Dynamics of Internal Models in Game Players

A new approach for the study of social games and communications is proposed. Games are simulated between cognitive players who build the opponent's internal model and decide their next strategy from predictions based on the model. In this paper, internal models are constructed by the recurrent neural network (RNN), and the iterated prisoner's dilemma game is performed. The RNN allows us to express the internal model in a geometrical shape. The complicated transients of actions are observed before the stable mutually defecting equilibrium is reached. During the transients, the model shape also becomes complicated and often experiences chaotic changes. These new chaotic dynamics of internal models reflect the dynamical and high-dimensional rugged landscape of the internal model space.Comment: 19 pages, 6 figure

Patched Together: cis-Regulatory Logic of the Hedgehog Response.

Understanding the processes that control how we develop from a fertilized embryo to a functional adult is paramount for treating the diseases that result when these processes are disrupted at any stage of life. My dissertation investigates the cis-regulatory logic underlying how cell signaling pathways utilize the genome to create and maintain the wide variety of cell types and tissues needed for proper development and survival. Surprisingly few cell signaling pathways are used throughout embryonic development; I have chosen to focus on Hedgehog (Hh) signaling, a pathway used in such diverse cellular contexts as digit specification, brain development, lung function, and reproductive maintenance. Disruption of this pathway results in developmental defects and cancer. It is essential to understand the mechanisms by which Hh signaling functions to treat these diseases more effectively. One relatively unexplored mechanism of Hh function is how its signal is transduced at the level of DNA, specifically through the regulation of gene expression. In this thesis, I explore the mechanisms that mediate tissue-specific, Hh-dependent gene regulation, and uncover an ancient cis-regulatory logic shared between flies and mice that has significant implications for the maintenance and evolution of cellular communication. I experimentally demonstrate that multiple enhancer elements, which control tissue-specific gene expression, rely on sub-optimal DNA sequences for binding of GLI proteins, the transcriptional effectors of Hh signaling. These sequences are essential to control gene expression in response to Hh and can influence the function of the pathway in a variety of cellular contexts. I also characterize several new transcriptional regulators of Hh signaling and introduce new tools to the field that allow for in depth analysis of the regulatory landscape of Hh target genes at any stage of development. My work presented here addresses a significant gap in our knowledge of how the Hh signaling pathway functions at the cis-regulatory level and describes a framework by which new advances can be made on this topic in the future.PHDCellular & Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135811/1/dslorber_1.pd

The evolution of cooperation and altruism--a general framework and a classification of models.

One of the enduring puzzles in biology and the social sciences is the origin and persistence of intraspecific cooperation and altruism in humans and other species. Hundreds of theoretical models have been proposed and there is much confusion about the relationship between these models. To clarify the situation, we developed a synthetic conceptual framework that delineates the conditions necessary for the evolution of altruism and cooperation. We show that at least one of the four following conditions needs to be fulfilled: direct benefits to the focal individual performing a cooperative act; direct or indirect information allowing a better than random guess about whether a given individual will behave cooperatively in repeated reciprocal interactions; preferential interactions between related individuals; and genetic correlation between genes coding for altruism and phenotypic traits that can be identified. When one or more of these conditions are met, altruism or cooperation can evolve if the cost-to-benefit ratio of altruistic and cooperative acts is greater than a threshold value. The cost-to-benefit ratio can be altered by coercion, punishment and policing which therefore act as mechanisms facilitating the evolution of altruism and cooperation. All the models proposed so far are explicitly or implicitly built on these general principles, allowing us to classify them into four general categories

Discovering new drug-drug interactions using data science: Applications to drug-induced Long QT Syndrome

