Intersection of Artificial Intelligence (AI) and Microbiology

I am enthusiastic about the intersection of artificial intelligence (AI) and microbiology. AI is an essential technology that can be integrated into various scientific fields, but I believe microbiology is especially suited to it. Bacteria, fungi and other microorganisms generate colossal amounts of data that require extensive amounts of time to process and analyze. Using AI, microbiologists can analyze data sets generated by these microorganisms to find significant patterns and information. AI can be employed to perform antimicrobial screening, identify bacteria and evaluate microbiological data sets.

One such application of AI in microbiology is antimicrobial screening, the identification of antimicrobial compounds. In recent years, there has been a growing concern regarding antibiotic resistance, making the discovery of new antimicrobial compounds a priority. Microorganisms are a diverse source of potential antimicrobial agents. However, there are millions of species of bacteria and fungi that still need to be analyzed for antimicrobial activity. Given the vastness of this data set, AI can be used to screen antimicrobial compounds quickly. Using data sets on microbial genome sequences, sequences of peptides and chemicals, and antimicrobial data, AI can predict what chemicals or peptides may possess antimicrobial properties. Then, the most promising candidates can be isolated and subjected to antimicrobial assays, Minimum Inhibitory Concentration (MIC) testing, and cytotoxicity assays. I believe that AI-powered antimicrobial screening may help identify new antimicrobial agents.

The other application of AI in microbiology is the prediction of antimicrobial resistance. When researchers get an isolate, they usually do antimicrobial susceptibility tests to determine which antimicrobial agents can inhibit its growth. Using AI, researchers can analyze the genome sequences and genes responsible for antimicrobial resistance in a given isolate to identify patterns that signal antimicrobial resistance. This information can be used to predict whether a certain bacterium is susceptible to a given antimicrobial agent. However, antimicrobial susceptibility testing still needs to be performed to confirm these findings experimentally.

One other application of AI in microbiology is microbial identification. Microorganisms can be identified using colony morphology and staining, microscopy, biochemical tests, and genetic analysis. Recently, researchers have been working on deep learning algorithms that allow for automatic identification of bacterial species upon inputting an image of a colony. These deep learning algorithms are constructed using convolutional neural networks that can recognize patterns in images. For instance, a neural network can be trained on thousands of bacterial colony images to identify a colony’s species according to an inputted image. Such technology can be instrumental in high-throughput screening of isolates. However, I believe that AI should be utilized as an optional tool for microbial identification.

Genomic analysis is one of my favorite areas of microbiology. When researchers sequence the genome of a bacterium or fungus, they get a wealth of information about this microorganism. Firstly, the sequence data can provide information about the genes that code for enzymes and various metabolic pathways. In terms of antimicrobial screening, such genes may hint at the antimicrobial potential of a given isolate. Furthermore, genome sequence data can provide information about genes that code for antibiotic resistance mechanisms possessed by the microorganism. Additionally, the genome can give information about secondary metabolite pathways, which can be used for antimicrobial screening, enzyme discovery, and drug development. Using AI, researchers can analyze genetic data and identify the most promising candidates for antimicrobial screening. Instead of manually analyzing gene sequences, researchers can utilize AI to identify potential genes and metabolic pathways.

AI plays an essential role in microbiome research as well. There are numerous microbiomes that house hundreds or even thousands of unique bacterial species. The human gastrointestinal tract microbiome, for instance, is one of the most diverse bacterial ecosystems on the planet. Using AI, researchers can find associations between different microbiomes and various health conditions. However, I believe that the causality between bacterial species and health problems has to be validated experimentally.

Speaking of diverse bacterial ecosystems, I am most enthralled with the application of AI in marine microbiology. Marine is an incredibly diverse region in terms of its microbiota. There are numerous species of bacteria, fungi, and other protists that inhabit marine flora, especially giant seaweeds known as Sargassum. I am confident that AI can be used together with sequencing and metabolomics to find new bioactive compounds or antimicrobial agents. Researchers can take advantage of the abundance of marine organisms by isolating various microorganisms from seaweed and identifying the most promising candidates using AI. These isolated microorganisms can be used to get sequence data, which can be employed to predict what enzymes, antimicrobial compounds, or other bioactive molecules they are capable of producing. Such molecules have the potential to become novel antimicrobial agents, drugs, or biochemical reagents.

