Accelerate Key Parts of Biotech R&D Pipeline Using Computer Vision
The Need Accelerating Production
In the realm of Biotech R&D, the cultivation of genetically engineered plants through tissue culture stands as a pivotal process, deviating from traditional seed-based methods to derive plants from embryos. Particularly in the case of corn, this intricate procedure spans 7-9 weeks, commencing with the manipulation of embryonic tissue, deliberately injured and exposed to agrobacterium tumefaciens, a specific bacteria facilitating DNA transfer. The outcome manifests as plant transformation, marked by the integration of foreign genes into the targeted specimen. Notably, the success rates of this process are dismally low, with a meager 2% or fewer embryos evolving into viable plants boasting the intended genetics. Hence, it becomes imperative to discern the success or failure of plant transformation at the earliest stages.
The Solution Succeed or Fail Fast
Historically, this determination was only feasible at the culmination of the 7-9 week period when plantlets emerged. Consequently, more than 98% of non-transformable embryos occupied valuable laboratory space and consumed essential resources. Given that plant transformation transpires within specialized chambers, maintaining stringent environmental conditions (temperature, humidity, and light), the inefficient utilization of space becomes a bottleneck in the downstream biotech R&D pipeline. To address this challenge, we conceptualized and implemented a groundbreaking solution: a Convolutional Neural Network (CNN) designed to scrutinize embryos and identify non-transformable ones within the initial two weeks post the initiation of plant transformation. This computer vision solution revolutionized the traditional approach, facilitating early detection and removal of approximately half of the non-transformable embryos. This, in turn, averted the necessity for a capital expenditure ranging between $10-15 million to expand the facility, effectively enhancing throughput by 1.5 to 2 times. Technologically, our approach incorporated an ensemble of deep learning models, achieving an impressive performance with over 90% sensitivity at 70% specificity during testing.
The Implementation Computer Vision Based on Deep Learning
Leveraging pre-trained models and neural transfer learning, we curated an extensive in-house dataset comprising 15,000 images meticulously labeled by cell biologists. These images, capturing various stages of embryonic development, were acquired using both an ordinary DSLR camera and a proprietary hyperspectral imaging robot. Our experimentation precisely determined the optimal timeframe for image acquisition post the initiation of plant transformation, establishing that images from a conventional DSLR were on par with those from the hyperspectral camera for the classification task. The impact of our work extends far beyond the confines of the laboratory, catalyzing a wave of innovations in computer vision within biotechnology R&D, spanning laboratories, greenhouses, and field applications. This progressive integration has not only optimized the R&D pipeline but has also significantly accelerated time-to-market, positioning our consultancy at the forefront of transformative advancements in the biotech sector
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