Team:ETH Zurich/human/essay/project

From 2014.igem.org

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(Complexity in our project)
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: 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 [http://2014.igem.org/Team:ETH_Zurich/modeling/int 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.  
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: 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 this [http://2014.igem.org/Team:ETH_Zurich/modeling/int 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 [http://2014.igem.org/Team:ETH_Zurich/modeling/parameters 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.
: Our modeling part is focused on parameter fitting (see our [http://2014.igem.org/Team:ETH_Zurich/modeling/parameters 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.

Revision as of 01:56, 18 October 2014

Complexity in our project

Wet lab
At the very beginning the work in the wet lab seemed straightforward. The way we planed our time was ambitious, but soon after the start we faced first problems. In different experiments we could observe cross-talk between the different quorum sensing molecules. In a next step we focused on quantifying the cross-talk. We found interactions between the subparts (AHLs, AHL binding molecules and promoters) on different levels. The various ways of crosstalk are characteristics of a complex system with emerging features. The whole is more than the sum of its parts; living beings, even if not multicellular have various naturally emergent properties.
In fact the observation of emergence was one of the central topics of our project. The techniques of rapid prototyping and 3D-printing followed by PDMS molding allowed us to use custom-designed millifluidic chips. The chip with the alginate bead grid on it enabled us to observe the phenomenon of guided emergence. In this case we do not have a simplification but a contextualisation. The design of our experiments aimed at the consideration of different interactive factors rather than a complete reduction of complexity. Since the knowledge of biological systems is limited such an approach seems to be nearby.
During our project we experienced that many times biological systems are not behaving as expected. While in some cases mistakes of the person planning or conducting the experiment were discovered, we could many times not localize a causation for the unexpected behavior. This taught us about the influence of parameters that have a priori not been taken into account or that are beyond our control. A cell is an open system that interacts with its environment; it is possible to reduce the number of factors influencing the system, but is not possible to eradicate them all.


Modeling
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 this 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.