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Accelerating Photosynthesis

Goal: Eliminate Competition between Food & Clean Air

We need food and air to live. Photosynthesis provides both.

Farms provide food; forests provide clean air. But, forests and farms are in accelerating competition for land - together they make up a 57% (and growing) share of Earth's total land area. The rest is desert, tundras and glaciers - not friendly places.

On balance, forests have been losing out to more farms. These land use changes account for 1/3 of human emissions. Despite the move from forests to farms, we're still on pace to underserve demand in rice & wheat by 30% within 30 years.

Photosynthesis may be life's engine, but that engine only operates at 0.1-2% efficiency. All life on earth - animals, humans, plants, microbes etc - is hindered as a result. To make food abundant and control CO2 levels, 10+% efficiencies are required. That is our goal.

Table of Contents

We begin with a history of food & CO2 on Earth in four charts. Food is created by taking CO2, keeping the C and releasing the O2. So, naturally, their stories are deeply entwined.

We then outline how we plan to achieve 10% efficiencies by co-evolving photosynthetic genetics and growing environment.







Food

Over the last 1000 years, the average person has become 40x more wealthy. Despite a 100x increase in the number of people, the price of wheat has fallen by 5x. However, the price has stopped dropping over the last twenty years.

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Food production is number of acres times production per acre.

We might think to double our allocation of land used for agriculture. We can't.

That leaves increasing production per acre. While real yields have continued a slow 0.3% increase year over year. However, max potential yields per acre have stagnated since 1982. Since we can't add land, we must increase output per acre.



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The problem is not input energy. Even a single photon has enormous energy relative to biological requirements. A photon of red light - a low energy wavelength - carries 3 ATPs worth of energy.

1000x more solar energy hits a bacterial cell than the bacterial cell requires to live.



Air

It's unclear what the optimal level of CO2 on Earth is.

In the last two hundred years, global co2 has grown at an all-time fast rate due to human emissions. The projections are not bright.

Agriculture emerged ~10,000 years ago - we believe an increase of global CO2 from 230ppm to 300ppm was required to make farming feasible.

So, optimal CO2 levels are not 0 ppm.

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Earth CO2 levels have varied enormously. Improvements in photosynthesis can dramatically change CO2 levels and CO2 levels are a principal evolutionary driver for photosynthetic organisms.

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    Around 400 million years ago, CO2 levels plummetted from 150,000 ppm to 10,000 ppm very quickly. While no causal link has been demonstrated, this reduction coincides with the evolution of carbon dioxide storage in photosynthetic bacteria.

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    100 million years ago, CO2 levels started to fall significantly. As a result, a new, more efficient photosynthesis (C4) pathway evolved to handle low CO2.

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    Many plants are not optimized for these higher CO2 levels reducing their capacity to remove CO2 levels and grow quickly.







Fixing Photosynthesis' Leaky Funnel

Why bother with environmental control and especially environmental optimization when the goal is accelerating photosynthesis genomically?

Environments and genomes are inextricably linked. Environments are a primary selection mechanism for genomes and can change the genome itself. Genomes are tailored to environments and small changes in photosynthetic genomics change the environment.

Determining optimal environmental conditions for a given genotype leads to better understanding of the phenotype. For instance, a 59bp change in a cyanobacterial strain led to a 3x increase in photosynthetic efficiency only under 1000mmol/m^2/s of light. By probing light levels, the improvement was traced back to the electron transport chain.

The physiology of a plant is changing every second. It's optimal light cycle is 10 microseconds on / 10 miliseconds off. Instead, we learn a neural net that inputs info about the plant and determine the appropriate environment.

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So, genomes and environments should be co-optimized. In alternating cadence, the genome is optimized to suit the environment and the environment is optimized to suit the genome.

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Photosynthesis Background

Photosynthesis as currently constructed is very wasteful. It operates at 0.1-2% efficiency.

Parts of photosynthesis are remarkably efficient. The solar cell has 83% efficiency in converting light energy into electrical energy. Unfortunately, converting electrical energy into carbohydrate energy is very inefficient.

Photosynthesis can be decomposed into 5 major subparts. Capturing photons, converting photons into electrons, transporting electrons, converting electrons into energy, and creating sugars.

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    Photosynthesis takes place within cells on plants' leaves.

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    Throughout the day, photons hit the plants leaves.

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    Within these halls of antennae, incident photons generate electrons via the photoelectric effect.

