Cytochromes are found in all biological oxidation Systems which involve transport of reducing equivalents through organized chains of membrane bound intermediates, regardless of the ultimate oxidant (Keilin, 1966; Bartsch, 1978; Meyer & Kamen, 1982). Thus, cytochromes are present not only in the aerobic mitochondrial and bac-terial respiratory chain, but are also found in much more diversified procariotic Systems, including all varieties of facultative anaerobes (nitrate and nitrite reducers), obligate anaerobes (sulphate reducers and phototrophic sulphur bacteria), facultative photoheterotrophes (phototrophic non-sulphur purple bacteria), and the photoautotrophic cyanobacteria (blue-green algae). Among the different types of cytochromes occurring in the cell, the soluble c-type cytochromes (‘class I’, Meyer & Kamen, 1982) are the most abundant and best characterized group of proteins (Bartsch, 1978; Meyer & Kamen, 1982; Dickerson & Timkovitch, 1975; Lemberg & Barrett, 1973; Salemme, 1977; Ferguson-Miller, Brautigan & Margoliash, 1979). The amino acid sequences of more than 80 mitochrondrial and close to 40 bacterial cytochromes c are known (Meyer & Kamen, 1982; Dickerson & Timkovitch, 1975; Schwartz & Dayhoff, 1976; Dayhoff & Barker, 1978).

After the work of Marmarelis & Naka (1972, 1973) in the catfish retina, systems analysis using stochastic stimuli has had a boom in the seventies (e.g. McCann & Marmarelis, 1975; Eckert & Bishop, 1975; French & Wong, 1977; Lipson, 1975; McCann, 1974; Naka, Marmarelis & Chan, 1975; Spekreijse & Reits, 1982; Trimble & Phillips, 1978; Terzuolo et al. 1982). White-noise analysis was considered to be a general tool for investigating nonlinear systems gaining a maximum of information with a minimum of assumptions about the system. The modification of the original Wiener theory (Wiener, 1958; Cameron & Martin, 1947; McKean, 1972) by Lee & Schetzen (1965) made the theory fairly easy to implement into widely available computers and thus accessible to a larger number of experimenters.

Chaos is a widespread and easily recognizable phenomenon that hardly anybody took notice of until recently. The reason may be that chaos has something profoundly counterintuitive about it. It will not fit easily into any familiar cause–effect frame. The best introduction to chaos is by the way of an example. Consider a leaking faucet (Shaw, 1984). When the weight of the accumulating drop exceeds the surface tension the drop falls and a new drop begins to form. If the leak is small and the pressure in the faucet is constant, the time taken for the drop to reach the critical weight is constant. The dripping is perfectly periodic, the period depending on the leak rate. If the leak is slightly increased, the period of dripping will decrease slightly and vice versa. However, somewhere beyond this point the leaking faucet becomes a nuisance. When the leak is increased beyond a certain point the dripping looses its regularity. The time interval between the drops will first alternate periodically between a short and a long time interval. After a further increase of the leak this double periodic pattern will become unstable and change into a new pattern where four different time intervals between the drops alternate periodically. As the leak is further increased the period will double again and again and finally the dripping becomes completely irregular without any repeating pattern. When this occurs we are observing chaos. At the same time we are posed with the problem of understanding how such a ridiculously simple system can show random behaviour.