Molecular Information Processing (MIP) is an academic discipline addressing both natural and artificial mechanisms by which molecules can process information, both individually and in consort. Whereas standard computer technology primarily uses non-molecular solid state (doped crystal) devices for information processing (e.g. CMOS gates), biological systems make strong use of molecular information processing in cellular biochemistry. Molecular information processing allows systems to direct processes of fine-grained self-construction, and hence to take dynamic control of the generation of both the building blocks and the “hardware” architecture that is doing the information processing. This closure is fundamental to allowing indefinite increase in complexity of computational systems, as investigated by von Neumann in his work on Self-reproducing Automata, and to allowing information processing systems to evolve autonomously.
While molecules may be designed to process information electronically in ways analogous to solid state devices, since molecules can be designed with complex electronic excitations in response to external stimuli (e.g. light or other chemicals), see especially the book by Szacilowski, this is not the only way molecular systems can process information. In particular, intermolecular recognition and self-assembly of higher-order molecular structures play a fundamental role in biochemical processes of genetics, protein translation, cell regulation and membrane compartmentalisation.
In 1992, coming from the Max-Planck-Institute für biophysical Chemistry, where he lead a research group in Molecular Self-Organisation, John McCaskill founded the first department for Molecular Information Processing at the Institute for Molecular Biotechnology (later a Leibniz Institute) while on the Faculty of the Friedrich Schiller University, Jena. Building on research on in vitro laboratory molecular evolution that involved sequence information directing RNA folding and replication kinetics, that could be studied algorithmically, the Molecular Information Processing research team went on to study the evolution of co-operating and spatially resolved molecular information processing systems in the 1990s.
In 1994 L.Adleman’s demonstration, that DNA could be used to address traditional computing problems (Hamilton Path) related to the computational class NP-hard, ushered in an enormous growth in the field of Molecular Computing. Molecular numbers are large and so such systems can exhibit massive parallelism, but number limitations ultimately limit the deployment of DNA computers as NP-machines, and so attention has shifted to computational capabilities linked to exploiting DNA MIP in embedded and autonomous information processing devices and machines and in controlling molecular construction directly (e.g. DNA self-assembly).
For many years, McCaskill’s group has been interested in fundamental issues in MIP, such as evolvability and increase in complexity, and a model was proposed as to how key complex exploitable systems such as protein translation can evolve. Since the turn of the century, McCaskill’s group has had a special interest in bringing together electronic information processing with molecular information processing to overcome limits in the construction of complex autonomous molecular systems. In 2003, the group turned its attention to the problem of constructing artificial cells, with the unique insight that electronic control via microfluidics might provide an effective route forward to bootstrapping cellular complexity from chemistry. Since then, in a variety of projects, McCaskill’s group has continued to investigate Molecular Information Processing in evolvable systems in connection with microelectronics. After projects investigating electronic chemical cells (ECCell) and a matrix for chemical IT (MATCHIT) the most recent work has concentrated (MICREAgents) on building autonomous hybrid electronic-chemical microparticles called lablets that can unite molecular and electronic information processing at space scale approaching that of cells.
1. McCaskill, J.S. Inaugural lecture FSU 1993.
2. Konrad Szacilowski, "Infochemistry: Information Processing at the Nanoscale” Wiley, ISBN: 978-0-470-71072-2