BTech in Biological Systems Engineering

The course will expose students to key concepts and fundamentals needed for material selection in the context of biomedical, sensing and green energy applications. 

Students will learn the basics of atomic structure and bonding, packing in materials, mechanical properties and testing, defects and dislocations, failure and cracks, phase rules and phase diagram, corrosion, metal and alloys for surgical implants and vascular stents; composites, ceramics and polymers for biomedical applications. Further, the course discusses on crystalline structure, shape and energetics of molecules and solid materials, orbital theories that shape their electronic structure and energy bands, crystalline and non-crystalline phases that materials grow into, nanostructured materials, their size dependent properties and some of the basic measurement techniques to characterize them. Concepts on optical absorption and emission in nanomaterials, chemistry in aqueous media, heterogenous catalysis and artificial photosynthesis (H2 generation), all useful for the technologies mentioned above are also looked into.  

During the course students will be encouraged to read through relevant scientific journal articles, and present a short seminar on any one application of materials within the purview of the syllabus. 

The course explores a broad spectrum of topics, ranging from comprehending biological databases and sequence analysis to phylogenetics, comparative genome analysis, gene expression regulation, biological pathways, structural biology, drug design, and computational neuroscience. Advanced bioinformatics areas will also be introduced, including RNA-seq, ChIP-seq, microarray, SNP/SSR, and NGS data analysis. 

The course on bioprocess engineering will provide you an overview of concepts involved in the design and engineering of such biological processes. To start with you will be learning fundamentals which are common to most bioprocesses. You will learn about enzymes – their mechanism and kinetics, cellular growth and metabolism – with a focus on microbial growth and product formation, basic principles of bioreactor design, and recovery and purification of products. You will focus on specific applications that will draw upon the fundamental concepts. Some of the examples will involve producing fermented products for food industry, wastewater treatment using immobilized microbial cultures, producing nutraceuticals from microalgae using photobioreactors. In all these cases, emphasis will be put on the design and operation of bioreactors to facilitate optimal performance and product separation and recovery. 

This course presents an introduction to biochemistry and molecular biology, building the foundation for other BSE program courses. Students learn structural attributes, functions, and metabolic processes for proteins, lipids, and carbohydrates. Enzymes, vitamins, and hormones are introduced, with a focus on their biological roles. Real-world applications are demonstrated for diabetes, hyperlipidemia, and inborn errors of metabolism, offering insights into associated challenges. In the second half, the course extends into molecular biology's historical evolution, the central dogma, nucleic acid structures, and their functions as information carriers. Topics span replication, transcription, translation, gene expression, regulation, and molecular biology techniques for nucleic acids and proteins. Practical applications in diagnostics, agri-food industries, and developmental biology are explored through model organisms and systems. Learners also go through a simultaneous laboratory course that trains them on basic laboratory techniques like glucose estimation, protein and lipid estimation and purification, nucleic acid extraction and purification, quality and quantitative assessment of nucleic acid samples, PCR, qPCR, gel electrophoresis, restriction enzyme digestion, and DNA sequencing.

Dive into the dynamic world of Machine Learning and Pattern Recognition. Here, you will explore essential principles, analysis techniques, and algorithms crucial for recognizing patterns in a variety of real-world data types, including audio, visual, text, and financial information. This course highlights the transformative impact of AI across multiple domains, with practical applications demonstrated through online search, voice recognition, facial identification, and medical diagnosis. As Machine Learning continues to evolve as a field with broad applications across disciplines, this interdisciplinary course provides students with a comprehensive foundation in the subject.

This course introduces the Grand Challenges at the interface of societal needs and technological capabilities. It will offer the opportunity for students to develop an interdisciplinary appreciation for engineering from a technical perspective as well as from a global and historical perspective. The primary focus of the IL/GC course is embarking on a multi-year project this semester. ILGC will help with connecting students with mentors of their choice. Since projects may be at different phases of the design process, specific activities throughout the term will assist in project progress and ensure project continuity. Projects could be taken up either in faculty exploratory research areas related to grand challenges, or service learning-based projects, or students’ own idea within Grand Challenges framework; or could even be tackling industry partner-led problems. 

This course introduces mathematical modeling in biology, with a strong focus on statistical analysis. Throughout the course, students explore diverse applications of stochastic modeling, from cellular processes to population dynamics. By gaining hands-on experience and working with real-world data, students develop analytical skills and a deeper appreciation for the intricacies of biological systems. This course equips students to navigate the complexities of biological data, make informed interpretations, and contribute to ground-breaking research, bridging the gap between theory and practice in the fascinating world of biology.

