SSBTR Think Tank: SSBTR International Webinar Lecture Series: Day 2020 Nov 30 (Monday) and Dec 06 (Sunday)

Society for Systems Biology & Translational Research (SSBTR) is a registered (and also both 12A & 80G certified) not-for-profit scientific society organizes an International Webinar Series Lectures.

All are cordially invited.

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

The date, timing and names of the eminent expert speakers and other detailing are as below.

GOOGLE MEET Link: SSBTRwebinarLecture or url (https://meet.google.com/qjb-utgp-ymk)

Please follow this page "SSBTR International Webinar Lecture Series" for up-datation.

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 Making
Abstract: 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".

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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.