Few people are capable of expressing with equanimity opinions that differ from the prejudices of their social environment. Most people are even incapable of forming such opinions." - The New Quotable Einstein
We
need to overcome the idea, so prevalent in both academic and
bureaucratic circles, that the only work worth taking seriously is
highly detailed research in a speciality. We need to celebrate the
equally vital contribution of those who dare to take what I call "a
crude look at the whole". - Murray Gell-Mann, Nobel Laureate in Physics,
1994
Day: 2020-Nov 30 (Monday) Time: IST 17:15 Hour
# Speaker: Dr. Heiko Enderling, H. Lee Moffitt Cancer Centre & Research Institute, at IST 17:30 Hour
Title: Integrating Computer Modeling of Cancer into Patient-specific Clinical Decision MakingAbstract: In close collaboration with experimentalists and clinicians, mathematical models that are parameterized with experimental and clinical data can help estimate patient-specific disease dynamics and treatment success. This positions us at the forefront of the advent of ‘virtual trials’ that predict personalized optimized treatment protocols. I will discuss a couple of different projects to demonstrate how to integrate calculus into clinical decision making. First, we show that a mathematical model can be calibrated from early treatment response dynamics in patients undergoing hormone therapy for prostate cancer. The learned model dynamics can then be used to forecast responses to subsequent treatment, and identify high risk patients who would benefit from concurrent therapies. In a second example, I will discuss how the pre-treatment tumor-immune ecosystem can be predictive of radiotherapy outcome, and how we can prospectively simulate treatment response dynamics to identify patient candidates for radiation dose escalation when needed, and treatment de-escalation without jeopardizing outcomes.
Bio-sketch: Dr. Heiko Enderling, an Associate Professor, H. Lee Moffitt Cancer Center & Research Institute. He is associated with Dept. of Integrated Mathematical Oncology, Dept. of Radiation Oncology, also working as Director for Education and Outreach Fellow. He is the Elected President of Society for Mathematical Biology. Before joining to Moffitt, he served as Assistant Professor, Center of Cancer Systems Biology, Tufts University School of Medicine, 2007-2013. His research interests include: Quantitative Personalized Oncology, developing calibrated and validated mathematical model driven by clinical data to aid patient-specific treatment decisions, Tumor-immune ecosystem dynamics and response to radiotherapy, and vision is to educate the next generation of interdisciplinary researchers. He was honored as Moffitt Educator of the Year 2017 and Moffitt Mentor of the Year 2020.
#
Speaker: Dr. Probir Kumar Dhar, Lead Scientist, SSBTR at IST 18:30
Hour
Title:
A
System Biology Based Dynamic Model to Limit Uncertainty in Cancer
Treatment
Abstract:
In
present cancer treatment scenario different chemotherapeutic
strategies like Maximum Tolerable Dosing (MTD), Metronomic
Chemotherapy (MCT), Antiangiogenic (AAG) drug, Hematopoietic stem
cell transplantation are available; however, the selection of the
best therapeutic strategy for an individual patient at any particular
stage of disease remains uncertain till now. Several analytical
models are proposed for each of the chemotherapeutic strategies;
however, no single analytical model is available that can make a
comparative assessment of the long-term therapeutic efficacy among
these strategies towards patient specific manner. To address this
issue we developed a composite synergistic system (CSS) model. Here
we have synergized the output of two models. One is based on fluid
dynamics (FD) principle and another is based on vasculature growth
(VG). In this developed model considered model variables are helpful
for fitting of initial clinical data of individual clinical cases and
successive model simulation runs may help in making therapeutic
decision/prediction. Again tracking tumor growth at a regular
interval of time is possible by matching the simulation output with
available clinical techniques like MRI or simple blood testing. Such
model may help the clinician to overcome the uncertainty in therapy
design at an individual patient level.
Bio-sketch:
Probir
Kumar Dhar, is a Lead Investigator in SSBTR. He received his
Masters' degree in Microwave Engineering and PhD degree from Jadavpur
University in the field of Cancer Systems Biology. Before this
present assignment, he has a teaching experience of 15 years in the
area of Electronics and Communication Engineering in different
institutions across India. In his credit more than 10 research
articles and policy publications, which were appeared in different
systems biology journals like Automatic Control of Physiological
State and Function, Computational Biology Journal, Journal of
Computational Systems Biology, Journal of Oncology and Translational
Research. He has presented his works in different International
conferences.
He
received and successfully completed a AICTE sponsored project MODROB
[Modernization and Removal of Obsolescence] titled as 'Advancement of
microwave antenna & transmission line experiment facilities for
academic purposes'. His present interest involves development of a
clinical software for leukemia therapy design and assessment and
e-learning modules "Coding for Future Systems Biologists".
Event photo:
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Day: 2020-Dec-06 (Sunday) Time: IST 14:45 Hour
#
Speaker: Dr. Igor Balaz, University of Navi Sad at IST 15:00 Hour.
Title:
Artificial
Intelligence and Multiscale Modeling of Tumor Treatments
Abstract:
From
the perspective of systems biology tumor can be regarded as a complex
adaptive system. It is a nonlinear system, composed of a number of
dynamically interacting elements with complex feedbacks between them.
To model such systems multiple scales should be taken into account.
Molecular scale simulations can give us insight into structural,
thermodynamic, kinetic and other properties of applied drugs and how
those drugs interact with cell membrane and receptors. At the
cellular scale, pharmacodynamics modeling simulates the efficacy and
toxicity of drugs. At the tumor scale, we need to integrate findings
from the molecular and cellular scale simulations with the
heterogeneous and adaptable tumor structure. To model tumor adaptive
dynamics and the resulting emergence of treatment resistance, we
should incorporate individualized genotype and phenotype data.
Finally, at the organism scale, we should use integrated PK/PD
approaches to model bio-distribution and clearance of drugs, as well
as whole-body physiologically based pharmacokinetic models (PBPK).
The set of all possible configurations of such modeling effort is
enormous. Computationally investigating all of them is not feasible
approach. However, with Artificial Intelligence we can speed-up and
automate the search for optimal treatment strategy. In this webinar
we will discuss strategies for developing such multiscale modeling
platform and how it can be integrated with artificial intelligence.
Bio-sketch:
Dr.
Igor Balaz is an Assistant Professor at the University of Novi Sad,
Serbia. He runs a computational biophysics lab in targeting the
systems biology of cancer. The work of his team blends nanomedicine,
molecular modeling, mathematical modeling and artificial intelligence
to search for novel drug delivery systems. He leads two multinational
European projects on the use of nanomedicine in cancer treatment:
EVO-NANO (Evolvable platform for programmable nanoparticle-based
cancer therapies) and PACE (Platform for Rapid Development of
Personalized Nanomedicine Drug Delivery Systems).
#
Speaker: Prof. Shubhabrata
Datta, SRM University at IST 16:00 Hour
Title: Machine Learning for Designing Hard Tissue Prostheses
Abstract:
To improve the
performance of the orthopedic and dental implants, i.e. to improve
several properties simultaneously, the constituent biomaterials
properties as well as implant structure needs to be designed
judiciously. The choice of the parameters for achieving optimum
performance of the systems is difficult to arrive experimentally, as
the process becomes expensive and laborious. In silico approaches
hold promise for searching the solutions for achieving the target
performance. Among the many approaches of designing materials and
structures for protheses computationally, machine learning attempts
to map the hidden relationship between the variables of complex,
nonlinear systems using available information of the system in a
high-throughput, statistically robust, and yet physically meaningful
manner. Machine learning tools, e.g. artificial neural network and
neuro-fuzzy inference systems are increasingly used in the domain of
informatics-based design of implants and biomaterials. In a
prescriptive analytics approach for improving several properties
together, many of them having conflicting objectives, the developed
models are used as objective functions for optimization with genetic
algorithm used in single as well as multi-objective mode.
Bio-sketch:
Prof. Shubhabrata Datta, is a Research Professor in the Department of
Mechanical Engineering, SRM Institute of Science and Technology, SRM
University, received his PhD from Indian Institute of Engineering
Science and Technology, Shibpur, India (previously known as B.E.
College Shibpur) in the field of Metallurgical and Materials
Engineering. He has more than three decades of teaching and research
experience. His research interest is in the domain of design of
materials using artificial intelligence and machine learning
techniques. More than 150 publications as journals and peer-reviewed
conference proceedings to his credit. Eleven of his graduate students
have been conferred with PhD degree. He was bestowed with the
Exchange Scientist Award from Royal Academy of Engineering, UK and
worked in the University of Sheffield, UK. He also worked Dept of
Materials Science and Engineering, Helsinki University of Technology,
Finland, Dept of Materials Science and Engineering, Iowa State
University, Ames, USA and Heat Engineering Lab, Dept of Chemical
Engineering, Åbo Akademi University, Finland as Visiting Scientist.
He is a Fellow of Institution of Engineers (India), Associate Editor,
Journal of the Institution of Engineers (India): Series D, and
editorial board member of several international journals.
Event Photo Day 2020-12-06
Lecture 1 by Dr. Igor Balaz, University of Navi Sad
Lecture 2 by Prof. Subhabrata Dutta, SRM University
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Dr. Durjoy Majumder
Organizing Secretary of the SSBTR International Webinar Lecture Series
