Evolution, Conservation, Change, Robustness, Complexity and Degeneracy

For species to be successful and adapt to an ever changing environment, they must strike a delicate balance between allowing for some DNA alterations, while at the same time preserving the key developmental processes encoded in their unique genomes. This poses a problem, as even small changes in any given gene might lead to deleterious effects that decrease the ability of any individual to survive. However, for a species to endure, it must allow for these variations among individuals in order that a population may be produced in which some individuals are more capable of survival in the new conditions. This suggests that survival of a species should allow for some changes in the DNA and therefore the production of gene products that provide improved fitness – but too much change is dangerous.

How is all of this managed? What kinds of mechanisms exist to allow for just enough change – while preserving necessary components and processes? Recent papers by Whitacre and Bender [1, 2] suggests a theoretical model of how this might work and another paper by Parikh, et. al [4] demonstrates some of the principles developed by Whitacre.

According to Whitacre and Bender [1, 2], robustness is often thought of in terms of the persistence of high level traits such as fitness, under variable conditions – it is the insensitivity of a biological system to sets of distinct conditions. Evolvability on the other hand, refers to the capacity for heritable and selectable phenotypic change [2]. Evolvability and robustness are intimately connected with the complexity of the system. Complexity is a term that can be defined differently in different contexts and systems. In biological contexts, complexity often refers to the number of different components (e.g. cells) that are involved in a system. For example, the kidney or the eye have many different kinds of cells that are needed for proper functioning. Complexity implies that the different components need to work together in a consistent and reliable way for the structure to function properly. This will involve many genes working in coordination across different cell types and responding to signals between different tissues. Complexity can also be considered at different levels, such as the pathways of reactions within cells that allow the higher level system to function (e.g. interaction between cells or tissues). Thus, complexity can be considered in terms of a hierarchical framework.

Using computational models, Whitacre and Bender [1] studied the impact of degeneracy and redundancy on system evolvability. Evolvability may be thought of as the ability of a system to innovate – the ability to create new capabilities that allow a species to respond to changes in the environment or to better take advantage of the current environment. Degeneracy, on the other hand, “is a system property that requires the existence of multi-functional components (but also modules and pathways) that perform similar functions (i.e. are effectively interchangeable) under certain conditions, yet can perform distinct functions under other conditions.”[2] Multiple proteins that perform similar functions are degenerate. Different adhesins have been shown to have specific roles in development, yet when one is missing or underproduced, another one can take its place [2, 3]. Looking at a large chart of metabolic pathways it becomes apparent that there are often multiple ways to get from one compound to another – degenerate pathways (e.g. glycolysis and pentose phosphate pathways both metabolize glucose). This degeneracy allows for changes in one protein to occur without killing the system. At the same time, the altered protein might provide new functionality and thus evolvability for the species. The computational models, in fact, show that it is this kind of degeneracy that actually creates robustness and therefore evolvability. Degeneracy also allows for more complexity since components can participate in multiple pathways, yet also remain distinct. The combinatorics lead to complexity.

Whitacre and Bender’s conclusion is that degeneracy is critical to evolvability, robustness, and complexity. This conclusion comes from computational modeling, but how does it apply to real biological systems?

In another paper that addresses this same question, Parikh, et. al [4] find that what appears to be conserved in some cases is a developmental process, even though different genes might be responsible for the same process in different species. They studied two different species of slime molds, Dictyostelium discoideum (DD) and Dyctostelium purpureum (DP). These are two species of social amoeba, that live part of their lives as single celled independent organisms. Upon starvation, however, thousands of individuals aggregate into a mound of cells, transform into a slug, migrate, and then form a structure that contains a ball of spores at the top of a dead stalk. This remarkable development is triggered by cAMP. You can see some excellent movies of this at dictyBase. The two species are similar enough that they can both co-aggregate into a single mass, but will sort out before differentiation [5].

