Biography:
Amin is a Professor of Systems Pharmacology at the Centre for Applied Pharmacokinetic Research (CAPKR) at the University of Manchester. He has an active program of training PhD students involving proteomics, physiologically-based pharmacokinetics and pharmacodynamics and precision dosing within CAPKR. Amin was a Professor of Systems Pharmacology at the University of Sheffield prior to joining the University of Manchester and numerous graduates from his team are currently active in pharmaceutical industry or academic research. Professor Rostami has authored/co-authored over 220 peer-reviewed full articles and serves on the Editorial Boards of several journals. He has been an invited speaker at over 170 national and international meetings and has led a number of hands on workshops in the area of in vitro-in vivo extrapolation as applied to ADME in Drug Development. Amin is also the Senior Vice President of Research & Development and Chief Scientific Officer at Certara, a company with a scientific team which includes almost 100 PhDs or MDs. His mission is to ensure that the latest scientific advances in the field of biosimulation are incorporated into all of the drug development efforts by various pharmaceutical companies.
He has now been listed in the 2017 ISI Highly Cited Researchers of the World (in the field of Pharmacology and Toxicology).

Biography:
Ali Shojaie is an Associate Professor of Biostatistics and Adjunct Associate Professor of Statistics at the University of Washington. Originally trained in Industrial and Systems Engineering, he obtained his PhD in Statistics from the University of Michigan, while completing Masters degrees in Applied Mathematics and Human Genetics. Dr. Shojaie's research lies in the intersection of machine learning for high-dimensional data, statistical network analysis and applications in biology and social sciences. Dr. Shojaie's team has developed methods and publicly available software for network-based analysis of various types of “omics” data, as well as high-dimensional time course data from molecular biology and neuroscience. Dr. Shojaie is a PI of the Statistical Learning Applied to Biostatistics Lab (SLAB LAB) and is Director of the Summer Institute in Statistics for Big Data (SISBID).

Biography:
Babak joined the department of biology at Boston College in 2015 as an Assistant Professor. He has a background in engineering and physics. He received his B.Sc. and M.Sc. from Sharif University of Technology in Electrical Engineering in 1997 and 1999, respectively. He then moved to Georgia Tech where he received an M.S. degree in Physics. He received his doctoral training in Photonics Research Group (Electrical Engineering) at Georgia Tech, where he designed and implemented compact on-chip photonic demultiplexers. He continued his work at Georgia Tech as a postdoctoral fellow to make spectrometers for biosensing applications. Intrigued by biological systems, he joined Shou lab at Fred Hutchinson Cancer Research Center, and studied stability and spatial organization of microbial communities in synthetic communities. His research interests include mathematical modeling of biological systems, community ecology, microbial systems biology, and applications of technology to biological research.

Biography:
Iman Hajirasouliha is Assistant Professor of Computational Genomics in the Institute for Computational Biomedicine at Weill Cornell Medicine of Cornell University and a member of the Institute for Precision Medicine, New York, USA. He completed a Postdoctoral Scholarship at the Computer Science Department, Stanford University, and a Simons Research Fellowship at the University of California, Berkeley. His research focuses on computational genomics, computational digital pathology, large-scale sequence analysis, and characterizing somatic variations and intra-tumor heterogeneity in cancer.

Biography:
Reza Salavati has a broad background in RNA biochemistry, molecular biology, and bioinformatics. He laid the groundwork for his research program by integrating computational and experimental approaches. His group developed methods to generate a comprehensive understanding of the mechanisms of gene regulation and protein-protein interaction network of trypanosomatid pathogens. He established a high throughput screen, identified potential inhibitors for RNA editing that kill T. brucei in vitro, and proposed the mechanism of inhibition. These are promising compounds which contribute to an exciting possibility for future drug development against these important pathogens.

Biography:
Dr. Ali Ghodsi is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He is also a member of the Centre for Computational Mathematics in Industry and Commerce and the Artificial Intelligence Research Group at the University of Waterloo. He was a researcher at the University of Toronto with the Probabilistic and Statistical Inference Group and at the University of Alberta at the Alberta Ingenuity Centre for Machine Learning. His collaborations with well-known researchers involved applying statistical machine-learning methods to deep learning, dimensionality reduction, and pattern recognition problems. Ghodsi's research spans a variety of areas in computational statistics. He studies theoretical frameworks and develops new machine learning algorithms for analyzing large-scale data sets, with applications to bioinformatics, data mining, pattern recognition, computer vision, and sequential decision-making. He has been published in high-quality proceedings and journals and some of his work is used by well-known companies such as Oracle and Golden Helix.

