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Don’t worry, systemic approach is not complex as it seems. Indeed, this approach is used for better understanding of complexity of the world that surrounds us. Ever since the term systemic approach was coined it changed the way we observe problems in various fields like biology, ecology, economy, town planning and many more. Four basic concepts of the systemic approach are: complexity, system, wholeness and interaction. This means that in the systemic approach we don’t approach to complex problems individually. Instead we look at them as a system, and we try to understand them as a whole and look at the interactions within the system. To achieve this we need theoretical, practical, and methodological approaches.

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Do you know that there is a type of jellyfish that glows in the dark? Yeah it is awesome. Now imagine that you can take that jellyfish to the laboratory, learn what makes it glow in the dark, and use what you have learned to make a cat that glows in the dark. Yeah, someone actually did that. I guess that guy doesn't need lamp in his room. Or you can use the same technology in your laboratory to make vaccines in banana, make cow's milk more nutritious or even make bacteria that eats plastics. But i guess that we scientist just prefer cats that glow in the dark.

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Feature extraction is a way of saying that we want to take some useful information of a signal. For example, we could have a signal that describes some phenomena, like the activity of the heart or the brain or even an accelometer recording that is in our smartphones. Those readings
could be electrical current, voltage, speed, acceleration,... we just have to record a phenomenon somehow.
With feature extraction tools, we would take those signals and use a computer software
that would analyse them with a mathematical formulation or even with an artificial intelligence implementation and  the result of this analysis would give us some information that is "hidden" or enconded
in the signal. Since real-life signals are not exactly equal in every situation, feature extraction aims to detect patterns and give precise meaning to those signals, which can help us identify and characterize the numerous new
signals around us that are waiting to be discovered :)

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Remember that glass of milk you had drank this morning? Your body has figured out many routes how should all nutrients from that milk come to every part of your body. Water and sugars and proteins need to somehow move from your intestine to your blood. When in blood they need to travel to every cell of your body. And when they arrive in your cells, they will be used to make you feel warm, give you energy and help you get strong. Learning more about that journey, and finding a way to repair it and make it more efficient is what Biotransport is all about.

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To modellize a system means to take part of the word and try isolate it from the rest to turn it into simple mathematical rules. This agregation of mathematical rules can predict and describe how the reality works with certain percision. Almost everything can be turned to a model, from the  something like the weather  to a pipeline. This can help experts to gain more insight about the actual dynamic of a system and try to build one more complex and more accurate

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Computer simulation
Systemic approach
Creative thinking

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Disease Modelling
Problem-Solving
Data Integration

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System model
Computer simulation
Result interpretation

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Signal Processing
Feature Extraction
Signaling Pathways

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Mathematical modelling
Biotransport
Synthetic biology