Gene Regulatory Networks: Modeling, Intervention and Context

abstract: Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.Dissertation/ThesisPh.D. Computer Science 201

Automatic Software Repair: a Bibliography

This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature

Modeling and Simulation of Biological Systems through Electronic Design Automation techniques

Modeling and simulation of biological systems is a key requirement for integrating invitro and in-vivo experimental data. In-silico simulation allows testing different experimental conditions, thus helping in the discovery of the dynamics that regulate the system. These dynamics include errors in the cellular information processing that are responsible for diseases such as cancer, autoimmunity, and diabetes as well as drug effects to the system (Gonalves, 2013). In this context, modeling approaches can be classified into two categories: quantitative and qualitative models. Quantitative modeling allows for a natural representation of molecular and gene networks and provides the most precise prediction. Nevertheless, the lack of kinetic data (and of quantitative data in general) hampers its use for many situations (Le Novere, 2015). In contrast, qualitative models simplify the biological reality and are often able to reproduce the system behavior. They cannot describe actual concentration levels nor realistic time scales. As a consequence, they cannot be used to explain and predict the outcome of biological experiments that yield quantitative data. However, given a biological network consisting of input (e.g., receptors), intermediate, and output (e.g., transcription factors) signals, they allow studying the input-output relationships through discrete simulation (Samaga, 2013). Boolean models are gaining an increasing interest in reproducing dynamic behaviors, understanding processes, and predicting emerging properties of cellular signaling networks through in-silico experiments. They are emerging as a valid alternative to the quantitative approaches (i.e., based on ordinary differential equations) for exploratory modeling when little is known about reaction kinetics or equilibrium constants in the context of gene expression or signaling. Even though several approaches and software have been recently proposed for logic modeling of biological systems, they are limited to specific contexts and they lack of automation in analyzing biological properties such as complex attractors, and molecule vulnerability. This thesis proposes a platform based on Electronic Design Automation (EDA) technologies for qualitative modeling and simulation of Biological Systems. It aims at overtaking limitations that affect the most recent qualitative tools

Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways

SyQUAL: a Platform for Qualitative Modelling and Simulation of Biological Systems

Qualitative modelling in systems biology is increasingly adopted as it allows predicting important properties of biological systems even when quantitative information of such systems are unknown. Even though different tools for qualitative modelling have been recently proposed, their lack of automatism and their unstructured simulation core limit their applicability to non-complex biological networks. This paper presents SyQUAL, a platform for qualitative modelling and simulation of biological systems. It consists of two main layers: a Web-based framework that allows users to (i) import models described in the standard Systems Biology Markup Language (SBML), (ii) easily define properties to observe, and (iii) run simulations by hiding the underlying layer, that is, a SystemC-based core simulator that allows simulating the systems through a discrete event-based model of computation at different levels of details. The paper shows how SyQUAL has been applied to identify the attractors and to analyse the system robustness/sensitivity under perturbations of the Colitis-associated Colon Cancer (CAC) network

Identification of Potential Drug Targets in Cancer Signaling Pathways Using Stochastic Logical Models

The investigation of vulnerable components in a signaling pathway can contribute to development of drug therapy addressing aberrations in that pathway. Here, an original signaling pathway is derived from the published literature on breast cancer models. New stochastic logical models are then developed to analyze the vulnerability of the components in multiple signalling sub-pathways involved in this signaling cascade. The computational results are consistent with the experimental results, where the selected proteins were silenced using specific siRNAs and the viability of the cells were analyzed 72 hours after silencing. The genes elF4E and NFkB are found to have nearly no effect on the relative cell viability and the genes JAK2, Stat3, S6K, JUN, FOS, Myc, and Mcl1 are effective candidates to influence the relative cell growth. The vulnerabilities of some targets such as Myc and S6K are found to vary significantly depending on the weights of the sub-pathways; this will be indicative of the chosen target to require customization for therapy. When these targets are utilized, the response of breast cancers from different patients will be highly variable because of the known heterogeneities in signaling pathways among the patients. The targets whose vulnerabilities are invariably high might be more universally acceptable targets

Neural disjunctive normal form: Vertically integrating logic with deep learning for classification

Inspired by the limitations of pure deep learning and symbolic logic-based models, in this thesis we consider a specific type of neuro-symbolic integration called vertical integration to bridge logic reasoning and deep learning and address their limitations. The motivation of vertical integration is to combine perception and reasoning as two separate stages of computation, while still being able to utilize simple and efficient end-to-end learning. It uses a perceptive deep neural network (DNN) to learn abstract concepts from raw sensory data and uses a symbolic model that operates on these abstract concepts to make interpretable predictions. As a preliminary step towards this direction, we tackle the task of binary classification and propose the Neural Disjunctive Normal Form (Neural DNF). Specifically, we utilize a per- ceptive DNN module to extract features from data, then after binarization (0 or 1), feed them into a Disjunctive Normal Form (DNF) module to perform logical rule-based classi- fication. We introduce the BOAT algorithm to optimize these two normally-incompatible modules in an end-to-end manner. Compared to standard DNF, Neural DNF can handle prediction tasks from raw sensory data (such as images) thanks to the neurally-extracted concepts. Compared to standard DNN, Neural DNF offers improved interpretability via an explicit symbolic representation while being able to achieve comparable accuracy despite the reduction of model flexibility, and is particularly suited for certain classification tasks that require some logical composition. Our experiments show that BOAT can optimize Neural DNF in an end-to-end manner, i.e. jointly learn the logical rules and concepts from scratch, and that in certain cases the rules and the meanings of concepts are aligned with human understanding. We view Neural DNF as an important first step towards more sophisticated vertical inte- gration models, which use symbolic models of more powerful rule languages for advanced prediction and algorithmic tasks, beyond using DNF (propositional logic) for classification tasks. The BOAT algorithm introduced in this thesis can potentially be applied to such advanced hybrid models