Research

The history of life is a four billion year long history of evolutionary innovations, qualitatively new traits that benefit their carrier and provide platforms for further evolutionary change. Eyes, flowers, and limbs are but few examples of innovations that transformed life once they were fully formed. Each of them was built on many microscopic, submicroscopic, and molecular changes, some of which were innovations in their own right. We know myriad evolutionary innovations, fascinating case stories of natural history, but we know much less about general principles that allow life to innovate.

Evolutionary innovations have their roots in molecular changes that affect three classes of biological systems. These are metabolic networks, regulatory circuits, and macromolecules, especially protein and RNA. We study these system classes separately, through a combination of comparative data analysis, computational modeling, and laboratory evolution, to identify common principles that underlie their ability to bring forth innovation.

This line of work has lead us to identify several principles important for innovation. These principles can unify very different kinds of innovations, and capture several known features of biological innovation. They help explain the role of environmental change, and of an organism's robustness to such change in innovation. We study how these principles can help us reconcile neutralism and selectionism, as well as explain the role of phenotypic plasticity, gene duplication, recombination, and cryptic variation in innovation. Finally, we also ask whether these principles apply to technological innovation, and thus open the powers of biological innovation to human engineering.

Selected Publications

Wagner, A. (2011) The origins of evolutionary innovations. Oxford University Press. [OUP]

Wagner, A. (2014) Arrival of the Fittest. Penguin Random House. [amazon]

Zheng, J., Guo, N., Wagner, A. (2020) Strong selection enhances protein evolvability by increasing mutational robustness and foldability. Science 370, 6521 [reprint request]

Payne, J.L., Khalid, F., Wagner, A. (2018) RNA-mediated gene regulation is less evolvable than transcriptional regulation. Proceedings of the National Academy of Sciences of the U.S.A. 115, E3481-E3490. [reprint request] 

Hosseini, S.-R., Martin, O.C., Wagner, A. (2016) Phenotypic innovation through recombination in genome-scale metabolic networks. Proceedings of the Royal Society B: Biological Sciences283: 20161536. [reprint request] 

Payne, J.L., Wagner, A. (2014) The robustness and evolvability of transcription factor binding sites. Science 343, 875-877.[link]

Barve, A., Wagner, A. (2013) A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature 500, 203-206. [link]

Hayden, E.J., Ferrada, E., Wagner, A. (2011) Cryptic genetic variation promotes rapid evolutionary adaptation in an RNA enzyme. Nature 474, 92-95. [reprint request] 

Raman, K., Wagner, A. (2011) The evolvability of programmable hardware. Journal of the Royal Society Interface 8: 269-281. [reprint request] 

Rodrigues, J., Wagner, A. (2009) Evolutionary plasticity and innovations in complex metabolic reaction networks. PloS Computational Biology 5(12): e1000613. [reprint request] 

Ferrada, E., Wagner, A. (2010) Evolutionary innovations and the organization of protein functions in genotype space. PLoS ONE 5(11): e14172. doi:10.1371/journal.pone.0014172. [reprint request] 

Ciliberti, S., Martin, OC, Wagner, A. (2007) Innovation and robustness in complex regulatory gene networks. Proc. Natl. Acad. Sci. U.S.A. 104, 13591-13596. [reprint request]

Robustness and Evolvability of Living Systems

Living things are unimaginably complex, yet they have withstood a withering assault of harmful influences over several billion years. These influences include cataclysmic changes in the environment, as well as a constant barrage of internal changes, mutations. And not only has life survived, it has thrived and radiated into millions of diverse species. Such resilience may be surprising, because complexity suggests fragility. If you have ever built a house of cards, you know what I mean: The house eventually comes tumbling down. Why is an organism not a molecular house of cards? Why do not slight disturbances (especially mutations) cause key organismal functions to fail catastrophically? And is the robustness of organisms to change itself a consequence of past evolution? How does it affect the potential for future innovation in evolution? These are some of the questions we ask in our research.

Selected Publications

Wagner, A. (2005) Robustness and Evolvability in Living Systems. Princeton University Press, Princeton, NJ. [link]

Zheng, J., Guo, N., Wagner, A. (2020) Strong selection enhances protein evolvability by increasing mutational robustness and foldability. Science 370, 6521. [reprint request] 

Hosseini, S.-R., Martin, O.C., Wagner, A. (2016) Phenotypic innovation through recombination in genome-scale metabolic networks. Proceedings of the Royal Society B: Biological Sciences283: 20161536. [reprint request] 

Hayden, E., Bendixsen, D.P., Wagner, A. (2015) Intramolecular phenotypic capacitance in a modular RNA molecule. Proceedings of the National Academy of Sciences 112, 12444-12449. [reprint request] 

