Gurinder Singh “Mickey” Atwal (Cold Spring Harbor Lab, U.S.A.)

Short bio

Fueled by massive amounts of data generated from DNA sequencing technologies, the Atwal Lab is currently focused on population genetics, cancer biology and high-performance computing. We often tackle scientific questions computationally by invoking theoretical concepts from statistical physics and machine learning.
A common thread in our research is the quest to understand collective biological phenomena from the perspective of the physical sciences. To this end, we develop and deploy mathematical and computational tools to address quantitative principles governing the behavior of many-body biological systems, ranging from molecular interactions in a single eukaryotic cell to the evolution of the species Homo sapiens.Our fantastic team of lab members and collaborators consists of physicists, biologists, mathematicians and computer scientists and we work closely with experimentalists and clinicians both here at Cold Spring Harbor Laboratory and around the world. For more details feel free to browse through our website.

Niko BeerenwinkelETH (Zurich, Switzerland)

Short Bio

Niko Beerenwinkel was born in Düsseldorf, Germany. He studied mathematics, biology, and computer science, and received his Diploma degree in Mathematics from the University of Bonn in 1999 and his PhD in Computer Science from Saarland University in 2004. He was a postdoctoral researcher at the University of California at Berkeley (2004-2006) and at Harvard University (2006-2007) before joining ETH Zurich as assistant professor of computational biology.

Niko Beerenwinkel’s research is at the interface of mathematics, statistics, and computer science with biology and medicine. His interests range from mathematical foundations of biostatistical models to clinical applications. Current research topics include haplotype inference from ultra-deep sequencing data, somatic evolution of cancer, reconstruction of signaling pathways from RNAi screens, HIV drug resistance, graphical models, and algebraic statistics.

He has authored over 50 research articles in the areas of computational biology, bioinformatics, biostatistics, virology, and cancer biology. His honors include the Otto Hahn Medal of the Max Planck Society and the Emmy Noether Fellowship of the German National Science Foundation.

Lecture outline

Cancer evolution is a stochastic evolutionary process characterized by the accumulation of mutations and responsible for tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to describe the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular profiling data. We discuss recent approaches to modeling the evolution of cancer, including population genetics models of tumorigenesis, phylogenetic methods of intra-tumor subclonal diversity, and probabilistic graphical models of tumor progression. Evolutionary modeling will play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Charles Cantor, Agena Biosciences, Sequenom, Retrotope (San Diego, USA)

Short Bio

Charles Cantor is currently affiliated with Agena BioSciences Inc., Sequenom Inc. and Retrotope Inc. He is Professor Emeritus, Biomedical Engineering
Professor of Pharmacology, School of Medicine at Boston University, and former director of the DoE Human Genome Project. Charles Cantor’s research is focused on identifying biological problems that are resistant to conventional analytical approaches and then developing new methodologies or techniques for solving these problems. His laboratory has developed methods for separating large DNA molecules, for studying structural relationships in complex assemblies of proteins and nucleic acids, and for sensitive detection of proteins and nucleic acids in a variety of settings. His current interests include the development of new methods for faster DNA sequencing, the development of new variations and analogs of the polymerase chain reaction, the development of bacterial strains suitable for environmental detoxification, and the discovery of human genes associated with sense and taste. He is also interested in exploring the possible use of biological molecules for applications in nanoengineering and microrobotics, and in making detectors capable of recognizing specific single molecules.

Francesca Ciccarelli, King’s College (London, U.K.)

Short bio

Francesca Ciccarelli graduated in pharmaceutical chemistry at the University of Bologna and was trained as a computational biologist at the EMBL-Heidelberg. Her early work focused on DNA and sequence analysis and phylogenetic reconstructions. In 2005, she started her group at the European Institute on Oncology in Milan with the aim to elucidate the role of mutations in the development of cancer. Her group applies a combination of experimental and in silico approaches and pursues two main lines of investigation. The first aims at characterizing the systems-level properties of cancer genes and to use them to identify novel targets for therapy. The second line involves extensive cancer genome sequencing to characterize the evolution of cancer clones. In 2014, she moved to London as an Associate Professor at the King’s College School of Medicine.