Commonly prescribed small molecule drugs can have net-positive and well-understood safety profiles when prescribed individually, but unexpected consequences when taken at the same time. Detection of these drug-drug interactions (DDIs) continues to be a critical and unmet area of translational research. The Centers for Disease Control and Prevention (CDC) estimate that one third of Americans are concurrently taking two or more prescription drugs, and DDIs are estimated to be responsible for 17% of all drug adverse events. The consequences of DDIs can be relatively minor (headache, skin rash) or much more severe (bleeding, liver toxicity). At a cellular level, DDIs can occur as a result of both drugs competing for metabolism (known as pharmacokinetic interactions) or targeting the same protein target or biological pathway (pharmacodynamic interactions). Clinical trials typically focus on the effects of individual drugs, leaving DDIs to usually be discovered only after the drugs have been approved. One of the most carefully studied drug adverse events is long QT syndrome (LQTS), an unexpected change in the heart's electrical activity that can lead to a potentially fatal ventricular tachycardia known as torsades de pointes (TdP). Some patients have genetic mutations that lead to congenital forms of LQTS, while drug-induced LQTS typically occurs via block of the hERG potassium channel (KCNH2) responsible for ventricular repolarization. After a number of high profile drugs were withdrawn from the market due to discovered risk of TdP, the FDA issued guidelines so that pharmaceutical companies could anticipate and test for this side effect before a new drug is approved. These recommendations have helped prevent new QT-prolonging drugs from entering the market, but nonetheless over 180 approved drugs have been associated with drug-induced LQTS. While information on individual QT-prolonging drugs is thus readily available to clinicians, little has remained known about DDIs (QT-DDIs). There are many more commonly prescribed drugs that are safe when given individually but could increase TdP risk when administered together. This troubling situation is compounded by the fact that traditional post-market surveillance algorithms are poorly equipped to sensitively and specifically detect DDIs. Data science – the application of rigorous analytical methods to large datasets – offers an opportunity for predicting previously unknown QT-DDIs. Some biomedical datasets (such as drug-target binding affinities and experiments to determine protein-protein interactions) have been collected explicitly for research, while other valuable datasets (such as electronic health records) were initially recorded for billing purposes. Each data modality has its own important set of advantages and disadvantages, and integrative data science approaches can incorporate multiple types of data to help account for these limitations. In this thesis we develop new data sciences techniques that combine clinical, biological, chemical, and genetic data. These approaches are explicitly designed to be robust to biased and missing data. We apply these new methodologies to (1) predict new QT-DDIs, (2) validate them experimentally, and (3) investigate their molecular and genetic mechanisms. We exemplify this approach in the discovery of a previously unknown QT-DDI between ceftriaxone (cephalosporin antibiotic) and lansoprazole (proton pump inhibitor); importantly, both drugs have no cardiac indications and are safe when given individually. The clinical data mining, drug target prediction, biological network analysis, genetic ancestry prediction, and experimental validation methods described in this thesis form the basis for a comprehensive pipeline to predict QT-DDIs rapidly and robustly. They also provide an opportunity for further enriching our understanding of LQTS biology and ultimately enabling the design of safer drugs

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling

Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs are not systematically evaluated in pre-clinical or clinical trials and so the FDA U. S. Food and Drug Administration relies on post-marketing surveillance to monitor patient safety. However, existing pharmacovigilance algorithms show poor performance for detecting DDIs exhibiting prohibitively high false positive rates. Alternatively, methods based on chemical structure and pharmacological similarity have shown promise in adverse drug event detection. We hypothesize that the use of chemical biology data in a post hoc analysis of pharmacovigilance results will significantly improve the detection of dangerous interactions. Our model integrates a reference standard of DDIs known to cause arrhythmias with drug similarity data. To compare similarity between drugs we used chemical structure (both 2D and 3D molecular structure), adverse drug side effects, chemogenomic targets, drug indication classes, and known drug-drug interactions. We evaluated the method on external reference standards. Our results showed an enhancement of sensitivity, specificity and precision in different top positions with the use of similarity measures to rank the candidates extracted from pharmacovigilance data. For the top 100 DDI candidates, similarity-based modeling yielded close to twofold precision enhancement compared to the proportional reporting ratio (PRR). Moreover, the method helps in the DDI decision making through the identification of the DDI in the reference standard that generated the candidate

Reflexion and reflection: A social cognitive neuroscience approach to attributional inference

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An Integrative Data Science Pipeline to Identify Novel Drug Interactions that Prolong the QT Interval

INTRODUCTION: Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia known as torsades de pointes (TdP). Over 40 drugs with both cardiac and non-cardiac indications are associated with increased risk of TdP, but drug–drug interactions contributing to LQTS (QT-DDIs) remain poorly characterized. Traditional methods for mining observational healthcare data are poorly equipped to detect QT-DDI signals due to low reporting numbers and lack of direct evidence for LQTS. OBJECTIVE: We hypothesized that LQTS could be identified latently using an adverse event (AE) fingerprint of more commonly reported AEs. We aimed to generate an integrated data science pipeline that addresses current limitations by identifying latent signals for QT-DDIs in the US FDA’s Adverse Event Reporting System (FAERS) and retrospectively validating these predictions using electrocardiogram data in electronic health records (EHRs). METHODS: We trained a model to identify an AE fingerprint for risk of TdP for single drugs and applied this model to drug pair data to predict novel DDIs. In the EHR at Columbia University Medical Center, we compared the QTc intervals of patients prescribed the flagged drug pairs with patients prescribed either drug individually. RESULTS: We created an AE fingerprint consisting of 13 latently detected side effects. This model significantly outperformed a direct evidence control model in the detection of established interactions (p = 1.62E−3) and significantly enriched for validated QT-DDIs in the EHR (p = 0.01). Of 889 pairs flagged in FAERS, eight novel QT-DDIs were significantly associated with prolonged QTc intervals in the EHR and were not due to co-prescribed medications. CONCLUSIONS: Latent signal detection in FAERS validated using the EHR presents an automated and data-driven approach for systematically identifying novel QT-DDIs. The high-confidence hypotheses flagged using this method warrant further investigation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40264-016-0393-1) contains supplementary material, which is available to authorized users