Food security has become a major discussion topic in recent years due to the exponential population growth, drastic climatic changes, and current geopolitical conflicts and wars, which collectively disrupt the overall world food production and distribution [1]. Moreover, traditional farming techniques are being criticized for emitting large amounts of harmful greenhouse gasses [2]. It is, therefore, important to explore alternative methods for future food supplementation. Microbial products, derived from bacteria, fungi, and microalgae, can provide promising alternatives to traditional food sources. Microorganisms can produce a wide range of high-value ingredients besides being an excellent source of nutritive proteins 3, 4. Therefore, it is important to explore microorganisms for future food supplementation.

Bacteria, such as Methylophilus methylotrophus, Rhodopseudomonas palustris, and Haloarcula sp., produce 50–80% protein (dry cell weight) and are characterized by small cell sizes and high multiplication [5]. Yeasts like Saccharomyces cerevisiae and Candida tropicalis have superior nutritional quality and can grow at an acidic pH level, making them excellent sources of protein [3]. Filamentous fungi, such as Aspergillus niger and Fusarium venenatum, contain up to 63% protein [6]. Microalgae, such as Chlorella sorokiniana and Arthrospira platensis (spirulina), can generate protein levels up to 70% of cell biomass and produce yields 20–50 times higher than soybeans [7].

Similarly, microorganisms have already been widely used in the manufacture of natural food ingredients and additives. Among these are food colorants derived from Monascus species filamentous fungi, which have been shown to possess antimicrobial and antioxidant properties [8]. Vitamins, terpenoids, steroids, amino acids, lactic acid, functional proteins (texturants), oligosaccharides, sweeteners, flavors, and enzymes are other examples of food ingredients produced successfully by metabolic engineering microorganisms [9].

The extraordinary achievements in molecular biology, biochemistry, real-time monitoring, and data management over the past several decades have led to the widespread usage of microorganisms. However, microbial bioprocesses face challenges from the social and marker perspective such as regulatory issues, safety concerns, sensory attributes, consumer perceptions, and social acceptance. Additionally, limitations such as production optimization and costs are impacting the future of alternative food as a viable option [10]. To overcome these barriers and promote microbial manufacturing processes, further research and innovations are required 11, 12.

Here we discuss digital twin (DT) modeling to address some of the key challenges. A DT model is generally defined as a virtual model of a process, product, or service that bridges the physical and digital (in silico) worlds in real time [13]. DT is becoming increasingly popular in a variety of industries, including medicine, manufacturing, engineering, and aerospace, and playing a vital role in their current revolutions. The DT modeling interest arises due to recent advances in the rapid collection, storage and sharing of data, and the development of computers that can use complex models and algorithms in a reasonable timeframe 29, 30. For example, it has been applied to integrate numerous clinical and molecular multiomic datasets and to predict outcomes for patients with pancreatic cancer and disease survival [14]. Here, we discuss how DT can be used to integrate multiomics datasets with machine learning (ML) analytics to understand microbial behavior, improve bioprocesses, and develop an interactive platform between a physical system and its digital replica. As a result, we hope for more accurate predictions, and the resultant platform can be adopted in a more sustainable way for microbial manufacturing.