Week 1 HW: Final Project Ideas

Biomanufacturing Neural Organoids Toward Defined Functional Phenotypes

(1) What you would like to biomanufacture and why?

I would like to biomanufacture a biohybrid neural construct: a human cortical organoid grown inside an instrumented, skull-inspired microenvironment with integrated electrodes, perfusion, and programmable sensory input. The enclosure is a functional engineering abstraction that provides mechanical support, stable geometry, nutrient exchange, optical access, and a reproducible electrical interface to the developing tissue.

This project is motivated by a broader question: can we manufacture living neural tissue toward a desired function, rather than merely grow tissue and characterize whatever function emerges?

Cultured neural networks have demonstrated adaptive behavior when coupled to closed-loop virtual environments (Kagan et al., 2022). Building on this direction, Brainoware demonstrated reservoir computing with a human brain organoid coupled to a multielectrode interface (Cai et al., 2023). Subsequent work has rapidly advanced the surrounding enabling technologies for long-term 3D electrophysiology, automated culture, neuromodulation, and closed-loop learning (Yang et al., 2024; Voitiuk et al., 2025; Acha et al., 2026; Robbins et al., 2026).

A camera would not connect directly to the organoid as an artificial eye. Instead, a fixed encoder would convert simple visual stimuli into reproducible spatiotemporal electrical stimulation patterns delivered through the electrode interface. Electrical activity from the organoid would then be recorded and passed to a deliberately simple decoder, such as logistic regression. The key test is whether the living neural network transforms the input into a representation that becomes more separable after structured developmental experience than after random or no stimulation.

The same platform could later support applications beyond biological computing. Because it measures network excitability, synchrony, and response to stimulation over time, it could also become useful for developmental neuroscience, neurotoxicity studies, drug screening, and disorders of network dynamics such as epilepsy. I would treat epilepsy as a possible downstream application rather than the defining purpose of the first prototype.

(2) What living system would you use?

The biological manufacturing chassis would be human induced pluripotent stem cells (hiPSCs) differentiated toward a cortical neural lineage. The simplified biological process would be: hiPSCs -> embryoid-body-like aggregate -> neural progenitor tissue -> cortical organoid -> electrically active neural network.

The inputs would include hiPSCs, defined culture media, neural differentiation signals, extracellular-matrix support as needed, oxygen, controlled temperature and CO2, and a perfused nutrient environment. The cells would provide the self-organization process. The engineered device would provide the boundary conditions within which that self-organization occurs.

For a first implementation, I would use a standardized healthy cell line rather than a disease-specific line. Published cortical-organoid protocols commonly begin with approximately 9,000 pluripotent stem cells per aggregate in a 96-well low-attachment format; I use that value only as a back-of-the-envelope starting assumption for the scale calculation below (Pagliaro et al., 2023).

(3) How would you use automation?

Automation would operate at three levels: routine culture, continuous sensing, and closed-loop stimulation. At the prototype stage, a desktop liquid-handling platform such as an Opentrons system could automate media exchange, reagent addition, sampling, and plate-to-plate handling. A later version would replace repeated bulk media changes with programmable microfluidic perfusion inside each cranial-like chamber.

The second automation layer would measure the state of each construct. Environmental sensors would track variables such as temperature, pH, and oxygen. Imaging would track growth and morphology. The integrated electrodes would measure spontaneous activity, evoked responses, network bursts, and synchrony. These measurements would provide a longitudinal record of both biological health and neural network development.

The third layer would automate experience. A camera would observe a simple visual scene. Software would transform that scene into a fixed electrical encoding and deliver the resulting spatiotemporal pattern to selected electrodes. The system would then record the neural response.

The recorded neural response will be fed back to the stimulation controller, allowing subsequent stimulation patterns to adapt to the organoid’s evolving activity rather than following a fixed schedule. This creates a true closed-loop developmental environment in which the system can probe, reinforce, or modify stimulation based on measured functional responses over time.

Importantly, the decoder would remain intentionally simple so that task performance cannot be attributed mainly to a powerful software model around the organoid.

(4) How much would you need to make, and how long would it take?

4a. How much? I would target 72 usable biohybrid neural constructs, divided into three groups of 24:

24 constructs receiving structured camera-derived sensory stimulation;

24 constructs receiving random stimulation matched for stimulation amount;

24 constructs receiving no sensory stimulation.

4b. How long?

For a first engineering estimate, I would assume approximately 60 days for neural differentiation and network maturation before the main functional test, followed by approximately 7 days of baseline recording and visual-discrimination experiments. Device preparation and cell seeding would require several additional days but could partially overlap with cell expansion and electrode fabrication. Therefore, I would estimate roughly 10 weeks for one complete manufacturing-and-test campaign after the instrumentation is available.

5. What don’t I know yet?

I am seeking a wet-lab partner with experience in cortical organoid culture, neural electrophysiology, or related tissue-engineering methods. Brain organoid manufacturing is not my primary area of expertise, so the biological feasibility, culture protocol, experimental scale, and quality-control criteria will need to be developed together with an experienced experimental group.

My contribution would focus on the system architecture: the closed-loop sensing and stimulation framework, experimental-control logic, computational analysis, functional readouts, and integration of the biological system with the intelligent-device platform. The wet-lab partner would provide the expertise needed to determine how the organoids should be produced, maintained, characterized, and safely interfaced with the stimulation and recording hardware.

An important early goal of the collaboration would be to translate the conceptual experiment into a biologically realistic protocol, including suitable organoid type and maturity, electrode-interface requirements, viability and quality-control measures, experimental duration, and the number of biological replicates required. The initial project could therefore begin with a small feasibility study before expanding to the larger comparative experiment described above.

