Modeling and Simulation Methods for Genetics and Genomics in Infectious Diseases

Role

Principal Investigator · Assistant Professor, Department of Biological Sciences, Purdue University

Bio

Evolution is the logical backbone of any biological phenomenon. Since the modern synthesis, many quantitative evolutionary models (especially in the field of population genetics) have been developed to decipher the mode, tempo, and impact of evolution on the diversification of populations, species, and communities. These models have been successful and sufficient until recently, with the explosion of genomic data. The advent of a vast amount of genomic resources not only grants us the luxury of asking new questions but also, at the same time, requires new models, new tests, and new statistical tools to understand the complexity of nature.

I am particularly passionate about building theoretical models and applying them empirically to understand the evolution and structuring of population diversities in time and space. I integrate concepts and tools from population genetics, genomic evolution, and disease ecology, and develop new methodologies to answer questions regarding rapid adaptation, range expansions and contractions, and parasite-pathogen coevolution.

Role

Postdoctoral Researcher

Bio

In my previous research, I used disease ecology models to study the effects of climate change on the interactions between a forest pest and its specific pathogens. I am interested in applying population genetics and statistical approaches to disease ecology, to explore how the evolutionary processes and pathogen diversity could affect disease control and prevention. Here I will study the Malaria system, which is famous for antigenic diversity. In my spare time, I like playing tennis and watching sports matches.

Role

Postdoctoral Researcher

Bio

I study genomics and allergenic properties of house dust mites, and the many fascinating ways they interact with the environment, humans, and other organisms. At Purdue, we will conduct a global survey of wild house dust mites, create a probabilistic model of mite invasion to human houses from wild habitats following climate change, and predict their allergenic propensities based on conservatism/variability of known IgE epitopes. We will also explore the possibility of creating a universal vaccine treating mite-induced allergic disorders using a murine model.

Role

Graduate Student

Bio

My interest is generally in infectious disease research. I am particularly interested in the use of molecular and genomic tools for the spatial and temporal tracking of malaria parasites and vectors in order to gain insight into transmission changes, and the spread of drug and insecticide resistance as control interventions are applied. I love cooking and playing soccer outside my working hours.

Role

Graduate Student

Bio

I am interested in antibody-antigen interactions and how this shapes pathogen protein diversity. Recently, I have worked on malaria and house dust mite projects mapping diversity statistics to protein surfaces in pursuit of promising vaccine leads. Both antibody diversity and antigen diversity represent combinatorially large spaces which can not be experimentally exhausted, so I am interested in machine learning models that extend our knowledge of immunodominant regions and epitope cross-reactivity. Last, I would like to build R and Python packages that capture my workflows to share with other research teams. If I am not in lab, I am home playing guitar.

Role

Graduate Student

Bio

I am broadly interested in using mathematical models to understand biological processes, particularly infectious disease dynamics. My current work focuses on malaria and the ecological and evolutionary factors that shape the establishment and persistence of drug resistance. Before coming to Purdue, I completed an integrated dual degree at BITS Pilani, India, earning a B.E. in Electronics and Instrumentation and an M.Sc. in Biological Sciences. Outside the lab, I enjoy desi hip-hop, trivia quizzes, sketching, badminton, and spending probably too much time deconstructing rap lyrics and constructing bad puns.

Overview

Plasmodium falciparum encodes a massive family of antigenically variable surface proteins (PfEMP1, encoded by var genes) that enable immune evasion. A single parasite genome carries ~60 var genes; across the parasite population, thousands of distinct antigenic variants coexist in a complex strain structure maintained by immune selection.

This diversity underlies the chronic nature of malaria in high-transmission settings — hosts accumulate immunity to individual strains but remain susceptible to novel ones for years. It also drives rapid rebound: the parasite population can maintain its antigenic repertoire despite temporarily reduced prevalence under intervention, causing fast resurgence when interventions are stopped.

Key questions

• What is the interaction between transmission intensity and immunological forces that shapes thousands of co-circulating strains?
• How do we infer SNP-haplotypes from mixed infections?
• What is the spatiotemporal structure of parasite genomes, and how do we infer the strength of immune selection?
• How does this information translate into actionable intervention schemes?

Our approach

• Agent-based and mathematical models of var gene competition, immune memory, and cross-reactivity
• Theoretical models to investigate transmission patterns and required intervention efforts
• Network analyses of genetic similarity to map strain structure and infer the strength of immune selection
• Threshold theory: R_div, an antigenic innovation number analogous to R₀, predicts when interventions collapse strain diversity to enable elimination
• Longitudinal genomic surveillance data from high-transmission sites in Ghana & Zambia
• Statistical tools to resolve haplotypes from mixed infections

Overview

Antimalarial drug resistance follows striking geographic patterns that simple population genetic models fail to explain. Chloroquine resistance emerged independently in Southeast Asia and South America before sweeping through Africa, and resistance to different drug combinations rises and falls differently across transmission zones — a pattern that standard resistance models, which ignore immune selection, cannot capture.

Our work shows that the same antigenic diversity shaping malaria's epidemiology also governs drug resistance evolution: in high-transmission regions, intense immune selection favors parasite diversification over resistance; in low-transmission zones, immunity is weaker and resistant strains spread with less competitive pressure.

