Student Presentation 1
Title: Can Exogenous Electric Fields Revert Cancerous Signals? Evidences and Challenges
Presenting author: Ishan Goswami – Mechanical Engineering, Virginia Tech
Research Group: Laboratory for Biomaterials and Tissue Engineering
Mammalian cells exhibit a remarkable co-ordination in self-assembly that form tissue and organs (and ultimately the organism). This self-assembly is at the heart of embryonic development, tissue repair and regeneration, while failures are implicated in disease. A considerable amount of literature demonstrates the importance of endogenous electric fields in the self-assembly process. It is no surprise, therefore, that cancerous transformations in cells are accompanied by a
gamut of aberrant electrical membrane properties and ion channel activities. Given this evidence, it could be argued that the perturbation of endogenous electric circuitry by external electric fields may be used to either eliminate or revert the tumorous phenotypes. We recently discovered a specific cell signaling (thymic stromal lymphopoietin) involved in pro-tumor immune modulation that is affected by pulsed electric field (used clinically in tumor ablation). However, mechanistic questions still remain unanswered, and our experimental and computational efforts in this regard will be briefly discussed.
Student Presentation 2
Title: CrossPlan: Systematic Planning of Genetic Crosses to Validate Mathematical Models
Presenting Author: Aditya Pratapa - Computer Science, Virginia Tech
Research Group: Computational Systems Biology
Mathematical models of cellular processes can systematically predict the phenotypes of novel combinations of multi-gene mutations. The challenge is that the number of possible combinations grows explosively, complicating the search for informative mutants and prioritizing them for experimental validation. Moreover, keeping track of the crosses needed to make new mutants and planning sequences of experiments is unmanageable when the experimenter is
deluged by hundreds of potentially informative predictions to test. We present CrossPlan, an algorithm for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. We base our approach
on a generic batch-based experimental workflow: in each batch, we can make a fixed number of mutants and characterize their phenotypes in parallel, with each mutant resulting from genetically crossing a pair of strains made in earlier batches. CrossPlan uses an integer-linear-program (ILP) to maximize the number of target mutants that we can make in a given number k of batches. We apply our method to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. Using CrossPlan, the number of target mutants we can make increases linearly with k. We also extend our solution to incorporate other experimental conditions such as delay and markers. The experimental flow that underlies our work is quite generic and our ILP-based algorithm is easy to modify. Hence our framework should be relevant in mammalian systems as well.