Monday, 28 December 2020

SSBTR International Webinar Lecture Series Day: 2021 Jan 12 (Tuesday) and Jan 24 (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. For date, time and link and other details aas below:

GOOGLE MEET Link: url (

Day: 2021-Jan-12 (Tuesday)     Time: IST 12:45 Hour

# Speaker: Prof. Olivier Gandrillon, Ecole Normale Superieure de Lyon at IST 13:00 Hour
Toward a dynamical network view on a differentiation process
A recent view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. Shannon entropy was used as as a measure of the cell-to-cell variability in gene expression. Entropy values showed a significant increase, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. We observed that the previous point of maximum entropy precedes an irreversible commitment to differentiation between 24 and 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.

Dr. Olivier Gandrillon received PhD degree in biology in 1989. After a two years post-doctoral stay at Caltech, he was appointed a permanent research position at Ecole Normale Supérieure de Lyon in 1989 and started his own team as an independent group leader in 1999 in Université Claude Bernard. He was awarded research director at CNRS in 2009. He moved back to ENS in 2015, where he is now heading a group entitled "Systems Biology of Decision Making". He has a long experience in multidisciplinary projects and interactions between Computer Science, Life Sciences and Mathematics. He is a member of the Dracula Inria team that is devoted to developing mathematical tools for multiscale modeling. He was elected director of the BioSyL research federation since 2011 when the federation was founded. He is the founder of the modeling seminar “Semovi” and of the international conference series “Integrative Post Genomics” (2001-2010) and “LyonSysBio” (since 2014). He was the co-chair of the very successful ICSB2018 conference. He co-authored 72 original publications in a very large range of different disciplinary fields. 

# Speaker: Dr. Bishwajit Das, SSBTR at IST 14:00 Hour
Information Theoretic Multivariate Dependence Analysis of HLA Immune-gene Regulation
Cell surface expression of Human Leukocytic Antigen (HLA) plays a significant role in immune recognition. HLA molecules are classified into two – class I and class II. In different cancers including leukemia, HLA (both class I and II) down-regulation is frequently reported. Regarding its regulation, different transcription factors (TFs) are responsible for its constitutive expression; however, it is also regulated by several inducible TFs. Using 1st order information theory based analysis for HLA and its associated TFs (human) gene expression data reveals that RFXB, an inducible controlling TF plays a major role in both myeloid and lymphoid types of leukemia; however, in lymphoid leukemia CREB1, another inducible TF may play an important role. Application of MaxEnt based multivariate dependence information theory for higher order analysis confirm the same finding along with an indication of the regulatory role of another combination of two TFs namely CIITA and IRF1 for myeloid type leukmic cells. However through this analysis no alteration is noted for HLA class II gene regulation.
Bio-sketch: Dr. Bishwajit Das, is an Investigator of SSBTR. He received his Ph.D. degree in 2021 from West Bengal State University. The field of his research is to understand HLA gene regulation in different non-communicable complex diseases by using different quantitative and analytical methods. He is interested in the development of different analytical tools for immune-informatics. He extensively used Information Theoretic approach in his doctoral thesis. So far eight international research papers are to his credits. He has presented his works in different International conferences and reviewer of leading bioinformatics review journal (Briefings in Bioinformatics).

Event Photos: Day 2021 Jan 12 (Tuesday)

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Day: 2021-Jan-24 (Sunday)     Time: IST 17:45 Hour 

# Speaker: Dr. Jianhua Xing, University of Pittsburgh at IST 18:00 Hour 
Title :
Reconstructing cell phenotypic transition dynamics from single cell data
Abstract :
Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, i.e., cell phenotypic transitions (CPTs). Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live cell imaging approaches provide temporal information but are technically challenging for multiplex long- term imaging. My lab is tackling these grand challenges from two directions, with the ultimate goal of integrating the two directions to reconstruct the spatial-temporal dynamics of CPTs. In one direction, we developed a live-cell imaging platform that tracks cellular status change in a composite multi-dimensional cell feature space that include cell morphological and texture features readily through fluorescent and transmission light imaging. We applied the framework to study human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT). In another direction, we aim at reconstructing single cell dynamics and governing equations from single cell genomics data. We developed a procedure of learning the analytical form of the vector field F(x) and the equation dx/dt = F(x) in the Reproducing Kernel Hilbert Space. Further differential geometry analysis on the vector field reveals rich information on gene regulations and dynamics of various CPT processes.
Bio-sketch :Dr Xing received B.S. in Chemistry from Peking University, M.S. in Chemical Physics from University of Minnesota, and PhD in Theoretical Chemistry from UC Berkeley. After being a postdoc researcher in theoretical biophysics at UC Berkeley and an independent fellow at Lawrence Livermore National Laboratory, he assumed his first faculty position at Virginia Tech, then moved to University of Pittsburgh in 2015. Currently Dr Xing is an Associate Professor in the Computational and Systems Biology Department, School of Medicine, a founding member of Center for Systems Immunology, and an affiliated faculty member of Department of Physics, University of Pittsburgh. He is also an affiliated member of University of Pittsburgh Hillman Cancer Center. Dr Xing’s research uses statistical and chemical physics, dynamical systems theory, mathematical/computational modeling in combination with quantitative measurements to study the dynamics and mechanics of biological processes. Recently his lab focuses on reconstructing information of cell phenotypic transition dynamics from live cell time-lapse images and snapshot high-throughput single cell data. Another related direction is to study how three-dimensional chromosome structure and dynamics, epigenetic modification, and gene regulation are coupled.

# Speaker: Dr. Mohit Kumar Jolly, Indian Institute of Science, Bangalore at IST 19:00 Hour
Title :
Systems biology of cancer metastasis: how do cancer cells coordinate, communicate, and cooperate?

Abstract : Metastasis (the spread of cancer cells from one organ to another) and therapy resistance cause above 90% of all cancer-related deaths. Despite extensive ongoing efforts in cancer genomics, no unique genetic or mutational signature has emerged for metastasis. However, a hallmark that has been observed in metastasis is adaptability or phenotypic plasticity – the ability of a cell to reversibly switch among different phenotypes in response to various internal or external stimuli. Phenotypic plasticity has also been recently implicated in enabling the emergence of resistance for many cancers across multiple therapies. However, a mechanistic understanding of these processes from a dynamical systems perspective remains incomplete. This talk will describe how mechanism-based mathematical models for phenotypic plasticity can enable our improved understanding of cellular decision-making at individual and population levels from these perspectives: a) Multistability (how many cell states exist en route?), b) Reversibility (do cells come across a ‘tipping point’ at specific time and/or dose of inducers beyond which they do not revert?), and c) Cell-cell communication (how do cells affect tendency of their neighbors to exhibit plasticity?). Collectively, our work highlights how an iterative crosstalk between mathematical modeling and experiments can both generate novel insights into the emergent nonlinear dynamics of cellular transitions and uncover previously unknown accelerators of metastasis and therapy resistance. 
Bio-sketch : Dr. Mohit Kumar Jolly received his B.Tech. and M.Tech. degree in Bio-engineering from IIT Kanpur and Ph.D. in Bio-engineering from Rice University He leads the Cancer Systems Biology group at the Centre for BioSystems Science and Engineering, Indian Institute of Science. He has made seminal contributions to decoding the emergent dynamics of epithelial-mesenchymal plasticity (EMP) in cancer metastasis, through mathematical modeling of regulatory networks implicated in EMP; his work has featured on the cover of Journal of Clinical Medicine, Cancer Research, and Molecular and Cellular Biology, and he won the 2016 iBiology Young Scientist Seminar Series – a coveted award for communicating one’s research to a diverse audience. Currently, his lab focuses on decoding mechanisms and implications of non-genetic heterogeneity in cancer metastasis and therapy resistance, with specific focus on mechanism-based and data-based mathematical modeling in close collaboration with experimental cancer biologists and clinicians. He is an elected fellow of Indian National Young Academy of Sciences (INYAS), and serves as Secretary of The International Epithelial-Mesenchymal Transition Association (TEMTIA).

Event Photos: Day 2021 Jan 24 (Sunday)





























































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 Dr. Durjoy Majumder
Organizing Secretary, SSBTR International Webinar Lecture Series

Thursday, 29 October 2020

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 ( 
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
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.

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
A System Biology Based Dynamic Model to Limit Uncertainty in Cancer Treatment
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.
Artificial Intelligence and Multiscale Modeling of Tumor Treatments
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.
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
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.
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

Friday, 31 January 2020

Experiments and Quasi-Experiments


An experiment is a study in which the researcher manipulates the level of some independent variable and then measures the outcome. Experiments are powerful techniques for evaluating cause-and-effect relationships. Many researchers consider experiments the "gold standard" against which all other research designs should be judged. Experiments are conducted both in the laboratory and in real life situations.

Types of Experimental Design:
There are two basic types of research design:
True experiments
The purpose of both is to examine the cause of certain phenomena.
True experiments, in which all the important factors that might affect the phenomena of interest are completely controlled, are the preferred design. Often, however, it is not possible or practical to control all the key factors, so it becomes necessary to implement a quasi-experimental research design.

