A project funded by the European Commission under the Sixth Framework Programme.

ESIGNET is coordinated by the University of Birmingham, UK, with partners: Technical University Eindhoven, Netherlands, Friedrich Schiller University Jena, Germany, and Dublin City University, Ireland.

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ESIGNET investigates the possibility to computationally evolve and simulate artificial cell signaling networks (CSNs) with pre-specified properties by means of Evolutionary Computation methods. It is required that the interactions between the simulated particles in the artificial chemistry are realistic with respect to the interactions found in real CSNs. In this project an artificial intelligence predictor for unknown components of cell signaling networks in organisms will be built.

Cell signaling networks (CSNs) are bio-chemical systems of interacting molecules
in cells. Typically, these systems take as inputs chemical signals generated within the cell or communicated from outside. These trigger a cascade of chemical reactions that result in changes of the state of the cell and/or generate some (chemical) output. CSNs can, therefore, be regarded as special purpose computers. In contrast to conventional silicon-based computers, the computation in CSNs is not realized by electronic circuits, but by chemically reacting molecules in the cell. The most important molecular components of CSNs are proteins. There are many different proteins, each of which can engage in interactions with other molecules with a high degree of specificity.  Their properties are often modified through interaction with other molecules. Often di erent CSNs are connected to one another through shared components. This is referred to as crosstalk. Crosstalk is an important means of modulating signal pathways according to events in the cell.  Scientific and Technological Objectives The overall goal of this project is to study the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs.