Speakers

Abstract:
The use of cancer model systems continues to enable deeper functional understanding of the drivers and dependencies in cancer. The creation of large panels of comprehensively characterized human cancer models has provided a rigorous backbone upon which to study genetic variants, candidate targets, small molecule and biologic therapeutics and to identify cancer dependencies. Here we describe the Cancer Cell Line Encyclopedia (CCLE) project, an effort to comprehensively annotate human cancer cell lines and describe different datasets available in this project and how they could be used for cancer research.

Biography:
Mahmoud Ghandi is a group leader in Levi Garraway's lab under the direction of Levi Garraway. He leads the Computational Biology group in the Cancer Cell Line Encyclopedia (CCLE) project — a public collection of cancer cell line data consisting of over 1000 cancer cell lines — as part of the Cancer Program at the Broad Institute of MIT and Harvard. His group uses high-throughput genomics integrated with large-scale small molecules, shRNA, and CRISPR screens to study the molecular mechanisms of cancer, find new vulnerabilities and therapeutic targets, and investigate the mechanisms of drug resistance. The group also develops and uses state-of-the-art computational methods for integrative analysis of next generation sequencing data as well as proteomic, metabolomic, microRNA, and epigenetic data to build predictive models for drug sensitivities and to advance our understanding of cancer biology.

Abstract:
I will discuss a number of physically relevant phenomena in the behaviour of bacteria when they interact with surfaces, starting from the swimming behaviour of bacteria near surfaces through to the reversible and irreversible attachment processes, the decision making to form an early colony of bacteria on surfaces, the self-organization of trail following bacteria on surfaces, and the shape of a growing biofilm front.

Biography:
Ramin Golestanian (born February 26, 1971) received his bachelor's degree from the Sharif University of Technology in Tehran, and his master's and doctoral degrees from the Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan. He received his PhD from Mehran Kardar at MIT, followed by a postdoctoral research fellowship at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara. He held academic positions at the IASBS, the University of Sheffield, and Oxford University, where in 2007 he became a Full Professorascended. He has a broad interest in many aspects of statistical physics of non-equilibrium, soft matter and biophysics. Golestanian has distinguished himself in particular for his role in the development of microscopic swimmers and active colloids through his contributions to the physics of active matter.

Honors, prizes, memberships
Ramin Golestanian was elected a Fellow of the American Physical Society and the Institute of Physics and received the Holweck Prize of the Société Française de Physique and the Institute of Physics, the EPJE Pierre-Gilles de Gennes Lecture Prize, the Martin Gutzwiller Fellowship of the MPI-PKS , the Nakamura Lecturer Award of the UCSB, and the 50th Anniversary Most Distinguished Alumni Award of the Sharif University of Technology.

Abstract:
The complexity and applications of Physiologically Based Pharmacokinetic (PBPK) models to predict drug disposition and drug-drug interaction has increased dramatically over the last 10-15 years. These models are matured enough that simulations results are used in lieu of clinical studies on approved drug labels (1). This increased use of PBPK models has been largely driven by the development of techniques that allow the PBPK models to be parametrised using information generated in human in vitro systems (e.g. scaling the rate of metabolism in human liver microsomes or hepatocytes in vitro to predict intrinsic clearance in vivo), the use of physiochemical and in vitro blood/protein binding to predict tissue distribution in vivo, the use of permeability in in vitro cell systems to predict intestinal permeability in vivo. As part of these developments much focus has been put on mechanistic prediction of drugs oral absorption. The main objective of oral absorption modelling and simulation is to predict the drug bioavailability. Oral drug absorption is affected by both the GI physiological parameters and the drug substance and formulation characteristics. To mechanistically predict oral drug absorption the Advanced Dissolution Absorption and Metabolism (ADAM) model has been developed since 2006 (2). ADAM is a multi-compartmental GI transit model fully integrated into the Simcyp human population-based Simulator (3, 4). There is a separate Paediatric module which includes systems differences between adult and various age bands of paediatric subjects where such information is available (5). Given the sophistication and complexity of oral drug absorption processes developing a mechanistic model requires integration of knowledge and expertise from various fields including GI tract anatomy, physiology and biology, drug metabolism and transport, drugs solubility and dissolution, drug release and formulation, mathematical modelling, and software engineering. In this presentation, the evolution of the ADAM model and its applications over the last decade are presented.

