted a survey and sought to outreach our experience and knowledge. Thereby we learnt new strategies of how to approach complexity, but also ways to handle it. Furthermore, we found a source of motivation and reinforcement in our human practice project. In town planning it is crucial to not reduce a problem too much. Otherwise, the architectural intervention will be an isolated and foreign object in its surrounding. Such interventions have a high risk to fail, explained D. Übelhör. As urban systems, biological systems are highly complex. In order to understand them we tend to reduce them to single subunits. However, reductionist approaches run the risk of neglecting important information, especially when transferring behavior of subunits to the behavior of the whole. That is also what we learnt from D. Garcia. He pointed out that in some situations reductionism is not only an unsuitable strategy but also unnecessary at the same time. Why should one struggle with details if they cannot explain the entity? Unexplainable noise is jointly responsible for complexity. There might ways to reduce the noise and thus increase the predictability of systems, however, we have to accept that we do not know everything. K. Chikkadi expressed this fact in a scientific context, while J. Fuisz highlighted the religious aspect of acceptance. The trust in a higher power helps to overcome complexity. Of course, this cannot be transferred to a research project literally. Anyhow, to accept that there is no human omnipotence can protect from frustration. We cannot - but we also do not have to - solve all complexity. To not get lost while interpreting experimental data, we followed E. Klein’s advice: we limited our ambitions in order to become efficient. Many times it is advisable to proceed in small steps rather than aiming at the full monty immediately. However, pedantic planning is always highly important especially when dealing with complexity, emphasized C. Veress. Looking back on the past weeks and months we can only agree with that. Spending time with planning instead of rushing quite often saves time, money and work. While spreading the word about synthetic biology we experienced the importance of a simple, comprehensible language. In fact, good communication and articulation is crucial for functional teamwork. Explaining concepts to high school students, interested visitors at the open house or during a science slam increased our awareness of the importance of communication and trained our skills at the same time. Many participants of our survey encouraged us in our research project. They agreed on the importance of analyzing complexity and the emergence of patterns. We were very happy to get such a positive feedback and motivated to continue our studies.

A model is always a simplistic representation of reality. It contains assumptions. Using standard descriptions, like chemical reactions and mass action, we analytically derived each formula we wanted to fit. This derivation was only possible thanks to some assumptions. As these page shows, every assumption was carefully made and thought about. The process of simplification was put into question because our human practice project tends to investigate why simplification is powerful but not enough to understand the surrounding world. In our case, simplifying was necessary. That's why we tried to understand what every assumptions could make sense in a biological way.
Our modeling part is focused on parameter fitting (see our parameter page). We use a classical deterministic model and tried to fit it to the experiments. Matching the reality level with the description level is a complicated task, as there is no optimal match. Differences and similarities between experiments and simulations give insights on where emergent phenomena could happen.
As randomness is a property of complex systems, it motivated us to derive a stochastic model. Some biological events, like binding, are typically stochastic phenomena. The human practice part inspired us in a certain sense.
We opted for an engineered representation (see the information processing page). It was natural to divide our systems into submodules. Thus, we took into account the fact that different levels of description can give several insights on the system works. The decomposition into interacting submodules (see the modeling overview page) was crucial to face this complex problem.