Sunday, January 14, 2007


Evolution of complexity in signaling pathways

Evolution of complexity in signaling pathways - An open access/free paper from the Proceedings of the National Academy of Sciences (PNAS) by Orkun S. Soyer and Sebastian Bonhoeffer:

Published online before print October 23, 2006, 10.1073/pnas.0604449103
PNAS | October 31, 2006 | vol. 103 | no. 44 | 16337-16342


It is not clear how biological pathways evolve to mediate a certain physiological response and why they show a level of complexity that is generally above the minimum required to achieve such a response. One possibility is that pathway complexity increases due to the nature of evolutionary mechanisms. Here, we analyze this possibility by using mathematical models of biological pathways and evolutionary simulations. Starting with a population of small pathways of three proteins, we let the population evolve with mutations that affect pathway structure through duplication or deletion of existing proteins, deletion or creation of interactions among them, or addition of new proteins. Our simulations show that such mutational events, coupled with a selective pressure, leads to growth of pathways. These results indicate that pathways could be driven toward complexity via simple evolutionary mechanisms and that complexity can arise without any specific selective pressure for it. Furthermore, we find that the level of complexity that pathways evolve toward depends on the selection criteria. In general, we find that final pathway size tends to be lower when pathways evolve under stringent selection criteria. This leads to the counterintuitive conclusion that simple response requirements on a pathway would facilitate its evolution toward higher complexity.

Opening paragraph:

A central question in evolutionary biology is the evolution of complex features. Research in this field has associated complexity at the organism level with complexity at the sequence level, with transcriptional control, or with cellular differentiation. Although these studies provided important insights into how complexity at organism level could have arisen, we still lack an explicit analysis of complexity in a specific system at molecular level. Here, we attempt such an analysis for biological signaling pathways. These pathways, i.e., systems of proteins that act in an orchestrated fashion, mediate the response of a cell toward internal and external signals. It is usually observed that pathways show high complexity, which manifests itself in various forms including number of components in a given pathway, interaction among different pathways, and compartmentalization. Although a sequential evolution as proposed by Darwin could explain the existence of such complex pathways, it is not clear why pathways should be driven toward a level of complexity that is higher than required by their function (i.e., producing a specific response). Both experimental and theoretical studies indicate that certain biological responses such as oscillations and switches or phenomena such as pattern formation and chemotaxis could be achieved via pathways that are simpler than found in nature. One possible explanation for pathway complexity (or biological complexity in general) is that selection for increased fitness necessitates an increase in complexity. Although there is support for such an explanation from studies in digital organisms, it is not clear why an increase in complexity should always be accompanied with an increase in fitness and vice versa.


The above paper cites:

The Origins of Genome Complexity
Michael Lynch and John S. Conery

Science 21 November 2003:
Vol. 302. no. 5649, pp. 1401 - 1404
DOI: 10.1126/science.1089370


Complete genomic sequences from diverse phylogenetic lineages reveal notable increases in genome complexity from prokaryotes to multicellular eukaryotes. The changes include gradual increases in gene number, resulting from the retention of duplicate genes, and more abrupt increases in the abundance of spliceosomal introns and mobile genetic elements. We argue that many of these modifications emerged passively in response to the long-term population-size reductions that accompanied increases in organism size. According to this model, much of the restructuring of eukaryotic genomes was initiated by nonadaptive processes, and this in turn provided novel substrates for the secondary evolution of phenotypic complexity by natural selection. The enormous long-term effective population sizes of prokaryotes may impose a substantial barrier to the evolution of complex genomes and morphologies.



Evolution of biological complexity
Christoph Adami, Charles Ofria, and Travis C. Collier
PNAS | April 25, 2000 | vol. 97 | no. 9 | 4463-4468

...In this paper, we skirt the issue of structural and functional complexity by examining genomic complexity. It is tempting to believe that genomic complexity is mirrored in functional complexity and vice versa. Such an hypothesis, however, hinges upon both the aforementioned ambiguous definition of complexity and the obvious difficulty of matching genes with function. Several developments allow us to bring a new perspective to this old problem. On the one hand, genomic complexity can be defined in a consistent information-theoretic manner [the "physical" complexity], which appears to encompass intuitive notions of complexity used in the analysis of genomic structure and organization. On the other hand, it has been shown that evolution can be observed in an artificial medium providing a unique glimpse at universal aspects of the evolutionary process in a computational world. In this system, the symbolic sequences subject to evolution are computer programs that have the ability to self-replicate via the execution of their own code. In this respect, they are computational analogs of catalytically active RNA sequences that serve as the templates of their own reproduction. In populations of such sequences that adapt to their world (inside of a computer's memory), noisy self-replication coupled with finite resources and an information-rich environment leads to a growth in sequence length as the digital organisms incorporate more and more information about their environment into their genome. Evolution in an information-poor landscape, on the contrary, leads to selection for replication only, and a shrinking genome size as in the experiments of Spiegelman and colleagues. These populations allow us to observe the growth of physical complexity explicitly, and also to distinguish distinct evolutionary pressures acting on the genome and analyze them in a mathematical framework.

If an organism's complexity is a reflection of the physical complexity of its genome (as we assume here), the latter is of prime importance in evolutionary theory. Physical complexity, roughly speaking, reflects the number of base pairs in a sequence that are functional. As is well known, equating genomic complexity with genome length in base pairs gives rise to a conundrum (known as the C-value paradox) because large variations in genomic complexity (in particular in eukaryotes) seem to bear little relation to the differences in organismic complexity. The C-value paradox is partly resolved by recognizing that not all of DNA is functional: that there is a neutral fraction that can vary from species to species. If we were able to monitor the non-neutral fraction, it is likely that a significant increase in this fraction could be observed throughout at least the early course of evolution. For the later period, in particular the later Phanerozoic Era, it is unlikely that the growth in complexity of genomes is due solely to innovations in which genes with novel functions arise de novo. Indeed, most of the enzyme activity classes in mammals, for example, are already present in prokaryotes. Rather, gene duplication events leading to repetitive DNA and subsequent diversification as well as the evolution of gene regulation patterns appears to be a more likely scenario for this stage. Still, we believe that the Maxwell Demon mechanism described below is at work during all phases of evolution and provides the driving force toward ever increasing complexity in the natural world...


Of related interest: "Balancing Robustness and Evolvability"

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