Tuesday, October 21, 2025

Computational Modeling in Physical Biology: The AI Opportunity

For decades, teaching computational modeling in biology at the senior undergraduate or early postgraduate level was profoundly challenging. Mastering it demanded a high level of understanding that was difficult for students, particularly those from traditional life sciences backgrounds with limited exposure to programming or advanced mathematics. The difficulty stemmed not only from the need to grasp the physics behind the models—including interpreting results, identifying assumptions, and adding parameters to enhance realism, but also, and crucially, the ability to code. Students often became slaves to the syntax, spending more time debugging semicolons than engaging with scientific principles.

The advent of powerful AI tools and prompting has fundamentally overcome the barrier of coding and syntax. When any student can instantaneously generate code or retrieve information, education focused on rote memorization or basic coding syntax becomes futile. The critical skill for the next generation of scientists is no longer the manual labor of programming, but the expert oversight required to interpret, critique, and guide the AI's output. This shift transforms the student from a slave to programming into the director of scientific thought.

What follows is an example of a five-part pedagogical framework that leverages AI to move beyond code generation and directly address the core challenge of modeling in physical biology. Using the central problem of the competition between deterministic forces and thermal energy—a key concept in physical biology—this framework outlines how students can be coaxed to transition to active scientific critics, equipped with the higher-order thinking skills necessary to thrive in an AI-assisted research environment.

This framework achieves multiple goals: it develops higher-order thinking abilities and computational thinking/modeling ability for biological phenomena, all while simultaneously teaching prompting as the essential bridge between scientific thought and computational directives.

 

Example Exercise

Part 1: Discovering the Basics 

Goal: Understand the code structure and how the two main parameters, Drift Force and Thermal Jiggle, affect the visual output of the particle's movement.


Task:
a)      Copy-paste the below code into Google Colab 


import numpy as np

import matplotlib.pyplot as plt

 

# --- Customizable Parameters ---

drift_force = 4.0       # F_drift: The constant pull (deterministic force)

thermal_jiggle = 0.5    # T_jiggle: The strength of random molecular impacts (thermal noise)

time_steps = 1000       # N: Total number of steps to simulate

dt = 0.01               # Delta t: Time step size

 

# --- Simulation Setup (Implicitly includes fluid drag, Gamma) ---

# For simplicity, we assume mass=1 and a constant friction coefficient (gamma=1).

# The movement update follows the heavily damped Langevin equation: dx = (F_drift/gamma) * dt + sqrt(2*kT*dt/gamma) * N(0,1)

# Here, T_jiggle is proportional to sqrt(2*kT/gamma).

 

# Initialize position and time arrays

position = 0.0

path = [position]

time = [0.0]

 

# --- Simulation Loop ---

for i in range(1, time_steps):

    # 1. Deterministic Movement (Drift)

    deterministic_step = drift_force * dt 

    

    # 2. Stochastic Movement (Thermal Jiggle)

    # np.random.normal(0, 1) generates a random number from a standard normal distribution (N(mean=0, std=1))

    noise_amplitude = np.sqrt(2 * dt) * thermal_jiggle

    stochastic_step = noise_amplitude * np.random.normal(01)

    

    # Update position: Total movement = Drift + Jiggle

    position += deterministic_step + stochastic_step

    

    # Record results

    path.append(position)

    time.append(i * dt)

 

# --- Plotting the Results ---

plt.figure(figsize=(105))

plt.plot(time, path, label=f'Drift={drift_force}, Jiggle={thermal_jiggle}')

plt.title("Particle Movement: Drift vs. Thermal Jiggle")

plt.xlabel("Time (s)")

plt.ylabel("Position (arbitrary units)")

plt.grid(True, linestyle='--', alpha=0.6)

plt.legend()

plt.show()

 


b)    Activity: Play and Plot


Run the simulation with the default settings (Trial A). Then, change only the bold parameter for the subsequent trials (B, C, and D) and record your observations.


Trial

drift_force

thermal_jiggle

Observation (Describe the path: erratic, straight, fast, slow, etc.)