Biography:
Dr. Kohandel has been graduated from Kharazmi University (Teacher Training University) and Institute for Advanced Studies in Basic Sciences, Zanjan. He did his PhD under supervision of Dr. Kardar (of MIT). Dr. Kohandel is currently an Associate Professor at the Department of Applied Mathematics, University of Waterloo. He is also a member of Center for Bioengineering and Biotechnology, and Center for Mathematical Medicine at the Field Institute, Toronto. His group is currently focused on the development and application of mathematical, computational and experimental models to gain a fundamental understanding of the complex process of cancer.

Abstract:
Life, as we know it, represents a complex, robust and, at the same time, an extremely fine-tuned organization of molecules and molecular processes that make use of stored and/or ambient free energy (and, as such, they are often referred to as ‘active’ processes), driving themselves into a hierarchy of nonequilibrium spatiotemporal forms known as the living matter. While the most basic questions concerning the origin of life and the emergence of its more complex forms still remain unanswered, contemporary advances have amassed into a substantial body of knowledge on the nature of the living matter, attracting interest also from other disciplines, including applied mathematics, computer science, chemistry, and physics. In this talk, we first provide a brief introduction on some of the basic notions that underlie our current understanding of active motion on the cellular level (including, for instance, the self-propelling motion, or swim, of E. coli and sperm cells in fluid media) from a physicist's point of view. We then review some of the results obtained from our recent studies in the field. These include computational modeling of the rheotactic and chemotactic responses of model swimmers in imposed shear flow in planar channels, in which case, swimmers are shown to exhibit intriguing behaviors such as population splitting and net upstream flux, paving the way for potential shear-induced separation strategies to be discussed in the end.

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:
Emerging evidence shows that endothelial cells are not only the building blocks of vascular networks that enable oxygen and nutrient delivery throughout a tissue but they also serve as a rich resource of angiocrine factors. The endothelial cells, in terms of cancer, play key roles in determining cancer progression and response to anti-cancer drugs. Furthermore, the endothelium-specific deposition of extracellular matrix is a key modulator of the availability of angiocrine factors to both stromal and cancer cells. Considering tumor vascular network as a decisive factor in cancer pathogenesis and drug response, these networks need to be an inseparable component of cancer models. Even though a large portion of our todays understanding of cancer is a product of animal studies, they have limitations leading scientists to look for a new generation of in vitro tumor models. Both computational and in vitro experimental models have been extensively developed to model tumor-endothelium interplay in synergy. While informative, they have been developed in different communities and do not yet represent a comprehensive platform. Here we discuss the necessity of incorporating vascular networks in both in vitro and in silico cancer models and discuss recent progresses and challenges of in vitro experimental microfluidic cancer vasculature-on-chip systems and their in silico counterparts. We further highlight how these two approaches can merge together with the aim of presenting a predictive combinatorial trend for studying cancer pathogenesis and testing the efficacy of single or multi-drug therapeutics for cancer treatment.

Biography:
Dr. Amir Sanati-Nezhad is an Assistant Professor at the Department of Mechanical Engineering and Manufacturing, with the joint affiliation with Biomedical Engineering program and Center for Bioengineering Education and Education, at the University of Calgary, Canada. He is also a full member of Hotchkiss Brain Institute and associate member of the Arnie Charbonneau Cancer Institute and the Libin Cardiovascular Institute. He received his MSc in Mechanical Engineering from Amirkabir University of Technology, Tehran, Iran, and a PhD from the Optical Bio-Microsystems Laboratory, Mechanical and Industrial Engineering, Concordia University, Montreal, Canada. He did two years of postdoctoral research in the Departments of Biomedical Engineering at the McGill University and at Harvard–MIT Health Sciences and Technology. His current research interests include BioMEMS, single-cell analysis, lab-on-a-chip, tissue engineering, organ-on-chip, and biosensing for applications in disease modeling, drug discovery and point-of-care.