Biography:
Masoud Jamei is the Vice President of Research and Development at Simcyp Division of Certara UK Limited where he leads a team of around 40 scientists and 15 software developers focusing on the design, development and implementation of various aspects of systems pharmacology models including in vitro-in vivo extrapolation techniques, Physiologically-Based Pharmacokinetics / Pharmacodynamics (PBPK/PD) models of small and large molecules and applying top-down Population PK (PopPK) data analysis to PBPK models in healthy volunteer and patient populations. He has been the author or co-author of over 70 peer-reviewed manuscripts and book chapters and over 150 abstracts in the field of modelling and biosimulation. He has also been an invited speaker and a session organiser/moderator at national and international meetings and also leads well-known Simcyp hands-on workshops on model-informed drugs development. He currently serves as a Vice-Chair of the Special Interest Group (SIG) on PK/PD and Systems Pharmacology of the Board of Pharmaceutical Sciences (BPS) of International Pharmaceutical Federation’s (FIP) and was the past Chair of the AAPS Systems Pharmacology Focus Group. He obtained his BSc and MSc from the Ferdowsi University of Mashhad, Iran in 1988 and 1991 respectively. In 2002 he earned a PhD in Control Systems Engineering at the University of Sheffield, UK, and carried out one year of post-doctoral research there. He was an Honorary Lecturer at the University of Sheffield (2008-2011) and a visiting Senior Lecturer at the University of Manchester (2011-2014). In 2003 he joined Simcyp.

Abstract:
Some aspects of mathematics is involved in all sciences. In some sciences, such as engineering, it is difficult and in some sciences, such as mechanics, it is impossible to define a border to stay away from mathematics. When it comes to biology, there is an extensive literature of mathematical activities in various fields related to biology such as bioinformatics, game theory, dynamical systems, perturbation theory and differential geometry. This is usually called “Mathematical Biology” or “Mathematical Bioscience”. There are many questions regarding this massive involvement of mathematicians in biological problems. Here, we address three bunch of such questions. The first, the standard one, concerns about the application of mathematics and its techniques in biology. The second, the non standard one, is the role of biological problems in progress of mathematics. The third and, the controversial one, concerns about the real value of such interactions. When it is obvious that mathematics has many applications in biology and helps finding the answers of so many difficult questions, why some people are skeptical about the role of mathematics in biology? In this talk, mainly, the first and the second bunch of questions will be discussed. The third bunch will be briefly addressed. The importance of answering these questions does not merely apply on research. It is worth noting that they will have a significant effect on education, educational plan, syllabus, and curriculum. In some extend, the first may have impact on budgets. But the latter has a longer effect for future human resources of the society. Therefore, answering these questions are not merely scientifically motivated. The wrong answers may negatively act on country’s development. Although, here in this abstract, little things about mathematical techniques are explicitly addressed, but, the answers of the above questions are highly depend on technical parts of mathematical activities in biology and biological activities in mathematics.

Biography:
Field on interest: Control Theory and dynamical systems and their applications in real world problems
Education: PhD in Applied Mathematics, University of Exeter
Msc in Pure Mathematics, Kharazmi University
Bsc in Electrical Engineering, Ferdowsi University

Abstract:
DNA is one of the fundamental ingredients of living cells and viruses. Since its discovery, DNA has been at the focus of studies not only by biologist but also by physicists who directly contributed in characterizing its famous double-helical structure. DNA packaging in cells and viruses has emerged as one of the most puzzling problems in DNA-biophysics. It reveals intriguing and even counterintuitive roles played by the long-ranged electrostatic interactions between DNA and other highly charged molecules, such as polyamines and colloidal nanoparticles, involved in the packaging process. In this talk, I will first give an overview of the challenges encountered in understanding the electrostatic properties of DNA in its densely packed states, such as DNA condensates in the bulk and in bacteriophages and the chromatin fiber in eukaryotic cells. I will then discuss our current understanding of the basic physics underlying these and other related phenomena, such as encapsidation of nanoparticles in virus-like particles, based on our recent developments in theoretical and computational modeling of strongly charged Coulomb fluids.

Biography:
Ali Naji is an associate professor of physics at the Institute for Research in Fundamental Sciences (IPM) in Tehran, Iran. He received his PhD in Physics from the Ludwig-Maximilian University of Munich, Germany, in 2005 on the theory and simulation of charged polymers. He carried out his postdoctoral work at the University of California, Santa Barbara (2006-2009) and, as a Royal Society Newton Fellow (2009-2011), at the University of Sheffield and at the Department of Applied Mathematics and Theoretical Physics of the University of Cambridge, UK. His research interests include physics of Coulomb fluids and strongly charged macromolecules (such as colloids, polymers and membranes), nano-particle/DNA complexes, electrostatic stability of virus-like nano-capsids, Casimir effect, electrostatics of soft disordered media and, more recently, fluctuating hydrodynamics of strongly confined fluids, and active self-propulsion in fluid media.

