Mechanics of DNA
Future Research Direction
Dr. Todd Lillian
Although my research area is traditionally pursued by biophysicists, I believe that my perspective, as an engineer, uniquely positions me to make significant contributions to our understanding of DNA. Below I outline two future research directions:
- Stochastic Elastic Rod Model
Previously, my research focused on modeling strain energy dominated DNA dynamics. Under these circumstances, the trajectory of DNA can be well described with a deterministic model, such as the one mentioned above, which neglects thermal energy. However, during many cellular processes, random thermal excitation contributes significantly to the molecule’s dynamics and in some cases is the driving force behind the process. Models that include the associated stochastic forces face the challenge of sampling the ensemble of possible trajectories a molecule may follow. Several of these ‘Brownian dynamics’ models have been developed; however, they lack the rich mechanical description provided by the elastic rod model. Therefore, I plan to develop a stochastic elastic rod model. In addition to providing an improved description of DNA mechanics, this new model has the potential of being computationally more efficient than existing Brownian dynamics models.
- Multi−Resolution Model
As already emphasized, the dynamics of DNA during many biological processes occur on many different length scales. During the process of transcription, for example, an enzyme (RNA polymerase) locally untwists a full turn of the double helix (~3.5 nm) and thereby induces conformational changes (including the formation of supercoils) which can effect the molecule on a longer length scale (>75 nm). However, on much longer length scales (1 μm to 2 cm), RNA polymerase will have almost unnoticeable effects. Therefore, it’s not only excessive to model an entire length of DNA with high spatial resolution, but it’s also computationally prohibitive. To this end, I plan to develop a multi−resolution model for long DNA molecules that patches together several DNA domains. Each domain will have a different resolution and will interface with its neighbors through the transfer of boundary conditions. For the case of transcription, this model could utilize an all−atom molecular dynamics description of RNA polymerase and the locally untwisted DNA. This detailed description could then provide boundary conditions for an elastic rod used to represent DNA conformational changes on the length scale of about 100 nm. At yet another level of resolution, a new reduced order model would be developed to describe interwound supercoiled structures. Finally, a lump model would be developed to represent the influence of the remaining bulk of the DNA.
These research efforts will contribute to a fundamental understanding of how the mechanics and dynamics of DNA influence its function. Although very limited, our present understanding of this key relationship has already led to significant drug therapies. For example, a chemotherapy drug (topotecan) prevents topoisomerase I enzymes from relaxing DNA supercoils in cancerous cells and thereby initiates cell death. Deepening our understanding of the mechanics and dynamics of DNA will therefore enable the development of new drug therapies that target diseases such as cancer. This research has additional broad implications in many areas of biology and engineering. In biology, these modeling strategies could also be applied to other biopolymers and bio-filaments such as RNA, microtubules, and actin. While in engineering, these modeling strategies could be applied to the design of nano structures and mechanisms that utilize long slender filaments (e.g., carbon nano−tubes) or even DNA molecules.