We have designed six models that cover various aspects of SugarGain, they are:

1. A model to describe our FruR-Cra system, along with molecular modelling and docking studies;
2. An in silico simulation of the kill switch;
3. A model to determine the composition and optimum viscosity of our novel polymer inoculant with biosafety considerations;
4. A virtual screening model to study invertase-anti-invertase interactions;
5. An auxotrophy model as part of our three tier biosafety mechanism; and
6. A model to predict the growth of bacteria in the sugarcane matrix

A few important ones have been highlighted below.

Growth Model

growth model helped us prove the fact was able to survive and replicate within the sugarcane matrix with a nontrivial growth rate. We were able to determine a closed-form function that modelled this growth.

Virtual Screening

The virtual screening model helped us determine the structure of sugarcane invertase. We also determined the structure’s correctness using its QMEAN score and Ramachandran plots. We found that Arabidopsis inhibitor had the lowest binding energy and therefore it could be used as a substitute for sugarcane anti-invertase for our downstream experiments.

pFruB-Cra Construct

Our model is the first-of-its-kind as we could not find any other mathematical model of the construct in literature. Teams in the future can now use this model in their projects and build upon it even further.

In silico Simulation of the Kill Switch

The kill switch model involved an in silico simulation of the kill switch construct. We determined the response time using the model, and we found using a perturbative sensitivity approach that this can be reduced using techniques like mutagenesis

Since our product, SugarGain, will be closely associated with sugar and sugarcane pb, we wanted to enforce robust measures to ensure biosafety. We came up with a three-tier biosafety mechanism.

As the first layer of biosafety, we wanted to render the bacteria auxotrophic to the sugarcane environment. Since nitrogenous amino acid levels remain constant inside the sugarcane even when nitrogen fertilisers are used, we planned to use HPLC to determine the exact amino acids that the bacteria will be auxotrophic to. The second failsafe will be our atmosphere regulated kill switch construct (please see project design section). The third failsafe lies in the processing of sugarcane juice itself. Various steps are involved in turning the juice into consumable sugar including heating the juice to temperatures beyond 100°C which should kill the bacteria.

Delivery Mechanism

The delivery mechanism had to be something easy to use and the inoculant needed to have a large shelf life so that the farmer could choose when to use the product. This inoculant would be supplied to the farmer in the form of a cartridge with an injector system.

Formulation of the Inoculant

The ingredients chosen for the final formulation were designed such that any remnants would be removed during the sugar manufacturing process (for which we have designed experiments for lab scale validation) and have minimal toxicity. This also gives the inoculant a long shelf life too.

Table 1: Optimal concentration of each component in the inoculant

Component	Optimised Concentration (w/w)
Culture Broth (LB)	95
Triton X 100	3.1
Carboxymethyl cellulose	0.1
Bentonite	1.4
Sorbic Acid	0.2
Potassium Sorbate	0.2

Industrial Scale-Up

We tried to scale up our inoculant to an industrial product. As a starting point in process development. These are the processes and operations that we identified.

Based on these, three primary processes we drew up the process flow diagram.

Unit/Stream	Component	Unit/Stream	Component
S1	Fresh Nutrient Media	S5	Concentrated Retentate
B1	Media Reservoir	S6	Permeate Nutrient Media
S2	Nutrient Media to Mixer	S7	Inoculant Mixture (See above table)
B2	Mixer	B5	Inoculant Storage
S3	Media to Bioreactor	S8	Inoculant Mixture to Hopper
B3	Bioreactor	B6	Hopper
S4	Fermented Cells with Media	S9	Packaging Outlet
B4	Crossflow Filter

We aim to test our enzyme on a small scale of 10,000 hectares as a pilot study.

We found land costs to be ₹2,500,000 via interactions with multiple stakeholders and then scaled up the rest of the costs shown in, and using the following pie chart. The proportions of costs to the total costs were calculated using Shuler & Kargi (2002). Using this, we calculate the total ‘other costs’ to be ₹25,000,000.

We calculated the price per kg for the inoculant. This price would be decreased in the future as we plan to carry out media formulation studies and come up with a cheaper, industrial and more suited inoculant for our solution.

Table 1: Manufacturing costs associated with our product

Ingredient	Price of one industrial lot (INR)	Mass needed for 75,000 kg of Inoculant	Mass of one lot (kg)	Lots required	Total Material Cost (INR)
Luria Bertrani	₹10,000	1800	2	900	₹9,000,000
Water	₹120	70,000	1,000	70	₹8,400
Triton X 100	₹2,000	2250	0.5	4500	₹9,000,000
CMC	₹5,000	150	25	6	₹30,000
Bentonite powder	₹4,000	1050	1000	1	₹4,000
Sorbic Acid	₹10,000	150	25	6	₹60,000
Potassium Sorbate	₹8,000	150	25	6	₹48,000
				Total cost	₹18,150,400
				Costs per kilogram	₹242

We assumed the inoculant to increase the recovery rate by 4 percent points above the current rate of 12%, that is to 16%.

We can market our product in two ways.If the recovery rate is under 3.1%, it can be distributed as a government handout to promote more efficient production of sugar from sugarcane.

On the other hand, if the increase in recovery rate is above 3.1% - which is more likely according to our preliminary calculation - it is a ‘profitable’ venture and will provide an incentive for farmers to buy it themselves.

Finally, economies of scale (reduction in costs as the scale of production increases) helps to increase profitability as we use the enzyme on a larger area of sugarcane plantations.