Team:ETH Zurich/human/essay/influence

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: A model is always a simplistic representation of reality. It contains assumptions. Using a 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 whhy is simplification 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 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 whhy is simplification 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 09:43, 15 October 2014

Influence on our scientific project

We encountered complexity everywhere. It quickly became a running gag among us to say "Anyway, it is too complex.." This section aims at describing the influence human practice had on our scientific project. First of all, our human practice part is intricately linked to the scientific project because we investigate in a philosophical way the concepts we tried to reproduce biologically: Emergence of a complex pattern from a simple rule. Our three key words were investigated as a whole. Human practice allowed to broaden our point of view.

Wet Lab
Cross-talk: observed a lot but never quantified. Identifying interactions on the subparts (molecules) level of the cell in order to understand the whole system behavior.
Observing emergence ==> guided emergence on a grid. No simplification but a contextualisation. In the design of our experiments, we took into account the different factors.
Living beings, even if not multicellular have naturally emergent properties.
Reproducibility of data ==> Bonnet's paper. Debugging constructs. See the influence of a priori not taken into account parameters. The cell as open system which interacts with its environment.
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 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 whhy is simplification 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.