Biography:
Dr. Hadi Shafiee is a faculty member at the Division of Engineering in Medicine and Renal Division of Medicine at Brigham and Women’s Hospital, Harvard Medical School. Dr. Shafiee’s lab aims to integrate biology/medicine with micro- and nanotechnology to develop innovative tools and solve unmet clinical problems, including applications in infectious disease diagnostics and treatment monitoring for global health, vaccine delivery, and fertility. His lab is particularly interested in developing point-of-care diagnostics through utilizing advances in consumer electronics, nanotechnology, and optical/electrical/magnetic sensing modalities. His work was featured on the cover of Science Translational Medicine, Advanced Functional Materials, Nanoscale, Small, and Lab-Chip, and was also highlighted by some of the major news outlets including CNN, theguardian, Boston Globe, CBS News, New York Times, STAT, NPR, Scientific American, and Yahoo, etc. His work on developing paper microchip technologies for point-of-care viral load measurement received several awards, including 2014 BWH Bright Futures Prize and 2015 Innovation Evergreen Award from Harvard Medical School.

Biography:
Alireza Mashaghi is a physicist and a medical scientist at Leiden University and Harvard Medical School. In the course of his academic career, he has been affiliated with various institutions including Max Planck Institute, ETH Zurich, Delft University of Technology, Massachusetts Institute of Technology, and Harvard. At Max Planck Institute, he was involved in the development of fluorescence correlation spectroscopy as well as high-resolution NMR spectroscopy for biological applications. Further, he did postdiploma work in materials science at ETH Zürich, where he designed and conducted various projects on nanotechnology, surface science, and optical/plasmonic sensing. He then moved to the Netherlands where he pioneered the use of single-molecule force methods for studying bimolecular folding processes. He received his Ph.D. with highest distinction in physics from Delft University of Technology. His current research at Leiden and Harvard encompasses a wide range of topics from single-molecule and systems biophysics to clinical medicine. He is an editorial board member of several well-established journals including NanoResearch, Medicine, and Scientific Reports.

Biography:
Professor Kevan M. Shokat is a leader in the field of chemical genetics and is particularly focused on development of new chemical approaches for studying protein and lipid kinase signaling. Professor Shokat completed his PhD at the University of California, Berkeley in the lab of Peter Schultz and began his own lab at Princeton in 1994 before moving to the University of California San Francisco/Berkeley in 1999. Among many awards he was a Life Sciences Research Foundation postdoctoral fellow and a Pew and Searle scholar and was elected to the National Academy of Sciences in 2010. His lab has pioneered the use of ATP analogs and kinase inhibitors as probes of engineered protein kinases.

Biography:
I am interested in developing methods to image anticancer immune responses to therapy. Tumors are often surrounded and invaded by bone marrow-derived cells. Imaging the infiltration of such immune cells into tumors may therefore be an attractive means of detecting tumors or of tracking the response to anticancer therapy. We developed a method to detect these cells noninvasively by positron emission tomography (PET) via the surface markers displayed by them. The ability to monitor the immune response in the course of therapy will enable early determination of the efficacy of treatment and will inform decisions as to whether treatment should be stopped or continued. Noninvasive monitoring could therefore change how therapies are applied and assessed, to the benefit of many patients.

Current Research:
Computational Cancer Genomics: Using high-throughput sequencing data, we are tying to understand different aspects of Cancer in Human.
The application of deep-learning techniques in Life-Science: Using state-of-the-art techniques in Big-Data analysis, we are trying to understand and model the behavior of living organisms.
Metabolic networks constraint-based modeling: As the best studied biological network, metabolic networks have a wide range of application from biotechnology to medicine. we try to improve in-silico models of metabolism by adding more realistic physical constraints. The ultimate goal could be having an in-silico whole-cell model that can respond to environmental fluctuations.

Abstract:
A metabolic network model provides a computational framework for studying the metabolism of a cell at the system level. The organization of metabolic networks has been investigated in different studies. One of the organization aspects considered in these studies is the decomposition of a metabolic network. The decompositions produced by different methods are very different and there is no comprehensive evaluation framework to compare the results with each other. In this study, these methods are reviewed and compared in the first place. Then they are applied to six different metabolic network models and the results are evaluated and compared based on two existing and two newly proposed criteria. Results show that no single method can beat others in all criteria but it seems that the methods introduced by Guimera & Amaral and Verwoerd do better on among metabolite-based methods and the method introduced by Sridharan et al. does better among reaction-based ones. Also, the methods are applied to several artificial networks, each constructed from merging a few KEGG pathways. Then, their capability to recover those pathways are compared. Results show that among metabolite-based methods, the method of Guimera & Amaral does better again, however, no notable difference between the performances of reaction-based methods was detected. Keywords: Metabolic network; Module; Decomposition; Comparison.

Biography:
Changiz Eslahchi graduated from the faculty of mathematical sciences of Teacher Training University of Tehran with BSc degree in 1987. He received his MSc degree in mathematics in 1989 from the University of Shiraz and PhD degree in 1998 from Sharif University of Technology. He is a professor in the department of computer sciences, school of mathematics, Shahid Beheshti University and a senior researcher at school of biological sciences, Institute for Research in Fundamental Sciences, Tehran, Iran. His main interests are system biology and structural biology. His research lies in the area of data-driven bioinformatics, creating algorithms to infer and exploit simple models of complex interactions. He also interest to understand the biology underlying genetic diseases such as cancer.

Abstract:
TBA

Biography:
Mohammad Ganjtabesh received his B.Sc. degree in Pure Mathematics (2001) from Tabriz University (Iran). Then, in 2003, he finished his M.Sc. program in Computer Science, at the University of Tehran. After the graduation, he accepted in Ph.D. program in Computer Science (University of Tehran) and graduated in 2009. At the same time, he was accepted for a double-degree Ph.D. program in Bioinformatics at the Ecole Polytechnique (Paris, France). Currently, he is an associate professor at the University of Tehran. His research interest includes the problems related to the RNA structures as well as Computational Vision Neuroscience.

Biography:
Zahra-Soheila Soheili Ph.D. is an associate professor of cell and molecular biology at National Institute of Genetic Engineering and Biotechnology (NIGEB).She graduated from the Institute of Biochemistry and Biophysics, University of Tehran, Iran in 2002. Subsequently, she completed her postdoctoral fellowship at the U268 INSERM, Paris France in myeloproliferative disorders, where she focused on the role of the extracellular matrix in primary myelofibrosis. In 2003 she was recruited to NIGEB and became an instructor and junior investigator at the “medical biotechnology department”, where she studied on human retinal pigment epithelium cells’ biology and behavior, in vitro. Recently, her lab discovered two types of RPE cell lines and wrote a license agreement for hRPE cell line trademark to distribute it through the international abmgood company. Since her arrival at the medical biotechnology department, she established herself within the NIGEB community and rapidly risen through the ranks to become a senior investigator, currently at the associate professor level. She is an associate member of the institute board advisory committee. She directs a multidisciplinary team of PhD students, who have a proven record of excellence in their respective fields. In the past five years, she pursued novel therapies for important eye diseases, such as age-related macular degeneration (AMD) and retinitis pigmentosa. She is working on designing and synthesis of chimeric protein molecules for molecular therapy of ocular diseases, specifically for anti-angiogenic therapies in CNV based disorders, and also for replacement of degenerated photoreceptors by targeted optogenetic engineering. Her team is working on AAV-based and Lentiviral vectors for gene delivery to the retinal cells, in vitro, and in animal models of the ocular disease. In 2017 Dr. Soheili got a chance to be an invited scientist at the “service of ophthalmology in neuroclinic of the faculty of medicine, university of Genève, Switzerland”, which is the coordinator of the European project of the “Target AMD”.