I am particularly interested in partnering with a laboratory that sees value in studying whether controlled developmental experience and closed-loop stimulation can influence the emergence of reproducible functional phenotypes in neural tissue.

Given that, here are few initial unknowns I would need to explore:

Unknown 1: What functional specification should define a successful product?

A cortical organoid can produce electrical activity without performing useful information processing. I need to determine which metrics are meaningful: stimulus-response reproducibility, separability of two stimulus classes, temporal memory, plasticity, network synchrony, or another measure. The first project would likely use a simple visual discrimination benchmark and require performance above chance and above matched controls using the same fixed encoder and decoder.

Unknown 2: Can the bioelectronic enclosure measure and stimulate throughout development without changing the biology in an uncontrolled way?

The electrode geometry must provide stable signals while avoiding excessive mechanical stress, heating, electrochemical damage, or interference with nutrient and oxygen transport. KiriE demonstrates long-term conformal recording with preserved organoid architecture, and shell MEAs now demonstrate 3D neuromodulation, but neither result eliminates the need to test how enclosure geometry, perfusion, and chronic sensory stimulation interact during development (Yang et al., 2024; Acha et al., 2026).

Unknown 3: Can developmental inputs actually make function more reproducible?

The central hypothesis is that structured sensory experience can be treated as a manufacturing variable. That may be wrong. Differences between organoids may remain larger than the effect of the sensory curriculum, or the observed discrimination may arise mostly from the external decoder. The critical experiment is therefore a controlled comparison of structured stimulation, matched random stimulation, and no stimulation, while keeping the software encoder and decoder fixed.

Why this is a biomanufacturing problem

The central manufacturing idea is to expand the definition of a product specification for living neural tissue. In conventional organoid production, function is often measured after tissue has been generated. In this project, neural activity and response to controlled sensory inputs would become process measurements during development and, eventually, quality attributes of the manufactured construct. The long-term question is whether living neural systems can be manufactured toward function by controlling both their biochemical environment and their experience.

References

Acha, C., George, D., Diaz, L. C., et al. (2026). 3D neuromodulation in neural organoids with shell MEAs. Advanced Healthcare Materials, 15(11), e03773. https://doi.org/10.1002/adhm.202503773

Cai, H., Ao, Z., Tian, C., et al. (2023). Brain organoid reservoir computing for artificial intelligence. Nature Electronics, 6, 1032-1039. https://doi.org/10.1038/s41928-023-01069-w

Huang, Q., Tang, B., Romero, J. C., et al. (2022). Shell microelectrode arrays (MEAs) for brain organoids. Science Advances, 8(33), eabq5031. https://doi.org/10.1126/sciadv.abq5031

Kagan, B. J., Kitchen, A. C., Tran, N. T., et al. (2022). In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron, 110(23), 3952-3969.e8. https://doi.org/10.1016/j.neuron.2022.09.001

Kopic, I., Dedousi, P., Schmidt, S., et al. (2024). Inkjet-printed 3D electrode arrays for recording signals from cortical organoids. Advanced Materials Technologies, 9(22), 2400645. https://doi.org/10.1002/admt.202400645

Pagliaro, A., Finger, R., Zoutendijk, I., et al. (2023). Temporal morphogen gradient-driven neural induction shapes single expanded neuroepithelium brain organoids with enhanced cortical identity. Nature Communications, 14, 7361. https://doi.org/10.1038/s41467-023-43141-1

Petzold, C. J., & Mukhopadhyay, A. (2025). From bench to biofactory: High-throughput technologies and automated workflows to accelerate biomanufacturing. Current Opinion in Biotechnology, 94, 103320. (Assigned course reading.)

Robbins, A., Schweiger, H. E., Hernandez, S., et al. (2026). Goal-directed learning in cortical organoids. Cell Reports, 45(2), 116984. https://doi.org/10.1016/j.celrep.2026.116984

Smirnova, L. (2024). Biocomputing with organoid intelligence. Nature Reviews Bioengineering, 2, 633-634. https://doi.org/10.1038/s44222-024-00200-6

Smirnova, L., Caffo, B. S., Gracias, D. H., et al. (2023). Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish. Frontiers in Science, 1, 1017235. https://doi.org/10.3389/fsci.2023.1017235

Voitiuk, K., Seiler, S. T., Pessoa de Melo, M., et al. (2025). A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring. Internet of Things, 33, 101671. https://doi.org/10.1016/j.iot.2025.101671

Yang, X., Forro, C., Li, T. L., et al. (2024). Kirigami electronics for long-term electrophysiological recording of human neural organoids and assembloids. Nature Biotechnology, 42, 1836-1843. https://doi.org/10.1038/s41587-023-02081-3

AI use disclosure

I used OpenAI ChatGPT to brainstorm the concept, organize the response according to the homework questions, identify and compare relevant recent literature, and check the arithmetic in the back-of-the-envelope scale estimate. I reviewed and selected the final scientific assumptions, citations, and framing within the limitations of my knowledge in the field.

Representative prompt: “Help me draft HTBAA Homework #1 around a function-directed neural biomanufacturing system: human cortical organoids grown in an instrumented skull-inspired enclosure with integrated electrodes and camera-derived sensory stimulation. Keep epilepsy as a possible future application rather than the primary objective. Address what to make, the living system, automation, meaningful scale and timing, and the biggest technical unknowns.”

Representative literature prompt: “Find recent 2024-2026 peer-reviewed papers that are directly adjacent to this concept, especially work on 3D organoid electrodes, automated organoid culture, neural-organoid stimulation, and closed-loop learning. Distinguish what those papers demonstrate from what remains novel in the proposed biomanufacturing experiment.”