Statistical Mechanics of Mutational Ensembles for Adaptive Evolution

This study aims to uncover which genes encode the specific traits and capabilities of an organism, a central question in biology with applications in bioengineering and biotechnology. The challenge lies in the large number and complexity of interactions among genes, obscuring the role of each in any given trait. This study addresses this problem through a novel method inspired by physics.

We will grow E. coli to undergo many mutations and become resistant to antibiotics, and we will seek to identify the genes conferring antibiotic resistance to it. This is a needle in a haystack problem, as many genes will have many mutations. To find the relevant ones, each mutation will be assigned a physical energy, and the entire mutational set will be analyzed as if it were a cloud of gas particles, using thermodynamics.

This is possible because to apply thermodynamics, we need only know the energy of individual mutations. If our definition of energy and use of thermodynamics are correct, then they should predict which mutated genes confer antibiotic resistance, which we will test experimentally. Preliminary data support the feasibility of this unconventional approach, which creates a new bridge between biology and physics.

Given that antimicrobial resistance contributed to 4.95 million deaths in 2019, the genes we find could identify effective new targets for drugs that combat antibiotic resistance. This technique, once validated, should also apply equally well to many other contexts, including bioremediation, ecological cleanups, and sustainable agriculture, domains for which the identification of adaptive genes and molecular mechanisms is of paramount importance.

Most broadly, this work will reveal a deep connection between thermodynamics and evolution, with many practical applications. In addition, an educational module on evolutionary theory for novice learners will be developed, with feedback from faculty and students at the University of St. Thomas in Houston.

The technical objective of this research is to demonstrate the thermodynamic structure of mutational ensembles in a testable system. A new physics theory of fitness landscapes is proposed that assigns a measurable energy to each mutation. This theory predicts that across a mutational ensemble, this energy will follow the Boltzmann distribution of statistical mechanics, and the law of equipartition should then apply and predict the selection pressure on each gene.

These hypotheses will be tested in E. coli through adaptive laboratory evolution experiments leading to antibiotic resistance. After sequencing the resistant strains, we will predict genes that confer antibiotic resistance based on changes in selection pressure, as measured by applying the law of equipartition of energy. These genes will then undergo experimental validation through knock-out and knock-in experiments.

Beyond identifying new drivers of antibiotic resistance, a significant contributor to human mortality, this study aims to demonstrate that the genotype-phenotype relationship adheres to statistical mechanics laws, potentially unraveling the genetics of polygenic traits by unifying biology and physics. Results, resources, and computational tools will be made publicly accessible via a dedicated website to foster further research and applications across biological fields.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.