Bratulic, S., Gerber, F., Wagner, A. (2015) Mistranslation drives the evolution of robustness but not translational accuracy in TEM-1 beta-lactamase. Proceedings of the National Academy of Sciences 112 , 12758-12763. [reprint request] 

Payne, J.L., Wagner, A. (2014) The robustness and evolvability of transcription factor binding sites. Science 343, 875-877. [link]

Wagner, A. (2012) The role of robustness in phenotypic adaptation and innovation. Proceedings of the Royal Society B: Biological Sciences 279, 1249-1258. [reprint request] 

Wagner, A. (2008) Robustness and evolvability: A paradox resolved. Proc. Roy. Soc. London Series. B 275, 91-100. [reprint request] 

Ciliberti, S, Martin, OC, Wagner, A. (2007) Robustness can evolve gradually in complex regulatory networks with varying topology. PLoS Computational Biology 3(2): e15. [reprint request] 

Wagner, A . (2005) Robustness, Neutrality, and Evolvability. FEBS Letters 579: 1772–1778. [reprint request] 

How do organisms adapt to changing environments? Does their robustness to mutations facilitate such adaptation? What is the role of cryptic variation in evolutionary innovation? How does cellular noise affect evolution? How do gene duplications influence evolutionary adaptation? These are some questions that laboratory evolution experiments can answer. Most of our experiments are motivated by such general questions with broad implications for evolution, rather than by the desire to understand the natural history of any one group of organisms. We have been using experimental evolution in bacteria, yeast, and fruit flies, but also the directed evolution of proteins and RNA molecules to answer specific research questions. Our experiments rely on genomic technology, such as genome sequencing and RNA sequencing to identify genetic changes that arise in the course of evolution.

Selected Publications

Mihajlovic, L., Iyengar, B.R., Baier, F., Barbier, I., Iwaszkiewicz, J., Zoete, V., Wagner, A., Schaerli, Y. (2024) A direct experimental test of Ohno’s hypothesis. Elife. https://doi.org/10.7554/eLife.97216.1.sa2. [reprint request] 

Karve, S., Wagner, A. (2022) Environmental complexity is more important than mutation in driving the evolution of latent novel traits in E. coli. Nature Communications 13, 5904. [reprint request] 

Toll-Riera, M., Olombrada, M., Castro-Giner, F., Wagner, A. (2022) A limit on the evolutionary rescue of an Antarctic bacterium from rising temperatures. Science Advances 8, eabk3511. [reprint request] 

Zheng, J., Guo, N., Wagner, A. (2020) Strong selection enhances protein evolvability by increasing mutational robustness and foldability. Science 370, 6521. [reprint request] 

Sprouffske, K., Aguilar-Rodriguez, J., Sniegowski, P., Wagner, A. (2018) High mutation rates limit evolutionary adaptation in Escherichia coli. PLoS Genetics 14, e1007324. [reprint request] 

Schaerli, Y., Jimenez, A., Duarte, J.M., Mihajlovic, L., Renggli, J., Isalan, M., Sharpe, J., Wagner, A. (2018) Mechanistic causes of constrained phenotypic variation revealed by synthetic gene regulatory circuits. Molecular Systems Biology 14, e8102. [reprint request] 

Bratulic, S., Toll-Riera, M., Wagner, A. (2017) Mistranslation benefits adaptive evolution through purging of deleterious mutations. Nature Communications 8, 15410. [reprint request] 

Hayden, E., Bendixsen, D.P., Wagner, A. (2015) Intramolecular phenotypic capacitance in a modular RNA molecule. Proceedings of the National Academy of Sciences 112, 12444-12449. [reprint request] 

Bratulic, S., Gerber, F., Wagner, A. (2015) Mistranslation drives the evolution of robustness but not translational accuracy in TEM-1 beta-lactamase. Proceedings of the National Academy of Sciences 112, 12758-12763. [reprint request] 

Dhar, R., Saegesser, R., Weikert, C., Wagner, A. (2013) Yeast adapts to a changing stressful environment by evolving cross-protection and anticipatory gene regulation. Molecular Biology and Evolution 30, 573-588. [reprint request] 

Hayden, E.J., Ferrada, E., Wagner, A. (2011) Cryptic genetic variation promotes rapid evolutionary adaptation in an RNA enzyme. Nature 474, 92-95. [reprint request] 

Much like humans, gene duplicates may be created equal, but they do not stay that way for long. Many of them get eliminated through deleterious mutations, others diverge beyond recognition in sequence and function, yet others retain similar functions for a long time. What role does natural selection play in this divergence? How do new gene functions arise? Do gene duplications cause an increase in gene expression? Does this increase carry a significant energy cost? These are some of the questions we ask about gene duplications.

