ShriyaPBhat Profile Banner
Shriya Bhat Profile
Shriya Bhat

@ShriyaPBhat

Followers
702
Following
241
Media
76
Statuses
139

Molecular Bio @Harvard College Host of the bio/acc podcast.

Cambridge, MA
Joined November 2023
Don't wanna be here? Send us removal request.
@ShriyaPBhat
Shriya Bhat
5 months
What’s next for gene editing, personalized genomics, and de-extinction? @geochurch is the godfather of synthetic biology—he helped sequence the human genome, pioneered CRISPR tech, and launched 50+ biotech startups. I had the chance to interview him. Here’s what I learned🧵
2
3
21
@ShriyaPBhat
Shriya Bhat
20 days
3/3: Extremophilic biofilms could have translational potential, from bioremediation of pollutants and heavy metals to inspiring new therapeutics and climate-resilient materials. Many remain untapped opportunities for medicine and biotechnology.
0
0
4
@ShriyaPBhat
Shriya Bhat
20 days
2/3: These adaptations support survival and often lead to the production of bioactive compounds: antimicrobials, antioxidants, anticancer agents, and cryoprotectants, some with remarkable stability under extreme conditions.
1
0
1
@ShriyaPBhat
Shriya Bhat
20 days
1/3: Biofilms in extreme environments use unique adaptations, like specialized extracellular polymeric substances, to survive heat, cold, acid, salt, and radiation. These structures act as shields, cryoprotectants, and even metal binders.
1
0
0
@ShriyaPBhat
Shriya Bhat
20 days
Excited to share my new review in Frontiers in Microbiology: Bioactivity of microbial biofilms in extreme environments. We look at how biofilms adapt at the limits, from hot springs to glaciers, and how the biomolecules they produce can be used in medicine and industry.🧵
1
1
3
@ShriyaPBhat
Shriya Bhat
1 month
3/3: Germline editing: embryo-scale delivery simplifies the hardest part. Focus on disease elimination, transparent IRB oversight, and ethics-by-design. Long term, costs could approach IVF, compared to multimillion-dollar somatic price tags.
0
1
5
@ShriyaPBhat
Shriya Bhat
1 month
2/3: D2C healthcare: works best for meds that can be prescribed digitally and for brands/manufacturers with existing audiences. Cutting PBMs can drop prices; insurance can still be integrated depending on the brand.
1
1
0
@ShriyaPBhat
Shriya Bhat
1 month
1/3: Deep tech vs software: biology front-loads technical risk; if it works, market risk is lower, then comes regulation. Capital efficiency + runway decides who makes it to clinical proof.
1
0
0
@ShriyaPBhat
Shriya Bhat
1 month
Watch the full conversation here:
1
0
1
@ShriyaPBhat
Shriya Bhat
1 month
Imagine if germline gene correction could become as routine--and affordable--as IVF. I talked to @CathyTie (Locke Bio, Manhattan Project) on Bio/Acc. How do we build in regulated biotech? Can D2C healthcare cut costs? And how do we do germline editing the right way? 🧵
1
1
15
@ShriyaPBhat
Shriya Bhat
1 month
Watch the full conversation here:
0
1
0
@ShriyaPBhat
Shriya Bhat
1 month
Watch the full conversation here:
0
0
5
@ShriyaPBhat
Shriya Bhat
1 month
Longevity escape velocity: are we getting close? “50% chance we reach it by the 2030s.” - @aubreydegrey Aging may be optional in 12 years...
13
17
137
@ShriyaPBhat
Shriya Bhat
2 months
Thanks to @Molecule_dao and @pumpdotscience for sponsoring!
0
0
3
@ShriyaPBhat
Shriya Bhat
2 months
Watch the full interview here:
1
0
3
@ShriyaPBhat
Shriya Bhat
2 months
US healthcare is "sick-care." Not preventative. And insurance companies are incentivized to keep it that way. That's why they won't pay for longevity drugs -- unless we can change that system. @aubreydegrey and @benjileibo talk about this further:
4
8
34
@ShriyaPBhat
Shriya Bhat
2 months
Meant to tag @benjileibo -- go check out @pumpdotscience!
0
0
5
@ShriyaPBhat
Shriya Bhat
2 months
3/3: Mice experiments are critical, but they’re not just about biology — they’re about changing the narrative. If researchers can show a dramatic increase in lifespan in mice starting late in life, it forces the world to take aging research seriously.
0
1
9
@ShriyaPBhat
Shriya Bhat
2 months
2/3: Biotech isn’t slowed by lack of ideas, but rather, a lack of open data. AI needs massive datasets, but most longevity data sits locked inside companies, dies with failed startups, or is hidden behind paywalls.
2
1
11