Physical constraints on accuracy and persistence during breast cancer cell chemotaxis - How are different characteristics of cancer cell migration related to each other? Cancer cells show biased migration in response to chemical gradients. This step is pivotal to metastasis, which is the leading cause of death in cancer patients. In experiments performed in Dr. Bumsoo Han's lab, we observed that breast cancer cells show a significant change in bias in response to changing chemical gradients, but very little change in speed and persistence of migration. To understand this constrain in cancer cell migration we performed computational analysis using Cellular Potts Model and Biased Persistent Random Walk Model. Our analysis suggested that this constrain is a feature of the migration and is independent of cell types, which was later also observed in pancreatic cancer cells. (###)

Antagonism in multiple-cue chemotaxis in cancer cells - What happens when cancer cells are simultaneously exposed to multiple chemical signals? Experiments performed in Dr. Bumsoo Han's lab on breast and pancreatic cancer cells revealed that cells show strong biased migration in response to individual chemical gradients, but when the gradients are combined there is a significant reduction in bias, which we call antagonism. To understand this better we performed biochemical network analysis by randomly searching over 10,000,000 parameter sets using MATLAB to explain antagonism in cancer cell migration in response to multiple chemical cues. Our analysis revealed that a mechanism of convergence with saturation of the convergent component can lead to antagonism in biased cell migration. (###)

Inference of signaling mechanism from cellular responses to multiple cues - Which networks and mechanisms show antagonistic behavior in response to two input signals? To understand this problem we performed an exhaustive search of ~500,000 minimal biochemical networks using steady state analysis of chemical reactions and numerical analysis of individual networks using MATLAB to understand cell behavior in response to multiple signals. We found that two common types of mechanisms can cause antagonism in response to chemical gradients, the first one involves convergence and the second involves repressive crosstalk, but when it comes to showing antagonism in response to chemical backgrounds, only the repressive crosstalk mechanism is successful.

Role of cell-cell communication in collective chemotaxis - What is the role of cell to cell communication in biased cell migration? To understand this problem, we numerically solved partial differential equations which depicted collective cell migration using MATLAB and performed cell migration simulations involving 10,000 cells and 1,000,000,000 time steps using Gillespie algorithm and C++ to verify numerical results. We found that communication is helpful only if it is coupled to the external environment. Otherwise, it can be detrimental to biased cell migration.

Effect of convection on cellular sensing precision for directed cell migration - What is the effect of flow on cancer cell migration in presence of chemical gradient? Experiments on pancreatic cancer cells in Dr. Bumsoo Han's lab revealed, compared to chemical signals cells are weak sensors of flow. To verify this, we mapped a biochemical network to a ternary logic gate by searching over 10,000 parameter sets using MATLAB. Our analysis helped us understand that cancer cells respond to different kinds of signals in a hierarchical manner. (###)

Constructing a gradient detector in cells - Is it possible to synthetically construct a gradient detector in cells? Cells are excellent gradient detectors. Many cells like E. coli, uses chemical gradient to its advantage to explore nutrient rich patches. But, is it possible to construct a gradient detector which cells can use to only report spatial gradient by communicating with each other? We have started to tackle this question by performing experiments in Dr. Matthew Bennett's lab and performing mathematical analysis of the biochemical networks. (***)

### All experiments performed by Dr. Hye-ran Moon, in Dr. Bumsoo Han's lab.

*** All experiments performed by Meidi Wang, in Dr. Matthew Bennett's lab.

Ph.D. Advisor - Dr. Andrew Mugler, University of Pittsburgh and Purdue University

Collaborators - Dr. Bumsoo Han, Purdue University, Dr. Kresimir Josic, University of Houston, Dr. Matthew Bennett, Rice University

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