Chris is a computational biologist with a focus on theoretical, experimental and computational Darwinian evolution, studying how biological systems evolve from the simplest molecules to the most complex structures such as the human brain.
A professor of Microbiology and Molecular Genetics as well as Physics and Astronomy at Michigan State University, he uses mathematics and computation to understand how simple rules can give rise to the most complex systems and behaviors.
Chris has pioneered the application of methods from information theory to the study of evolution, and spearheaded the development of Avida, an Artificial Life simulator that uses mutating and adapting computer viruses as a tool for investigating evolutionary biology.
The author of over 120 peer-reviewed articles in biology and physics, Chris also wrote the textbook “Introduction to Artificial Life” (Springer, 1998). He was a principal scientist at the Jet Propulsion Laboratory at NASA, where he conducted research into the foundations of quantum mechanics and quantum information theory.
Chris is the recipient of NASA’s Exceptional Achievement Medal, and he was also elected as a Fellow of the American Association for the Advancement of Science.
Chris earned a B.S. in Physics and Mathematics and a Diplom in Theoretical Nuclear Physics from the University of Bonn in Germany. His M.A. and Ph.D. degrees in Theoretical Physics are from Stony Brook University in New York.
Industry Expertise (5)
Areas of Expertise (8)
Excellence in Research Award (professional)
Awarded by KGI
Space Act Award (professional)
Awarded by NASA
Exceptional Achievement Medal (professional)
Awarded by NASA
SUNY at Stony Brook: Ph.D., Theoretical Physics 1988
SUNY at Stony Brook: M.A., Physics 1988
University of Bonn (Germany): Diplom, Theoretical Physics 1988
University of Bonn (Germany): B.S., Physics/Mathematics 1982
- American Association for the Advancement of Science
- American Physical Society
- American Society for Microbiology
- International Society for Artificial Life
Should We Ban Autonomous Killing Robots?
Huffington Post online
By Christoph Adami
We live in exciting times. Self-driving cars are just around the corner, and we can start imagining how our daily lives will be changed by the changes in traffic, our schedule, and transportation infrastructure in general. These cars may well even be all-electric, and we may not even own them, but just hail them using an app. We can see how in the not-so-distant future we can order a product and have it delivered by a drone within an hour. For those of use who grew up with the promises of future technology à la “The Jetsons“, it seems that we are finally seeing what that future may look like.
Using Evolution to Trap HIV
MSU Today online
Chris Adami, professor of microbiology and molecular genetics at MSU, found that in patients who have never taken medications to treat HIV, the virus’ proteins don’t evolve. However, patients who were on anti-viral drugs saw their viral proteins evolve quickly, and in a very particular way...
Plugging the Hole in Hawking's Black Hole Theory
MSU Today online
Recently physicists have been poking holes again in Stephen Hawking’s black hole theory – including Hawking himself. For decades physicists across the globe have been trying to figure out the mysteries of black holes – those fascinating monstrous entities that have such intense gravitational pull that nothing – not even light – can escape from them. Now Professor Chris Adami, Michigan State University, has jumped into the fray...
Journal Articles (5)
The origin of life can be understood mathematically to be the origin of information that can replicate. The likelihood that entropy spontaneously becomes information can be calculated from first principles, and depends exponentially on the amount of information that is necessary for replication. We do not know what the minimum amount of information for self-replication is because it must depend on the local chemistry, but we can study how this likelihood behaves in different known chemistries, and we can study ways in which this ...
Consecutive measurements performed on the same quantum system can reveal fundamental insights into quantum theory's causal structure, and probe different aspects of the quantum measurement problem. According to the Copenhagen interpretation, measurements affect the quantum system in such a way that the quantum superposition collapses after the measurement, erasing any knowledge of the prior state. We show here that counter to this view, unamplified measurements (measurements where all variables ...
A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large—but not intermediate-sized—populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.
In a comment on our manuscript "Strong selection significantly increases epistatic interactions in the long-term evolution of a protein", Dr. Crona challenges our assertion that shared entropy (that is, information) between two residues implies epistasis between those residues, by constructing an explicit example of three loci (say A, B, and C), where A and B are epistatically linked (leading to shared entropy between A and B), and A and C also depend epistatically (leading to shared entropy between A and C), so that loci B and C are correlated (share entropy).
Most mutations are deleterious and cause a reduction in population mean fitness known as the mutational load. In small populations, weakened selection against slightly-deleterious mutations results in an additional reduction in fitness: the drift load. Many studies have established that populations can evolve a reduced mutational load by evolving mutational robustness, but it is uncertain whether populations can evolve a reduced drift load. Here, using digital experimental evolution, we show that small populations do evolve reduced drift loads, that is, they evolve robustness to genetic drift, or "drift robustness". We find that, compared to genotypes from large populations, genotypes from small populations have a decreased likelihood of small-effect deleterious mutations, thus causing small-population genotypes to be drift-robust. We further show that drift robustness is not under direct selection, but instead arises because small populations preferentially adapt to drift-robust fitness peaks. These results have implications for genome evolution in organisms with small effective population sizes.