A (Default)

1.0

0.5

B

1.0

2.0 (Increase Jiggle)

C

4.0 (Increase Drift)

0.5

D

0.5

2.0

 

Question: Look at the plot for Trial B. The path becomes very erratic, or “messy”. Why do you think the particle’s movement is so jagged and unpredictable when you increase the thermal_jiggle parameter?


Part 2: Questioning the Physics

Look closely at your plot for Trial C (high drift force). Even though the force is constant, the particle’s speed does not increase infinitely; it reaches a steady, constant average speed.Isn’t this contradictory to Newton’s law F=ma where a constant force F should cause constant acceleration a)? What do you think is happening here, and what physical process is secretly included in the model’s math to prevent the particle from accelerating forever?

Part 3: Contextualizing the Biology: The Motor Protein in a Changing Cell

Imagine this simulation models a myosin motor protein walking along a cellular track.

·       The drift_force is the energy driving the motor.
·       The thermal_jiggle is the water molecules pushing it around.

This ideal situation is rarely the case in a real cell. What other parameters do you think can be added to the model to make it more realistic (e.g., related to fuel, physical environment, or biological obstacles)?

Part 4: Learning to Prompt

The code for the above simulation is reproduced below for ease of reference: 


import numpy as np

import matplotlib.pyplot as plt

 

# --- Customizable Parameters ---

drift_force = 4.0       # F_drift: The constant pull (deterministic force)

thermal_jiggle = 0.5    # T_jiggle: The strength of random molecular impacts (thermal noise)

time_steps = 1000       # N: Total number of steps to simulate

dt = 0.01               # Delta t: Time step size

 

# --- Simulation Setup (Implicitly includes fluid drag, Gamma) ---

# For simplicity, we assume mass=1 and a constant friction coefficient (gamma=1).

# The movement update follows the heavily damped Langevin equation: dx = (F_drift/gamma) * dt + sqrt(2*kT*dt/gamma) * N(0,1)

# Here, T_jiggle is proportional to sqrt(2*kT/gamma).

 

# Initialize position and time arrays

position = 0.0

path = [position]

time = [0.0]

 

# --- Simulation Loop ---

for i in range(1, time_steps):

    # 1. Deterministic Movement (Drift)

    deterministic_step = drift_force * dt 

    

    # 2. Stochastic Movement (Thermal Jiggle)

    # np.random.normal(0, 1) generates a random number from a standard normal distribution (N(mean=0, std=1))

    noise_amplitude = np.sqrt(2 * dt) * thermal_jiggle

    stochastic_step = noise_amplitude * np.random.normal(01)

    

    # Update position: Total movement = Drift + Jiggle

    position += deterministic_step + stochastic_step

    

    # Record results

    path.append(position)

    time.append(i * dt)

 

# --- Plotting the Results ---

plt.figure(figsize=(105))

plt.plot(time, path, label=f'Drift={drift_force}, Jiggle={thermal_jiggle}')

plt.title("Particle Movement: Drift vs. Thermal Jiggle")

plt.xlabel("Time (s)")

plt.ylabel("Position (arbitrary units)")

plt.grid(True, linestyle='--', alpha=0.6)

plt.legend()

plt.show()


Task: 

a)     Generate a prompt that can reproduce the above code. Use the commented lines in the code as hints to develop your prompt. 
b)    Check if the output from your code the output from the code above is qualitatively similar (for instance, the behavior of the plot observed in Trial B or Trial C). 


Part 5: Revise the Model using Prompting

In Part 3, you identified a few parameters that can be added to make the simulation more realistic. Use prompting to add the parameters to your simulation and critically evaluate the behavior. [You can either evaluate by making observations as in Part 1, or, evaluate the underlying physics as you did in Part 2.]

Conclusion: Teaching Scientific Directorship in the AI Era

This pedagogical framework for computational modeling in physical biology represents a fundamental strategic pivot: leveraging AI to address a historical teaching bottleneck and, in doing so, maximizing the development of higher-order cognition in students. The ultimate goal is not merely to teach students with AI, but to teach them how to lead AI.

The challenge in teaching computational concepts to students from traditional biology backgrounds was that the necessity of mastering coding and debugging created a significant extraneous cognitive load. This mandatory struggle with syntax diverted the student's finite working memory away from the actual germane load—the complex intellectual work of scientific analysis and model creation. It is crucial to emphasize that this strategy does not undermine the ultimate value of coding; rather, it makes a strategic, context-dependent choice to remove this technical barrier for a specific audience.