Similarities between true and quasi-experiments:
Study participants are subjected to some type of treatment or condition
Some outcome of interest is measured
The researchers test whether differences in this outcome are related to the treatment

Differences between true experiments and quasi-experiments:
In a true experiment, participants are randomly assigned to either the treatment or the control group, whereas they are not assigned randomly in a quasi-experiment  
In a quasi-experiment, the control and treatment groups differ not only in terms of the experimental treatment they receive, but also in other, often unknown or unknowable, ways. Thus, the researcher must try to statistically control for as many of these differences as possible
Because control is lacking in quasi-experiments, there may be several "rival hypotheses" competing with the experimental manipulation as explanations for observed results

Key Components of Experimental Research Design
The Manipulation of Predictor Variables. In an experiment, the researcher manipulates the factor that is hypothesized to affect the outcome of interest. The factor that is being manipulated is typically referred to as the treatment or intervention. The researcher may manipulate whether research subjects receive a treatment (e.g., antidepressant medicine: yes or no) and the level of treatment (e.g., 50 mg, 75 mg, 100 mg, and 125 mg). Suppose, for example, a group of researchers was interested in the causes of maternal employment. They might hypothesize that the provision of government-subsidized child care would promote such employment. They could then design an experiment in which some subjects would be provided the option of government-funded child care subsidies and others would not. The researchers might also manipulate the value of the child care subsidies in order to determine if higher subsidy values might result in different levels of maternal employment.

Random Assignment. Study participants are randomly assigned to different treatment groups
All participants have the same chance of being in a given condition
Participants are assigned to either the group that receives the treatment, known as the "experimental group" or "treatment group," or to the group which does not receive the treatment, referred to as the "control group"
Random assignment neutralizes factors other than the independent and dependent variables, making it possible to directly infer cause and effect
Random Sampling. Traditionally, experimental researchers have used convenience sampling to select study participants. However, as research methods have become more rigorous, and the problems with generalizing from a convenience sample to the larger population have become more apparent, experimental researchers are increasingly turning to random sampling. In experimental policy research studies, participants are often randomly selected from program administrative databases and randomly assigned to the control or treatment groups.
Validity of Results. The two types of validity of experiments are internal and external. It is often difficult to achieve both in social science research experiments.
Internal Validity. The extent to which researchers provide compelling evidence that the causal (independent) variable causes changes in the outcome (dependent) variable. To do this, researchers must rule other potential explanations for the changes in the outcome variable.
When an experiment is internally valid, we are certain that the independent variable (e.g., child care subsidies) caused the outcome of the study (e.g., maternal employment)
When subjects are randomly assigned to treatment or control groups, we can assume that the independent variable caused the observed outcomes because the two groups should not have differed from one another at the start of the experiment  
For example, take the child care subsidy example above. Since research subjects were randomly assigned to the treatment (child care subsidies available) and control (no child care subsidies available) groups, the two groups should not have differed at the outset of the study. If, after the intervention, mothers in the treatment group were more likely to be working, we can assume that the availability of child care subsidies promoted maternal employment
One potential threat to internal validity in experiments occurs when participants either drop out of the study or refuse to participate in the study. If particular types of individuals drop out or refuse to participate more often than individuals with other characteristics, this is called differential attrition. For example, suppose an experiment was conducted to assess the effects of a new reading curriculum. If the new curriculum was so tough that many of the slowest readers dropped out of school, the school with the new curriculum would experience an increase in the average reading scores. The reason they experienced an increase in reading scores, however, is because the worst readers left the school, not because the new curriculum improved students' reading skills.
differential attrition Differential or selective attrition occurs when the rates of dropping out or leaving a study with several data collection waves (e.g., longitudinal study or experimental research) vary across different study groups. This is particularly troublesome when the characteristics of those who drop out are systematically different from those who remain, and may introduce bias in the study findings.
External Validity. The degree to which the results of a study can be generalized beyond the study sample to a larger population. 
External validity is also of particular concern in social science experiments
It can be very difficult to generalize experimental results to groups that were not included in the study
Studies that randomly select participants from the most diverse and representative populations are more likely to have external validity
The use of random sampling techniques makes it easier to generalize the results of studies to other groups
For example, a research study shows that a new curriculum improved reading comprehension of third-grade children in Iowa. To assess the study's external validity, you would ask whether this new curriculum would also be effective with third graders in New York or with children in other elementary grades.

Ethics. It is particularly important in experimental research to follow ethical guidelines. Protecting the health and safety of research subjects is imperative. In order to assure subject safety, all researchers should have their project reviewed by the Institutional Review Boards (IRBS). The Natonal insttutes of Health supplies strict guidelines for project approval. Many of these guidelines are based on the Belmont Report (pdf).
The basic ethical principles:
Respect for persons
-- requires that research subjects are not coerced into participating in a study and requires the protection of research subjects who have diminished autonomy
-- requires that experiments do not harm research subjects, and that researchers minimize the risks for subjects while maximizing the benefits for them
-- requires that all forms of differential treatment among research subjects be justified
Advantages and Disadvantages of Experimental Design
Advantages. The environment in which the research takes place can often be carefully controlled. Consequently, it is easier to estimate the true effect of the variable of interest on the outcome of interest.
Disadvantages. It is often difficult to assure the external validity of the experiment, due to the frequently nonrandom selection processes and the artificial nature of the experimental context.