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    Generated electrons are then immediately drawn into a biological wire (literally proteins with attached conductive metals).

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    These electrons provide the energy required to create life's intermediate energy forms (ATP & NADPH).

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    CO2 is breathed in and harvested energy is used to chain long carbon chains of sugars for use by the plant.



The overall process has 0.5-2% efficiency. Some transitions are remarkably efficient (83% quantum yield of photons to electrons); while others (Rubisco turnover rate) are not. In any chain process, addressing the current limiting factor yields a new one. In our repairman's tour of photosynthesis, we'll detail potential fixes; refactor for each transition as well as consider possibilities of cutting entire steps.

Below is a "sales" funnel of energy. At each step, some amount of energy is lost. Overall, we start with 1000 Joules and end up with 13 Joules!

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Photosynthetic Energy Conversion Funnel (Joules at each step indicated on right).

The current approach to improving plants requires too much watching grass grow. Phenotyping the results of a genomic change requires waiting until the plant is fully grown and most measurements are currently done by hand and written down on paper.

We are building a pipeline from FASTA files of genomes to deployment in our customer's facilities. Every step is automated to create a high throughput of candidates. Every step is expressed in software so that machine learning algorithms can filter candidates for the next change.

Real-world data and experiments is required. However, the more build-test-iterate cycles that can be executed in tissue culture (plant cells) vs full-grown plants the faster improvements will be made.

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    There are three main stages. Plant genomes, plant cells and full-grown plants. Plant genomes are added to plant cells which are then grown into full plants via tissue culture.

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    Entirely in software, hundreds of thousands of candidate genetic edits are generated. Machine learning algorithms then accept (green) or reject these candidates. Accepted genes are then expressed in cells (green arrow).

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    The build-test-iterate pipeline has historically been futile in plants since the cycle requires 3+ months to wait for the plant to grow. Instead, we incorporate detailed photosynthetic efficiency measurements at the tissue culture stage.

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    Convolutional neural nets then evaluate the photosynthetic efficiency of every cell in every plant in order to accept or reject candidates for deployment in full-scale production.

    Rejects again provide the data for training models at the tissue culture and gene candidate stage so that hopefully more can be evaluated and rejected on the day timeframes of tissue culture vs months timeframes of full plants.

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    Overall, at every stage candidates are evaluated by machine learning models and either progressed (green) or rejected (red). Every reject yields data (blue) with which to train the prior step. By automating, the flywheel moves more quickly; by training accurate models, the flywheel becomes more efficient.



Proposed Genomic Candidates

Below we list a few more targeted avenues of genomic improvements. For each improvement, we share the potential gain, the cause of inefficiency and which part of the funnel is being addressed.

Improvement Cause of Efficiency Loss Component Potential Gain Notes
Add Green & Infrared Antennae Proteins Unused Solar Radiation Photon Capture 210% 52% of solar energy outside of accceptable wavelengths. Some photosynthetic bacteria (bastochloris viridis & cyanobacteria acaryochloris) have antennae tailored for near-infrared wavelengths (700-1000nm).
Avoid Expensive Antennae Synthesis. Poor resource allocation. Photon Capture 50% Some antennae proteins are expensive to synthesize. Recently, novel cyanobacterial strain was found that reduced doubling time from 4.9hrs to 1.5hrs. Only differed by 59 bps. Didn't use expensive antennae (phycobilisome); instead, key proteins of electron transport chain (plastocyanin, cytochrome b6f, etc...) were expressed at 1.5-2.7 higher levels. 50% improvement in efficiency from photosystem II led to a 3x reduction in the doubling rate.
Modify Photosynthesis Inhibition Regulation. Slow recovery from excessive light. Converting Photons to Electrons 20% Too much light => too many free electrons. Plants respond by quenching new photon energy as heat. Restarting photosynthesis to changed environmental conditions is slow. Recently, 20% photosynthesis acceleration by down regulating slow-response proteins. With LED light control, entire regulatory network can be removed.
Add Carbon Concentration Mechanism Rubisco's Specificity for CO2 vs O2 Chaining Carbons 20% Cyanobacteria and C4 plants disentangle CO2 storage and consumption. CO2 kept at 4000-40000 ppm near Rubisco to prevent respiratory reactions. Substantial improvement: C4 plants make 4% of plant species but 30% of biomass. Higher CO2 => choose fast catalyzing / low selectivity Rubisco => increase turnover rate to 48CO2 / second.
Intra-leaf CO2 Conductance Rubisco's Specificity for CO2 vs O2 Chaining Carbons 30% CO2 levels are 3x lower at Rubisco site => requires slow but highly selective Rubisco.
Express Rubisco in only Nuclear DNA Slow Evolutionary Progress in Rubisco Other ? Currenly 1/2 protein synthesized from chloroplast DNA; 1/2 protein synthesized from nuclear DNA. Single point mutations disable functionality impairing incremental progress. Make CRISPR-based edits simpler to execute. Cyanobacteria and C4 plants disentangle CO2 storage and consumption. CO2 kept at 4000-40000 ppm near Rubisco to prevent respiratory reactions. Substantial improvement: C4 plants make 4% of plant species but 30% of biomass. Higher CO2 => choose fast catalyzing / low selectivity Rubisco => increase turnover rate to 48CO2 / second.
Move Rubisco Synthesis to Nucleus Scientific Process 20% Selectively knock out regulatory networks that regulate photosynthesis inhibition due to excessive light. Too much light => too many free electrons. Plants respond by quenching new photon energy as heat. Restarting photosynthesis to changed environmental conditions is slow. Recently, 20% photosynthesis acceleration by down regulating slow-response proteins. With LED light control, entire regulatory network can be removed.
TODO TODO TODO TODO TODO

