{"id":173298,"date":"2026-05-08T00:07:01","date_gmt":"2026-05-08T06:07:01","guid":{"rendered":"https:\/\/tecscience.tec.mx\/en\/?post_type=sciencecommunication&p=173298"},"modified":"2026-05-09T00:16:55","modified_gmt":"2026-05-09T06:16:55","slug":"scaling-biotechnological-processes","status":"publish","type":"sciencecommunication","link":"https:\/\/tecscience.tec.mx\/en\/science-communication\/scaling-biotechnological-processes\/","title":{"rendered":"Biotechnology Simulators for Industrial-Scale Applications"},"content":{"rendered":"\n

By Carolina Ram\u00edrez Mart\u00ednez<\/strong><\/a> and Alberto Ordaz Cort\u00e9s<\/strong><\/a><\/p>\n\n\n\n

A biotechnological process may work flawlessly in a 250-milliliter flask or a 2-liter bioreactor, but replicating it in a 50,000-liter industrial system presents monumental challenges.<\/p>\n\n\n\n

Although scientific breakthroughs are born in Petri dishes and laboratory flasks within university labs, most never reach real-world industrial application.

The reason lies in the challenge of executing science at an industrial scale. Many biotech companies succeed in raising funds to advance their technologies, only to fail because of operational shortcomings and weak business models [1].<\/p>\n\n\n\n

To rescue this knowledge from the so-called \u201cvalley of death\u201d and prevent industrial failure, techno-economic analysis (TEA) has become an essential navigation tool.<\/p>\n\n\n\n

A TEA begins with a mass and energy balance: it calculates how much raw material, water, and energy a process requires, as well as how much product it can generate. From there, researchers can estimate production costs, resource consumption, and commercial feasibility.<\/p>\n\n\n\n

At the same time, TEA provides the methodological framework to anticipate three fatal mistakes repeatedly identified in the industry: 1) failing to understand manufacturing costs at scale, 2) targeting low-margin markets \u2014such as biofuels, which account for 56.7% of failures\u2014 and 3) overestimating how much consumers are willing to pay a \u201cgreen premium.\u201d<\/p>\n\n\n\n

In short, techno-economic analysis integrates financial indicators that translate experimental data into potentially viable projects with a clear business outlook.<\/p>\n\n\n\n

Science vs. Business<\/h2>\n\n\n\n

An investor does not ask the same questions as a scientist. The question is not whether microorganisms can grow, but how much it costs to make them produce at an industrial scale.<\/p>\n\n\n\n

One example is the study Economic and technical insights into carotenoids and lipids large-scale production in Rhodotorula glutinis: A study based on pulse-feeding culture, cell disruption strategies and cytotoxicity, which evaluates the technical and economic feasibility of a biotechnological process for the large-scale production of carotenoids and lipids \u2014compounds used in the food, cosmetics, pharmaceutical, and biofuel industries\u2014 through the oleaginous yeast Rhodotorula glutinis [2].<\/p>\n\n\n\n

The research suggests that R. glutinis<\/em> could become a more sustainable alternative to traditional sources because it requires less agricultural land and can make use of industrial waste and byproducts.<\/p>\n\n\n\n

The TEA revealed that downstream processing \u2014particularly the cell disruption stage required to release lipids and carotenoids for purification\u2014 is one of the most critical bottlenecks in the process. Without this type of evaluation, researchers could spend years optimizing fermentation conditions without realizing that extraction and purification costs ultimately make the final product commercially unfeasible.<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
Research on the yeast Rhodotorula glutinis conducted at Tecnol\u00f3gico de Monterrey combined fermentation, industrial simulation, and techno-economic analysis to evaluate whether large-scale carotenoid and lipid production could be commercially viable.<\/em> (Image: Courtesy of the research group)<\/figcaption><\/figure>\n\n\n\n

Tools like SuperPro Designer function as virtual laboratories, allowing researchers to model an entire industrial plant before laying a single brick. However, they are not \u201cblack boxes\u201d that generate random numbers; their power lies in technical precision [3].<\/p>\n\n\n\n

In the case of the yeast Rhodotorula glutinis, the simulation made it possible to achieve:<\/p>\n\n\n\n