A Safeguard Against Cognitive Offloading

The scientific merit of this five-part structure lies in its meticulous sequencing, which serves as a safeguard against cognitive offloading—the central tension identified in AI education literature.

1.    Instruction First, Prompting Later: The student is rigorously taught analysis, critique, and model enhancement in Parts 1-3. The provided code is used as a neutral object of study, allowing students to develop mastery of the scientific process (e.g., interpreting implicit assumptions like fluid drag, proposing biological revisions like ATP concentration) before touching the AI tool.

2.   AI as Expert Assistant: The student is coaxed to prompting only after mastering the scientific requirements. The subsequent task of generating a computational directive (Parts 4-5) becomes the highest-order learning activity. This ensures the student is performing the necessary mental work (germane load), using the AI to execute their demands.

This intentional scaffolding operationalizes the expert oversight that is now the critical ability of the next generation. By automating the extraneous technical burden, the framework effectively elevates students into the “learner-as-leader” paradigm. They are taught to be the director of scientific thought, validating their ability to govern and refine complex computational systems—a necessary prerequisite for innovation in the AI-driven research environment of tomorrow.

Ultimately, this strategy transforms a technological challenge into a pedagogical triumph, ensuring that computational tools accelerate, rather than replace, genuine scientific education.



*This document and the exercises were refined and enhanced using Gemini 2.5 with the initial idea and subsequent prompts given by the author, Vigneshwar Ramakrishnan. In essence, AI was used as an expert assistant in developing this document. 

Friday, June 06, 2025

ஓரு/இரு வரிக் கவிதைகள்

 

வேர்கள் இரண்டானாலும் தோள் கொடுப்பது தோழமையே! 

பின்னிப் பிணைந்த உறவென்றாலும் வேர்கள் வெவ்வேறு தான்! 

படைப்பின் அழகு பன்மை! 

பல்துறை வல்லுநராக, பன்முக கலைஞராக குடை, சாளரத்தின் வெளியே சிறகடித்து பறக்க காத்திருக்கிறதோ?

சுதந்திர சிங்கமாய் இருக்க ஆசைதான், வாழ்க்கை, கடமை என்ற கூண்டுகளுக்குள் அடை(க்க)பட்ட மனிதர்க்கு! 

Saturday, April 12, 2025

My Journey In Academia: Lessons Learned

27 March 2025

 Below are some learnings from my personal experience — as a student, educator, researcher and an administrator. 


Executive Summary of the Learnings

 

As a student: Setting the right research culture is important and needs deliberate, concerted and collective effort over time so that it gets into the ethos of every member of the institute. 

 

As an educator & researcher: Academic knowledge construction happens at different levels. Moving beyond research questions of narrow focus to questions that can serve to integrate seemingly different knowledge is important to advance our horizons. 

 

As an administrator: Explicit setting up of “platforms” for the integration of the academic talent pool is crucial and requires clear separation from administrative duties.      

 

 

As a Student — Setting the Right Research Culture

Undergrad at PSG Tech, Coimbatore

I distinctly remember the lab sessions we used to have as undergrads — particularly the viva questions. The questions would be something like, “Why can’t we eat grass?” or “Why don’t bacteria grow on toothbrushes?” etc. These questions, though I didn’t know the answers to them, served to trigger the curiosity and search for answers. This curiosity was not an isolated phenomenon, but almost everyone in the classroom was driven by such questions. In retrospect, I understand that the faculty members had a clear focus — to trigger curiosity, and nudge us to ask questions and to seek answers ourselves. Furthermore, they were not judgmental when we didn’t know the answers — only more nudging. In the recent alumni meeting, celebrating the 25th year of the department, alumni across the batches recollected similar experiences and attributed their current mindset to this culture in the department. 

Lesson

Not being judged and being driven by curiosity is key to learning and growth. 

