I am passionate about mathematics in general, and specifically about mathermatical biology in my own research. I envision research in quantitative biology as a constant exchange of ideas between experts in quantitative fields (i.e. computer science, engineering, mathematics, physics, statistics) and experts in biological sciences. This conversation enriches biology with the insight provided by quantitiative methods, and it enriches quantitiative fields with the demands of complex problems originated in biology. Joel Cohen put it succinctly and brilliantly: “mathematics is biology’s next microscope, only better; biology is mathematics’ next physics, only better“.
I have worked on recently on the following areas:
Machine learning: We use machine learning on a regular basis, but we are also developing new algorithms that improve the performance of artificial neural networks as compared to the state of the art. Also, I have been involved in applications of machine learning to analysis of cardiac signals, text summarization, and student success at the university level.
Data harmonization: We are able to represent heterogeneous and complex data sets of arbitrary size with a reduced set of data primitives (well-defined mathematical objects that are the building blocks to represent reality). Our analysis pipelines only consume data primitives, and only produce data primitives. The ultimate goal is to design and implement an agent capable of automated knowledge discovery.
Adaptive learning: The training of interdisciplinary scientists poses tremendous challenges. This is particularly true when teams are comprised of people with heterogeneous backgrounds. We have developed technology that minimizes the cognitive overhead to train individuals, and to integrate teams into a research project.
Multi-scale analysis of infectious disease: We study mechanisms that connect multiple scales, from milliseconds through evolutionary time, and from quantum interactions through continental dynamics of infection. This endeavor requires advances in multiple areas, as described below. We have been able to produce advances in every single aspect, and we are capable of integration of all these dimensions.
Epidemiology of asymptomatic carriers: We study the effect of asymptomatic hosts in the dynamics of malaria transmission. The results can be extrapolated to other diseases.
Dispersal of vectors: We study methods to model the dispersal of mosquitoes in a heterogeneous landscape dominated by species distribution, climate, and vegetation cover.
Physiology: We are able to identify when a host is infected before the onset of symptoms occurs. We accomplish this via high-frequency measures of accelerometers, blood pressure, ECG, and temperature.
Cellular models of immune interaction: We are using flow-cytometry data and cytokine information during a malaria infection to model cellular-level interactions between the adaptive and innate immune system, and healthy and infected red blood cells.
Multi-omic integration: We are able to analyze transcriptomic data, and integrate it with proteomics, metabolomics, immunomics, and other -omic technologies. Using this type of integration we have been able to determine factors that confer resilience against an infection.
Computational Drug Design: Given a gene regulatory network (that we can reconstruct from a time series of transcriptomic data), we can identify the most sensitive elements of the network, and target them with molecular docking studies against of databases of drug-like molecules.
Recently funded projects are:
(PI Gutierrez) ALICE (Adaptive Learning for Interdisciplinary Learning Environments, 2016-2018, $299K), NSF award #1645325: ALICE is a Web-based information system that generates individualized development plans, according to previous experiences and current challenges. Furthermore, ALICE is designed to connect lexias from multiple subject matters, thus bypassing disciplinary barriers that in many cases are artificial. The principles behind ALICE are generalizable, and hence it has the potential to be used in K-16, graduate, and continuing education.
(Co-PI Gutierrez, PI Galinski) Technologies for Host Resilience (2016-2019, $1.9M UGA out of $6.5M) – Host Acute Models of Malaria to study Experimental Resilience (THoR’s HAMMER), DARPA contract contract #W911NF-16-C-0008, 2016-2019: This project will explore the molecular mechanisms of resilience, susceptibility and resistance of non-human primate hosts when challenged with a malaria infection.
(Co-PI Gutierrez, PI Barbour) Collaborative Research: NSF INCLUDES: An Integrated Approach to Retain Underrepresented Minority Students in STEM Disciplines (2016-2018, $117K). NSF award 1649226: The University of Georgia, Florida International University, Savannah State University, Clark Atlanta University and Fort Valley State University will lead this Design and Development Launch Pilot to address enhancing recruitment, retention, productivity and satisfaction of historically underrepresented minority (URM) undergraduate students who enroll in STEM graduate programs at primarily white (PWI) and research intensive (RI) universities
(Co-I Gutierrez, PI Galinski) Malaria Host-Pathogen Interaction Center (2012-2017, $19M), MaHPIC, NIAID contract #HHSN272201200031C, 2012-2017. PI Mary Galinski: MaHPIC involves the multidisciplinary study of malaria infections, immunity and pathogenesis of P. falciparum, P. vivax and P. knowlesi in the context of host-pathogen interactions, in humans and nonhuman primates, using a systems biology approach. Three nonhuman primate malaria species will be studied: P. coatneyi to model P. falciparum, P. cynomolgi to model P. vivax, and P. knowlesi, a monkey malaria species that has been causing illness and cases of death in humans in Southeast Asia.
(Co-I Gutierrez, PI Herrera) International Centers of Excellence in Malaria Research (2011-2017, $5.5M) – Center for non-Amazonian regions of Latin America – CLAIM, NIAID cooperative agreement #U19AI089702-01, 2010-2017. PI Sócrates Herrera: CLAIM is divided into three projects: Project 1 is evaluating the diversity of the ecology and parasite populations related to the epidemiology and clinical findings to establish a scientific framework that supports the development of new intervention strategies for malaria elimination in non-Amazonian areas of Latin America. Project 2 is addressing major gaps in understanding of the ecology, behavior, vector potential, and control of Anopheles malaria vectors to guide the development and implementation of more effective integrated vector management (IVM) strategies of National Malaria Control Programs (NMCPs). Project 3 aims to determine the clinical outcomes and their association with parasite and host features of malaria-infected individuals living in non-Amazon regions of LA with different intensities of malaria transmission.