About Systems Biology

Fig 1: The three main competences involved in systems biology

Systems biology is a relatively new field, but which has grown out of many previous fields, such as biotechnology, theoretical biology, nonlinear dynamics, bioinformatics and omics-technologies, systems theory and cybernetics, biophysics, physiology, and cell- and molecular biology. The name systems biology is well-chosen since the main emphasis is on moving from studying isolated components to studying the components relation to the intact system. This desire has two important consequences, which are central to systems biology:

Experimentally, it means a shift from in vitro experiments (studying the components in test tubes or isolated from their intact systems), to in vivo and in situ set-ups (studying the components while still present within the living systems). It also means that one seeks to collect information from as many components in parallell as possible; this is done using omics technologies like metabolomics, proteomics, genomics, etc. Similarly, the data is also collected with an as high time-resolution as possible, and experimental setups are standardized to move from qualitative to quantitative data. All these experimental improvements are central to systems biology, and means that much more information about the system has suddenly become available.

Theoretically, this means that biochemical reasoning is no longer sufficient to grasp the true implications of the data, as the complexity of the gatherered data and underlying systems generally implies that such reasoning generates erroneous or at least incomplete conclusions. For this reason, mathematical modelling is a central part of systems biology. This modelling makes use theory from many different backgrounds. Regarding the model formulations, one usually makes use of classical biochemical and physiological experessions, which are put together based on the mechanistic knowledge or assumptions about the systems. Regarding the model analysis, one uses methods from nonlinear dynamics, systems theory, and system identification. All these improvements are generally made to draw as many correct conclusions as possible from the data without drawing any erroneous ones, and to make strong predictions that can be tested experimentally.

Fig 2: The mathematical modelling in systems biology is to a large extent thought of as a kind of data-analysis, which contains the two main steps "hypothesis testing", and "model analysis", as is illustrated in this figure.

Sidansvarig: gunnar.cedersund@liu.se
Senast uppdaterad: 2010-05-12