Basic understanding of cell biology is essential for any individual who wishes to pursue a career in life sciences or any other associated field. This course explores fundamental cellular processes such as cell cycle, cell division, cell signaling, replication, recombination, repair, cell death and ageing which is critical to comprehend the differences between various physiological and disease states. Rationalizing these cellular processes from a molecular perspective is pivotal in grasping the entirety of a cell and the associated complexities in maintaining a homeostatic environment that supports life. A significant part of the course also includes a ‘Journal Club’ type presentations, where some of the most groundbreaking cell biology discoveries will be discussed. It will not only inculcate the habit of reading research papers but will also create an environment where students will be involved in a scholarly discussion. In parallel, the cell biology course also has a weekly lab component and was designed to provide hands-on experience with molecular cloning (preparation of competent cells, transformation, plasmid purification), mutagenesis, protein induction and protein purification followed by in vitro analysis by setting up biochemical assays. Exposure to such core research methodologies will be expected to augment their practical and analytical skills. 

As a fundamental building block, all industrial automation, including robotics, CPS, and biomedical instrumentation requires monitoring by comprising several sensors embedded in situ or in a remote environment. This course will equip students with fundamental knowledge and skills on sensors, actuators, associated materials & processes, and generic electronic conditioning circuits and systems.  

It includes an introduction to operating principles, materials, and characterization of various sensing technologies and their selection based on functional characteristics and performance criteria. Students will learn about typical analogue and digital processing approaches, simulation techniques, display and analyses, etc. while physically implementing it in some representative applications as a PBL component.

In the rapidly evolving field of Biological Systems Engineering, staying up-to-date with the latest advancements is crucial. This dynamic, 1-credit seminar and journal club course offers students a unique opportunity to immerse themselves in the cutting-edge trends shaping the future of the BSE discipline. Through a series of engaging presentations, research discussions, and seminars led by esteemed academicians and industry specialists, students will gain invaluable insights into the latest innovations and methodologies. Participants will learn to critically read and analyze scientific papers, deliver compelling academic presentations, and engage in thought-provoking discussions on emerging topics. This course aims to cultivate a deep understanding of current and future trends in Biological Systems Engineering, preparing students to contribute to and lead in this ever-evolving field.

Deep Learning has made impressive advances in various domains. The backbone of these advances has been the learning of representations enabled through big data. In this course, one would get a conceptual and practical introduction to the elements of deep learning. Module 1 will discuss the building blocks: different types of neural networks (conv, recurrent, graph), and how to learn effective embeddings through state-of-the-art architectures like attention modules, transformers, memory networks, GPT, etc. We will also discuss perception and generation in text as well as images. Module 2 will be reinforcing these blocks through applications in NLP – summarization, sentiment analysis, translation and applications in computer vision - object detection, segmentation, monocular depth estimation, stable diffusion, GANs, etc. Module 3 covers different optimization algorithms and settings – SGD, Adam, Minimax games and provides insights into methods for learning i) from large data but no labels and ii) small data but with labels, i.e., self-supervised learning and energy-based models.

This course covers fundamental concepts in statistical theory and methodology. Topics include -  

a. Principles of inference (including Bayesian inference), maximum-likelihood estimation, likelihood ratio tests, goodness-of-fit tests, bootstrap and computer-intensive methods, and least squares. 

b. Generalized linear and nonlinear models, including models for count and categorical responses; generalized additive models. 

c. Fixed, random and mixed-effects ANOVA models. 

d. Models for Dependent Data including time series data. 

If time permits, we will cover topics in Multivariate Analysis and Statistical Learning.

Human activities, over a period have profoundly altered the balance of planetary health which in turn is directly linked to human health. In recent times, there is a global recognition that a balance is needed among global systems of land, air, water and biodiversity to sustain and preserve life. This has been the genesis of studying the interdependencies of human and planetary health. This course will introduce the concept of planetary health, what are the current impacts due to human activities and the solutions to mitigate the risks at local and global levels. The solutions that will help populations with sustainable ways of living will be discussed and exemplified.  

All the human conditions have a genetic underpinning while some conditions show complete genetic determination, others arise from a complex gene-environment-time interaction. This course is designed with two areas of focus, genetics and genetic engineering. The first part — genetics — will introduce the students to the principles of genetics, organization of the human genome, patterns of genetic inheritance, genetic bases of human medical conditions and genetic treatments. The students will learn about molecular mechanisms that enable gene-environment interaction; genetic bases of single gene and complex genetic disorders; risk calculations and emerging genetic treatments like gene therapy, CAR-T cell therapy, etc. The second part of the course — genetic engineering — will familiarize the students with research technologies used to engineer genomes, for example, DNA modification, cloning methods, DNA editing, transgenesis, etc. The students will also practice DNA isolation, DNA modification, whole genome library preparation, gene editing using CRISPR Cas system. 

The course delves into the intricate study of biological systems and their interconnectedness. Students explore how molecular components within living organisms interact to form complex networks, allowing a comprehensive understanding of biological processes. The course covers various computational and analytical tools used to model and analyze these intricate systems. Topics include signaling pathways, gene regulatory networks, and the integration of omics data. Through practical applications, students gain insights into the dynamics of cellular processes and the emergent properties of biological systems. This course equips participants with the skills to unravel the complexities of living organisms at the molecular and systems levels. 

