Abstract: Recent studies suggest that certain cellular sensory systems display fold-change detection (FCD): a response whose entire shape, including amplitude and duration, depends only on fold-changes in input and not on absolute levels. We show the significance of FCD behavior in organisms that search in fields with scalar symmetry, i.e. have a constant pattern up to a multiplicative scalar. Furthermore we present a wide class of mechanisms that have FCD. Throughout, we use the example of bacterial chemotaxis, which has been shown to display FCD behavior both at the single bacteria and the community levels. Further details

Abstract: Realistic in-silico study of biological systems using empirically-grounded computer simulations is a powerful yet underutilized field of research. In contrast with the more traditional modeling efforts, realistic in-silico study seeks a comprehensive view of the system rather than constructing toy-models that simplify its complexity. Once achieved, realistic modeling facilitates analysis of the combined system-level effects of multiple factors such as time, anatomical constraints and cellular regulation. This tackles a central layer of biology that is currently experimentally unfeasible and thus often overlooked. In my talk, I will discuss the challenges of realistic in-silico study and the expected long-term impact of the field. I will present three simulations of organs and stem cell population of three distinct, evolutionarily diverse, biological systems. I will detail the in-silico analysis and the subsequent experimental testing of the predictions. Finally, I will discuss applications of in-silico modeling and in-silico analysis in disease development and medical research.

Abstract: Bacteria, the first and most fundamental of all organisms, lead rich social life in complex hierarchical communities. Collectively, they gather information from the environment, learn from past experience, and make decisions. Bacteria do not store genetically all the information required to respond efficiently to all possible environmental conditions. Instead, to solve new encountered problems (challenges) posed by the environment, they first assess the problem via collective sensing, then recall stored information of past experience and finally execute distributed information processing of the 109-12 bacteria in the colony, thus turning the colony into super-brain. Super-brain, because the billions of bacteria in the colony use sophisticated communication strategies to link the intracellular computation networks of each bacterium (including signaling path ways of billions of molecules) into a network of networks. I will show illuminating movies of swarming intelligence of live bacteria in which they solve optimization problems for collective decision making that are beyond what we, human beings, can solve with our most powerful computers. I will discuss the special nature of bacteria computational principles in comparison to our Turing Algorithm computational principles, showing that we can learn from the bacteria about our current and future social networks – the need for semantic communication, about new game theory, about new approach to fight cancer by recognizing the social behaviors of cancer - that cancer is a meta-community of smart social beasts, about how to design swarms of communicating smart robots, about social intelligence of human and more.

Abstract: Chromosomal aneuploidy, i.e. the gain or loss of chromosomes, is the most common abnormality in cancer. While certain aberrations are known to be strongly associated with specific cancers and contribute to their formation, most aberrations appear to be nonspecific, arbitrary, and do not have a clear effect. The understanding of chromosomal aneuploidy and its role in tumorigenesis is a fundamental open problem in cancer biology. We report on a systematic study of the characteristics of chromosomal aberrations in cancers, using over 15,000 karyotypes and 62 cancer classes in the Mitelman Database. Remarkably, we discovered very high co-occurrence rate of chromosome gains with other chromosome gains, and of losses with losses. Gains and losses rarely show significant co-occurrence [1]. This finding was consistent across cancer classes and was confirmed on an independent CGH dataset of cancer samples. The results of our analysis are available for further investigation via our website http://acgt.cs.tau.ac.il/stack/. The broad generality and the intricate characteristics of the dichotomy of aneuploidy, ranging across numerous tumor classes, are revealed here rigorously for the first time using statistical analyses of large-scale datasets. A possible explanation of our finding is that aneuploid cancer cells use extra chromosome gain / loss events to restore a balance in their altered proteins ratios, needed for maintaining their cellular fitness. We will report on some tests of this hypothesis.

Abstract: To appear

Abstract: The majority of mammalian genes contain multiple poly(A) sites in their 3'UTRs. Alternative cleavage and polyadenylation (APA) is emerging as an important layer of gene regulation as it generates transcript isoforms which differ in their 3'UTRs, thereby modulating gene response to 3'UTR-mediated regulation. We apply deep-sequencing technique to obtain global maps of polyadenylation sites and examine APA modulation associated with cellular proliferation and transformation. We have also identified a novel regulator of APA and demonstrate, for the first time, a link between APA mis-regulation and a genetic disorder.

Abstract: It is very easy to define new classes of random graphs, but there are only very few models that we understand reasonably well. I will first give a quick survey of several such models. The main question that I want to address is this: In the study of biological systems we encounter many large graphs. It seems crucial to have a theory that allows us to properly model such graphs. In other words, what is being sought are meta-models of random graphs that with a proper choice of (few) parameters will reproduce those graphs that we encounter in biological data. In particular I will explain several fairly recent papers concerning the local structure of random graphs and I will discuss the relevance of these theorems to the problem at hand.

Abstract: A major factor in the functional uniqueness of human tissues is the repertoire of genes and proteins that each tissue expresses. To obtain a view into this functional uniqueness we constructed these repertoires from recent data of gene and protein expression. By integrating these repertoires with the currently known set of protein-protein interactions we created tissue-specific molecular interaction networks (interactomes). We found a common network backbone covering 59%-73% of the interactions in each tissue. This common backbone contains many of the hub proteins, which are highly enriched for primary regulatory roles. We then turned to investigate an important manifestation of the functional uniqueness of each tissue: the tissue-selectivity of genetic diseases. Surprisingly, we found that genes associated with tissue-selective genetic diseases are often expressed in many more tissues beyond their diseased tissue. We suggest that tissue-selectivity of genetic diseases might result, in part, from the unique set of molecular interactions within the diseased tissue.

Abstract: To appear

Abstract: No robust set of "metrics" for human immunological health currently exists. Yet immunological insufficiency or dysregulation underlie many known medical conditions and have been implicated in many more. In this talk I will discuss the research in my lab whose objective it is to understand how immunity is orchestrated in man and to apply this knowledge towards advancing genomic medicine. To achieve this goal, we intertwine three main themes, spanning between clinical and basic research: First, we aim to identify biomarkers monitoring health and immune health in particular, second, we aim to harness evolution to efficiently translate our understanding of biology from model organisms to humans and third, we aim to understand the system level properties of immunity and harness these insights to understand complex systems in general.