Welcome to the Evolutionary Health Group of the City University of New York Graduate School of Public Health and Health Policy and the National Center for Biotechnology Information. Employing diverse computational approaches, we seek to uncover and explain deeply conserved mechanisms of host adaptation for pathogens ranging from bacteriophages to cancer cells to...words?
The term parasite typically refers to an organism participating in a sustained inter-species relationship (symbiosis) in which this organism benefits at the cost of another. Parasites engaging in extremely asymmetric relationships are often bestowed the higher title of pathogen.
This asymmetry makes pathogens selfish, often so selfish that hosts are under strong evolutionary pressure to evolve defenses against the pathogen or risk population collapse. In response, the pathogen evolves to evade these new host defenses, giving rise to a conflict which rapidly generates genomic novelty and reveals the rules governing a stable ecology.
We endeavor to learn these rules for diverse pathogens ranging from large multicellular eukaryotes down to the smallest RNA viruses and establish conserved features. Relaxing our definition of organism, we may zoom in even further to consider this same conflict playing out within an individual genome among selfish genetic elements living in the blurry boundary of life. In turn, we believe we can begin to better understand the propagation of selfish elements in the complex nonliving systems that compose our public health infrastructure, from social media to government policy.
The growth in the number of nucleotides in publicly available genome repositories beats Moore's Law. Together, advances in computer engineering and next-generation sequencing technologies enable computational groups like ours to study a vast diversity of organisms.
We have a special interest in viruses, and RNA viruses in particular. Almost all "priority pathogens" with recognized pandemic potential are viruses and the majority of zoonotic mammalian viruses are RNA viruses. Viruses evolve fast providing the opportunity to correlate genomic variation to environmental change as it happens in real time.
A typical workflow involves aligning homologous sequences, inferring phylogeny, and estimating ancestral reconstructions to establish mutations. We focus on understanding the biology rather than software development but we build our own tools as necessary.
The other key element of our work is the construction of mathematical models to explain and hopefully predict evolutionary dynamics. Evolution is inherently random and yet while the combinatorial space of all possible sequences long enough to make even the smallest genomes is incomprehensibly vast, under the same selective pressures, the same mutations are observed to repeatedly emerge.
One area of particular interest is epidemic modelling. In this direction, we evaluate the predicted effects of public health intervention, including the long term impact of intervention on pathogen evolution. This most often begins with a system of ordinary differential equations (curves: S E I R model) and reassessed if complex spatial or stochastic effects are substantial over the timescale of interest.
We are very applied mathematicians and are not experts in a particular discipline but instead seek to learn new techniques best suited to our biological interests. We often employ large scale simulations relying on straightforward numerical methods, but are always excited to apply new analytical approaches.
Bacteria maintain diverse immune machinery to protect against phage infection. If that fails, an infected individual may undergo programmed cell death (PCD), an altruistic behavior, reducing the probability that the infection will spread within the community. Building on our prior work focusing on understanding the features which govern the optimal strategy for somatic damage mitigation, we seek to understand how bacteria decide when to take one for the team.
Modern sequence alignment tools can establish homology among highly divergent proteins. The resulting deep alignments may then be organized into clusters given an appropriate metric over the sequence space. Percent identity with respect to a reference is often used to designate just two clusters - "the same" and "different". What percent identity threshold represents functional divergence, however, varies based on the genomic and ecological context. Using information theory, we are building a pipeline to segment deep alignments into groups that constitute the most efficient (in bits) representation of the underlying sequence space. It is our hope that this threshold-free approach is able to reproduce manually curated designations of evolutionary divergence as well as predict new functional groups.
Addressing tumor evolution remains a major challenge for cancer treatment. Despite the availability of thousands of human cancer genomes through programs including TCGA and COSMIC, substantial patient heterogeneity makes predictions of individual responses to treatment noisy. Interactions between tumor mutations and germline variations remain poorly explored and even well characterized driver genes likely play additional, unknown roles in the process of metastasis. Cancer is also frequently observed in diverse non-human animals from clams to clydesdales but tumors from most species are rarely sequenced. This data could dramatically improve our understanding of the interactions between germline variations in driver genes present in other species and tumor mutations which, motivated by our prior work, we expect to overlap with the landscape of human tumors even for distant relatives. We are building a database to collect publicly available tumor genomes across species and hope to identify several organisms for which the observed cancer incidence is high and the germline variations in driver genes will be informative for human health.
Generative text algorithms trained using much of the internet provide a mechanism to access technical information that can reduce the barrier to entry for non-experts. We are exploring the incorporation of these tools into the research pipeline at 3 levels. First, for researchers, how can we responsibly use algorithmic summarization to improve our literature reviews, reducing the time spent searching for relevant work and broadening the scope of what we can read in detail? Second, for students, how do we establish quantitative guidelines for scientific consensus to narrow the gap between our textbooks and peer-reviewed publications? Third, for the general public, how can we quantitatively evaluate biases in the answers these tools provide for clinical queries?
Virus ecology and evolution is climate dependent and successful epi/pandemic prevention and response requires the incorporation of climate variables into epidemiological models and biostatistical workflows. Free, robust remote sensing data is made available through NASA; however, data accessibility remains a challenge for the epi/biostats community. As a part of the NIH Climate Change and Health Initiative we are building a web portal to help policy makers and public health practitioners utilize this data. In parallel, we are clustering metaviromes based on climate zone classification to determine climate-sensitive trends in viral abundance and evolutionary selection pressures.