This course is designed to equip learners with the essential skills required to develop biosensors that detect specific nucleic acid sequences and/or proteins. Students will learn fundamental concepts of biosensor design and development including topics on sample processing, biorecognition elements, biorecognition events, signal generation and amplification, and transduction. A significant focus will be on developing point-of-care diagnostics facilitated by discussions on the latest research in the domain. A non-exhaustive list of techniques discussed in this course includes mechanical and chemical methods for biomarker extraction, nucleic acid amplification techniques, nucleic acid/protein-based lateral flow devices, ELISA, sandwich ELISA, CRISPR-based assays, and optical and electrochemical sensing. The course incorporates project-based learning throughout the semester with specific milestones designed in sync with the topics being covered in the lecture sessions.

Neuroscience is an interdisciplinary field with an overlap of diverse subjects like physics, mathematics, computer science, information theory, signal processing, physiology, psychology, and ethology. It also inspires the field of artificial intelligence, and more specifically, artificial neural networks and reinforcement learning. This course will introduce students to the fundamental mechanisms of neuronal communication and information processing in the brain. It will also cover topics in neuroethology to bring awareness of biological intelligence that has evolved across the animal kingdom in diverse forms.  

The course will introduce students to the world of quantum computing. We will cover the nuts and bolts of qubits, entanglement, and quantum algorithms along with the necessary physics and mathematics background, including linear algebra, probability, complex numbers, and partial differential equations. Through projects, students will have the opportunity to explore the potential advantages of quantum computing in areas such as finance, medicine, logistics and machine learning. Through the lab part of the course, students will gain hands-on experience using quantum SDKs and implementing quantum algorithms, enhancing their practical understanding of the subject matter.

This course provides the basics for understanding biomedical and pharmaceutical polymers, as well as polymers used in our daily life, and the foundation for the in-depth study of a variety of polymers. By the end of this course, students will be able to (i) understand the basics of polymer chemistry, polymer history, polymer properties, and applications; (ii) improve their ability to collect and use new information related to polymers; and (iii) enhance their ability to present their knowledge and opinions.  

The primary objective of this course is to develop an understanding of the economics of health and contemporary issues in public health delivery in developing countries with a special focus on India. The course builds upon a trio of economic theory, empirical readings, and hands-on experience working with nationally representative health data. The course will involve a discussion on the demand and supply of health and health care, information asymmetry in health insurance markets, models of healthcare delivery, global health inequities, and challenges in public health delivery.

Have you considered how Netflix recommends movies to you? Or how you are recommended items to buy on Amazon? Recommender systems are systems that recommend restaurants, movies, or content to watch, etc., by learning a user's preferences. When a new user signs up, the system has no prior knowledge of the user and must improve its recommendations on-the-fly by observing the user's behavior. Such a paradigm of machine learning where the system must learn "on-the-go" is broadly termed as online learning. Online learning is a major paradigm of machine learning and has a wide array of applications in the real world like the recommender system. The goal of online learning is to make a sequence of accurate predictions based on given knowledge of the correct answer to previous prediction tasks and possibly additional available information. The effectiveness of the prediction, for instance in recommendations, is critical to long term engagement of the users and the success of the platforms. It is particularly relevant where the users themselves can be dynamic and the standard machine learning approach of batch updating can be expensive in terms of performance and scaling. This course will introduce the algorithmic techniques through various practically relevant problems such as classification, portfolio management, recommender systems, etc. The course will then discuss some of the basic algorithmic techniques to solve these problems. The course is an introductory level course that is aimed at exposing the students to the basics of online learning.

To couple engineering techniques relevant to devices & systems and material characterization techniques with medical diagnostics demands.  

To address the fundamentals of human body systems in relation to the physical principles and design of typical bio-mechatronic diagnostic techniques. To appraise and understand the physical and operating principals of various imaging devices and modalities used in medical and industrial applications - as representative case studies, System models, ISO standards, classification of medical diagnostic devices in terms of safety and regulatory regimes worldwide, including relation to the development and deployment (entrepreneurial aspects) of new (without predicates) devices.

Immerse yourself in the fascinating study of Human-Tech Interaction, a course designed to delve into the complex interactions between humans and technology through a multi-modal sensory approach. This approach harnesses technologies such as bio-sensors, computer vision, and electro-mechanical sensors to monitor and model human physiological and behavioral responses. Aligned with industry needs, the course also focuses on strategies to enhance safety, productivity, and creativity across various environments—from industrial settings to office spaces. This is essential for designing technology that improves user experience and effectiveness in different work contexts. Machine Learning Principles and Practices (MLPR) is a prerequisite.