The other aspect of private sector involvement is perhaps more mixed in its consequences, compared to individual farmers’ efforts. Indian agriculture has long been heavily influenced by powerful intermediaries, who may combine participation in credit and input, and even output and land markets, to earn economic rents associated with market power, in a phenomenon wellstudied as interlinkage . Market intermediaries and other private actors in the agricultural supply chain certainly provide essential products and services for the success of Punjab’s present agricultural system, but it is not clear that their incentives for enabling innovation are aligned with maximizing social welfare, just as, with imperfect competition, static resource allocation may not satisfy that optimality property. Given the foregoing discussion, as well as the issues highlighted in previous sections, it is reasonable to suggest that beneficial innovation in Punjab agriculture will not occur solely through the private sector. At an abstract level, the problems of asymmetric information, externalities, the public good nature of innovations and imperfect competition in various markets along the agricultural value chain all point towards some public sector involvement in facilitating greater innovation, especially that which incorporates crop diversification. It is arguably the case that the state government can make targeted interventions that provide effective nudges towards innovation, as well as adoption and diffusion of innovations, even in the face of the severe constraints imposed by the state’s own fiscal situation, and the conduct of national food procurement policy. Some of the barriers to innovation have to be overcome by relatively large financial investments in physical infrastructure, but the state government can catalyze the private sector to undertake these investments by improving the ease of doing business in the state. The public sector’s focus can and should be on improving the knowledge available to farmers, finding ways to overcome their switching costs, and providing them with better insurance as they move towards activities that involve greater risk and uncertainty.Myoblast, myocytes, and fibroblasts are cells of greatest interest for the field of cellular agriculture. For texture and taste, plastic pots for planting adipocytes may be used and grown either separately or co-cultured with muscle cells. The choice of animal will also have an effect on the final product and production process because cells from different animals will have different growth characteristics, morphology, and product qualities.

The majority of these cell lines are adherent, meaning they require a suitable substrate to grow. Ideally, cells may be grown in suspension culture , bringing cellular agriculture in line with typical pharmaceutical practice such as CHO cells. Micro-carriers may also be used to increase the surface area of the total surface. Proliferating many cells is not the only consideration in cellular agriculture. Stem cells differentiate into more complex tissue structures depending on time and environmental conditions, which is critical in forming final products that consumers are willing to purchase. For example, C2C12 immortalized murine skeletal muscle cells differentiate into myotubes at high density and when exposed to DMEM + 2% horse serum . However, because cell differentiation often precludes further proliferation, cells must be periodically pass aged to provide more physical space for growth. This is typically done by detaching the cells from the substrate using trypsin enzyme and physically placing the cells onto additional surface area. Fundamental techniques in cell culture can be found in and a general overview of mammalian cell culture for bio-production uses can be found in . Figure 1.1b shows a high level overview of the cellular agriculture process. Throughout this entire process, media is used to support cells by providing them with nutrients, signal molecules, and an environment for growth. We are focused on reducing the cost of the media while supporting cell proliferation. This is because the media has been identified as the largest contribution to cost . The main considerations for the design of cell culture media in cellular agriculture are the media must be inexpensive, it must be free of animal products, and it must support long-term proliferation of relevant cell lines and final differentiation into relevant products. The most basic part of a cell culture medium is the basal component, which supplies the amino acids, carbon sources, vitamins, salts, and other fundamental building blocks to cell growth. The optimal pH of cell culture media is around 7.2 – 7.4 which is achieved through buffering with the sodium bicarbonate – CO2 or organic buffers like HEPES. Temperature should be maintained at around 37◦C at high humidity to prevent evaporation of media. Osmolarity around 260 – 320 mOsm/kg is maintained by the concentration of inorganic ions salts such as NaCl as well as hormones and other buffers. Inorganic salts also supply potassium, sodium, and calcium to regulate cell membrane potential which is critical for nutrient transport and signalling.

