My research interest is aimed at the study of the mechanisms of emergence of collective behavior in complex systems, in particular, how information dynamics and complex interconnections of individual subunits can lead to adaptive capabilities of living systems.
During my doctoral work at Goldsteins lab at the University of Arizona, I studied the collective behavior of swarming self-propelled bacteria. This research helped to elucidate the relationships between body alignment, coherent motion, and cell concentration in the large-scale coherent phase of these microorganisms. An important implication I learned from this research is that collective responses in living systems can emerge as an effect of cell interactions with other cells and their environment, and thus their ecological context, rather than limited to their specific individual programming. And appropriate collective responses lead to the adaptive fitness of populations of cells. These ideas motivated my current line of research interest. In my postdoctoral work, I took a systems-biology perspective to explore the phenomenon of cancer, understood as a breakdown of multicellularity. Namely, neoplasms exhibit a switch in the selective process from the organismal level to the cellular level, promoting cell heterogeneity and competition, the inverse process to the transition to multicellularity. As a member of Dr. Paul Davies team in the ASU-PSOC program, I developed a model of metastasis revealing that early stages of organ invasion could be driven by rare event dynamics rather than a selective advantage of the invasive tumor cells. This result emphasized the importance of diversity and niche construction in the progress of the disease, bringing about eco-evolutionary arguments concerning cell-cell and cell-tissue interactions. In collaboration with Dr. Kimberly Bussey, we unveiled an evolutionary signature in cancer genomes. Specifically, our bioinformatic studies correlated gene evolution with observed patterns of mutations and expression changes in cancer, demonstrating that ancient genes tend to be robust and that their genomic modifications are associated with advanced stages in cancer. This work revealed that cancer samples carry a signature of stress-induced mutagenesis, an ancestral biological mechanism of evolvability and diversification. On a different line of research, in collaboration with Dr. Julia Bos from the Institut Pasteur in Paris, we used image processing and particle tracking techniques to study the dispersion process of membrane vesicles in bacteria challenged with antibiotic stress. This research uncovered some important details in the dynamics of the mechanism of the emergence of drug resistance, a subject that is also very pertinent in the study of cancer cells via orthologous mechanisms in multicellular tissue cells.
A recurrent feature in these lines of research is that tumor diversification and evolution are central properties of cancer. And while tumors are well recognized as an ecological and evolutionary process of populations of cells, very little progress has been done in drawing clinically relevant distinctions that identify how different tumors are evolving and how to manipulate the corresponding mechanisms to maximize benefits to the patient. Modeling of the spatial heterogeneity, complexity, and evolutionary dynamics of populations of cancer cells is an ideal framework to assess the disease recurrence, to study the clonal expansion of resistant strains challenged with different cancer therapies and explore for best strategies of dosage modulation to achieve optimal control of tumor growth, prolong the expected time to recurrence and minimize the effects of treatment. As such, the study of the evolution of resistance to therapy is a natural focus of interest of major clinical importance that I have pursued as part of Carlo Maleys lab at ASU. An important direction my current work has taken is the adaptation of methods of spatial statistics into cancer research. This is an active collaboration with Dr. Joel Brown from the Moffitt Cancer Center in which I have translated several statistical packages generally implemented in fields like landscape ecology and geographical information systems into an analysis pipeline that allows us to investigate the ecological aspects of tumor microenvironments using digitalized cell location data from histopathological images.