Associate Prof @MITChemE ; mom^4 + wife; enjoys building cell-fate circuits, exploring dna topology, reprogramming the living world, and soccer; soli gloria deo
Based in United States
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were R01s smaller in the 1980s? ...sure many were but this one is cracking up toward $200K in directs!
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The biotech era debuted in the 1980s, and this is the type of lab the NIH thought a single R01 could support.
Using the NIH entry-level postdoctoral stipend as a consistent benchmark, a single R01 with $250,000 in annual direct costs could theoretically pay for about 15.6 postdoc-years in 1987, when the stipend was $15,996 per year.
How about today? At the FY2026 stipend of $63,480 per year, that same $250,000 would pay for only about 3.9 postdoc-years.
This means that while a single R01 could theoretically support the equivalent of about 3 entry-level postdocs over 5 years in 1987, it could support fewer than one (one for four years) today—a roughly 75% loss in personnel purchasing power.
And, of course, this assumes that every dollar goes directly to postdoc stipends. It does not include fringe benefits, PI salary, graduate students, technicians, supplies, equipment, animals, core-facility fees, travel, publication charges, or any of the other costs required to run a modern laboratory.
Same nominal R01. Roughly one-quarter of the scientific labor.
The Simpsons debuted in 1987 and this is the type of house the shows creators thought a single-income family could afford.
According to an episode in Season 1, Homer made 25k a year(60k in 2026 dollars).
In 1987 that house would cost about 100k(which was the national average) with a mortgage of roughly $700/month(assuming 20% down and interest rates consistent with that time)
How about today? The same house would cost 450k with a mortgage of roughly $2,500/month.
This means that while Homer was spending ~30% of his income on his mortgage in 1987 he would need to spend closer to 50% of his income on his mortgage today.
And of course, this doesn't include other areas of inflation such as food, gas or etc.
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Reflects a deep truth... time is the most precious resource we can share with people because it is so limited. A recent conversation at MIT led by @PhirstDown hit on this very topic... ironically I was too busy to attend 🫠
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Josh Leonard @TheLeonardLab kicking mSBW 2026 with a 🔥 keynote! Build for robustness then layer complexity!
#msbw2026
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Today at MIT come hear UNSW Prof John Mattick's seminar "RNA is the architect of human development and diversity"
Monday July 27th at 4 pm
Location: 66-110
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Great talks and posters! Congrats to the prize winners and congratulations @arjunrajlab, @PavithranRavind and Asmus on CellTrails! Very cool work!
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Great meeting in NYC for CZI @biohub ! Lovely intersection of Immunology, tech, and cell engineering!
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Hey Boston folks, da BOMB is back!
Abstract submission closes Wednesday July 22nd! Link below.
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Congrats, @emmapeterman_ on a terrific defense!
An whirlwind journey to characterize gene elements at high resolution and to build improved circuit designs and frameworks that accelerate the Design-Build-Test-Learn loop!
Cheers to a job well done! Excelsior!
@MITChemE
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Heading to @ISSCR annual meeting? If you have a talk, poster, etc, please share info! Glad to drop on by and chat!
Come by Mary Ehmann's poster tomorrow (July 8th) 4-5 pm poster is W1335 and highlights DASIT!
You can find me early tomorrow and Friday at the sessions below!
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My 1.5 hour flight to Montreal turning into a red-eye on my way to ISSCR to speak in an 8 am session
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Bye Chicago! Great time thinking about theory in math and biology! #NITMB
Thanks Yogesh Goyal for a great meeting!
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Graduation and hooding for these three Galloway Lab PhDs! Congrats, Brittany, Sneha, and Chris! Excelsior!
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The Galloway Lab is at the American Society of Gene & Cell Therapy Annual Meeting. Come check out our new work in building genetic controllers, optimizing lenti design, and titrating cell-fate regulators to optimize scalable production of neurons. Hope to see you at the meeting! #ASGCT2026.
Details below 👇
Tuesday May 12th Poster session at 5:00PM-6:30PM
Kasey Love @kaseyslove
"Engineering high-titer lentiviral vectors for robust expression of RNA-based gene circuits"
Abstract ID: 1067
Derin Gumustop and Paulina Naydenkov
"Single-transcript, microRNA-based incoherent feedforward loop delivers precise transgene expression in primary cells"
Abstract ID: 1067
Thursday May 14th Poster session at 5:00PM-6:30PM
Brittany Lende-Dorn
"Chemogenetic tuning reveals optimal MAPK signaling for cell-fate programming"
Abstract ID: 3467
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Thanks also to our funders including NIH, NSF, AFOSR, Pershing Square and others! In a challenging year, this funding has made a huge difference in our ability to finish the work! (60/n)
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We had excellent contributions across the lab and from the Zandstra, Dekosky, Davis, and Mummery labs which helped us really push the limits of the engineering and exploration of genomic circuits. (59/n)
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Chris was joined by Kasey who first showed that syntax could be used to optimize gene circuits, demonstrating that the divergent substantially outperformed the tandem design. Her leading efforts in using syntax in forward design helped drive the success of many of the papers from our lab in the last year. (58/n)
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This project was expertly led by the talented Christopher Johnstone who started on this project in early 2020 just as the pandemic was hitting. Over the first two years, Chris worked to develop the biophysical model that led to our predictions of how syntax could tune supercoiling-mediated feedback and built the experimental systems to test inducible control and then really cranked on getting the genomics assays to test the prediction that transcription induces folding and changes in the patterns of supercoiling. These were all new assays for our lab and he built them from end-to-end! A truly tour-de-force in relentlessly pursuing his hypothesis from cell engineering to genomics! (57/n)
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Lessons learned: Optimal control comes from working with those that are around you in a symphony, harmonizing requires thoughtful coordination and the right pairing. Those around us really influence how productive and awesome our work can be which is a great transition to recognizing all those that made this work come alive! (somebody queue up Teddy Swims!)(56/n)
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Where can get to in the next decade? Something like this. If you were at SynBio 5.0, you may recognize the cat with tophat and monocle. (55/n)
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For our own work this has been a huge step forward in forward design, enabling more rapid DBTL cycles and improved single-shot cell engineering that is so much easier and successful than when we started in 2019…so now we can update the meme (54/n)
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As someone who always dreamed of building dynamic genetic control systems, this feels like an inflection point in our understanding and capabilities which have been limited in primary cells!