One other application of AI in microbiology is bioremediation. There are numerous strains of bacteria that can break existing pollutants in the environment, rendering them harmless. Using AI, researchers can analyze data sets on such bacteria and the chemical properties of the pollutants that they can break down. The researchers can utilize AI to find the most suitable bacteria or conditions under which bioremediation is effective. The chemical properties of the pollutants that need to be removed from the environment are of a paramount importance. Researchers can employ AI to identify patterns in environmental conditions and determine what variables (pH, temperature, nutrients, etc.) are crucial in bioremediation. Such information will allow scientists to determine the most optimal conditions for bioremediation.

Many fields of microbiology rely on the properties of microorganisms. One such field is industrial microbiology, which utilizes bacteria, fungi, and other protists to create various products. For instance, there are dozens of products made with the help of fermentation. Fermentation requires optimal conditions, namely, temperature, pH, oxygen content, nutrients, and the duration of the incubation. Using AI, researchers can analyze such data sets and identify patterns that result in optimal substrate breakdown and maximal product yield.

I believe that the relationship between microbiology and AI will be exceptionally instrumental in the future of science. However, I do not think that researchers in microbiology will be replaced by AI. Rather, I believe they will have an opportunity to utilize AI as a discovery tool. It will dramatically increase the rate of scientific breakthroughs while allowing researchers to spend their time and effort on more crucial matters, such as laboratory validation of the AI-predicted results.

When I think of the future of microbiology, I imagine an intersection between AI, genomic data, antimicrobial screening, and marine microbiology. In my opinion, such an approach will result in the rapid identification of novel bioactive compounds. The application of AI in microbiology will allow researchers to find valuable data sets generated by diverse ecosystems and unknown microorganisms. The laboratory experience will still be necessary for microbiologists in the future, although AI will allow for more opportunities. Using AI, researchers will be able to navigate the vast amounts of genetic data to find new potential enzymes, antimicrobial compounds, drugs, and biochemical reagents.

What Researchers say about Black Holes

When I started reading what researchers have said about black holes, I realized that scientists are not simply interested in them because they are mysterious or because they look fascinating in space images. Researchers consider black holes to be some of the most extreme natural laboratories in the universe. NASA describes them as objects that are still not fully understood, particularly because we do not yet know exactly what happens inside the event horizon. At the same time, scientists have strong evidence for their existence through their effects on surrounding matter, the motion of nearby stars, X-ray emission and gravitational waves.

One thing researchers repeatedly emphasize is that we cannot directly observe the inside of a black hole. Since light cannot escape from inside the event horizon, astronomers study the environment surrounding it instead. According to NASA, material falling toward a black hole can become extremely hot and produce X-rays, allowing scientists to investigate the black hole indirectly. The intense gravity of a black hole can also affect the motion of nearby stars and distort light travelling around it. For me, this is one of the most fascinating aspects of black-hole research because scientists are essentially trying to understand an invisible object by studying the evidence it leaves around itself.

Researchers are particularly interested in accretion, which is the process by which matter moves toward and potentially falls into a black hole. Scientists studying Sagittarius A, the supermassive black hole at the center of our Milky Way, are trying to understand how the black hole collects, processes and sometimes expels material. NASA researchers have described accretion as one of the major ongoing questions in black-hole research because understanding how black holes feed helps scientists understand how black holes and other cosmic structures grow.

Researchers also study what happens to the gas surrounding a black hole. As gas moves inward, it can form an accretion disk, become compressed and heated to extremely high temperatures, and produce X-rays. NASA researchers have used sophisticated computer simulations to study the turbulent magnetic fields and particle interactions occurring in the hot gas near black holes. These studies help explain the X-ray signals that astronomers actually observe from black-hole systems.

Another major area of research is the spin of black holes. Scientists are interested in how rapidly a black hole rotates because its rotation can affect the surrounding spacetime and the behavior of matter and radiation near it. This is one reason X-ray observations are so valuable. According to ISRO, X-ray polarization measurements can provide information that helps researchers investigate the mass and spin of accreting black holes, the geometry of the emitting region, accretion flows, outflows and jets.

This is exactly where I found the connection with XPoSAT very interesting. ISRO explains that X-ray polarization gives researchers two additional parameters the degree and angle of polarization which can help distinguish between different theoretical models of how X-rays are produced. In black-hole systems, X-rays from the accretion disk can become polarized, while strong gravity can bend the radiation and change its polarization properties. Researchers can use these signals to investigate the geometry and physical conditions around the black hole.

Researchers are also interested in black-hole jets. Some black holes can produce powerful outflows in which energetic particles are launched away from the region surrounding the black hole. These jets can travel enormous distances and emit radiation across different wavelengths. Scientists study their polarization because the polarization can contain information about the direction of the jet and the magnetic-field structure. ISRO specifically identifies jets and their magnetic fields as one of the areas where X-ray polarization measurements can provide important information.

Another important question researchers are trying to answer is what happens when black holes merge. When two black holes orbit each other and eventually collide, they produce gravitational waves ripples in spacetime. The first direct detection of gravitational waves in 2015 came from a black-hole merger. Since then, gravitational-wave observatories have detected many more such events. For me, this shows how researchers are developing completely different ways of studying black holes: through X-rays, visible and radio observations, the motion of stars, and even gravitational waves.

Researchers are also trying to understand the relationship between black holes and galaxies. Supermassive black holes are found at the centres of most large galaxies, including our own Milky Way. Scientists are investigating how these black holes formed, how they grew, and how their activity may influence the evolution of their host galaxies. One of the biggest unanswered questions is how some supermassive black holes became extremely massive so early in the history of the universe. NASA notes that the origin of supermassive black holes remains poorly understood.

What researchers say about the inside of a black hole is even more interesting. We have strong observational evidence for black holes, but we still do not know exactly what happens after matter crosses the event horizon. Current theories based on general relativity lead to the concept of a singularity under certain conditions, but scientists do not yet have a complete theory that combines general relativity with quantum mechanics in this extreme environment. NASA also describes what happens to matter after it passes the event horizon as an unresolved mystery.

For me, this is what makes black-hole research so exciting. Scientists already know a great deal about black holes, but every discovery seems to create another question. We have observed the shadow of a black hole, measured the movement of stars around supermassive black holes, detected gravitational waves from black hole mergers and studied X-rays produced by material near them. Yet we still do not have a complete picture of what happens in the most extreme regions of a black hole.

What XPoSAT adds to this research

From my perspective, XPoSAT is especially important because it adds another dimension to black-hole observations. ISRO explains that POLIX measures the degree and angle of polarization of X-rays in the 8–30 keV range, while XSPECT provides X-ray spectroscopy and timing information in the 0.8–15 keV range. Combining polarization with spectroscopy and timing can help researchers better understand the physical processes taking place around black holes and other extreme X-ray sources.

ISRO has also involved the wider Indian astronomy community in XPoSAT research. A national user meeting brought together researchers from around 20 institutes and universities to discuss how X-ray polarization could be used to investigate black holes, neutron stars, active galactic nuclei and other astronomical objects. ISRO subsequently released XPoSAT scientific data to the research community in October 2025, allowing researchers to work with the mission's observations.

So, when I look at what researchers are saying, I feel that black holes are no longer just mysterious “dark objects.” They have become powerful tools for investigating some of the biggest questions in physics. Researchers are using every possible clue X-rays, polarization, gravitational waves, light, stellar motion and computer simulations to understand them. And what excites me most is that missions like XPoSAT mean India is not just watching this research from the outside; Indian scientists are developing instruments and generating data that can contribute to our understanding of these extreme objects.