Quantifying Deception: A Case Study in the Evolution of Antimicrobial Resistance
Proceedings of the Genetic and Evolutionary Computation Conference 2016, , 101-108, 2016
Abstract: The concept of ‘deception’ in fitness landscapes was introduced
in the genetic algorithm (GA) literature to characterize
problems where sign epistasis can mislead a GA away
from the global optimum. Evolutionary geneticists have long
recognized that sign epistasis is the source of the ruggedness
of fitness landscapes, and the recent availability of a growing
number of empirical fitness landscapes may make it possible
for evolutionary biologists to study how deception affects adaptation
in a variety of organisms. However, existing definitions
of deception are categorical and were developed to
characterize landscapes independent of population distributions
on the landscape. Here we propose two metrics that
quantify deception as continuous functions of the locations
of replicators on a given landscape. We develop a discrete
population model to simulate within-host evolution on 19
empirical fitness landscapes of Plasmodium falciparum (the
most common and deadly form of malaria) under different
dosage levels of two anti-malarial drugs. We demonstrate
varying levels of deception in malarial evolution, and show
that the proposed metrics of deception are predictive of some
important aspects of evolutionary dynamics. Our approach
can be readily applied to other fitness landscapes and toward
an improved understanding of the evolution of antimicrobial
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).