Lecture Outline

In my lectures I will discuss on the recent advances in on our understanding of cancer genetics and evolution. I will start by reviewing the accumulating evidence of cancer heterogeneity in terms of acquired genetic mutations and genomic rearrangements. I will then describe the impact of these novel results on our modelling of cancer networks. Finally, I will describe how the characterization of cancer driver genes in terms of network can help in identifying cancer-specific targets to be used in therapy.

Francesca Demichelis, Università degli Studi di Trento (Trento, Italy)

Short Bio

Dr Demichelis trained as a physicist at the University of Trento, Italy, and at the Imperial College of Science, Technology and Medicine in London, UK. She then obtained a PhD from the International School in Information and Telecommunication at the University of Trento where she worked on integrated and automated analyses to finally model in situ protein expression data from large scale tumour samples collections. She was then a postdoctoral fellow at the Harvard Medical School in Boston working on the characterization of the genomic landscape of solid tumours using high-density oligonucleotide platforms data and computational genomic approaches.In 2007, she joined Weill Cornell Medical College as Instructor and Institute Fellow in Computational Biomedicine and later joined the Faculty as Assistant Professor in Pathology and Laboratory Medicine in 2008. Since 2011 she is Assistant Professor at the Centre for Integrative Biology at the University of Trento where she directs the Laboratory of Computational Oncology with focus on the understanding of clonality and evolution to identify tumour driver events and on bridging germline polymorphisms and somatic aberrations to dissect tumour subclasses.  She is the recipient of multiple awards including the New Investigator Award from the U.S. Department of Defense and the Prostate Cancer Foundation Competitive Award. She is co-recipient of the first American Association for Cancer Research (AACR) Team Science Award.

Lecture outline

Approaches to infer tumor evolution from single base level data will be presented and discussed in the context of the identification of driver events that are key in tumor progression both at gene and pathway levels. In addition, examples of tumor dynamics based in circulating DNA (plasma) from advanced patients will be presented.


Bhubaneswar MishraCourant Institute of Mathematical Sciences (New York University New York, NY, U.S.A.)

Short Bio

Professor of Computer Science & Mathematics, Courant Institute, New York University; Principal Investigator, NYU/Courant Bioinformatics Group; Principal Investigator, SEI/CMU/NYU Center for Malicious Behavior and Model Checking; Professor of Cell Biology, NYU School of Medicine, New York University; QB Visiting Scholar Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory; Adjunct Professor Department of Human Genetics, Mt. Sinai School of Medicine; Adjunct Professor Tata Institute of Fundamental Research

Lecture outlines

Cancer: From Endless Complexity to Simplicity (Introductory Lecture)

Cancer biologists have been celebrating the powers of reductionist molecular biology and its major successes for four decades. Many of those who have participated in cancer research during this period have witnessed wild fluctuations from times where endless inexplicable phenomenology reigned supreme to periods of reductionist triumphalism. However, the advent of massive amounts of -omics data in recent years has tampered that enthusiasm and is pointing to a move back to confronting the endless complexity of this disease. We will discuss how this summer school may help us to create a roadmap for the next generation of analysis. Such a roadmap can be built on statistical inference from data, philosophical models of causality, mathematical basis of phenomenological models, bio-chemical frameworks for mechanistic models, logical approaches based on model checking and control-theoretic approaches to therapy design.