Photosynthetic Wind Tunnels

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Photosynthetic Wind Tunnel

To accelerate a process, one must first measure it well and quickly. For example, a wind tunnel creates a variety of wind conditions and measures aerodynamic efficiency against them at a far lower cost than iteratively crashing planes. Similarly, our photosynthetic wind tunnels evaluate photosynthetic efficiency against every possible climate on earth as well as determine the optimal environmental dynamics for a particular genotype.

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Measuring photosynthesis

Plants rely on photons to drive their chemistry. So, it's not entirely surprising that photon-counting cameras can determine a fair amount re: plants. We have many mechanisms for measuring photosynthetic efficiency.

Our current optics enable 30um resolution measurements - enough to recognize the photosynthetic efficiency of individual cells in every plant. Transferring control logic to software reduced the cost from 30k$ to 2.5k$. Mounting these sensors onto a robot means we can now measure the photosynthetic efficiency of every cell in every plant every few minutes.

Chlorophyll Fluorescence

We count fluorescent photons emitted by chlorophyll. When photons hit a plant's chloroplast, some number of electrons are excited. Some percentage of said electrons are harvested, but others drop an energy level and emit photons at 720nm. By precisely probing the plant with varying LED light levels and counting the fluoresced photons, one can determine photosynthetic efficiency.

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In particular, when photons (yellow p) hit a plant's leaf some number of electrons (e) are generated. Many of those electrons are eventually used to power sugar synthesis. Others are fluoresced in red spectrum (red p). Our photon counting cameras then count these photons.

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Top Left: Black & white photo of an orchid - flowers in white. Top Right: Same Orchid. Image of photosynthetic efficiency - notice the flowers are black (representative of no photosynthesis). When photons hit a plant's chloroplast, some number of electrons are excited. Some percentage of said electrons are harvested, but others drop an energy level and emit photons at 695nm. Bottom Left: A cut leaf. Location where leaf was cut (extreme right hand side) has near zero photosynthesis - far fewer chloroplasts inside leaves - can also see traces of lower photosynthetic efficiency along veins of leaf. Bottom Right:Raw measurements

Segmentation Neural Networks

Segmentation neural nets measure photosynthetic leaf area every few hours. These provide a longer term signal of plant growth.

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Left:Field of strawberries Right:Results of strawberry segmentation


Measuring Stomata

Stomata are plant mouths. It's how they breathe. In the photos below, they are open. Plants vary how wide they open their mouths depending on how much CO2 their carbon cycle is currently capable of chaining. As such, stomatal dynamics are an excellent view into a plant's real-time cellular photosynthetic state.

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Measuring width of open stomata.

Environmental Optimization

Reinforcement learning is a challenging technique to apply and often not appropriate for many problems it’s suggested for. However, the paradigm of sensed environment, controlled actions and optimizes rewards is a natural factoring for dynamics problems.

Environment control is associated with indoor growing however almost every form of human overseen plant growth involves environmental control. In field agriculture, farmers control water, fertilizer, hormones, microbiomes, planting timing and even weather through cloud seeding. Even in carbon sequestration forests, governments have large areas of different weather, ecologies and environments to select from in siting these forests.