Abstract:
Understanding the molecular etiology of cancer is challenging with various complexities including the multifactorial nature of the disease as well as the heterogeneity that exists at both genome and phenome levels. Advances in next-generation sequencing (NGS) have made it possible to profile multiple levels (e.g. genomic, epigenomic, transcriptome) of molecular landscapes of patient samples at a high resolution. This has enabled us to identify driver abnormalities of several cancers, in particular those with a less heterogeneous molecular landscape. However, identifying the aberrations that are functionally relevant among the plethora of abnormal genomic patterns, particularly given the presence of many passenger events, has remained challenging for many cancers. We have established a multi-disciplinary program, which leverages systems biology approaches that combine genomics datasets involving sequences from hundreds of cancers coupled with detailed clinical data and functional studies to identify driver aberrations in cancer. In my talk, I will describe the components of this program spanning over molecular profiling, computational biology and functional genomics. I will also present examples of contribution of this program to major cancer studies in our centre, and will discuss novel findings from our studies of solid tumors including renal cell carcinoma.

Biography:
Dr. Yasser Riazalhosseini received a BSc degree in Microbiology from University of Isfahan in 2002, a MSc degree in Molecular and Cellular Biology from Khatam Institute of Higher Education in 2005, and the PhD in Molecular Biology from Heidelberg University in 2010. After undertaking post-doctoral studies on Cancer Genomics at the Deutsches Krebsforschungszentrum (DKFZ; German Cancer Research Centre), Dr. Riazalhosseini joined the Department of Human Genetics at McGill as an Assistant Professor, and was appointed Group Head of the Cancer Genomics program at the McGill University and Genome Québec Innovation Centre (the McGill Genome Centre). His principal activity has been to initiate and lead a multidisciplinary, applied research program on cancer genomics, with a primary focus on renal cell carcinoma. Dr. Riazalhossieni’s research is focused on understanding the genomic causes of solid cancers with the goals of obtaining better preventions and treatments. His research program uses systems biology approaches that combine genomics datasets involving sequences from hundreds of cancers coupled with detailed clinical data, and high-throughput functional studies. Contributing to his research goals, Dr. Riazalhosseini has a major role in the Cancer Genomics of the Kidney (CAGEKID) consortium, part of International Cancer Genome Consortium (ICGC), where he has assumed joint responsibility for the data analysis and leads the down-steam validation of results.

Abstract:
Inferring Gene Regulatory Networks (GRNs) from gene expression data is a major challenge in systems biology. The Path Consistency (PC) algorithm is one of the popular methods in this field. However, as an order dependent algorithm, PC algorithm is not robust because it achieves different network topologies if gene orders are permuted. In addition, the performance of this algorithm depends on the threshold value used for independence tests. Consequently, selecting suitable sequential ordering of nodes and an appropriate threshold value for the inputs of PC algorithm are challenges to infer a good GRN. In this talk, I propose heuristic algorithms to infer GRNs. The effectiveness of proposed methods is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The results indicate that the new algorithms are suitable for learning GRNs and it considerably improves the precision of network inference.

Biography:
Rosa received her B.Sc. in Statistiucs from Shahid Beheshti University and her M.Sc. in Statistics from Tehran University. She was awarded her Ph.D. from Shahid Beheshti University. Rosa Aghdam is Post-Doctoral Research Fellow of Bioinformatics in the Institute of Research in Fundamental Sciences (IPM), where she has been consistently involved in research since 2009. Her research focuses on Learning Gene Regulatory Networks and detecting significant genes in cancer, Bayesian Network, System Biology, Hidden Markov Models and Regression Model.

Abstract:
Most studies of gene regulatory network (GRN) inference have focused extensively on identifying the interaction map of the GRNs. However, in order to predict the cellular behavior, modeling the GRN in terms of logic circuits, i.e., Boolean networks, is necessary. The perturbation techniques, e.g., knock-down and over-expression, should be utilized for identifying the underlying logic behind the interactions. However, we will show that by using only transcriptomic data obtained by single-perturbation experiments, we cannot observe all regulatory interactions, and this invisibility causes ambiguity in our model. Consequently, we need to employ the data of multiple omics layers (genome, transcriptome, and proteome) as well as multiple perturbation experiments to reduce or eliminate ambiguity in our modeling. In this paper, we introduce a multi-step perturbation experiment to deal with ambiguity. Moreover, we perform a thorough analysis to investigate which types of perturbations and omics layers play the most important role in the unambiguous modeling of the GRNs and how much ambiguity will be eliminated by considering more perturbations and more omics layers. Our analysis shows that performing both knock-down and over-expression is necessary in order to achieve the least ambiguous model. Moreover, the more steps of the perturbation are taken, the more ambiguity is eliminated. In addition, we can even achieve an unambiguous model of the GRN by using multi-step perturbation and integrating transcriptomic, protein-protein interaction, and cis-element data. In conclusion, relying on the results of only knock-down experiments and not including as many omics layers as possible in the GRN inference, makes the results ambiguous, unreliable, and less accurate.

