Postdoctoral Position in Computational Drug Repurposing from COVID to Alzheimer's
Posted 05 May 2020
Beth Israel Deaconess Medical Center/Harvard Medical School
A postdoctoral traineeship is available in the Hide laboratory to leverage its researchers' unique and powerful computational systems medicine platform. The platform has a track record for prediction of powerful drugs that have been effective against conditions from sepsis to neurodegeneration.
The successful candidate will research (a) drug discovery of response to infectious agents such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2)—the causative agent of COVID-19, and (b) explore the development of drugs promoting resilience against Alzheimer’s.
Seeking to understand how the body responds to pathological insults such as infection or against the pathologies of neurodegenerative disease, this traineeship is intended to promote the development of the career of an individual who is willing to rapidly and efficiently develop molecular models from signatures of infection and resilience to apply to drug repurposing.
The lab will explore two systems: (a) Coronavirus disease 2019 using spatial transcriptomics of in-house postmortem infected samples from multitissue and single cell assays, and (b) powerful neurodegeneration organoid assays of Alzheimer’s models where researchers can closely monitor pathway activity produced by Alzheimer’s pathology.
Developing signatures from these diseases, the candidate will employ an in-house network of drug-disease-pathway relationships to elucidate mechanism of action and prioritize drug combinations for therapeutic intervention.
This opportunity requires a candidate who is well versed in computational techniques, and has broad and deep experience in systems approaches to translational bioinformatics.
COVID-19: The postdoctoral trainee will apprehend and curate COVID-19-related datasets from our in-depth spatial transcriptomics assays of infected tissues of postmortem subjects that have tested positive for SARS-CoV- 2, and relevant publicly available datasets. They will be used to evaluate and synthesize molecular signatures and so integrate models that pertain to concepts of disease. Datatypes will include but are not restricted to mRNAs, miRNAs, ncRNAs, single-cell and tissue-level transcriptomes, methylation, acetylation and genome variant data. Integrated molecular signatures from human subject data, will be assessed and incorporated into pathway-disease-drug-network models. The project is expected to expose several layers of pathological pathway cascades, and these will need to be evaluated and modeled. A major role will be to predict, test, and provide prioritized intervention strategies, such as drugs, miRNAs, and potential diagnostics.
Alzheimer’s: The postdoctoral trainee will apprehend and curate Alzheimer’s disease datasets from the lab's collaborators and the public domain to evaluate and synthesize molecular signatures and so integrate models that pertain to concepts of disease and resilience. Datatypes will include but are not restricted to mRNAs, miRNAs, ncRNAs, single-cell and tissue-level transcriptomes, methylation, acetylation and genome variant data. Integrated molecular signatures from human subject data, and from model organoid data will be assessed and incorporated into pathway-disease-drug-network models. A major role will be to predict, test, and provide prioritized intervention strategies, such as drugs, miRNAs and potential diagnostics.
- Ph.D. in a quantitative field related to bioinformatics (e.g. with a specialization in bioinformatics related to genetics, neurosciences, disease modeling, pathway modeling).
- Extensive experience working with multi-omic datasets.
- Ability to generate computational disease models and hypotheses.
- Superb communication skills.
- Ability to work independently and as part of a team.
- Ability to drive a research project from design stages to data analysis, figure preparation and manuscript writing.
- A passion for scientific research.
- Strong organization and time-management skills.
- Meticulous attention to detail.
- Excellent working knowledge of R and other scripting language.
This position is a fundamentally important one and requires a highly motivated individual who relishes a challenge and is not shy about diving into complex datasets.
- Sound knowledge of statistics,
- Experience with manipulating and curating Alzheimer’s transcriptome datasets.
- Experience using large-scale datasets to rank gene and pathway candidates, and to define key network events that may be driving a disease process.
- Extremely comfortable with network-orientated bioinformatics.
- Knowledge of the aging and neurodegeneration research field.
- A strong understanding of genetics.
- Experience with human-derived model systems.