Key questions

• How do transmission intensity and immune selection modulate drug-resistance evolution?
• What are the impacts of recombination and the number of affecting alleles?

Our approach

• Compartmental models coupling antigenic diversification with drug resistance dynamics
• Calibration to field resistance surveillance data from sub-Saharan Africa and Southeast Asia
• Prediction of which drug combinations will succeed or fail under different transmission regimes
• Population genomic analysis of selection signals in parasite genomes from Zambia and elsewhere

Selected publications

Software

• varmodel2 — agent-based malaria transmission model (GitHub)
• DrugResist — drug resistance simulator (GitHub)
• SNP-slice — haplotype inference from mixed infections (GitHub)

Overview

What determines how many pathogen strains co-circulate at equilibrium — and at what rate are they replaced by new variants? These questions are central to predicting epidemic dynamics, evaluating the long-term impact of interventions, and understanding pathogen evolution. Most existing models treat population ecology and evolutionary genetics separately; when coupled, qualitatively new behaviors emerge that neither framework alone predicts.

We develop a general eco-evolutionary framework that integrates within-host immune dynamics with between-host transmission and strain-level evolutionary processes to predict long-term strain diversity and its turnover rate. Unlike disease-specific models, this framework is designed to apply across pathogens with diverse life histories and antigenic architectures and infer the dominant forces of maintaining or disrupting strain diversity.

Key questions

• What ecological and evolutionary forces jointly determine the equilibrium number of co-circulating strains?
• How does strain turnover — the rate at which variants are replaced — depend on transmission intensity and selection strength?
• How does this framework translate into predictions about epidemic rebounds and long-term intervention failure?
• Are there general mechanisms that explain the observed diversity and turnover patterns across pathogens?

Our approach

• Analytical and computational eco-evolutionary models coupling immune dynamics with strain emergence and extinction
• Derivation of diversity-turnover relationships spanning a spectrum of pathogen life histories
• Application to several human diseases, such as influenza A, HIV-1, malaria and pneumococcus as primary test cases, with broader generalization to other multi-strain pathogens

Selected publications

Overview

The same immune pressure that drives pathogen evolution leaves different signatures in genomic data at the 2D and 3D levels. Our lab bridges population genetic theory and structural biology to decode these signals, revealing new insights of genetics signature from three-dimensional protein architecture that can be learned.

Our first relevant work published is evo3D, an R package that maps haplotype-level statistics (such as diversity, selection, differentiation) onto protein structure surfaces. By mapping evolutionary history from sequence data onto protein surfaces, we can identify which regions of a pathogen protein are most intensely targeted by different types of selection forces.

Ongoing applications

Current approaches to identifying antibody epitopes are constrained by the scarcity of experimentally solved antibody-antigen structures, which are expensive to obtain and often biased toward a narrow range of antigens. Evolutionary signals offer a principled, scalable alternative grounded in the collective immune history of host populations — applicable anywhere sequence and structural data exist.

We are building on the evo3D foundation to develop systematic tools that span a broad range of human pathogens, integrating evolutionary genomics with structural feature analysis to expand what can be learned about immunodominance and vaccine targets at scale. Ongoing work, supported by NIH, is pushing this framework toward new predictive applications in antigen biology.

Research directions

• Projecting population genetic statistics onto 3D protein surfaces to map immune selection in structural space
• Building comprehensive immune selection profiles across diverse antigen families from sequence and structural databases
• Applications to malaria surface antigens, respiratory viruses, and other pathogens of public health relevance

Selected publications

Software

• evo3D — maps evolutionary signals onto 3D protein structures (GitHub)

Overview

Mites (Acari) are the most diverse arthropod group on Earth, with over 55,000 described species associated with virtually every terrestrial organism, including parasites on mammals, birds, plants, insects (such as bees) and free-living organisms in nests, houses and soil. Their host associations provide a rich natural experiment in how parasites colonize new hosts, diversify with them, and respond to environmental change. Soil mites provide important ecological services to terrestrial ecosystems by shredding organic materials and making it available for bacterial and fungal decomposition.

Our lab studies the evolutionary ecology of host–mite interactions across multiple scales: from the molecular biology of allergens in dust mites, to macroecological patterns of host switching across 1,300+ mammal species, to the deep evolutionary history of mites across geological time.

Current projects

• Global host-switching prediction: Using machine learning and a database of 10,000+ mammal–mite associations, we model the ecological and phylogenetic predictors of host range expansion and apply them to forecast invasion risk
• Brood parasitic bird symbionts: Feather mites on cuckoos and their hosts reveal how horizontal transmission shapes host specificity and codiversification at macroevolutionary scales
• Dust mite allergen genomics: Analyzing Dermatophagoides genomes to understand allergen diversity and identify targets for immunotherapy
• Ancient soil mites: Reconstructing the evolutionary history and timeline of mites in ancient soils using comparative genomics

Selected publications

Data resources

• Global Mammal–Mite Association Database — 10,000+ records across 1,300+ mammal species (Zenodo)