The Scale Challenge: Unlocking Cellular Reprogramming’s Next Chapter in Reproductive Medicine

To achieve in vitro gametogenesis, we’re taking computation and experiment to the next level.

At e184, we aim to enable reproductive rights for everyone. That means rethinking reproduction from the earliest stages, to make sure that individuals who struggle to produce sperm or eggs, or are unable to at all, can conceive. It means developing techniques for in vitro gametogenesis, creating gametes in a laboratory setting.

That, in turn, requires a virtuoso performance of cell biology. We need to take cells and change which gene programs they express, leading them to develop in new ways: a process called cellular reprogramming. Academic researchers have made remarkable discoveries in this field since Takahashi and Yamanaka's groundbreaking work on induced pluripotent stem cells in 2006, in areas as diverse as treating Parkinson’s, addressing macular degeneration, and performing large-scale drug screenings. But the next chapter demands something different: systematic exploration of cellular plasticity at an industrial pace.

Academic research excels at discovery and fundamental understanding, uncovering new biological principles, developing novel methodologies, and pushing the boundaries of what's possible. These contributions form the foundation of everything we build. But translating these scientific discoveries into robust, reproducible therapies for human patients requires a complementary approach: one optimized for systematic exploration, standardization, and scale.

From discovery to systematic exploration

Traditional academic cellular reprogramming operates within natural constraints. PhD and postdoc timelines favor projects that can yield publications within reasonable timeframes. Grant structures like R01s support focused investigations rather than massive systematic projects.

These constraints have driven incredible creativity and fundamental insights among academic scientists, but they also leave a niche for a different approach. Where a typical academic project might test dozens of regulatory factors, industrial platforms can systematically explore hundreds or thousands. Where publication timelines favor demonstrating proof-of-concept with single cell lines, clinical translation demands protocols that work robustly across diverse genetic backgrounds.

These platforms don’t replace academic discovery. They build on it. Academic innovations provide biological insights and foundational tools, while industrial platforms engage in the systematic exploration needed to translate these discoveries into reliable, scalable applications.

The combinatorial frontier

Consider the mathematics of cellular reprogramming. Takahashi and Yamanaka began with a screen of 24 transcription factors in order to discover their Nobel Prize-winning cocktail of four. Academic research has gradually expanded this combinatorial scale, with many studies now considering combinations from pools of 50-100 factors.

There are 10,626 different ways to choose four transcription factors out of 24, a space of combinations still within reach of a systematic screen. Choosing four out of 50 requires considering 230300 different possible combinations, which already stretches the limits of the feasible.

Robust protocols for complex cell types like gametes may require exploring much larger spaces. Modern screening approaches systematically evaluate extensive libraries of regulatory factors, not because massive numbers are necessarily required in final protocols, but because discovering optimal combinations often necessitates screening considerably larger pools to navigate complex regulatory landscapes.

In order to achieve this, we’ll need a different approach. We won’t be able to test every combination, so we’ll need to guide our search, using machine learning models to pick out which combinations are most likely to be promising. We’ll be following in the footsteps of companies like NewLimit, building models that predict the ability of different transcription factor combinations to enable transformation to gamete cells with the same precision that their models predict effects on cell age.

The challenge isn't just computational, though. Even screening hundreds of combinations requires standardized experimental procedures, automated approaches, consistent quality control, and sophisticated data analysis. It demands the kind of systematic engineering mindset that naturally follows after academic discovery.

The research opportunities

Our IVG approach creates unique research opportunities that bridge fundamental biology and clinical application. We're building a team to work on problems that span multiple scales:

Fundamental discovery: Constructing causal gene regulatory networks through systematic perturbation experiments. Understanding how epigenetic features influence transcription factor efficacy. Exploring combinatorial control mechanisms where factor combinations achieve what individual factors cannot.

Technical innovation: Developing accelerated differentiation protocols. Creating robust, reproducible methods that work across diverse cellular contexts. Building predictive approaches for cellular reprogramming outcomes.

Clinical translation: Advancing therapies for infertility through systematic understanding of human gamete and embryo biology. Creating protocols that work across diverse patient populations. Contributing to scalable approaches for future cell-based therapies.

More than just scale, our approach is one of integration. Computational insights directly inform experimental design. Experimental results immediately feed back into predictive approaches. Fundamental discoveries connect directly to clinical development.

What we're looking for

This work requires deep expertise across multiple disciplines, and more importantly, researchers excited by interdisciplinary collaboration. We need:

Cell biologists and developmental biologists who understand the fundamental mechanisms of fate conversion, cellular reprogramming, and developmental pathways. Experience with transcription factor-based reprogramming, stem cell differentiation protocols, or reproductive biology is particularly valuable.

Data scientists and bioinformaticians skilled in machine learning, multi-omics data analysis, and predictive modeling. We need people who can work with multi-modal datasets, build interpretable and predictive models of cell fate conversion, and translate computational insights into testable hypotheses. Experience with single-cell transcriptomics, chromatin accessibility data, proteomics, or other high-throughput biological measurements is valuable.

Engineers and automation specialists who can design systematic experimental approaches, implement reproducible protocols, and scale biological processes. Experience with laboratory automation, quality control systems, or bioprocess development is highly relevant.

Researchers at the intersections - people passionate about solving problems that combine computational methods with experimental rigor. Whether you're a computational biologist interested in experimental validation, or an experimentalist eager to incorporate predictive modeling, we value and encourage interdisciplinary thinking.

The advantages of our approach

Beyond pure scale, systematic approaches to cellular reprogramming offer key advantages:

Speed: Well-designed factor-based approaches can dramatically accelerate cell fate conversion research compared to traditional methods.

Efficiency: Systematic screening identifies more effective factor combinations that increase differentiation success rates.

Reproducibility: Standardized, systematic protocols ensure consistent results across experiments and researchers.

Novel insights: Large-scale perturbation studies reveal regulatory relationships and combinatorial effects that smaller studies cannot capture.

Robustness: Protocols developed across multiple cellular contexts and genetic backgrounds are more likely to work reliably in diverse applications.

Career development and culture

We offer unique growth opportunities that few academic or industry settings can match:

Leadership development: Early-career scientists can lead cross-functional projects where biology, computation, and engineering intersect. You'll work directly with team members across disciplines rather than within traditional silos.

Clinical exposure: Direct involvement in translational challenges and the path from scientific discovery to therapeutic application.

Technical breadth: Exposure to cutting-edge approaches across computational biology, experimental automation, and clinical development.

Scientific freedom: Substantial independence to pursue creative approaches within our shared mission. We believe the best innovations come from empowering researchers to leverage their unique insights while maintaining a collective focus.

Rapid iteration: See your ideas tested and refined quickly rather than waiting years for validation.

Join, and build something that matters

We're assembling a team that can bridge the gap between advanced research and clinical reality. If you're excited by systematic exploration at an unprecedented scale, if you want to see fundamental biological insights translated into reproductive technology that helps people become parents, if you're drawn to research problems that require both deep thinking and systematic execution - we'd love to hear from you.

Whether you're a PhD student interested in how systematic approaches can amplify research impact, a postdoc ready to take on interdisciplinary leadership challenges, or an experienced researcher excited by the intersection of computation and experimental biology, there may be a place for you on our team.

The next breakthrough in reproductive medicine won't come from a single experiment or a single lab. It will emerge from systematic integration of prediction, validation, and clinical insight - all working together at scale.

Ready to be part of that future? Connect with us to start the conversation.

Here at e184, we're not just building better experiments - we're building a better approach to biological discovery. One that honors fundamental insights from academic research while creating the systematic capabilities needed for clinical impact.

You can be part of that.