 

Research Intern at NCBS, Bangalore

1.     As an intern at NCBS, I was part of the regular lab meetings. During these lab meetings, which usually ran into a few hours — students would take turns in presenting either the current literature, or, their work, or a software demo (I was in a computational lab, and the PI was Prof. Sowdhamini). What was unique about the lab meetings was that the PI also used to be on the schedule! The very first time I understood that one could use the distance formula to calculate distance between atoms in PDB structure was when Prof. Sowdhamini mentioned it during her illustration as part of a larger calculation. This clearly not only signaled the importance attached to lab meetings, but also served to emphasize that learning is a life-long activity. 

Lesson

Explicit demonstration that learning is a life-long activity is important to set the right research culture. 

2.     I was also fortunate to attend some of the annual work seminars — which no student or faculty missed for any reason! Right from the Director to any project intern attended it and witnessed the exchange of ideas and contentions on ideas! 

Lesson

Setting the right research culture is a collective effort in an institution. 

Graduate Student at NUS, Singapore 

1.       My first experience that I distinctly remember with my advisor Prof. Raj Rajagopalan, then the Head of the Department, was his holding the door for me to enter! This gesture of his — a Head of the Department holding the door for a young student — was startling to say the least! Later he would say to us that graduate students are junior faculty members of the department, and he would even discuss some of the departmental functioning with us informally. 

Lesson

Treating students and younger colleagues as equals is important for grooming them as next-generation academics. 

2.     I proposed a journal club forum in our department even as a first-year graduate student. The idea was to create a platform to discuss contemporary topics and also trigger new thoughts in the areas of interest. The proposal was immediately approved by my advisor, then the HoD, who forwarded it to the faculty members and encouraged it. While the journal club was primarily run by the students, faculty members and visiting professors to the department would also join, at least occasionally. I distinctly remember having Prof. Bill Krantz in one of the journal clubs; he threw questions for thinking that are beyond, but related to, the topic of discussion (including ethical issues). This was an eye-opener for me personally on the different dimensions that a particular research idea has. I am told that this journal club still continues, albeit with a different focus. 

Lessons 

a)    It is important to have a supportive senior mentor who can envision the impact of an initiative on the overall growth of the department/institution, and facilitate the realization of the idea. 

b)    It is important to create “platforms” which can serve as sounding boards for ideas, and bring in like-minded people, senior and junior, to be members of such platforms.   

 

3.    My PhD — Research as “Discovery” of “Truth” 

 

As with any PhD student, my trajectory was not smooth — I expected that my advisor would hand down research questions and I just had to figure out how to do it. But this is what my supervisor, Prof. Raj had to say during a conversation: “If I know what to do, I only need a technician to the complete the work — I don’t need a PhD student!” Unfortunately, the educational culture that I had grown up looked up to the “professor” and assumed the professor knew it all. This was very confusing to me and I couldn’t come to terms with it until about 4th year into my PhD — which is when I had the fun of “discovering” Nature. As we were analyzing the data I had, my supervisor and I were “connecting” the data logically and constructing a “story” out of it. That was eye-opening for me — because all through the days I had assumed that knowledge would present itself — but here we had to look at the data from multiple perspectives and be creative (and logical) in framing an explanation for the data. I was so exhilarated to discover Nature’s little secrets (=The Truth) at the end of my PhD. Little did I realize that I was conveniently ignorant of the larger issues in the field. I also did not realize at that time the huge difference in the ontological assumptions behind using the words “constructing” knowledge vs “discovering” knowledge. 

 

Lesson

PhD is just a beginning. 

 


 

As an Educator & Researcher — Knowledge Dissemination, Consumption, and Construction

At SASTRA, Thanjavur

 

As a young faculty member, fresh after PhD, I was all enthusiastic about teaching — wanting to convey to students everything I knew! I did my best to disseminate the textbook knowledge into their little brains. But this was unsettling for me. I felt that I was not tapping into the potential of students — but didn’t know how to. I introduced my students to newer frontiers in the courses I taught — but I realized that all I was doing was making them consume more knowledge. I wasn’t giving them the joy of ‘discovering’ that I experienced as a PhD student. 

 

Lesson

A PhD is no license to teach; knowledge dissemination and consumption does not give the joy of discovery and learning.  