Despite the divergence of most duplicate genes, some genes with overlapping functions have been retained, in some cases for hundreds of millions of years. Examples include the many partially redundant genes in vertebrates, the result of ancient gene duplications in primitive chordates. Gene duplicates with similar functions may provide robustness against mutations, environmental change, and noise. Does natural selection retain some duplicates with similar functions because of the robustness they can provide? Using the apparatus of mathematical population genetics, we showed that natural selection can maintain redundant gene duplicates in sufficiently large populations.

In other work, we characterized the sequence and functional divergence of thousands of duplicate genes in different organisms, and showed that this divergence is often asymmetric. That is, one of two duplicates diverges much more rapidly than the other, a process that may be driven by the functional divergence of one of the duplicates.

We also ask about the costs of gene duplication, and whether gene duplications would rise to fixation neutrally or aided by natural selection. Through comprehensive analyses of functional genomic data, we could show that the increases in gene expression caused by gene duplication carry an energy cost that is high enough to be selected against in large populations. In such populations, gene duplications could thus not go to fixation neutrally.

Selected Publications

Mihajlovic, L., Iyengar, B.R., Baier, F., Barbier, I., Iwaszkiewicz, J., Zoete, V., Wagner, A., Schaerli, Y. (2024) A direct experimental test of Ohno’s hypothesis. Elife. https://doi.org/10.7554/eLife.97216.1.sa2. [reprint request] 

Karve, S., Wagner, A. (2022) Environmental complexity is more important than mutation in driving the evolution of latent novel traits in E. coli. Nature Communications 13, 5904. [reprint request] 

Toll-Riera, M., Olombrada, M., Castro-Giner, F., Wagner, A. (2022) A limit on the evolutionary rescue of an Antarctic bacterium from rising temperatures. Science Advances8, eabk3511. [reprint request] 

Zheng, J., Guo, N., Wagner, A. (2020) Strong selection enhances protein evolvability by increasing mutational robustness and foldability. Science 370, 6521. [reprint request] 

Sprouffske, K., Aguilar-Rodriguez, J., Sniegowski, P., Wagner, A. (2018) High mutation rates limit evolutionary adaptation in Escherichia coli. PLoS Genetics 14, e1007324. [reprint request] 

Schaerli, Y., Jimenez, A., Duarte, J.M., Mihajlovic, L., Renggli, J., Isalan, M., Sharpe, J., Wagner, A. (2018) Mechanistic causes of constrained phenotypic variation revealed by synthetic gene regulatory circuits. Molecular Systems Biology 14, e8102. [reprint request] 

Bratulic, S., Toll-Riera, M., Wagner, A. (2017) Mistranslation benefits adaptive evolution through purging of deleterious mutations. Nature Communications 8, 15410. [reprint request] 

Hayden, E., Bendixsen, D.P., Wagner, A. (2015) Intramolecular phenotypic capacitance in a modular RNA molecule. Proceedings of the National Academy of Sciences 112, 12444-12449. [reprint request] 

Bratulic, S., Gerber, F., Wagner, A. (2015) Mistranslation drives the evolution of robustness but not translational accuracy in TEM-1 beta-lactamase. Proceedings of the National Academy of Sciences 112, 12758-12763. [reprint request] 

Dhar, R., Saegesser, R., Weikert, C., Wagner, A. (2013) Yeast adapts to a changing stressful environment by evolving cross-protection and anticipatory gene regulation. Molecular Biology and Evolution 30, 573-588. [reprint request] 

Hayden, E.J., Ferrada, E., Wagner, A. (2011) Cryptic genetic variation promotes rapid evolutionary adaptation in an RNA enzyme. Nature 474, 92-95. [reprint request] 

Genetic and metabolic networks drive all biological processes. You can think of them as bridges between the organism and the individual molecules – proteins and genes – that form all living cells. We need to understand their structure, function, and evolution if we want to understand whole organisms. One research focus in the lab is the study of genome-scale transcriptional regulation networks, protein interaction networks, and metabolic networks. Another is the analysis of smaller regulatory circuits through detailed mathematical models of their molecular interactions. We are specifically interested in evolutionary questions, such as how natural selection shapes the structure of such networks, how evolutionary innovations arise in such networks, and what the evolutionary history of such networks looks like.

For instance, we have characterized the structure of the core metabolic network of the bacterium Escherichia coli, the evolution of the protein interaction network and the transcriptional regulation network of the yeast Saccharomyces cerevisiae. The metabolic network is an example of a small-world network, a type of network found in many unrelated areas of science, such as sociology and computer science. The structure of many small world networks contains information about their history. This means that we can use the structure of a metabolic network to infer which metabolites appeared early in the evolution of life. In our work on transcriptional regulation networks, we have focused on the evolution of small transcriptional regulation circuits. We have shown that such circuits have arisen by convergent evolution, a strong indicator that a network’s structure is optimal for a given purpose.