Biography:
Amir Alizad joined the School of Biological Sciences at the IPM in 2017 as a postdoctoral fellow. He has a background in engineering and worked on Systems biology in his Ph.D. He received his B.Sc. and M.Sc. in Electrical Engineering from Amirkabir University of Technology and University of Tehran, in 2003 and 2006, respectively. He received his Ph.D. at University of Alberta in 2014, where he worked on gene regulatory network inference by utilizing information theory and statistics. Moreover, He has 7 years of industrial work experience in Iran and Canada as a system designer and project manager. His research interests include gene regulatory network inference, causality inference, biological time series analysis, and mathematical modeling of biological systems.

Abstract:
Dictyostelium discoideum amoeba is a well-established model organism for studying the crawling locomotion of eukaryotic cells. These amoebae extend pseudopodium - a temporary actin-based protrusion of their body membrane to probe the medium and crawl through it. Experiments show highly-ordered patterns in the growth direction of these pseudopodia, both in the absence and presence of external chemical stimulants. Here, we propose a discrete model for studying and investigating the cell locomotion and chemotaxis based on the experimental evidences.

Abstract:
To ask why there are species is to ask one of the most fundamental questions in evolutionary biology. The presence of reproductive isolation between species, in the form of inviable and/or sterile hybrids, poses a serious challenge: why would natural selection favor a trait as seemingly disadvantageous as hybrid inviability/sterility? Our current understanding of the genetics of speciation enables us to answer this question in general terms, but a more detailed understanding of the genetics of speciation demands rigorous theoretical models to explore this “mystery of mysteries”. In this talk, I explore how speciation can occur in two different fitness landscapes: Gavrilets’ holey fitness landscape and a tunably rugged fitness landscape. Studying speciation in these artificial fitness landscapes sheds some light on the genetic changes that can result in the emergence of reproductive isolation.

Biography:
Dr. Ata Kalirad received his BSc in cell & molecular biology from the University of Tehran in 2011 and his PhD in evolution from the University of Houston in 2016. During his PhD, he worked under the supervision of Dr. Ricardo B. R. Azevedo on the question of speciation, which resulted in proposing the “spiraling complexity model” to explain the effect of epistasis on the accumulation of genetic incompatibilities. He has been working at the School of Biological Sciences (IPM) since 2017, focusing on the role of stochasticity in living systems.

Abstract:
Based on previous studies, empirical distribution of the bacterial burst size varies even in a population of isogenic bacteria. Since bacteriophage progenies increase linearly with time, it is the lysis time variation that results in the bacterial burst size variations. Here, the burst size variation is computationally modeled by considering the lysis time decisions as a game. Each player in the game is a bacteriophage that has initially infected and lysed its host bacterium. Also, the payoff of each burst size strategy is the average number of bacteria that are solely infected by the bacteriophage progenies, after lysis. For calculating the payoffs, a new version of ball and bin model with Time Dependent Occupation Probabilities (TDOP) is proposed. We show that Nash equilibrium occurs for a range of mixed burst size strategies that are chosen and played by bacteriophages, stochastically. Moreover, it is concluded that the burst size variations arise from choosing mixed lysis strategies by each player. By choosing the lysis time and also the burst size stochastically, the released bacteriophage progenies infect a portion of host bacteria in environment and avoid extinction. The probability distribution of the mixed burst size strategies is also identified. Keywords: Game theory; Mixed Nash equilibrium; Lysis; Lysogeny; Ball and Bin model; Probability distribution; Bacteriophages; Bacterium; Multiplicity of Infection (MOI).

Biography:
Seyed Amir Malekpour received his B.Sc. in 2006, M.Sc. in 2008, and Ph.D. in 2016 in Statistics, all degrees from University of Tehran. His research interests include stochastic processes in the genome-wide variation studies, Bayesian models in studding the population dynamic of virus, and game-theoretic approaches in modeling the natural behavior of micro-organisms, in system biology. He is currently a Post-Doctoral Research Fellow at School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran.