Environment-sensing Mechanisms of Gene Expression and their Effects on the Dynamics of Genetic Circuits across Cell Generations

In genetic circuits, the constituent genes do not interact only between themselves, they are also affected by regulatory molecules of the host cells that support the circuits’ operation and by the environmental conditions. These factors, along with the intrinsic noise in gene expression, affect the functioning of the circuits. As such, to understand the structure of natural circuits and to engineer functional synthetic circuits, one needs to characterize thoroughly how external factors and perturbations from the environment may affect their behavior.This thesis focused on two cellular mechanisms through which the dynamics of gene expression becomes environment dependent: the intake of gene expression regulatory molecules from the media and the σ factor competition. The first mechanism determines the dynamics by which inducer molecules in the media enter the cell cytoplasm and trigger or repress the expression of the target gene. The second mechanism allows cells to change its gene expression profile to adapt to specific stress conditions.Following the characterization of the effects of these mechanisms on the expression dynamics of individual genes from live, single cell measurements, we then performed in silico assessments on how these effects at the single gene level propagate to the circuit level. Here, the dynamics of genetic circuits was observed in both non-dividing and dividing cell populations, where errors in the partitioning of molecules in cell division occur and introduce significant variance between sister cells.From these studies, with the knowledge on the factors of the host cells and their environment sensing mechanisms, more predictive models of the circuits’ dynamics are expected to emerge. The models would further help in identifying what circuit composition, properties of the host strains and environmental conditions are needed for the circuits to exhibit the desired behavior

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp