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Subject: “Genetics,Biotechnology”

  • What changes when AI moves from reading viral genomes to designing them?

    Why in the News

    Researchers at Stanford University and the Arc Institute used Artificial Intelligence (AI) to design complete genomes of bacteriophages, viruses that infect bacteria. Of 285 AI-generated designs physically synthesised and tested in the laboratory, 16 produced functioning phages, and some overcame bacterial resistance that had defeated the original virus. Humans have been synthesising viral genomes and deliberately modifying viruses for decades, so what is new is not the physical manufacture of a virus. AI has entered the design stage of biology, deciding what the genome should be rather than executing a design a human specified. The tension is that the same capability that could transform antimicrobial resistance research, vaccines and therapeutics could also accelerate harmful biological engineering.

    What is a genome language model?

    1. What it is: A genome language model is a machine learning system trained on genetic sequence data rather than on text, and the two used in this experiment were Evo 1 and Evo 2.
    2. How it works: The principle resembles a large language model, except that instead of learning patterns in words, it learns patterns in DNA.
    3. What it reads: It studies the genetic alphabet of A, C, G and T across vast numbers of genomes, and then generates new genetic sequences from the patterns it has learned.
    4. How it was specialised: For this experiment the models were further trained on thousands of bacteriophage genomes related to ΦX174, so the sequences they generated stayed within a known biological family.

    What did the Stanford-Arc experiment actually do?

    1. The design step was handed over: The scientists already knew the ΦX174 genome and already knew how to synthesise viral DNA and recover functioning phages. What changed was who, or what, proposed the genome.
    2. The output was constrained, not open-ended: AI did not invent a completely unrelated virus from nothing. It generated previously unseen ΦX174-like whole genomes within a known biological framework.
    3. The build step was conventional: Scientists selected some of these sequences, physically manufactured the DNA and introduced it into E. coli. Where the genetic instructions were biologically coherent, the bacterial machinery produced new phage particles.
    4. The yield: Of the 285 designs tested, 16 succeeded in producing functioning phages.
    5. Some designs beat the natural virus: Combinations of AI-designed phages overcame resistance in E. coli strains against which the original ΦX174 failed.
    6. The most significant result was combinatorial: An AI-designed phage successfully combined a viral protein with other genetic changes in a way conventional engineering had struggled to achieve, which suggests the system can identify multiple genetic changes that work together across an entire genome.

    How did biology get from reading genomes to writing them?

    1. Phages are old and abundant: Bacteriophages, literally “bacteria eaters”, have been known for more than a century and are among the most abundant biological entities in nature.
    2. Reading came first: In 1977, one particularly small phage, ΦX174, became the first complete DNA genome to be sequenced.
    3. Writing came next: By the early 2000s, scientists had shown that viral genetic material could be synthesised from known sequence information and used to recover functioning viruses.
    4. Deliberate modification followed: The controversial influenza gain-of-function experiments of 2011-12 showed that genetic changes could modify important properties such as transmission in experimental animals.
    5. The unresolved dilemma: That research highlighted a dilemma that remains open, since the same science that can improve pandemic preparedness may also create biosafety and biosecurity risks.
    6. Design is the fourth step: The progression runs from reading viral genomes, to writing them, to modifying them, and now to AI helping decide what should be written.

    What does this open up in medicine?

    1. Phage therapy is the nearest application: Antibiotic resistance is steadily eroding conventional treatment options, and bacteriophages offer another way of killing bacteria.
    2. Specificity is the limitation: A phage effective against one bacterial strain may fail against another, and bacteria can also develop resistance to phages.
    3. The search model has limits: Researchers have traditionally searched nature and phage libraries for suitable candidates, or modified existing viruses, which caps the available options at what already exists.
    4. The question changes: Generative biology moves medicine from asking whether the needed phage can be found to asking whether it can be designed.
    5. The applications extend well beyond phages: AI can assist the design of vaccine antigens, antibodies, therapeutic proteins and the viral vectors used to deliver genetic treatments, and may eventually help optimise oncolytic viruses that selectively attack cancer cells.
    6. The real shift is broader than viruses: The larger revolution is AI becoming capable of designing biological function, rather than AI making viruses.

    Is the simplicity of the target a safeguard, or is the risk the acceleration?

    1. The reassuring reading: ΦX174 is an exceptionally simple bacteriophage, while dangerous human viruses are vastly more complicated.
    2. Human pathogens are harder targets: They must negotiate receptor binding, host range, tissue tropism, replication, immune escape and transmission, each of which is a separate design problem.
    3. Complexity is not a defence: Human scientists already understand much about these determinants, and decades of virology, reverse genetics and gain-of-function research have linked many genetic changes to viral behaviour.
    4. AI does not need to rediscover virology: Its power lies in integrating what humanity already knows, examining vastly more combinations than humans can explore manually, and accelerating the path from hypothesis to experimental design.
    5. The concern is capability amplification: The relevant question is not whether an untrained individual can ask today’s chatbot to generate a pandemic virus. It is whether increasingly capable AI could make a knowledgeable and well-equipped laboratory substantially more effective at designing biological systems.
    6. A low success rate is a temporary comfort: The yield reported above is low, but digital systems can generate enormous numbers of candidates, so a low success rate is reassuring only while the number of attempts remains small.

    How must biosecurity change?

    1. Current screening looks for resemblance: Traditional DNA-synthesis screening often asks whether an ordered sequence resembles a known pathogen or toxin.
    2. Resemblance fails against generated sequences: A previously unseen sequence generated inside a known family may not resemble anything on a watchlist while still doing the same thing.
    3. Screening must move to function: In the age of generative biology, screening must also consider what a sequence might actually do, not simply whether it looks dangerous.
    4. Over-restriction has its own cost: Claude Fable 5 was initially deployed with strong safeguards around biology, chemistry and cybersecurity, and legitimate scientific work could sometimes trigger a fallback to a less capable model.
    5. The correction points to graduated access: Those safeguards have since been refined to reduce false-positive biology fallbacks while more sensitive capabilities remain restricted, which points toward graduated, auditable access under institutional and security controls.
    6. Model refusal is not a strategy: Biosecurity cannot rest entirely on what an AI model agrees or refuses to answer, so safeguards are needed throughout the chain: AI systems, DNA-synthesis providers, laboratories and institutional biosafety oversight.

    Why does this matter for India?

    1. Frontier AI becomes scientific infrastructure: If frontier AI becomes central to drug discovery, genomics, vaccines, protein engineering and experimental design, access to advanced AI becomes part of national scientific infrastructure.
    2. Sufficiency and compulsion are different things: Smaller and specialised models will be sufficient for many tasks, but a country should choose a small model because it is sufficient, not be forced to use one because somebody else owns the frontier.
    3. Restricted access compounds over time: If researchers elsewhere receive trusted access to highly capable biomedical models while Indian scientists depend on restricted public versions, the disadvantage accumulates across drug discovery, vaccines and antimicrobial resistance.
    4. The investment exists but needs a scientific arm: India is already investing through the IndiaAI Mission and indigenous foundation-model programmes, and that ambition should extend to scientific and biomedical AI, secure compute and high-quality datasets.
    5. Trusted access needs a framework: Legitimate researchers need a defined route to stronger capabilities, which requires an institutional trusted-access framework rather than case by case negotiation with model providers.
    6. The two goals are not separable: AI sovereignty without biosecurity would be reckless, and biosecurity without AI sovereignty could leave the country scientifically dependent.

    Challenges to AI-designed genomes

    1. Sequence screening cannot see intent: Order screening matches against known pathogen sequences, so a generated sequence within a benign-looking family passes even where its function is hazardous. Eg. Screening protocols built around named agents on an export control list match those names, so a functionally equivalent sequence outside the list is not flagged. Fix. Require DNA-synthesis providers to run function prediction alongside sequence matching, with a reporting duty on flagged orders.
    2. Automated laboratories compress the safety window: Combining generative design with robotic experimentation shortens the interval in which oversight can intervene. Eg. Future systems may compress months or years of literature review, modelling and experimental planning into much shorter cycles. Fix. Mandate institutional biosafety committee sign-off at the design stage rather than only before physical synthesis.
    3. Volume defeats low success rates: A weak per-attempt success rate becomes a strong aggregate capability once attempts are cheap and unlimited. Eg. The design pool in this experiment was generated computationally, so the number of candidates was bounded by compute rather than by laboratory effort. Fix. Impose volume-based reporting thresholds on synthesis orders from a single requester within a stated period.
    4. Model safeguards obstruct legitimate research: Blunt refusal policies block the research they were meant to protect, which pushes scientists toward unsupervised alternatives. Eg. Legitimate scientific queries triggered fallback to a less capable model under initial biology safeguards. Fix. Operate tiered credentials, where verified institutional researchers receive higher-capability access under audit logging.
    5. Governance is nationally fragmented: Biosecurity rules stop at borders while synthesis orders and model access do not. Eg. The 2011-12 gain-of-function controversy produced divergent national moratoria rather than a common standard. Fix. Negotiate a common minimum synthesis-screening standard through the Biological Weapons Convention review process.
    6. India lacks a biosecurity institution for generative biology: Existing oversight bodies were designed for genetically modified organisms and field trials, not for computational design of pathogens. Eg. The Genetic Engineering Appraisal Committee and the Review Committee on Genetic Manipulation are structured around organism release rather than sequence design. Fix. Create a statutory biosecurity review function covering generative design, synthesis orders and model access, reporting jointly to the Department of Biotechnology and the Ministry of Electronics and Information Technology.

    Conclusion

    The experiment does not show that AI can casually manufacture dangerous human viruses. It shows something more precise: computers are beginning to move from analysing biological information towards proposing biological designs that scientists can physically build, a capability that serves therapeutic research and harmful engineering alike. The answer is neither prohibition nor unrestricted access, but controlled acceleration, with safeguards rising as capability and risk rise. The unresolved question is no longer whether AI should be allowed to understand biology, but how to govern it once understanding biology becomes the ability to design it.

    Back2Basics: IndiaAI Mission

    1. What it is: The IndiaAI Mission is the national artificial intelligence programme approved in 2024 with an outlay of ₹10,371 crore, implemented by IndiaAI under the Ministry of Electronics and Information Technology.
    2. Its stated vision: “Making AI in India and Making AI Work for India”, built around seven pillars covering compute, applications, datasets, foundation models, skills, startup financing and safe and trusted AI.
    3. Compute pillar: It operates a national AI compute grid with over 38,000 graphics processing units, offering up to 40 per cent lower compute costs to eligible users.
    4. Safety arm: The IndiaAI Safety Institute is its national trust framework, covering bias mitigation, privacy, explainability and AI governance.

    Matching Previous Year Question

    “[2026] Which of the following statements with regard to genetic medicine is/are correct? 1. Genetic medicines correct/compensate for the faulty genes responsible for disease. 2. Engineered viruses and lipid nanoparticles are used as carriers of the genetic medicine. 3. Genetic medicines alter the entire DNA sequence. (a) 1 only (b) 2 and 3 only (c) 1 and 2 only (d) 1, 2 and 3 ANSWER: C”

  • Gene Editing’s Bold Move: Permanently Shut Down PCSK9

    Why in the News

    VERVE-102, an experimental in vivo base editing therapy delivered as a single intravenous infusion, permanently switches off the PCSK9 gene inside liver cells and cut LDL cholesterol by about 62 percent in a phase 1 trial. Cholesterol control has until now been a lifelong compliance problem, and a one time genetic change replaces that problem with a permanent, irreversible one.

    How does VERVE-102 work?

    1. What it is: VERVE-102 is not a traditional drug. It is a form of in vivo gene editing, meaning the editing is done inside the patient’s body rather than on cells removed and returned.
    2. Step 1, delivery: Genetic instructions are delivered through a single intravenous infusion.
    3. Step 2, the edit: Those instructions make a one time targeted change to the DNA inside liver cells, altering a single base in the PCSK9 gene.
    4. Step 3, the effect: The edited liver cells permanently lose the ability to produce PCSK9.
    5. Step 4, the outcome: With PCSK9 production switched off, the liver clears more LDL cholesterol from the blood, and the effect persists without repeat dosing.
    6. The stated goal: A single infusion that permanently reduces the liver’s ability to produce PCSK9, so that a one and done cholesterol treatment could eventually replace conventional medicines.

    What is LDL cholesterol?

    1. Definition: LDL (low-density lipoprotein) is called bad cholesterol because high levels make it stick to artery walls and form hard fatty deposits called plaque.
    2. Why it matters: These deposits narrow the arteries and block blood flow, which raises the risk of heart attacks and strokes.

    What is PCSK9 and why is it the target?

    1. What it is: PCSK9 is a protein involved in regulating LDL cholesterol in the blood.
    2. The natural experiment: People who naturally carry certain loss-of-function changes in the PCSK9 gene have lower LDL cholesterol throughout their lives and a lower risk of coronary heart disease.
    3. The inference: Reducing PCSK9 activity is therefore a safe and effective route to lowering cardiovascular risk.
    4. Confirmed by drugs: PCSK9 monoclonal antibodies substantially reduce LDL cholesterol and cardiovascular events, confirming the target.
    5. The limitation VERVE-102 addresses: Traditional medicines temporarily block PCSK9 or reduce its production, so their effects require continued treatment.

    What did the phase 1 trial find?

    1. LDL reduction: LDL cholesterol fell by about 62 percent in the highest dose group after four weeks.
    2. PCSK9 reduction: PCSK9 levels in that group fell by about 88 percent.
    3. Absolute fall: LDL cholesterol decreased by approximately 78 mg/dL on average.
    4. Follow up length: Some participants were followed for at least one year, and the longest follow up reached 18 months.
    5. Durability so far: The reductions in PCSK9 and LDL cholesterol were relatively stable across that period.

    How much cardiovascular risk does that reduction translate into?

    1. The established ratio: For every 1 mmol/L reduction in LDL cholesterol, cardiovascular risk falls by 20 to 22 percent.
    2. Worked case: An LDL cholesterol of 4.0 mmol/L, approximately 155 mg/dL, falling to 1.6 mmol/L is a 60 percent reduction.
    3. Effect of that case: That fall halves the patient’s cardiovascular risk.
    4. What remains unproven: VERVE-102 has not yet been shown to prevent heart attacks or strokes directly.
    5. The supporting evidence: All cholesterol lowering trials so far have shown that lower cholesterol means fewer cardiovascular events, and drugs blocking the PCSK9 protein have been shown to reduce heart attacks.

    How does it compare with the treatments already in use?

    1. Statins: Usually the foundation of treatment. They are relatively inexpensive, widely available, and supported by extensive evidence showing reductions in cardiovascular events.
    2. Ezetimibe: A cholesterol absorption inhibitor, taken orally, that works by blocking cholesterol from being absorbed in the small intestine.
    3. PCSK9 antibody medicines: They produce powerful LDL reductions and have demonstrated cardiovascular benefits, but require repeated injections.
    4. Inclisiran: It reduces PCSK9 production and can lower LDL cholesterol by roughly 50 percent, with less frequent dosing that makes long term treatment easier. It does not permanently modify DNA.
    5. The distinguishing feature of VERVE-102: Every existing option acts temporarily and must be continued. VERVE-102 makes a permanent change to DNA.

    Does permanence justify the loss of reversibility?

    1. The compliance case: Repeat prescriptions and remembering daily doses are a standing burden, and a safe one time treatment would remove that burden entirely.
    2. The unknown: This is a permanent change and the long term consequences are not yet known, so treated patients will need close observation.
    3. The reassurance from biology: Naturally occurring loss-of-function mutations of the gene exist, and people carrying them have less heart disease and live longer, which is the basis for the trial.
    4. The evidence horizon problem: An 18 month period is very different from proving that an effect will last for decades, and that requires further research.
    5. The current standing of the therapy: It is a potential future option for selected high risk patients, not a replacement for statins, ezetimibe, PCSK9 inhibitors or inclisiran.
    6. Trial breadth: More diverse trials are needed to establish whether the effect holds across populations over decades.

    Who would be considered for it first?

    1. Familial hypercholesterolemia: An inherited condition producing very high LDL cholesterol from birth, whose patients have the most to gain from a permanent reduction.
    2. Very high cardiovascular risk patients: Those whose risk is not controlled by existing therapy would be the second group.
    3. The staging logic: Beginning with these groups allows observation for problems before any wider use.
    4. What it is not yet: It is not a population level cholesterol intervention and is not positioned as one.

    Challenges to VERVE-102

    1. Irreversibility of a permanent edit: A therapy that cannot be stopped removes the physician’s ability to withdraw treatment, e.g. a statin prescription can be discontinued the day an adverse effect appears, while an edited liver cell population cannot be restored.
    2. Evidence horizon is short: Durability is established only to 18 months, e.g. statin cardiovascular outcome evidence rests on trials such as the Heart Protection Study that ran over five years in more than 20,000 participants.
    3. Delivery vector and off target risk: Gene therapy delivery carries historical safety precedent, e.g. the 1999 death of a participant in an adenoviral vector gene therapy trial in the United States halted the field for years.
    4. Cost and access: One time genetic therapies have been priced far beyond public health budgets, e.g. Casgevy, the first approved CRISPR based therapy, is priced at over two million dollars per patient in the United States.
    5. Population applicability: Early phase cohorts do not establish effect across differing lipid profiles, e.g. coronary artery disease in South Asians presents roughly a decade earlier and at lower body mass index than in western populations.
    6. Regulatory pathway for permanent somatic edits: Approval frameworks for irreversible somatic edits are still forming, e.g. India’s National Guidelines for Gene Therapy Product Development and Clinical Trials, 2019 permit somatic editing under review but bar germline editing outright.
    7. The competing benchmark is already cheap: A one time therapy must justify a large upfront price against an existing generic, e.g. statins cost a few rupees a day in India and are on the National List of Essential Medicines.

    Conclusion

    The central finding is that a permanent genetic switch off of PCSK9 through a single infusion produces LDL reductions larger than any daily medicine achieves, and that the reduction has held for 18 months. What remains unresolved is whether a permanent change is safe across a lifetime, and whether the LDL reduction converts into fewer heart attacks and strokes, neither of which the phase 1 data can answer. Until large outcome trials report, the therapy stands as an option for familial hypercholesterolemia and very high risk patients rather than a replacement for statins, ezetimibe, PCSK9 inhibitors or inclisiran.

    PYQ Relevance:

    Question (2021, GS3): “What are the research and developmental achievements in applied biotechnology? How will these achievements help to uplift the poorer sections of society?
    Linkage: Applied biotechnology is the primary field where gene editing techniques (like CRISPR) are developed to address challenges in health and agriculture, which can specifically benefit the underprivileged

  • What psychiatric genetics can and cannot tell an Indian family

    Why in the news?

    Families of patients with psychiatric illness increasingly ask whether the condition is in their blood and whether a genetic test can settle their child’s future. There is a tension between the real progress of psychiatric genetics and its limited power to predict individual outcomes, especially for Indian populations underrepresented in genomic databases. The central point is that genes load the dice but do not determine destiny.

    What is a genome-wide association study (GWAS)?

    1. About: A GWAS compares millions of common genetic variants across very large groups of people with and without a condition, to find variants that appear more often in one group. . It compares DNA markers, most often single-nucleotide polymorphisms (SNPs, between individuals with a condition and healthy control groups.
    2. What it yields: It behaves like a satellite map highlighting genomic areas of interest, showing where to look for biological mechanisms rather than pinpointing a cause.

    What does polygenic risk mean?

    1. About: In common psychiatric disorders no single gene variant has a large effect, unlike single-gene diseases such as Tay-Sachs disease or Duchenne muscular dystrophy.
    2. Mechanism: Risk is polygenic, emerging from the combined influence of thousands of variants together with rare genetic changes, development, environment, and chance.

    What is a polygenic risk score?

    1. About: A polygenic risk score (PRS) compresses many small genetic effects into a single number meant to estimate a person’s inherited susceptibility.
    2. Limits: It cannot say whether a person will become ill, at what age, how severe it will be, or which medicine will work, because it captures only part of genetic liability.

    How Polygenic Risk Works

    1. Many small changes: Instead of one major gene causing an illness (like in cystic fibrosis), polygenic conditions involve hundreds or thousands of tiny DNA changes called single nucleotide polymorphisms
    2. Adding it up: Each individual variant adds or subtracts a tiny amount of risk; a PRS totals these up to estimate your overall genetic predisposition.
    3. Common conditions: It applies to complex diseases like heart disease, type 2 diabetes, schizophrenia, and certain common cancers

    What have the major GWAS findings shown?

    1. Schizophrenia: A 2022 landmark study identified associations at 287 genomic regions and pointed to genes active in neurons and synapses.
    2. Bipolar disorder: A large 2021 study identified 64 associated regions.
    3. Regulatory signals: Many signals lie in DNA that regulates when and where genes switch on, not in stretches that directly encode a protein.
    4. Shared risk: A December 2025 study in Nature reported that some inherited risk is shared across schizophrenia and bipolar disorder.

    Why is prediction unreliable, especially in India?

    1. Score does not contain life: A person with a higher score may remain well while a person with a lower score may fall ill, because the score does not contain childhood adversity, sleep disruption, substance use, medical illness, or access to care.
    2. Expert caution: The International Society of Psychiatric Genetics has cautioned that current scores for schizophrenia, bipolar disorder, and depression are not accurate enough for routine clinical prediction.
    3. Ancestry bias: Genomic databases have drawn disproportionately from people of European ancestry, so scores are often less accurate in other populations.
    4. Indian diversity: The GenomeIndia project generated whole-genome data from 10,000 healthy, unrelated Indians across 83 population groups and documented extraordinary genetic diversity, so a score developed elsewhere cannot simply be imported.

    What can genetics usefully change in the clinic today?

    1. Reduces blame: A mother did not cause schizophrenia by being too strict and a father did not transmit bipolar disorder through a moral failing, and biology matters.
    2. Avoids fatalism: Genetic vulnerability should not be converted into a verdict, and no test can declare a person safe or doomed.
    3. Focus on modifiable risk: The useful approach is to track early warning signs, avoid intoxicants, sleep well, seek help promptly, and focus on recovery.
    4. Visible risks: Many risks are visible without sequencing, such as lost sleep before a manic episode, escalating cannabis use, treatment stopped due to stigma, and distance from specialist care.

    Conclusion

    The central idea is that psychiatric genetics will not identify people before they fall ill, but it can replace superstition and blame with a more accurate account of vulnerability. Prediction will remain probabilistic even as datasets grow larger and more representative. The task is to keep probabilities from being misunderstood, stigmatised, or commercialised, and to involve diverse populations while protecting privacy.

    Back2Basics:

    GenomeIndia Project

    1. Convening body: Funded by the Department of Biotechnology (DBT), Government of India.
    2. Aim: To build a catalogue of the genetic diversity of the Indian population.
    3. Scale: Generated whole-genome data from 10,000 healthy, unrelated Indians across 83 population groups.
    4. Significance: Provides an India-specific reference against which imported genetic risk scores can be tested rather than assumed to apply.

    Genomics in India: About

    1. Definition: Genomics studies the complete set of an organism’s DNA, including how variants relate to disease.
    2. Diversity: India’s population carries extraordinary genetic diversity across many groups, making a single national reference essential.
    3. Clinical caution: Risk scores derived from European-ancestry datasets can mislead when applied to Indian populations.

    Challenges in Psychiatric Genetics

    1. Weak prediction: Scores cannot forecast onset, severity, or treatment response for an individual.
    2. Ancestry gaps: European-dominated databases reduce accuracy elsewhere.
    3. Commercial overreach: Enthusiasm of commerce can outrun the science.
    4. Privacy risk: Genomic data raises serious privacy and consent concerns.
    5. Stigma: Misread probabilities can label people as patients-in-waiting.

    Way Forward

    1. Diversify datasets: Include diverse populations in genomic research.
    2. Community involvement: Involve clinicians and communities in deciding how data are used.
    3. Protect privacy: Enforce strong safeguards on genomic data.
    4. Integrate data: Combine genetic findings with developmental, clinical, and environmental information.

    PYQ Relevance

    [UPSC 2026] Which of the following statements with regard to Genome India Project is/are correct?

    1. It is a part of the Human Genome Project.

    2. The project is funded by the Department of Biotechnology (DBT), Government of India.

    3. Its primary aim is to build a catalogue of genetic diversity of the Indian population.

    (a) 1 only

    (b) 2 and 3 only

    (c) 1 and 2 only

    (d) 1, 2 and 3

  • Evidence of non-Mendelian inheritance in mice

    Why in the News

    Researchers have reported evidence of non-Mendelian inheritance in mice, involving DNA methylation, genomic imprinting and paramutation. Nanopore sequencing helped detect these epigenetic marks.

    What is Epigenetic Inheritance?

    1. Definition: Transmission of heritable changes in gene activity without altering the underlying DNA sequence.
    2. Major mechanism: Chemical modifications such as DNA methylation can influence whether genes are switched on or off.
    3. Non-Mendelian: Unlike classical Mendelian inheritance, the inherited information is not limited to changes in the DNA sequence.
    4. Genomic imprinting: Expression of certain genes depends on whether they are inherited from the mother or father.
    5. Paramutation: One allele can induce a heritable change in the expression of another allele without changing its DNA sequence.
    6. Nanopore sequencing: Can detect certain DNA modifications, including methylation, while sequencing DNA.

    Why does it matter?

    • Expands inheritance theory: Heritable information can involve regulatory/epigenetic states in addition to DNA sequence.
    • Environment and inheritance: Some environmental factors can influence epigenetic states, though not every acquired epigenetic change is necessarily inherited.
    • Disease relevance: Abnormal epigenetic regulation is associated with cancers and other diseases.
    • Biotechnology: Advanced sequencing can help identify epigenetic modifications alongside DNA sequences.

    “[2021, GS3, 15 marks] What are the research and developmental achievements in applied biotechnology? How will these achievements help to uplift the poorer sections of society?

    [2021] In the context of hereditary diseases, consider the following statements:
    1. Passing on mitochondrial diseases from parent to child can be prevented by mitochondrial replacement therapy either before or after in vitro fertilization of egg.
    2. A child inherits mitochondrial diseases entirely from mother and not from father.
    Which of the statements given above is/are correct?

    [A] 1 only

    [B] 2 only

    [C] Both 1 and 2

    [D] Neither 1 nor 2

  • AI tool can shrink and rewrite proteins

    Why in the News

    A university team has developed Raygun, an AI tool that can redesign and miniaturise proteins while retaining their function. This could improve the delivery of protein-based therapies and accelerate drug development.

    What is AI-based Protein Engineering?

    1. Protein engineering: Designing or modifying proteins to obtain desired properties such as smaller size, stability or specific biological functions.
    2. AI-based design: AI models trained on protein sequences can predict and generate redesigned protein structures.
    3. Raygun: The tool can shrink proteins while attempting to preserve their function, potentially making them easier to deliver.

    Why does it matter?

    • Gene therapy: Delivery vectors have limited cargo capacity. Smaller functional proteins can make therapeutic delivery easier.
    • Drug development: AI can reduce dependence on lengthy trial-and-error approaches in protein design.
    • Precision medicine: Engineered proteins could potentially be tailored for specific therapeutic functions.
    • Biosafety: Powerful AI-enabled biological design raises concerns regarding misuse, unintended effects and governance.

    Protein engineering ≠ gene editing

    • Protein engineering: Modifies/designs the protein to alter its properties.
    • Gene editing: Directly modifies DNA sequences.
    • AI protein design: Uses computational models to predict or generate useful protein sequences/structures.
    • Gene therapy: Uses genetic material or biological mechanisms to treat disease.

    [2026] Which of the following statements with regard to genetic medicine is/are correct ?
    1. Genetic medicines correct/compensate for the faulty genes responsible for disease.
    2. Engineered viruses and lipid nanoparticles are used as carriers of the genetic medicine.
    3. Genetic medicines alter the entire DNA sequence.
    Select the answer using the code given below :

    [A] 1 only

    [B] 2 and 3 only

    [C] 1 and 2 only

    [D] 1, 2 and 3

  • BioE3 Policy Reports Early Biomanufacturing Gains

    Why in the News

    The Government has highlighted the early achievements of the Biotechnology for Economy, Environment and Employment (BioE3) Policy, demonstrating growing investments and capacity in India’s biomanufacturing sector.

    What is the BioE3 Policy?

    • Full form: Biotechnology for Economy, Environment and Employment (BioE3) Policy.
    • Approved: 2024 by the Union Cabinet.
    • Implementing Agency: Department of Biotechnology (DBT).
    • Objective: Promote high capacity biomanufacturing to drive economic growth, environmental sustainability, and employment generation.

    Key Features of the Policy

    • Focus Areas: The policy identifies six thematic sectors:
      • Bio based chemicals.
      • Smart proteins.
      • Precision biotherapeutics.
      • Climate resilient agriculture.
      • Biofuels and carbon capture.
      • Marine and space biotechnology.
    • Funding Pattern:
      • Government support of up to 70% of project cost.
      • Remaining contribution from the private sector.
    • Industry Participation:
      • Over 600 beneficiaries have utilised BioE3 facilities.
      • Private investment commitments have reached about ₹602 crore.
    • Long term Goal: Support India’s vision of a $300 billion bioeconomy by 2030.

    What is Biomanufacturing?

    • Definition: The production of chemicals, fuels, materials, pharmaceuticals and other products using biological systems such as microorganisms, enzymes or engineered cells.
    • Benefits:
      • Reduces dependence on fossil fuel based manufacturing.
      • Promotes sustainable industrial production.
      • Supports the circular bioeconomy.

    What is a Biofoundry?

    • A highly automated research facility that designs, builds, tests and analyses biological systems.
    • Accelerates the development of new biotechnology products through automation and artificial intelligence.

    [2026] Which of the following statements with regard to GenomeIndia Project is/are correct ?
    1. It is a part of the Human Genome Project.
    2. The project is funded by the Department of Biotechnology (DBT), Government of India.
    3. Its primary aim is to build a catalogue of genetic diversity of the Indian population.
    Select the answer using the code given below:

    [A] 1 only

    [B] 2 and 3 only

    [C] 1 and 2 only

    [D] 1, 2 and 3

  • Why is there so much activity in the field of biotechnology in our country? How has this activity benefitted the field of biopharma?

    Biotechnology involves using living organisms and biological systems to develop useful products and processes. India is now among the world’s top 12 biotechnology hubs.

    Activity in the field of Biotechnology in India

    Robust Government Policy: Initiatives like the National Biotechnology Development Strategy 2021-2025 have provided a roadmap for a $150 billion bio-economy by 2025.

    Institutional Framework: The Department of Biotechnology (DBT) and BIRAC provide critical seed funding and mentorship to over 5,000 startups.

    Cost-Effective R&D: India offers a significant cost advantage (nearly 33% lower) in R&D and manufacturing compared to developed nations, attracting Global Capability Centers (GCCs).

    Vast Biodiversity and Genetic Pool: India’s diverse climatic zones and ethnic genetic diversity provide a massive “natural laboratory” for genomic research and agricultural biotech.

    Human Capital: A steady influx of STEM graduates (over 2 million annually) provides the technical workforce required for high-end lab work and clinical trials.

    Infrastructure Growth: The establishment of specialized Biotech Parks offers “plug-and-play” facilities for rapid scaling. Eg- Genome Valley in Hyderabad.

    FDI Liberalization: 100% Foreign Direct Investment (FDI) is permitted under the automatic route for greenfield projects, boosting capital infusion.

    Digital Integration: The use of AI and Big Data in bioinformatics, supported by the National Supercomputing Mission has accelerated drug discovery and protein folding research.

    Pandemic Legacy: The successful indigenous development of vaccines (e.g., Covaxin) proved India’s “Proof of Concept” to the world, triggering massive reinvestment in the sector.

    Activity benefitting the field of Biopharma

    Global Vaccine Leadership: India now supplies approximately 60% of the world’s vaccines, earning the title Pharmacy of the World.

    Increase Economical Value: The Indian bioeconomy reached an estimated $130-$165.7 billion in 2024, with projections to reach $300 billion by 2030.

    Shift to Biosimilars: Biotechnology has enabled India to move beyond simple generics to complex Biosimilars. India has the highest number of biosimilars approved globally.

    Precision Medicine: Allowed biopharma companies to develop targeted therapies for cancer and rare genetic disorders tailored to the Indian populations

    Clinical Trial Hub: Improved regulatory frameworks such as New Drugs and Clinical Trial Rules, 2019 and biotech expertise have made India a preferred destination for multi-centric global clinical trials.

    Reduced Import Dependency: Local production of Active Pharmaceutical Ingredients (APIs) and Key Starting Materials (KSMs) through fermentation technology is reducing reliance on imports.

    Innovation in Biologics: Companies like Zydus Cadila and Dr. Reddy’s are now shifting from “imitative” to “innovative” R&D, focusing on novel biologics for autoimmune diseases.

    Diagnostics Revolution: The biotech boom led to the rapid development of low-cost, molecular diagnostic kits such as RT-PCR, CRISPR-based ‘Feluda’ tests, improving healthcare penetration.

    Major challenges

    High Capital Intensity: Developing a single biosimilar costs $100-250 million, deterring smaller Indian firms from competing.

    Complex Manufacturing Requirements: Biologics require ultra-pure environments, even a 1°C temperature shift can spoil entire production batches.

    Innovation Deficit: India still invests only 7-8% of revenue in R&D compared to 20%+ by global innovators.

    Skill Gap in Advanced Tech: Shortage of professionals trained in bioinformatics, transcriptomics, and computational biology slows down innovation.

    Global Intellectual Property (IP) Conflicts: Navigating the “patent thickets” of global biopharma giants remains a major legal challenge for biosimilars.

    Infrastructure Deficit in NAMs: Lack of standardized, industry-ready laboratories for non-animal methodologies across the country.

    Supply Chain Fragility: India remains dependent on imported raw materials like specialized cell culture media for biotech production.

    Way forward

    Strengthening Regulatory Cadre: Creating a dedicated “Scientific Review Cadre” within CDSCO to match global approval timelines.

    Expanding Clinical Trial Capacity: Establishing a national network of 1,000 accredited clinical trial sites to accelerate drug development.

    Investing in Biofoundries under BioE3 Policy to provide common infrastructure for startups to test and scale.

    Academic-Industry Collaboration: Upgrading seven NIPERs into “Centers of Excellence” for translational research and high-end skilling.

    Strategic Use of Free Trade Agreements: Leveraging FTAs with the EU and UK to harmonize quality standards and boost exports.

    By bridging the gap between laboratory research and commercial biopharma, India is moving toward Atmanirbhar Bharat in healthcare.

  • How can biotechnology improve the living standards of farmers?

    Karoly Ereky coined the term “Biotechnology” in 1919 to describe the fusion of biological and technological processes aimed at enhancing life on Earth. For agriculture, biotechnology has emerged as a significant boon, elevating crop quality and yield through innovative approaches.

    Role of Biotechnology in Improving Living Standards of Farmers

    Provides disease-free planting material through tissue culture. Eg- Tissue culture banana (G-9 cultivar) increases yields by 30-40%.

    Enhances crop yields through high-yielding and hybrid varieties. Eg- “Swarna Sub-1” flood-tolerant rice and “DRR Dhan 42” drought-tolerant rice.

    Reduces pesticide cost through pest-resistant GM crops. Eg- Bt cotton reduced pesticide use by 40-60%.

    Lowers fertilizer expenses using biofertilisers. Eg- Rhizobium and Azotobacter cuts nitrogen fertilizer requirement in pulses/oilseeds.

    Increases resilience to climate shocks with stress-tolerant seeds. Eg- Drought Tolerant High-Yielding Chickpea Variety “SAATVIK (NC 9)”

    Reduces post-harvest losses using improved shelf-life varieties. Eg- Delayed-ripening tomato (Arka Rakshak) reduces spoilage.

    Nutritional security through biofortified crops. Eg- Iron-rich pearl millet (ICMH 1202).

    Kisan-Kavach: An anti-pesticide suit designed to combat the threat of pesticide-induced toxicity in agricultural settings.

    Enables diversification into high-value crops. Eg- Tissue-culture strawberries (“Chandler”) in Himachal Pradesh.

    Boosts dairy income through microbial feed supplements. Eg- Yeast-based probiotics increase milk yield by 8-12%.

    Enhances fishery productivity using improved seed varieties. Eg- Jayanti Rohu shows 17-20% higher growth rates.

    Generates rural employment – Eg- Tissue culture labs and biofertiliser units run through FPOs in Telangana.

    Supports women-led microenterprises – Eg- SHGs in Tamil Nadu producing vermicompost.

    Challenges

    Regulatory Complexity: Approval processes for GMOs and biotech tools are lengthy. Eg- delay in approval of GM Mustard (DMH-11)

    Public skepticism about GMOs. Eg- opposition to Bt Brinjal.

    Environmental and Ethical Concerns: Gene flow to non-target species, biodiversity risks, and ethical considerations around gene editing. Eg- concerns over “playing God”

    Access and Equity: High development costs and IP protections limit access for smallholders.

    Health concerns – Eg- StarLink corn incident (2000) – animal-feed-only GM corn entered the human food chain.

    Limited private sector participation – Eg- Policies such as the Cotton Seed Price Control Order (2015) and mandatory tech transfer provisions have discouraged private R&D

    Illegal Cultivation and biosafety risks – Eg- HT-Bt cotton is illegally cultivated on up to 25% of cotton acreage in India

    Declining Cotton Productivity – Yields have fallen from 566 kg/ha (2013-14) to 436 kg/ha (2023-24), far below China and Brazil’s 1,800-1,900 kg/ha.

    Rising Import Dependence – India has shifted from net exporter to net importer, with cotton imports reaching $0.4 billion in 2024-25.

    Undermining seed sovereignty due to intellectual property rights. Eg – Monsanto-Mahyco Bt cotton disputes

    Way Forward

    Science-Based Regulation- Ensure transparent field trials, publicly accessible data and independent monitoring,

    Promote public-private partnerships in biotech research and support region-specific GM crops

    Implement robust GM labeling and enforce strict action against illegal cultivation and counterfeit seeds.

    Prioritise biofortified GM crops such as Golden Rice, iron-rich pulses, and zinc-rich wheat to combat micronutrient deficiencies

    Effective implementation of BioE3 mission can help realise Vajpayee’s vision of Biotech for Bharat – “What IT is for India, BT is for Bharat”

  • What are the research and developmental achievements in applied biotechnology/? How will these achievements help to uplift the poorer sections of society?

    Applied biotechnology focuses on the practical application of these biological insights to solve real-world problems in sectors like agriculture, healthcare, environment, and industry.

    R&D Achievements in Applied Biotechnology

    Genomics: Genome India Project sequenced 10,000 Indian genomes. It provides a baseline for understanding genetic diseases unique to the Indian population.

    Climate-Resilient Crops: Eg- Sahbhagi Dhan for drought and Swarna-Sub1 for flood- prone areas has secured yields in disaster-prone regions.

    Human health

    Indigenous Vaccine Platforms: Eg- Development of the world’s first DNA-based COVID-19 vaccine (ZyCoV-D) and the indigenously developed HPV vaccine (Cervavac) for cervical cancer.

    Bio-fortification: R&D has led to the creation of nutrient-rich crop varieties, such as Sakti-1 maize (high lysine and tryptophan) and CR Dhan 310 (high protein rice).

    Bio-remediation and Waste-to-Wealth: Success in developing “Microbial Consortia” for cleaning oil spills (OilZapper) and converting agricultural waste into ethanol (2G Biofuels).

    Restorative Health

    Regenerative Research: Eg- LV Prasad Eye Institute (LVPEI) in Hyderabad has pioneered significant advancements in using limbal stem cells to restore vision.

    Synthetic Biology: Research into metabolic engineering has allowed for the microbial production of high-value compounds like Artemisinin (anti-malarial drug), reducing dependence on plant extraction.

    Molecular Diagnostics: The creation of low-cost, paper-based diagnostic strips (like the FELUDA test) for various infectious diseases has decentralized high-end testing.

    Uplifting Poorer Sections of Society

    Food and Nutritional Security: Bio-fortified crops directly combat “Hidden Hunger” among the rural poor by providing essential vitamins and minerals through their daily staple diet.

    Increased Farm Income: Biotech seeds like Bt Cotton and bio-stimulants reduce the cost of chemical pesticides and fertilizers, increasing the net profit margin for farmers.

    Affordable Healthcare: Local manufacturing of biologicals and biosimilars through biotech processes makes life-saving drugs like insulin and monoclonal antibodies affordable.

    Animal Husbandry and Dairy: Achievements in In-vitro Fertilization (IVF) for cattle and sex-sorted semen technology have helped landless laborers increase milk yield and improve livestock quality.

    Clean Environment and Sanitation: Biotech-based Bio-toilets utilize anaerobic bacteria to treat human waste in areas without sewage systems, improving hygiene and dignity for urban slum dwellers.

    Employment Generation: The growth of the Bio-Economy (targeted at $300 billion by 2030) creates a range of jobs from high-end research to low-skilled manufacturing.

    Energy Security: The production of bio-gas and ethanol from farm residue provides a secondary source of income for farmers while offering cheaper, cleaner fuel for cooking and transport.

    Resilience to Climate Change: For the poor who are most vulnerable to weather shocks, biotech-developed salt-tolerant or heat-resistant seeds provide a safety net against crop failure.

    Applied biotechnology is no longer a luxury science but a fundamental pillar for inclusive growth.

  • Discuss several ways in which microorganisms can help in meeting the current fuel shortage.

    Microorganisms are microscopic organisms such as bacteria, fungi, archaea, and microalgae that can break down organic matter and produce useful energy compounds. Due to these capabilities, they are becoming important for sustainable energy production and the global clean energy transition.

    Ways Microorganisms Help in Meeting Fuel Shortage

    Bioethanol: Saccharomyces cerevisiae and Zymomonas mobilis ferment sugars and agricultural waste into ethanol. India achieved 10% ethanol blending in 2022 and targets 20% (E20) by 2025-26.

    Biodiesel: Microalgae such as Chlorella and Dunaliella produce lipid-rich biomass, which is converted into biodiesel through transesterification.

    Biogas through Anaerobic Digestion: Methanogens decompose sewage, food waste, and cow dung to produce methane-rich biogas. Eg- India’s GOBAR-dhan scheme.

    Biohydrogen Production: Certain photosynthetic bacteria and cyanobacteria can split water or organic compounds to release Hydrogen gas, the cleanest burning fuel.

    Microbial Fuel Cells (MFCs): Bacteria break down organic waste in wastewater and release electrons, generating electricity while simultaneously treating the wastewater.

    Biobutanol Production: Species like Clostridium acetobutylicum produce butanol through ABE (Acetone-Butanol-Ethanol) fermentation. Biobutanol is considered superior to ethanol.

    Syngas Fermentation: Acetogenic bacteria can convert synthesis gas (CO and H2 from industrial emissions or biomass gasification) into liquid fuels like ethanol and acetic acid.

    Microbial Enhanced Oil Recovery (MEOR): Microbes are injected into depleted oil wells where they produce surfactants and gases that decrease oil viscosity.

    For a country like India, which imports over 80% of its crude oil, scaling up microbial fuel technologies is essential for achieving Urja Atmanirbharta (Energy Self-reliance) and meeting the Panchamrit targets for net-zero emissions.