Good day, Dr. Sanga Mitra. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Please start by introducing your professional journey in academia.

I'm Doctor Shonga Mitra. I have done my PhD in bioinformatics from Indian Association for the cultivation of science and my PhD was was on on head and neck cancer and identifying biomarkers as including both coding and non coding RNAs, especially micro RNAs and how how they affect the downstream pathways. Along with that I've also studied.

Next, in during postdoctoral time at NIH USA I have studied about Pyrn which is a jamline non coding RNA which helps in transposon depression.

The structural and evolution evolutionary role of trns from Procrastio.

After that, I joined as a senior research scientist at IIT Madras. There have studied the gene network rewiring across brain regions in Alzheimer's disease and found out the hub genes which controls the network and the pathways which it alters us as a result of this rewiring. Currently, I'm doing the experimental validations along with the collaborative lab at UCI Arvind to test this biomarkers and also studying cellular heterogeneity using cellular.

Publisher method.

Next please.

Thank you for sharing that, Professor Mitra.

Thank you, Professor Sanga Mitra. Let's proceed to evaluate your candidacy based on the role requirements for the Cancer Bioinformatics Professor position. I'll begin by verifying specific skills and qualifications relevant to the role. First, let's discuss your expertise in Cancer Bioinformatics. Could you provide an example of a research problem you have worked on in this area, describing the approaches and methods you implemented?

So who Recently he I've been working on neurodegenerative disease generally, particularly eczema disease, particularly if for any brain disease, a single region started at a time.

Instead, we have you have studied like seen multiple brain regions, taken multiple brain regions and have seen how how the network or the gene network across region changes because one region does not work alone, it works in coordination with multiple regions and.

Suppose in Alzheimer disease means any given region like all regions are not affected affected similarly.

The and it varies. The effect varies so that we have studied based on gene networking thing and we have used differential correlation. We have used Balkans data and on that we have applied differential correlation method. From there we have formed the differentially correlated network and then you have used those Luvian algorithm to bipartite this to see in each region how the network is changing and the corresponding pathway. Then we have extracted the.

Chins in this reward network and now we are trying to validate it. Whether the mechanistic take role of this.

Thank you. Next please.

Understood, Professor Mitra. Your work analyzing changes in the gene network across multiple brain regions in neurodegenerative diseases, particularly Alzheimer's, using differential correlation and network analysis, is noted and appreciated. Next, let's evaluate your teaching capabilities. Can you share an example of a theory or laboratory course you have taught or would propose to teach in Cancer Bioinformatics? How would you structure it to effectively engage graduate students?

I would like to to teach his role of non coding RNA in cancer biology.

Where I'll I'll first help us make students understand what is the difference between coding and on coding RNAs, How many types of non coding RNAs are there and whether this non coatings in RNAs, how they are functional like take one example at a type suppose micrna, how they're processed is what are the the way they have like affect the genes in the downstream. Team whether or what is known about them. Like for microarray you already know how the Biogenesis occurs, where the.

How they, uh, target the genes, but for TRN fragments, for example, Biogenesis and as well as the target, as is known, but not so well studies that micro RNA. And so how the are studied, like what gaps are there that students can and in future can do research on and also introduce to them the tools also through which they can analyze data on microns, the databases where they can already find the existing.

Data so they can use them to analyze and also along with that introduce them to the like smaller sequencing and other types of experimental analysis which they can use to understand and better this non putting RNAs.

Thank you.

Thank you, Professor Mitra. I appreciate your detailed response outlining your course structure on the role of non-coding RNAs in cancer biology. It’s clear you emphasize foundational understanding, critical gaps in research, and practical tool-based learning, which is essential for graduate-level teaching. Moving forward, let's focus on guiding student research and projects. Could you describe a project you've mentored or designed for students that involved a multidisciplinary approach? How did you ensure its successful execution?

So the project which I have like worked on or mentored is developing a tool called multisense. I've like mentored at Mtech students along with my recent mentor at IIT Madras. So there I have tried to analyze how I would like the the tool. The multisense tool is based on analyzing different layers. So.

US and how different genes in different layers or if you see like in different regions, how they're connected through different genes or the network so.

Try to find out how the biological implications of this network and then and how we can develop the tool further. We can use expression matrix instead of layer. We can use expression matrix as one and layer like. Each expression matrix can be each layer and we can see the interaction between different tissues like.

Issue 1 and versus tissue 2, how the genes interact between these two tissues. So oh that is the current project. I've mentored multiple Mtech students and in future I would like to develop projects on the role of micro RNA in and tRNA in your identity disease and how they cross talk with each other and how they control all their downstream targets and also.

How when the enhancers are compromised due to SNPs is how the TF bounding is compromised and as a result the downstream team target or the downstream gene expression is compromised?