DD and DP diverged about 400 million years ago. This is about the length of time between the divergence of jawed fish and humans – not an insignificant span. There are significant differences between the genomes, in which orthologous genes only share about 62% sequence similarity. About 50% of the genes have orthologs in the other species. Despite this, their differentiation processes are remarkably alike. Each takes about 24 hours to go from single celled amoeba to a fruiting body containing spores atop a stalk. Once the cells aggregate they transform into a slug that contains two types of cells – prestalk and prespore. These two types of cells differentiate into a stalk and into a cluster of spores, respectively. In DP, the stalk is generated while the slug is still migrating, but in DD the stalk does not form until migration of the slug stops moving. Another prominent difference between the two species is the timing of aggregation and culmination, which are delayed by 4-hours in DP compared to DD [4].

Parikh, et. al [4] compared the cellular population of mRNAs, the transcriptomes, of DP and DD as the differentiation process occurred. They performed RNA sequencing (RNA-seq, [6]) on cell populations every 4 hours up to 24 hours and quantified the amount of each mRNA present at each time point. There are two phases during which DP shows a 4 hour delay when compared to DD development – once at 4 hours and again at 16 hours. The levels of expression of almost every mRNA in the transcriptome change at some time during the differentiation process. Even though the orthologs have diverged significantly, the sets of genes that are coordinately regulated are highly conserved, although at different times in the developmental process (and some gene sets, such as stalk development, are expressed in a different order as well). Although not much is known about the transcription factors in Dictyostelium, it is clear that the regulation of sets of genes during development is conserved and that for some genes the order of expression is changed as well.

It is hard to identify transcription factor binding sites because the genomes (especially the upstream sequences) are highly AT-rich. Even though we don’t know how it is conserved, it is clear that the developmental process IS conserved. It is also clear that there has been some change in the program of differentiation since the two species have diverged. This is in agreement with the results of Whitacre’s, which suggest that degeneracy and redundancy are important parts of evolution. The ability to reconfigure the developmental program in different species of Dictyostelium suggests a redundancy that allows for this kind of change in whole sets of coordinately expressed genes without serious consequences to the species. The presence of redundancy, in turn, suggests that there are degenerate genes (perhaps transcription factors?) that have been allowed to change and thereby regulate gene sets in a different timescale and order.

The RNA-seq process appears to be more accurate and sensitive than microarray technology [6]. Together with computational analysis as done by Parikh et al [4], transcriptome comparisons have become more feasible. It will be interesting to see if this new approach will lead to a better understanding of gene regulation and, therefore, a better understanding of development, pathological conditions, responses to environmental change, and evolutionary processes.


1. Whitacre, J. and A. Bender, Degeneracy: a design principle for achieving robustness and evolvability. J Theor Biol, 2010. 263(1):143-153.

2. Whitacre, J.M., Degeneracy: a link between evolvability, robustness and complexity in biological systems. Theor Biol Med Model, 2010. 7:6.

3. Guo, B., C.A. Styles, Q. Feng, and G.R. Fink, A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating. Proc Natl Acad Sci U S A, 2000. 97(22):12158-12163.

4. Parikh, A., E.R. Miranda, M. Katoh-Kurasawa, D. Fuller, G. Rot, L. Zagar, T. Curk, R. Sucgang, R. Chen, B. Zupan, W.F. Loomis, A. Kuspa, and G. Shaulsky, Conserved developmental transcriptomes in evolutionarily divergent species. Genome Biol, 2010. 11(3):R35.

5. Raper, K. and C. Thom, Interspecies mixtures of the Dictyosteliaceae. Am. J. Bot, 1941. 28:69-78.

6. Shendure, J., The beginning of the end for microarrays? Nat Methods, 2008. 5(7):585-587.

This entry was posted in computational biology reflections blog and tagged , , , , , . Bookmark the permalink. Trackbacks are closed, but you can post a comment.

Post a Comment

Your email is never published nor shared. Required fields are marked *


You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>