Deciphering the 1-D Sequence Code for Life
Proteins are workhorses of cell that performs a wider range of functions essential for all forms of life. All of the necessary information for fold and function is encoded in the 1-D sequence for proteins. Proteins exquisitely translate this code to fold and function very efficiently, yet deciphering this encoded information remains a challenging task. In this project, we will use physics-based computational methods developed in Ozkan lab to predict structure and structure encode-dynamics using 1-D sequence code to map sequence to function.
Skills: Physics or chemistry background with a minimum knowledge in coding is preferred for this project.
Mentor: Banu Ozkan
Viscoelastic Properties of Cells and Proteins
This is a theoretical project aiming at viscoelastic properties of living cells and biological macromolecules (proteins). They are modeled with the soft-matter theories of viscoelasticity. Shear and bulk moduli depending on the frequency of the applying force will be calculated based on the available experimental input.
Mentor: Dmitry Matyushov
Examining the Role of Mechanical Forces in the Origin of Life
The molecular machinery of biological cells is largely encapsulated within a cell membrane consisting of a lipid bilayer. How such encapsulated structures could have arisen in the prebiotic world is a topic of considerable interest. We hypothesize that the mechanical forces of the impact between raindrops and an ocean/lake surface coated with a hydrocarbon film could drive the production of cell-like encapsulation. To evaluate our hypothesis, we study the formation of cell-like-structures consisting of water-in-oil-in-water (w-o-w) droplets by a controlled impact of water drop on oil-water systems.
Skills: Physics or chemistry background is preferred for this project. Priority will be given to students who will spend his/her summer research on ASU Tempe campus.
Mentor: Rizal F. Hariadi
Using New Statistical Tools to Learn Models of Life
As theorists, we use mathematical models to understand trends and draw predictions for the behavior of complex systems when, as is often the case, trends and predictions are not obvious "by eye" from a glance at the data. Here we want explore a novel tools of Statistics–new tools of Bayesian nonparametrics (BNPs)–to help us construct plausible models from complex data. BNPs use flexible (nonparametric) model structures to efficiently learn models from complex data sets. We want to adapt BNPs to address important questions in Biophysics directly from the data itself which is often limited by fundamental factors (such as the impossibility of directly imaging features of interest inside cells which are too small compared to the wavelength of light used to probe them). More specifically, we want to show that BNPs hold promise by allowing complex data to be transformed into principled models describing the dynamics of life – conformational dynamics of single proteins to the dynamics of protein clusters and beyond.
Mentor: Steve Pressé