Trace metals such as iron, zinc, copper, and selenium are also found in basal media for a variety of tasks like enzyme function. Vitamins, particularly B and C, are found in many basal formulations to increase cell growth because they cannot be made by the cells themselves. Nitrogen sources, such as essential and non-essential amino acids, are the building blocks of proteins so are critical to cell growth and survival. Glutamine in particular can be used to form other amino acids and is critical for cell growth. It is also unstable in water so is typically supplemented into media as L-alanyl-L-glutamine dipeptide . Carbon sources, primarily glucose and pyruvate, are essential as they are linked to metabolism through glycolysis and the pentose-phosphate pathway. Fatty acids like lipoic and linoleic acid act as energy storage, precursor molecules, and structural elements of membranes and are sometimes supplied through a basal medium like Ham’s F12. Having a sufficient concentration of all of these components is required for proliferating mammalian cells across multiple passages as per above. Having a robust basal media is a necessary but not sufficient condition for long-term cell proliferation and differentiation. Serum is a critical aspect of cell culture because it provides a mix of proteins, amino acids, vitamins, minerals, buffers and shear protectors . Serum stimulates proliferation and differentiation, transport, attachment to and spreading across substrates, and detoxification. Serum has large lot-to-lot variability, zoonotic viruses and contamination , as well as the ethical issues associated with collecting serum from animals. Therefore, while it often simplifies cell growth and differentiation, it is critical to remove serum as per point . Supplementation with growth factors like FGF2, TGFβ1, TNFα, IGF1, or HGF is a common way to induce growth of mammalian muscle cells without the use of serum. Transferrin, another protein found in serum, fulfills a transport role for iron into the cell membrane. PDGF and EGF are polypeptide growth factors that initiate cell proliferation. Such components enhance cell growth but are expensive and comprise the vast majority of the cost of theoretical cellular agriculture processes. Much work has been done on developing serum-free media. The E8 / B8 medium for human induced pluri potent stem cells is based on Dulbecco’s Modified Eagle Medium / F12 supplemented with insulin, transferrin, FGF2, TGFβ1, ascorbic acid, and sodium selenite. Beefy-9 by is similar to E8 but with additional albumin optimized for primary bovine satellite cells. The approach we will take in this dissertation is to use prior knowledge of biological processes to construct a list of potential media components, and use design-of-experiments methods to optimize component concentrations based on cell proliferation. This will be particularly useful for cellular agriculture because by developing and using these statistical tools, as we will see in the next section, DOEs will help develop media quickly and efficiently.

One of the most difficult aspects of this work is measuring the quality of media. Viable cells must be counted after a period of time over which the scientist believes the medium will have an effect, which changes depending on cell type, media components, cell density, ECM, pH, temperature, osmolarity, and reactor configuration. If cells grow by adhering to a substrate, then sub-culturing / passaging may play a role on the health of a cell population,drainage for plants in pots so discounting this effect may have deleterious effects on media design quality. Counting using traditional methods like a hemocytometer or more advanced automatic cell counters using trypan blue exclusion are labor-intensive and prone to error. Cell growth / viability assays are chemical indicators that correlate with viable cell number such as metabolism or DNA / nuclei count and can also be used to quantify the effect of media on cells. In chapter 5 we conducted many experiments with different assays and show the inter-assay correlations in Figure 1.3. Notice no assay is perfectly correlated with any other assay because they are collected with different methodologies and fundamentally measure different physical phenomena. For example, Alamar Blue measures the activity of the metabolism in the population of cells, so optimizing a media based on this metric might end up simply increasing the metabolic activity of the cells rather than their overall number. As some of these measurements can be destructive / toxic to the cells , continuous measurements to collect data on the change in growth can be tedious. Collecting high-quality growth curves over time may be accomplished using image segmentation and automatic counting techniques. Using fluorescent stained cells and images, segmentation can be done using algorithms like those discussed. Cells may even be classified based on their morphology dynamically if enough training data is collected to create a generalizable machine learning model.The primary means by which this dissertation will improve cell culture media is through the application of various experimental optimization methods, often called design-of-experiments . The purpose of DOEs are to determine the best set of conditions xto optimize some output yby sampling a process for sets of conditions in an optimal manner. If an experiment is time / resource inefficient, then optimizing the conditions of a system may prove tedious. For example, doing experiments at the lower and upper bounds of a 30-dimensional medium like DMEM requires 2 30 ≈ 109 experiments. This militates for methods that can optimize experimental conditions and explore the design space in as few experiments as possible. DOEs where samples are located throughout the design space to maximize their spread and diversity according to some distribution are called space-filling designs. The most popular method is the Latin hypercube , which are particularly useful for initializing training data for models and for sensitivity analysis. Maximin designs, where some minimum distance metric is maximized for a set of experiments, can also allow for diversity in samples, with the disadvantage being that in high dimensional systems the designs tend to be pushed to the upper and lower bounds. Thus, we may prefer a Latin hypercube design for culture media optimization because media design spaces may be >30 factors large. Uniform random samples, Sobol sequences, and maximum entropy filling designs, all with varying degrees of ease-of-implementation and space-filling properties, also may be used. It cannot be known a priori how many sampling points are needed to successfully model and optimize a design space because it is dependent on the number of components in the media system, degree of non-linearity, and amount of noise expected in the response. Because of these limitations, DOE methods that sequentially sample the design space have gained traction, which will be talked about in the next section. A more data-efficient DOE is to split up individual designs into sequences and use old experiments to inform the new experiments in a campaign. One sequential approach is to use derivative-free optimizers where only function evaluations y are used to sample new designs x. DFOs are popular because they are easy to implement and understand, as they do not require gradients. They are also useful for global optimization problems because they usually have mechanisms to explore the design space to avoid getting stuck in local optima. The genetic algorithm is a common DFO where a selection and mutation operator is used to find more fit combinations of genes . In Figure 1.7, notice the GA was able to locate the optimal region of both problems regardless of the degree of multi-modality.