Principal Investigator
Nash Rochman is an Assistant Professor in the CUNY SPH
Department of Epidemiology and Biostatistics; an
Institute for Implementation Science in Population Health
Investigator; an NIH Special Volunteer; and an editor at
Biology Direct.
Nash was drawn to a career in biology to help distil complex and confusing data into predictive models for disease. He completed his undergraduate education at Bard College of Simon's Rock and Brown University and pursued his PhD advised by Sean Sun in The Johns Hopkins University Cell Biomechanics Lab. Nash went on to pursue a postdoctoral fellowship centered on pandemic viral evolution advised by Eugene Koonin
in the NIH Evolutionary Genomics Research Group. Prior to joining CUNY, Nash was a Principal Investigator at the NIH in the Independent
Research Scholar Program. Nash splits his time between NYC and DC where he lives with his wife Anita.
In many locations, Nash can be found playing jazz trumpet.
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Senior Research Associate
Peter Vlasov has over 20 years experience in computational biology. He completed his PhD in applied physics and mathematics from Moscow Institute of Physics and Technology. Peter began his scientific career in the area of structural bioinformatics at the Institute of Molecular Biology (Russia). After spending several years applying these techniques in the private sector for the drug-design company Algodign LLC, Peter returned to publicly funded research within the Center for Genomic Regulation (Spain) and the Institute of Science and Technology (Austria). Peter's current research focuses on the development of computational methods in evolutionary and systems biology. In addition to his research, Peter has made advances in computational biology through his contributions to educational initiatives for both university students and children designed to expand and diversify the biomedical workforce. Peter currently resides in Barcelona with his family where he can be found lifting heavy things before heading out to a gallery opening.
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MPH Scholar
Delaney Collins completed her undergraduate studies in Cell and Molecular Biology at the University of Utah. Graduating during the COVID-19 pandemic; her work in oncology; and volunteering in a children's hospital all played important roles in shaping her interest in public health. Her research interests lie in finding ways to use molecular data to solve large-scale public health problems. Outside of work and school, Delaney enjoys reading, baking, and hiking all over the diverse Utah terrain.
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Doctoral Scholar
Eslam Abousamra completed his undergraduate degree in molecular biology and applied statistics at Connecticut College. He went on to pursue his MPH in Epidemiology at the University of Washington advised by Prof. Trevor Bedford centered on infectious disease forecasting, surveillance, and the complex dynamics of respiratory viral interference. His current research focuses on applying machine learning methods to improve public health surveillance and intervention, in particular, with respect to antibiotic resistance. Originally from Alexandria, Egypt, Eslam lives in NYC.
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Doctoral Scholar
Bridget Dela Akasreku comes to public health with over seven years of clinical experience as a physician assistant in Ghana. Her current work focuses on HIV epidemic forecasting to evaluate the impact of public health interventions among marginalized populations in Kenya and beyond. Bridget is committed to applying these computational techniques to drive evidenced-based policy decisions. Beyond research, she teaches MPH courses in policy and intervention design. Bridget splits her time between New York and New Jersey.
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Research Associate
Abir Bhuiyan completed his M.S. in Population Health Informatics at CUNY SPH. His research focuses on measuring the accessibility of LLM responses to common medical queries. Outside of the lab, Abir wears a few different hats, as both a data analyst with the City of New York and manager of a small business.
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MPH Scholar
Nicole Perez completed her B.S. in Biochemistry at UC Riverside. Working in a hospital laboratory inspired her to pursue a career in public health. Nicole's research focuses on understanding how LLMs encode associations between demographic characteristics and common health conditions. In her spare time, Nicole enjoys relaxing with her dog and searching for the best matcha latte in SoCal.
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MPH Scholar
Ivy Kosater holds a bachelor's degree in biochemistry from Florida State University. After spending the years following her undergraduate education working in the field of bioinformatics, she decided to pursue an MPH in Epidemiology and Biostatistics at CUNY SPH to apply her computational skills towards improving population health outcomes. Her current work focuses on understanding how antibiotic exposure affects the human gut microbiome. Outside of school and research, you can find her biking around Brooklyn or at the cinema.
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Mebrahtom Zeweli completed a B.S. in Health Promotion from Jimma University and an MPH from Addis Ababa University, Ethiopia. His research spans health risk factor epidemiology and health information systems with particular interests in population movement as a risk factor for vector-born disease and public health data quality management, respectively. Mebrahtom has also managed national disease control and elimination initiatives and previously served as a volunteer during the 2014-6 Ebola Virus Disease outbreak in West Africa. His current research focuses on understanding the impact of climate and environmental exposures on chronic disease. Outside the lab, he enjoys hiking and exploring new places.
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MPH Scholar
Safa Amir comes to public health with a background in psychology, motivated to bring a behavioral lens to infectious disease modeling. Here current work focuses on examining how diversity in individual daily activities and epidemic awareness impacts the spread of infectious diseases. Safa lives in Dallas, where she enjoys gardening, hiking, and taking long road trips to unwind and explore new places.
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We are always eager to discuss possibilities for collaboration. If you are interested in joining the group, please do not hesitate to email Nash. Opportunities for candidates at all career stages from high school students to senior research associates may be available. Positions are fully remote, or hybrid based in NYC or DC.