- Genomics & computational biology
- Applied statistical/machine learning
- Integrative analysis of large-scale genomics/biomedical/clinical data
- Genome-wide molecular network models
- Age-specificity and sexual-dimorphism in health & disease
- Cross-species models for human disease
- Genetic heterogeneity of complex diseases
- Precision medicine
The Krishnan lab develops and applies genomics/computational approaches to find mechanistic explanations for how our genome relates to health and disease.
Using statistics and machine learning, we leverage large-scale genomic/biomedical/clinical data to systematically build genome-scale models and generate data-driven hypotheses in biology and medicine. We are specifically interested using this approach to understand how genetic components (and their natural variants) relate to physiological processes and diseases manifestations that vary across the human lifespan and between the sexes. We work in synergy with experimental and clinical researchers to motivate our methods and put our computational models/predictions to test in human and model systems.
Our overarching goal is to: a) gain more nuanced and accurate insights into the genes and networks underlying physiology, complex diseases, and clinical phenotypes, and b) use these insights to mechanistically link an individual's genomic profiles to a precise assessment of her/his physiological traits, disease risks, and clinical outcomes. Towards this goal, we focus on three complementary directions:
1. Impact of sex and age on human physiology.
The fundamental influences of sex and age on tissue physiology remain largely under-studied in the genomic context. We want to understand the genome-wide changes that underlie 1) sex-differences in tissues throughout the body at each developmental stage, and 2) temporal effects of development and aging on specific tissues in males and females. We will develop statistical-/machine-learning methods to characterize the genome-wide expression/epigenetic signatures and molecular networks specific to each context. These efforts will provide us, and the broader biomedical community, with computational frameworks to shed light on the mechanisms underlying, say, renal development in males or cardiovascular aging in females.
2. Model organisms for developmental stage-specificity and sexual dimorphism.
Model organisms are key to studying sex-differences and development, but uniformly relating this knowledge across organisms remains a major challenge. Our aim is to develop genomic datasets and tools for 1) delineating the evolutionary relationships between sex-/stage-specific molecular mechanisms, and 2) seamlessly translating this knowledge across human and model organisms. We collaborate closely with experimental biologists to create and analyze high-resolution panels of gene-expression and epigenetic profiles of tissues in human and model organisms across developmental stages. We are also developing network-based methods to systematically transfer the resulting sex-/age-specific pathways and phenotypes between models and human.
3. Disease heterogeneity and precision medicine.
The genetic basis of complex diseases and clinical outcomes is perplexingly heterogeneous and context-dependent. The third focus of our group is to understand how mutations linked to complex diseases exert their functional effects in specific tissues in a sex- and age-dependent manner. We are developing a new network-based approach that can sub-classify diseases into functionally coherent and mechanistically interpretable ?genetic strata?. This approach will also help us predict novel candidate genes and appropriate model organism phenotypes for each disease stratum. These results will be used to subsequently develop a comprehensive analytical framework for mechanism-guided precision medicine that will help in classifying individual patients to specific disease strata and linking them to actionable clinical information. We want to deploy this framework to tackle the troves of data from efforts such as the national Precision Medicine Initiative, now gathering a research cohort of >1 million participants.