Thanks @ProfTomEllis 😊for helping articulate how hard this is and @Roth_Lab for noting the timescale! (52/n)
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To compare across delivery modes and cell types, we can compute the noise ratio. Across 293Ts and hiPSCs, we see that divergent syntax minimizes the noise ratio whether in lenti delivery. Remarkably the noise ratios for divergent syntax of the all-in-one system delivered in lenti or integrated at single copy in landing pads are very similar, suggesting that we don’t need precise, site-specific integration to build systems with minimal intrinsic noise. Indeed, the site-specific tandems perform worse-- by noise ratio -- than the divergent lenti vectors, indicating that syntax is MORE important for reducing noise than controlling for locus of integration! I don’t think we would have expected that apriori. (51/n)
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In quantifying noise, we see that these vectors don’t affect extrinsic variance (which is good!) But we see 3-fold reduction in intrinsic noise in divergent syntax compared to the tandem. This is excellent evidence that syntax biases the probability of co-expression as predicted by our model of supercoiling-mediated feedback! (50/n)
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The antidiagonal dimension measures the correlation between genes, inherently controlling for the other variables (e.g. extrinsic variance). By examining the width of this distribution you get an idea of the intrinsic noise and so what do we see? The divergent distributions are much narrower along the y-axis whereas the tandem is broader across the antidiagonal, just as predicted! (49/n)
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We can most directly see supercoiling-mediadated changes in RNAPII binding probability by looking at intrinsic noise.
What’s intrinsic noise? Intrinsic noise is the variance of expression between genes. If you examine two genes such as the inducible gene and constitutive activator, you will get a distribution of both in a joint plot (shown here). The diagonal is the extrinsic variance which is how the genes scale together and this is often a function of cell size, metabolic activity etc. For example, bigger cells make more of both genes, smaller cells make less of both. (48/n)
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Because of the effects of supercoiling-mediated feedback, syntax can generate toggle switches that are biases or balanced in bistability or completely monostable. Syntax can determine if oscillations are coupled or damped. These dynamic behaviors emerge from changes in binding probability. So can we see any evidence of these predicted changes? Even if we see supercoiling as predicted, is there any reason to believe you can design a better dynamic system? So glad you asked!... (47/n)
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Synthetic biology offers enormous potential in for building sense-and-respond systems and other dynamic circuits such as toggle switches and oscillators for cell and gene therapies. However, accomplishing this vision has been limited by challenges in predictive design including poor choices of syntax (e.g. syntax couples expression in previously unappreciated ways). But with syntax, we also have predictions about how to *improve the design of dynamic systems*. (46/n)
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Excess light chain is expected to support higher titers of intact mAb. Indeed by ELISA, we saw a 4-fold increase in antibody titers across different syntaxes. (note: all integrated at single copy at the same site in 293Ts). So, yes, even when parts and copy number are constrained, we can use syntax to optimize expression of a biologic! But what about dynamic systems… (45/n)
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One nice thing about syntax is that you can use it to optimize expression *even if you don’t change parts*! Most of the time people will swap out parts such as promoters to change expression but what if you have limited parts or don’t want to change them? Can this work for optimizing production of biologics? To answer that, we tried producing a difficult-to-produce Yellow Fever antibody discovered by the Dekosky lab. (44/n)
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Do syntax trends persist as the length of the gene increases? Yes, syntax trends are preserved BUT increasing the length reduces expression of the inducible gene…which is interesting. (42/n)
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What about if we add in insulators? No, insulators exert no significant effect on coupling. Syntax dominates the profiles. (41/n)
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Do these couplings change when we increase the distance between the promoters? Not within ~2kb (40/n)
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This coupling is similar but slightly different in “all-in-one” vectors where the activator is expressed from the same virus and thus the same locus instead of separately. (39/n)
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Also, the coupling between genes differs by syntax. Divergent syntax couples expression, resulting in a narrower ratio between genes, whereas the tandem allows for a great range of ratios. So syntax can be used to different effects. (38/n)
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Yes, syntax strongly affects expression across delivery modes including lentivirus. Here, in titer-matched viruses, you can see that syntax defines how much induction you can achieve. Notably, the tandem design with the upstream tet and the divergent syntax is the most common in published lentis (by a wide margin!). (37/n)
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Ok so we do see changes in chromatin folding and DNA supercoiling which match our predictions and the syntax-specific patterns of expression, but what can you do with syntax? Can it be used as a design parameter? Can it be useful?... (36/n)
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Looking across other marks at this locus, we can see induction changes the transcription activity, elongating RNA Pol II, and histone marks. Curiously, we do see lots of RNA at the downstream gene in tandem but these transcript appear poorly productive in making the protein, possibly representing readthrough transcripts. (34/n)
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