Reward

Our game score is plant growth speed. Our score is a fusion of real-time photosynthetic efficiency and daily added biomass.

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State Space

The state of the plant & its environment is our environment includes all of the above environmental information and adds detailed celllular-level plant physiology information. As mentioned above, we capture real-time photosynthetic efficiency photos of with 30um resolution. At a similar resolution, thermal cameras determine whether plants' pores are open - when open, the leaf's surface is being evaporative cooled. Periodically, we capture 3d spectral images - since plants respond physiologically to most visible light, near & far infrared is very useful to receiving clearer signals of plant structure & water retention.

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    State Space (microns -> millimeters)

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    State Space (microns -> millimeters)

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    State Space (microns -> millimeters)

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    State Space (microns -> millimeters)





Action Space

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Bank of PLCs for environmental control & control. NVIDIA Jetson JTX2 for inference.

Our goal with the action space is to control every environmental variable relevant to plant growth.

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    Watering sounds simple. However, there are many combinations of variables to control: pH, quantities, schedule, root application or leaf application, size of rain droplets, etc...

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    Light can be broken down to: for every wavelength, how many photons per m^2 per second at what times of day? Its effect is very complicated though. While red and blue light are the dominant source of energy, all spectra contribute to plant regulatory control.

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    Plant consume nutrients as ions. TODO list of nutrient ions.

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    Air

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    Hormones

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    Microbes





Conclusion

Solar energy is extraordinarily abundant - our entire species' power usage is 10000x lower than incident solar energy. All energy to drive living systems is based off that energy - the vast majority harvested via photosynthesis. All aerobic life (humans, microbes etc...) lives of photosynthesis' work in converting water and carbon dioxide in oxygen and food. Increasing photosynthetic efficient from 0.2-2% to 10% improves the engine on the left so that all of the animals on the right can benefit.



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On the left, a photo of plant cells photosynthesiizing. On the right, a video of the Earth photosynthesizing. It can be hard to visualize how small changes in the genomes of the cells on the right can have any bearing on the health of the planet's atmosphere or its residents. However, abundance and clean air can achieved by accelerating that molecular engine.

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Appendix

Photosynthesis Background

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Photon Capture

Each leaf contains halls of light-harvesting antennae (red/blue discs). When a photon (yellow triangle) hits these antennae, an electron is generated. These electrons are then gathered towards a reaction center (green mesh).

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Photons are created via fusion in the sun and propelled against the Earth's surface. Within a plant's leaf, a hall of light-harvesting 1 nm2 antennae greet those photons. Each antenna is excited via the photoelectric effect within a narrow band of wavelengths - resulting excitons "hop" into a central reaction center via a quantum walk. Since blue light has higher energy, antennae that select for blues are further from the reaction center (RC). Every exciton enters the reaction center with roughly the energy of 1 red photon. 50-350 antennae feed each reaction center.

Converting Photons into Free Electrons

This first reaction center concentrates electrical energy to split water to create electrons, protons and oxygen. We breathe the oxygen. The electrons and protons supply downstream energy.

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Photosystem II concentrates energy from said excitons to shard water into a proton, an electron and O2. The O2 is released for us to breathe. The proton creates a proton gradient for downstream ATP energy synthesis. Process is very remarkable - only biological process to oxidize water and generates the largest potential difference found in nature at 1.2V-1.8V. Due to amount of intensity of solar & electrical stress, the core protein (D1) must be replaced every 19 min.

Transporting Electrons

Disorganized free electrons would tear apart cellular infrastructure and greatly increase entropy. Instead, electrons are immediately transported down a 25nm biological wire to a second reaction center.

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Electron transport chain. In low light, when fewer electrons are being transported the wire shortens to 20nm. In high light, the wire extends to 30nm.

Converting Electrons into Energy

The second reaction center re-energizes electrons after their journey down the biological wire. These excited electrons then create energy intermediates.

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The second reaction center (Photosystem I) also concentrates energy from 50-300 light-harvesting antennae to form a 0.5V potential difference. That energy then re-energizes the electrons flowing out of the electron transport chain which in turn oxidize NADP+ to create NADPH - an energy intermediate for the carbon cycle.

Converting Energy + Carbon Dioxide into Carbohydrates

The core enzyme of photosynthesis, Rubisco, then consumes the energy generated & co2 in order to chain carbons into sugars. Its' crucial functionality but also its' slow work-rate (3-5 co2 per second vs >100 per second) make it Earth's most abundant enzyme.

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