Abstract:
The human intestinal tract is colonized by a myriad of microbes that have developed intimate interactions with the host.The interactions between gut microbiota and the host involve a complex network of metabolic pathways and of biologically active molecules secreted by intestinal bacteria, some of which are packed into nanoparticles known as outer membrane vesicles (OMVs). Dysbiosis between gut microbiota and the host promote the loss of the intestinal barrier, thus increasing intestinal permeability, allowing for gut microbiota to travel across the intestinal epithelium and into systemic circulation. This phenomenon is often referred to as “leaky gut” syndrome, and it enables gut microbiota to impact the entire body and immune system. In this situation,even commensal bacteria and probiotic have unfavorable effects on the host metabolism and immune system. Several components and also DNA, RNAs are packaged in OMVs. Recently, new studies demonstrate these molecules could be effective on eukaryout and prokaryout interactions. We are going to study the effects of dominant microbiota derived extracellular as new approach for prevention, control & treatment of metabolic syndrome specially obesity, type 2 diabetes and atherosclerosis. To perform this project, we need to determine the pattern of gut microbiota & metabolic profile/ endotoxemia, the immune response and the Fecal SCFAs concentration in obese, T2D & atherosclerosis patients. Next, OMVs of Akkermansia muciniphila , faecalibacterium prausnitzii, Bacteroides fargilis & B. thetaiotaomicron will be extracted. Then, RNAs will be extracted from OMVs. By RNA sequencing, this molecule will be aligned to the sequenced chromosomal DNA of related bacteria and the human genome. Finally, the effects of these bacteria and their OMVs will be studied on prevention, control & treatment of metabolic syndrome in vitro and in vivo. As it mentioned, the gut microbiota is essential to human health and the immune system and plays a major role in the bidirectional communication between the gut and the brain. Based on evidence, the gut microbiota is associated with metabolic, neuropsychiatric and major depressive disorders. In addition recent studies showed that gut microbes can shape responses to cancer immunotherapy. Also, the type of microbe was also linked to differences in responses to treatment. Epigenetic modulation of gene activity occurs in response to non-genetic factors such as bacterial infection, physical activity, dietary factors, and environmental toxins. In addition, each of these factors is thought to affect and be affected by the gut microbiota. The core human gut microbiota contributes to the developmental origin of diseases by modifying metabolic pathways. So, researchers need to learn more about how those microbes exert their influence on the metabolic and immune system and need more information about epigenetic and gut microbiota interactions.

Biography:
Seyed Davar Siadat is Professor of Medical Microbiology at Pasteur Institute of Iran. He has an extensive research portfolio in gut microbiota field; especially in “Outer Membrane Vesicle” as well as subunit vaccines, conjugate vaccines, etc. He has published more than 120 papers in scientific journals (National & International) and serving as editorial board member/reviewer of several scientific Journals from the field of Medical Microbiology and Infectious Diseases. He has mentored and supervised many students for their thesis or summer scholarship program. He has guided 30 Ph.Ds and More than 50 MSc students.

Abstract:
The efforts in our laboratory in the School of Biology at the University of Tehran has mainly been directed at identification of disease causing genes. The diseases we have focused on have been neurologic disorders including glaucoma (Glc), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Glaucoma is the leading cause of blindness in the world, PD is a common neurodegenerative disease with an estimated prevalence of 2% among individuals older than 65 years, and ALS is an adult onset fatal motor neuron disease and the third most common neurodegenerative disease. All three diseases are complex and their etiologies have a significant genetic component. Genetic aspects of these important diseases had not previously been investigated in the Iranian population. In addition to screening of known causative genes, we have used genome wide linkage analysis and exome sequencing to identify causative genes in affected families without mutations in the known genes. We have collected large Iranian patient cohorts affected with each of the diseases, and data on the patients have revealed clinical and epidemiological information specific to the Iranian patients. With respect to glaucoma, we have shown that mutations in CYP1B1 are the cause of disease in 70% of Iranians affected with primary congenital glaucoma and we identified LTBP2 as a novel causative gene. For PD, we found a novel locus that has been named PARK15 and identified FBXO7 as the causative gene within the locus. Additionally, we identified ADORA1 that encodes an adenosine receptor as cause of a form of Parkinsonism accompanied with intellectual deficiency, and GTPBP2 that encodes GTP binding protein 2 as cause of a complex presentation that includes cognitive dysfunctions. Finally, we have screened the three most common ALS causing genes, SOD1, C9ORF72, and TARDBP among Iranians affected with ALS. We have also identified patients affected with HMSN-P. This is a disease that is related to ALS and has previously been reported only in individuals of the Far East. Our genetic approach has in several cases led to correct diagnosis of disease in patients who had been misdiagnosed. Causative genes of several non-neurological disease have also been identified. We emphasize the genetic heterogeneity of the Iranian population marks it as a powerful target for genetic studies. We also emphasize the strength of novel genetic approaches including whole genome exome sequencing for identification of novel disease causing genes.

Abstract:
TBA