 

At ThinQ

 

Three years into teaching and research, I was perceived successful in research because I managed to get a grant and also managed to publish independently. My research proposal was a very specific problem and was driven by curiosity. But I was clueless of how to build a research career out of it. I was solving the research problem — but then stood alone, unable to connect with the larger questions. 

 

It is at this juncture that I bumped into Mohanan and Tara in 2015 — through the course “Inquiry and Integration in Education”. The course introduced us to the epistemological basis of knowledge in various domains — ranging from math, history, linguistics, physics and morality. It was clear that these seemingly different fields had something in common — reasoning & creativity. Secondly, the learning triggers in the course enabled us to construct our own knowledge — with the only condition that it adheres to the epistemic norm of rationality. This was liberating in the sense that I didn’t have to worry whether what I arrive at is the “correct” answer. And, alas, I realized that is what I was doing in research! You can never know if your conclusion from your research is “correct” — all that one can say is, given the grounds, this is the conclusion one can arrive at. We assume that this conclusion that we arrive at is what is TRUE about the world we live in. In other words, knowledge is constructed based on certain epistemic norms. We constantly make progress towards understanding Nature, but we will never be able to find out THE TRUTH — because truth is what we construct based on the epistemic norms. Over the years, I have also come to understand that certain ontological concepts can serve to integrate the seemingly different strands of academic knowledge. 

 

In this backdrop, I revisit my view of research and my own research works, only to understand that I was working on a very narrow problem (of protein-DNA specificity), which has its roots in much larger issue of robustness and repair in biological systems. However, one quickly notes that robustness is also a property of non-biological systems. Thus, there is a specific aspect of robustness that is relevant to the current work that I am doing — but there is also a broader aspect of robustness that is relevant to many other domains of study.   

 

Lessons

1.     Academic knowledge across disciplines shares epistemic and ontological commonalities. 

2.     Ground-breaking research can happen if one traces the research question beyond the immediate and tangible relevance.  

 

My view of Research — Now 

 

While research can in general be attributed as knowledge construction, we may note that it occurs at different levels. The questions I dealt with in my PhD had to do with a very specific problem where I focused on a specific system of study and generated/collected data to explain intriguing questions about that particular system (EcoRI enzyme). However, I now realize that one needs to go beyond a narrow focus of a specific problem of immediate interest. For instance, if only I had expanded the question to other DNA-binding proteins, I would have quickly realized that the problem of specificity is linked to the question of robustness in biological systems. Research questions and academic knowledge generation at that level have the potential to make a big positive dent and will not only be useful to give insights to a specific problem, but also serve to integrate several fields of study. 

 

As an administrator

At SASTRA

Since the time I was given an administrative role in our university, I have been mainly involved in streamlining processes, ensuring things are happening as per the policies of the university and dealing with huge amounts of paper work. While this has been a refreshing experience and has given me new opportunities to be part of making new policies and initiatives (I have, together with colleagues, conducted critical thinking workshops, designed courses that blend disciplinary knowledge and critical thinking for PhD students, and so on) on the one hand, it has also been a dull experience on the other. I no longer have the luxury of spending sufficient time with my PhD and PG students, or read latest research news. I no longer spend time with students after my classes — rushing immediately for a meeting or walking away, preoccupied with pending paper work and cheap ego tricks.   

 

Lesson

Academicians should be freed from administrative duties to fully realize their academic potential. 

 

At NCBS, Bangalore

 

I had the opportunity to visit NCBS again — this time, as the Associate Dean of Research (SCBT) at SASTRA — to attend their annual talks. 

 

1.     What became clear this time, after about 20 years of my initial contact with NCBS was their focus: frontiers in fundamental biology. But what was even more clear is that the research areas of faculty members working there spanned different spatial and temporal scales in biology — thus complementing one another, and not competitive

 

Lesson

In setting up an institution, it is important to have fellows/members who work on complementary ideas in order to promote healthy collaboration, and to avoid unnecessary competition. 

 

2.     Another thing I noticed at NCBS is that their administrative team took good care of all logistics for my arrival at NCBS, and gave clear instructions. Later, I found that their "meetings officer" had a PhD from UBC, Canada! 

 

Lesson

A clear separation of administration and academia is important, with a very creative and able administrative head.