Thank you.

So to assess the student the best thing is to give them hands on exercise and see how actively they can solve problems. So if it's a complex problem, the best way is to tackle or to make student understand is to break down them into small problems and solve each its problem at a time. I'm like have solutions for like compartmentalize it and then solve one at a time and then stitch it together to see the bigger.

Thank you, Professor Mitra, for providing an insightful overview of your mentoring efforts, particularly with the MultiCens tool development, and your forward-looking vision for projects on microRNAs, tRNAs, and enhancer dysfunctions. Your emphasis on multidisciplinary research and the ability to guide students through such complex inquiries is certainly commendable. Next, I’d like to address evaluation and pedagogical impact. How do you approach assessing student performance, particularly in research-heavy or project-based courses? Could you provide an example?

And also to deal with students, one should keep in mind that and to understand the learning ability of that students like some students may prefer audio visual learning, some may prefer kind of stick so or to make them understand. And I would like to use those like a mixture of teaching in procedures.

Thank you.

Thank you, Professor Mitra, for outlining your thoughtful and adaptive approach to evaluating students, particularly through hands-on problem-solving and accommodating diverse learning preferences. It's evident that you prioritize both clarity and inclusivity in your teaching methods. Let’s now move to your academic contributions. Can you highlight a significant peer-reviewed publication you have authored, detailing its impact on the field of bioinformatics or cancer research?

So till that I have authored 17 peer reviewed applications. Autopsy is the most noteworthy is the one in Nature Communications which was in the role of IRNA and structure which are done during my post-doc. Recently he I have published in PLOS Computational Biology which is about the methodology of the multisense tool and in NPG systems Biology and applications which is is on. Gene network rewiring in user generated disease as.

Other than that I've developed a database on hidden neck cancer which has been published in oral oncology and also have studied the effect downstream effect of 53 pathway involving Micron and target genes in head and neck cancer which have the even the target genes where the target genes have been validated experimentally and have been published in scientific reports. Other than publications. I have edited the book OK called cancer and non coding RNA which has.

Published in elsewhere and there I've authored 3 chapters. I've detailing thing about the.

DRNA and cancer like roles of TRNATN fragments in cancer as well as different resources is in cancer.

Thank you.

Thank you, Professor Mitra, for sharing your academic achievements, including your impactful publications in prestigious journals such as *Nature Communications*, *PLOS Computational Biology*, and *npj Systems Biology and Applications*. Your editorial contributions, particularly the book on cancer and non-coding RNAs, further demonstrate your depth in the field. Lastly, let's discuss any experience with industry collaboration or consultancy. Have you been involved in any projects or partnerships with industry, and how did you align academic research with industry needs?

So this, uh, the multi source for the multisons tool development, we have collaborated it with umm, industry called Exlorum where they're helping us with.

Like developing the background of the tool and hosting in them in Amazon cloud and some nitty gritties of the, uh, background on coding. So uh yeah, my industry experience is limited, but definitely it was a great experience talking with them how the thing of dealing with the project and how they're developing and how we academicians sing. And currently with, we are in the stage of writing a tool paper together.

Thank you.

Thank you, Professor Mitra. Your collaboration with Exlorum in developing and hosting the MultiCens tool demonstrates your ability to bridge academia and industry effectively, even with limited experience. It’s clear you possess the aptitude for integrating innovative academic insights into practical applications. With all the required skills and qualifications discussed, I will move forward appropriately.

Professor, I'd like to begin by understanding more about your teaching philosophy. Could you share with me which foundational concept or course in cancer bioinformatics you find particularly enjoyable or impactful to teach, and why?

So in cancel bioinformatics particularly I am interested how how the like cellular heterogeneity which is the basis of any cancer cell or like tumor you can say. So understanding that basis the cellular heterogeneity, how to deal with cellular heterogeneity, what is cellular heterogeneity? What are the experiments like single cell RNA like if single cell RNA sequencing is not available then deconvolute the bulk RNA data.

Uh, and understand the basis, that's the foundational concept, like several heterogeneity should be understood. Then what are the regulatory layers as like the coding? There is of course a coding layer, the gene can be mutated and then there is a, a post transcriptional layer where non coating RNS can be involved. Then people have to understand that there are CIS regulations. And so it's regulates this genes and as a result of the different regulations like, and there's, there's epigenetic regulations.

So there are multiple regulations like different layers of regulations as a result and the genes are altered as a result, cancerous cells are oncogenes are are produced and on the other hand housekeeping genes are the important genes which maintains the homeostasis of forgiven tissue or organ and are compromised that is as the the genes which we call tumor suppressor genes. So these are like some of the foundational concepts to.

Understand cancer biology or cancer informatics.

