A pair of UCSF scientists has developed a model explaining how simple chemical and physical processes may have laid the foundation for life.
The basic idea is that simple principles of chemical interactions allow for a kind of natural selection on a micro scale: enzymes can cooperate and compete with each other in simple ways, leading to arrangements that can become stable, or “locked in,” says Ken Dill.
The scientists compare this chemical process of “search, selection, and memory” to another well-studied process: different rates of neuron firing in the brain lead to new connections between neurons and ultimately to the mature wiring pattern of the brain. Similarly, social ants first search randomly, then discover food, and finally build a short-term memory for the entire colony using chemical trails.
They also compare the chemical steps to Darwin’s principles of evolution: random selection of traits in different organisms, selection of the most adaptive traits, and then the inheritance of the traits best suited to the environment (and presumably the disappearance of those with less adaptive traits).
Like these more obvious processes, the chemical interactions in the model involve competition, cooperation, innovation and a preference for consistency, they say.
In its simplest form, the model shows how two catalysts in a solution, A and B, each acting to catalyze a different reaction, could end up forming what the scientists call a complex, AB. The word “complex” is key because it shows how simple chemical interactions, with few players, and following basic chemical laws, can lead to a novel combination of molecules of greater complexity. The emergence of complexity – whether in neuronal systems, social systems, or the evolution of life, or of the entire universe -- has long been a major puzzle, particularly in efforts to determine how life emerged.
“A major question about life’s origins is how chemicals, which have no self-interest, became ‘biological’ -- driven to evolve by natural selection,” he says. “This simple model shows a plausible route to this type of complexity.” link
Stochastic innovation as a mechanism by which catalysts might self-assemble into chemical reaction networks. 2007. Justin A. Bradford and Ken A. Dill. PNAS