Another important aspect of our work on networks regards the origin of new phenotypic traits. In a metabolic network, such new traits include the ability to thrive on new food sources; in transcriptional regulation circuits they comprise new expression patterns of regulatory genes; and in signaling circuits, they include new and beneficial signaling behaviors. By studying and comparing all three classes of systems, we can reveal powerful general principles that facilitate the origin of new traits. One of these principles is the existence of vast genotype networks, connected sets of circuit genotypes with the same phenotype. Such genotype networks allow a system to preserve an old existing phenotype while exploring many new phenotypes, until a superior phenotype is found.

To pursue these lines of inquiry, we analyze large genomic data sets and develop new computational and mathematical tools, such as tools to sample high dimensional parameter spaces efficiently.

Selected Publications

San Roman, M., Wagner, A. (2021) Diversity begets diversity during community assembly until ecological limits impose a diversity ceiling. Molecular Ecology 30, 5874-5887 [reprint request] 

Hosseini, S.-R., Martin, O.C., Wagner, A. (2016) Phenotypic innovation through recombination in genome-scale metabolic networks. Proceedings of the Royal Society B: Biological Sciences 283: 20161536. [reprint request] 

Hosseini, S.-R., Barve, A., Wagner, A. (2015) Exhaustive analysis of a genotype space comprising 1015 central carbon metabolisms reveals an organization conducive to metabolic innovation. PLoS Computational Biology 11 (8), e1004329. [reprint request] 

Barve, A., Wagner, A. (2013) A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature 500, 203-206. [link]

Barve, A., Rodrigues, J.F.M., Wagner, A. (2012) Superessential reactions in metabolic networks. Proceedings of the National Academy of Sciences of the U.S.A. 109 (18), E1121-E1130. [reprint request] 

Raman, K., Wagner, A. (2011) Evolvability and robustness in a complex signaling circuit. Molecular BioSystems 7, 1081-1092. [reprint request] 

Rodrigues, J., Wagner, A. (2009) Evolutionary plasticity and innovations in complex metabolic reaction networks. PloS Computational Biology 5(12): e1000613. [reprint request] 

Ciliberti, S., Martin, OC, Wagner, A. (2007) Innovation and robustness in complex regulatory gene networks. Proc. Natl. Acad. Sci. U.S.A. 104, 13591-13596. [reprint request]

Ciliberti, S, Martin, OC, Wagner, A. (2007) Robustness can evolve gradually in complex regulatory networks with varying topology. PLoS Computational Biology 3(2): e15. [reprint request] 

Conant, G.C. Wagner, A. (2003) Convergent evolution in gene circuits. Nature Genetics 34, 264-266 [reprint request] 

Modern biology raises fundamental conceptual questions. One of them relates to the notion of causality, central to understanding the world around us. Do conventional notions of causality break down in the face of the extraordinary complexity of biological systems? Relatedly, what do we mean when we say that we can understand an organism (phenotype) by studying its DNA (genotype)? While questions like these have implications far beyond the sciences, other questions we ask are long-standing puzzles in evolutionary biology. How important is neutral change for evolutionary adaptation and innovation? And what is the role of randomness in Darwinian evolution? We aim to answer questions like these with approaches that range from the analysis of experimental data to mathematical modeling.

Selected Publications

Wagner, A. Adaptive evolvability through direct selection instead of indirect, second-order selection. (2021) Journal of Experimental Zoology Part B: Molecular and Developmental Evolution 338: 395–404. [reprint request]

Wagner, A. (2015) Causal drift, robust signaling, and complex disease. PLoS ONE 10(3), e0118413. [reprint request] 

Wagner, A. (2012) The role of randomness in Darwinian evolution. Philosophy of Science 79, 95-119. [reprint request] 

Wagner, A. (2008) Neutralism and selectionism: A network-based reconciliation. Nature Reviews Genetics 9, 965-974. [reprint request] 

Wagner, A. (1999) Causality in complex systems. Biology and Philosophy 14, 83-101. [reprint request]

Wagner, A. (1997) Models in the biological sciences. In: Dialektik 1997 (1) Falkenburg, B.; Hauser, S. (Eds.),  43-57. Felix Meiner, Hamburg. [reprint request] 

Wagner, A. (1995) Reductionism in Evolutionary Biology: A Perceptional Artifact? in 1993 Lectures in Complex Systems, eds. D. Stein and L. Nadel, Santa Fe Institute Studies in the Sciences of Complexity, Lect. Vol. VI, Reading, MA:Addison-Wesley, 603-611. [reprint request]