And also there are a lot of data is available. So only we also need to understand what are the different types of data available to to tackle like in cancer biology, how you can tackle them and what analysis can be done to further gain insights into that data as a bioinformatician.

And how we can present different hypothesis to the society which can be experimentally validated and later clinically?

Like clinically translated for therapeutic purpose?

Thank you.

Thank you, Professor, for your detailed insight. It’s evident that you emphasize a robust understanding of cellular heterogeneity, regulatory mechanisms, and data-driven hypothesis generation in cancer bioinformatics, which are cornerstones of this discipline. Let’s proceed.

Yes.

Your resume mentions that you have contributed to the development of "MultiCens," a web application for multi-layered network analysis. Could you elaborate on the research problem this project aimed to address and how the application contributes to the field of cancer bioinformatics?

So this tools, this tool called multi science which we are currently.

He developed like developing and hope to public make it public very soon. So basically this focuses on how suppose two different tissues or different layers like multilayer means suppose each layer is a tissue and how different tissues interact with each other like. And when I say that tissues interact, that means the genes within that tissue who interact within that tissue as well as across. So there is like local interactions as well as global.

Actions. So this tool calculates different kinds of centrality of this gene network, which is present and in any given tissue and in any given time point.

We calculate global centrality, how the different gene networks are connected and like weak genes across tissue. Suppose with muscle and pancreas, how the genes in pancreas affects is the genes in muscle in layer. And there's another thing we calculate is query set centrality. Suppose first insulin producing genes or this gene 13 pancreas that's a predicate. How it affects is the gene layers in in muscle. So these are the three different types of centrality which we calculate and based on the centrality.

And so we calculate local centrality, like how important the genius centrality team means. Centrality of a gene means how important the gene is in a gene network.

Score we rank the gene in a given tissue that's overall fundamental, fundamental like understanding of this tools and we can use this on any cancer to understand how different cancer layers or the metacity layers are connected, how the primary tissue or the primary origin of the cancer is affecting the secondary origin of the like region where the cancer submit exercise. So there is like multiple applications of.

In cancer biology and also in any given disease.

Thank you.

Your project highlights an impressive integration of computational biology into understanding disease mechanisms. Now, I'd like to understand how you connect this research to a teaching environment. How would you explain the concept of "gene network centrality" to graduate students with minimal bioinformatics background while maintaining academic rigor?

So to move on, like to make understand our student first I I'll use who's the help of diagrams, I'll show them and that like gene network means like suppose the genes are nodes each nodes and how they're connected. Like gene one is node one and how it's connected to another gene two other node two. First I'll draw a drag gram and so like in a given network. There can be like multiple genes, like multiple nodes and then and slowly I'll.

I'm like, I'll start with a small example where I'll count how many nodes are there and how many connections are there and then I'll show them. Suppose gene one has 10 connections, whereas gene 2 has 2, gene 3 has four by that and make them understand like gene one, if it has 10 connections, that means it is central to that and network and it's most important in maintaining.

So that is the way like like a simple way to make them understand like what is centrality? What is that is the importance of a gene in a given network. And then there can be like multiple networks. I'll draw them and show them like there are multiple networks. So gene one in network one, how is it like also affecting globally another network? It's like important locally, maybe globally gene two who is more important and is more connected to other network. So in this way I'll make them understand first the basic.

A particularly given network or pathway in in a given condition.

Concert and then we'll bring in the like the expression data for cancer, like how we can show them like which genes are important in which cancel, uh, layer in this way, I'll make them understand. Thank you.

So I'll give them some data and and like list of genes, the edge, edge distance, number of note, like number of connections and ask them to solve a problem to to realize whether they've understood the basic concept of centrality and and then also to ask them to rank them do like a small problem solving thing that will clarify whether they've understood the basic concept. Once they understand the basic concept, then of course I have to explain the concept.

That's a clear and structured approach to teaching a complex concept. Using graphical illustrations and gradually layering complexity is an effective way to build understanding. How do you assess whether your students have truly grasped the concept of centrality and its application in cancer research?

Gene and expression matrix if they don't know like again new, we can also depends whether their student topology or not. They have to explain the concept of genes and all and and other details on the cancer and how they're connected and network. If they're students of computational like from computational perspective, they'll understand the net network systems and the centrality things. So depending on the background of the student, I'll try to give them problem like I'll ask them to solve some problem and from that I can reflect whether they've.

Include the concept or not.

Thank you.

Thank you, Professor. Your approach effectively tailors instruction to a varied student background, ensuring they grasp foundational concepts through practical problem-solving. This adaptability and depth of engagement reflect both your research acumen and pedagogical commitment.

Professor, let us delve into your research productivity and fiscal sustainability for this position. Could you present a three-year research roadmap outlining how you propose to advance your research agenda in Cancer Bioinformatics? Specifically, how would you align your efforts with the priorities of the institution, leveraging VIT University's resources and infrastructure efficiently?

Right. I have I have two projects in mind. One of the projects is I'd like to study in how the enhancer like the role of like enhancer controls transcription via a promoter, right involving promoter. So if there are SNPs is like SNP or single impulated problem within the enhancer. This is regulation and will be Hanford. So enhancer will not be able to activate.

Motor and and as a result the transcription and downstream like the the TF binding will be compromised. So first I would like to list like in in glioblastoma and particularly in interested in glioblastoma as well as neuroblastoma.

So I'll still least turn like least of enhances. I'll find the a list of enhances which are already reported to be in brain cancer or the glioblastoma. Then we'll find the SNPs from Geo studies is then we'll see in we'll see use attack and chipset to understand their epigenetic whether that's regions are active or repressed. And then we'll see that transcription factor binding sites and and that TFs.

Whether if there is a SNP he present, whether the tier binding is compromised or not. But we'll understand from TF expression as well as the motive side of the transcription factor. This is a tier binding site. And finally, we checked whether the downstream genes whether they're expressed or not from the expression. So it'll be a multi omics integration. And for that I'll use multimodal AI, a network system and develop a tool. With which we can generate a complete network and then validate.

With them using different experimental approach. Thank you.

Thank you, Professor, for the detailed overview of your first project initiative. It is clear that your approach integrates multi-omics data and advanced computational tools, which aligns well with a precision approach to Cancer Bioinformatics. Allow me to challenge this for scalability—how do you plan to mentor and involve junior students during the development of such computationally and experimentally intensive studies to ensure a consistent pipeline of high-quality publications and skills transfer?

I already know the VIP is a legacy institute and this is what this is one of the very few institutes we've had bioinformatics department and and I'm well aware of its reputation. So as such, I don't have any questions. I look forward to the first to first interview and more interactions with the faculty members and committees over there. Thank you.

Our scheduled time for the interview is now up. Before we conclude, do you have any questions related to the role or the company?

So.

Professor, your role would involve teaching and mentoring in Cancer Bioinformatics, guiding student research, and engaging in innovative teaching methods. How would you implement an active learning strategy, like a Flipped Classroom, for a large-enrollment Cancer Bioinformatics course without relying on slides? Could you share a specific example or structure?

So for active learning I will involve them like. I'll ask them to present and to like. Suppose if I teach what? Whatever I teach in the first class, at the end of the class I'll ask some of the members to present to like in a brief presentation, give a pre presentation without any slide or anything. Then I'll ask them to have a cheat cheat shipment to just to summarize in few sentence to three or four sentence what they've understood. Heard and from that I will understand.

I can understand like whether they are, the concept is clear to them. And also I'll have global classroom where they can post their questions and and see in the next day whether I can answer them or not. And and yeah, they're like, yeah, of course there are like different ways that can help them or improve the students and improve both mind teaching ability and the learning ability of the students.

Thank you.

Thank you for sharing, Professor. Let's explore further. Suppose you're mentoring diverse students at VIT University in Cancer Bioinformatics research. How would you tailor your engagement strategies to accommodate their varying academic backgrounds and cultural contexts?

So yes, in bioinformatics, I expect that students may come from a biology background and from computational background. And it can be combination of both or, or like in general, like any science background. So first, first in the first few classes I'll.

Ask them like I'll check what are the backgrounds and what's the combination of background. Based on that I'll tell her the slides like if they need mobility input, I'll tell her it in that manner. If they need both computational, if it's a mix, I'll tell her in that manner. And also I'll use first questionnaire called verb questionnaire to understand the learning preference. And based on that I'll design the slides and my lectures and all like hands on experience accordingly.

Thank you.

So I'll use like a hands on a little hands on test like I'll ask them to demonstrate it tool, tool and generate result or use a database of a cancer database and show like show the pipeline how to download all the data, what data is present in a given database, how to download them and what how they can be used along with like written exams or presentation or also to like at the end of the class or after a course to summarize them.

Understood, Professor. One last question in this area: How would you design evaluation tools to measure real competency in your Cancer Bioinformatics course, beyond just standardized testing methods?

Presentations or some written in projects.

Thank you.

You're welcome, Professor. Let's proceed to the next topic. How would you systematically approach improving a specific departmental metric, such as "Research per Faculty," to align with high-level accreditation standards at a university like VIT?

So I'd like to build common resources or like a cancer bioinformatics center where common resources are like available and like contribute to the resource as well as see like the resource which are already there should be present on the one umbrella. So and then an approach different faculty members. Has worked within VIT and across and and other institutes who are involved in cancer bio research to form a network.

Group as well as a journal club so that we can and like help each other or increase each others Like like keep us like informed and updated.