Charles Ofria | |
---|---|
Born | New York, NY (United States) | December 13, 1973
Alma mater | Stony Brook University (B.S., 1994) California Institute of Technology (Ph.D., 1999) |
Known for | Digital evolution, Avida |
Awards | Withrow Distinguished Scholar Award (2006, 2016) [1] NSF CAREER Award (2007) Withrow Teaching Excellence Award (2010) [2] |
Scientific career | |
Fields | Computer Science, Evolutionary Biology |
Institutions | Michigan State University |
Doctoral advisor | Christoph Adami |
Other academic advisors | Richard Lenski |
Website | www |
Dr. Charles A. Ofria is a Professor in the Department of Computer Science and Engineering at Michigan State University, the director of the Digital Evolution (DEvo) Lab there, and Director of the BEACON Center for the Study of Evolution in Action. He is the son of the late Charles Ofria, [3] who developed the first fully integrated shop management program for the automotive repair industry. Ofria attended Stuyvesant High School and graduated from Ward Melville High School in 1991. He obtained a B.S. in Computer Science, Pure Mathematics, and Applied Mathematics from Stony Brook University in 1994, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology in 1999. Ofria's research focuses on the interplay between computer science and Darwinian evolution. [4]
Ofria is one of the designers of Avida, an artificial life software platform to study the evolutionary biology of self-replicating and evolving computer programs (digital organisms, see also Digital organism simulators). Avida has been used extensively to study the basic processes that underlie Darwinian evolution. [5] Avida is under active development in Ofria's Digital Evolution Lab at Michigan State University and was originally designed by Ofria, Chris Adami and C. Titus Brown at Caltech in 1993.
Ofria received the NSF Career Award in 2007 [6] and the Withrow Excellence Award for Excellence in Teaching in 2010 [7] and for Excellence in Research in 2006 and 2016. [8] He was also a 2017 winner of the William J. Beal Outstanding Faculty Award. [9]
Michael Joseph Behe is an American biochemist and an advocate of the pseudoscientific principle of intelligent design (ID).
A digital organism is a self-replicating computer program that mutates and evolves. Digital organisms are used as a tool to study the dynamics of Darwinian evolution, and to test or verify specific hypotheses or mathematical models of evolution. The study of digital organisms is closely related to the area of artificial life.
Avida is an artificial life software platform to study the evolutionary biology of self-replicating and evolving computer programs. Avida is under active development by Charles Ofria's Digital Evolution Lab at Michigan State University; the first version of Avida was designed in 1993 by Ofria, Chris Adami and C. Titus Brown at Caltech, and has been fully reengineered by Ofria on multiple occasions since then. The software was originally inspired by the Tierra system.
Karl Sims is a computer graphics artist and researcher, who is best known for using particle systems and artificial life in computer animation.
Experimental evolution is the use of laboratory experiments or controlled field manipulations to explore evolutionary dynamics. Evolution may be observed in the laboratory as individuals/populations adapt to new environmental conditions by natural selection.
Evolvability is defined as the capacity of a system for adaptive evolution. Evolvability is the ability of a population of organisms to not merely generate genetic diversity, but to generate adaptive genetic diversity, and thereby evolve through natural selection.
In population genetics and population ecology, population size is a countable quantity representing the number of individual organisms in a population. Population size is directly associated with amount of genetic drift, and is the underlying cause of effects like population bottlenecks and the founder effect. Genetic drift is the major source of decrease of genetic diversity within populations which drives fixation and can potentially lead to speciation events.
Richard E. Lenski is an American evolutionary biologist, the John A. Hannah Distinguished Professor of Microbial Ecology at Michigan State University. He is a member of the National Academy of Sciences and a MacArthur Fellow. Lenski is best known for his still ongoing 36-year-old long-term E. coli evolution experiment, which has been instrumental in understanding the core processes of evolution, including mutation rates, clonal interference, antibiotic resistance, the evolution of novel traits, and speciation. He is also well known for his pioneering work in studying evolution digitally using self-replicating organisms called Avida.
Mutation frequency and mutation rates are highly correlated to each other. Mutation frequencies test are cost effective in laboratories however; these two concepts provide vital information in reference to accounting for the emergence of mutations on any given germ line.
Christoph Carl Herbert "Chris" Adami is a professor of microbiology and molecular genetics, as well as professor of physics and astronomy, at Michigan State University.
The evolution of biological complexity is one important outcome of the process of evolution. Evolution has produced some remarkably complex organisms – although the actual level of complexity is very hard to define or measure accurately in biology, with properties such as gene content, the number of cell types or morphology all proposed as possible metrics.
The E. coli long-term evolution experiment (LTEE) is an ongoing study in experimental evolution begun by Richard Lenski at the University of California, Irvine, carried on by Lenski and colleagues at Michigan State University, and currently overseen by Jeffrey Barrick at the University of Texas at Austin. It has been tracking genetic changes in 12 initially identical populations of asexual Escherichia coli bacteria since 24 February 1988. Lenski performed the 10,000th transfer of the experiment on March 13, 2017. The populations reached over 73,000 generations in early 2020, shortly before being frozen because of the COVID-19 pandemic. In September 2020, the LTEE experiment was resumed using the frozen stocks. When the populations reached 75,000 generations, the LTEE was transferred from the Lenski lab to the Barrick lab. In August 2024, the LTEE populations passed 80,000 generations in the Barrick lab.
Dr Peter John Bentley is a British author and computer scientist based at University College London.
Hypercube-based NEAT, or HyperNEAT, is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorithm developed by Kenneth Stanley. It is a novel technique for evolving large-scale neural networks using the geometric regularities of the task domain. It uses Compositional Pattern Producing Networks (CPPNs), which are used to generate the images for Picbreeder.orgArchived 2011-07-25 at the Wayback Machine and shapes for EndlessForms.comArchived 2018-11-14 at the Wayback Machine. HyperNEAT has recently been extended to also evolve plastic ANNs and to evolve the location of every neuron in the network.
The BEACON Center for the Study of Evolution in Action is a science and technology center in the United States, focused on experimental and applied research on evolutionary dynamics, sponsored by the National Science Foundation. The consortium of universities that make up BEACON is led by Michigan State University with partner institutions of North Carolina A&T State University, the University of Idaho, the University of Texas at Austin, and the University of Washington.
In evolutionary biology, robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.
Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs that experience the same major ecological interactions as biological organisms. Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology.
Epistasis is a phenomenon in genetics in which the effect of a gene mutation is dependent on the presence or absence of mutations in one or more other genes, respectively termed modifier genes. In other words, the effect of the mutation is dependent on the genetic background in which it appears. Epistatic mutations therefore have different effects on their own than when they occur together. Originally, the term epistasis specifically meant that the effect of a gene variant is masked by that of different gene.
Jeffrey E. Barrick is a Professor in the Department of Molecular Biosciences at The University of Texas at Austin. His research uses the tools of genomics, synthetic biology, and molecular biology to study the evolution of microorganisms, including symbionts of insects. Since 2022, Barrick has directed the E. coli Long-Term Evolution Experiment (LTEE), which has been underway since 1988.
The Program in Ecology, Evolution, and Behavior (EEB) at Michigan State University is a graduate-level program that was founded in 1987. As of 2023, the EEB Program had more than 75 core faculty, 40 affiliated postdocs, and 100 graduate students, along with more than 500 alumni. Elise Zipkin is the current Director of the Program.