Causality and Cancer: Multiple Facets: (Sept 30 Lecture)

Existing techniques to reconstruct tree  or DAG models of progression for accumulative processes such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In these lectures we define a novel theoretical framework to reconstruct such models based on the probabilistic notion of causation defined by Suppes, and extended by Skyrm, Dupre and Cartwright. This view of causality differs fundamentally from that based on correlation. We consider a general reconstruction setting complicated by the presence of noise in the data, owing to the intrinsic variability of biological processes as well as experimental or measurement errors. Our analysis suggests the applicability of the method on small datasets of real patients.

Victor Moreno, ICO (Barcelona, Spain)

Short bio

Dr. Moreno is Professor of Preventive Medicine and Director of the Cancer Prevention and Control Program at the Catalan Institute of Oncology-IDIBELL in Barcelona, Spain. He has been long experience in genetic and molecular epidemiology studies on CRC. He has designed and conducted several case-control studies on CRC and contributed to the identification of genetic susceptibility loci, and molecular mechanisms involved in CRC etiology and progression. He leads the Unit of Biomarkers and Susceptibility at ICO, with strong expertise in biostatistics and bioinformatics. His team has experience both in the design of epidemiological and clinical studies and in the analysis of omics data. In his most recent project, COLONOMICS, (www.colonomics.org) tumors and adjacent normal mucosa from a sample of 100 CRC patients have been extensively characterized at molecular level (gene expression, micro-RNA expression, methylation, genetic variation, CNVs and somatic mutations in exome). Also samples of normal mucosa from 50 healthy donors have been analyzed. This resource is the bases for diagnostic and prognostic biomarker discovery and to elucidate the mechanisms involved using systems biology approaches.



Lecture outline

Transcriptional regulatory programs of normal and tumor colon cells

Dysregulation of transcriptional programs leads to cell malfunctioning and can have an impact in cancer development. Within the COLONOMICS study (www.colonomics.org) we have characterized global differences between transcriptional regulatory programs of normal and tumor cells of the colon. Expression array data (Affymetrix Human Genome U219) from 100 samples of colon tumor and their paired adjacent normal mucosa were used to reconstruct. transcriptional networks. ARACNe algorithm was used to infer a consensus network for each cell type. Networks were compared regarding topology parameters and identified well-connected clusters, which were characterized by functional enrichment. ENCODE ChIP-Seq data curated in the hmChIP database was used for in silico validation of the most prominent transcription factors. A large loss of transcriptional interactions in the tumor network was observed, together with a subgroup of emergent or up-regulated transcription factors related to relevant colon cancer mechanisms. In a second analysis, microRNA data has been related to gene expression data to identify specific relevant regulatory clusters.

James OsborneCS Department,University of Oxford, (Oxford, U.K.)

Short bio

I am running a workshop on Cell Based and Individiual Based Modelling (CBIBM) as part of the 2014 International Conference on Computational Science, 10th-12th June 2014, Cairns Australia. For more information and to submit a paper or abract for a talk go here.

A recent poster I presented at the “Workshop on Mechanics and Growth of Tissues: from Development to Cancer” in paris in January 2014 is availiable here.

In 2000-2004 I completed an undergraduate degree in Mathematics at New College. From there I went to the Life Sciences interface Doctoal Training Centre (LSI DTC) to begin my DPhil studies. For research component of my DPhil I was based in the Computational Biology Group under the supervision of Jonathan Whiteley, my thesis was entitled “Numerical and Computational Methods for Simulating Multiphase Models of Tissue Growth”.

From 2008-2011 I was working as a Post Doctoral Research Asistant, in the Computational Biology Group, looking at “Computational Approaches to Multiscale Modelling in Systems Biology” as part of the Oxford Centre for Integrative Systems Biology (OCISB). Between 2009 ans 2013 I returned to the DTC (www.dtc.ox.ac.uk) as an Associate Director with my time split betwen research and the DTC.

Since 2011 I have been a Senior Researcher in the Computational Biology Group and lead the cell based modeling group.

Since July 2013 I have been working at Microsoft Research Cambridge as part of the Boiological Computation Group in the Computational Science Laboratory.