Modeling multi-valued biological interaction networks using Fuzzy Answer Set Programming

Fuzzy Answer Set Programming (FASP) is an extension of the popular Answer Set Programming (ASP) paradigm that allows for modeling and solving combinatorial search problems in continuous domains. The recent development of practical solvers for FASP has enabled its applicability to real-world problems. In this paper, we investigate the application of FASP in modeling the dynamics of Gene Regulatory Networks (GRNs). A commonly used simplifying assumption to model the dynamics of GRNs is to assume only Boolean levels of activation of each node. Our work extends this Boolean network formalism by allowing multi-valued activation levels. We show how FASP can be used to model the dynamics of such networks. We experimentally assess the efficiency of our method using real biological networks found in the literature, as well as on randomly-generated synthetic networks. The experiments demonstrate the applicability and usefulness of our proposed method to find network attractors

Therapeutic target discovery using Boolean network attractors: improvements of kali

In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modeling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are i) the possibility to work on asynchronous Boolean networks, ii) a finer assessment of therapeutic targets and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system, such as a Boolean network, are associated with the phenotypes of the modeled biological system. Given a logic-based model of pathological mechanisms, kali searches for therapeutic targets able to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing their likeliness. kali is illustrated on an example network and used on a biological case study. The case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results. However, like any computational tool, kali can predict but can not replace human expertise: it is a supporting tool for coping with the complexity of biological systems in the field of drug discovery

Methods for control strategy identification in Boolean networks

Understanding control mechanisms present in biological processes is crucial for the development of potential therapeutic applications, for instance cell reprogramming or drug target identification. Experimental approaches aimed at identifying possible control targets are usually costly and time-consuming. Mathematical modeling provides a formal framework to study biological systems and to predict potential successful candidate interventions. A common modeling framework is Boolean modeling, which stands out for its ability to capture the qualitative behavior of the system using coarse representations of the interactions between the components, overcoming the usual parametrization problem. The main goal of this thesis is the study of the control problems present in biological systems and the development of efficient and complete approaches for control strategy identification. In particular, we aim at developing methods to identify sets of minimal controls that are able to induce the desired states in biological systems modeled by Boolean networks. With the goal of making our approaches attractive for application, we establish two key factors: efficiency and diversity. We want our approaches to be able to deal with state-of-the-art networks in a reasonable amount of time while providing as many different optimal control sets as possible. With these factors in mind, we developed two different approaches. Our first method is based on value percolation, one of the most simple and efficient approaches to control strategy identification in Boolean networks. Percolation-based methods can be implemented efficiently but are limited and might miss many control strategies. Our approach introduces the use of trap spaces, regions of the state space closed under the dynamics. This allows us to increase the number of control strategies identified while still benefiting from an efficient implementation. Our second approach focuses on exhaustivity and flexibility. Based on model checking techniques, it allows us to identify all the minimal control strategies for a given target. This approach is also able to deal with more complex control problems, since it can handle any type of target. To overcome the higher computational costs associated with the comprehensiveness of the method, we also introduce several reduction techniques to improve its performance. In the last chapter, we show the applicability of our approaches to different biological systems. We study the control strategies obtained for a network modeling the epithelial-to-mesenchymal transition, considering different control targets and types of interventions. We also explore the relevance of the intervention strategies identified in the biological context. Finally, we compare our approaches to other current control methods in different Boolean networks.Das Verständnis von Kontrollmechanismen in biologischen Prozessen ist von entscheidender Bedeutung für die Entwicklung potenzieller therapeutischer Anwendungen, z. B. die Reprogrammierung von Zellen oder die Identifizierung von Zielstrukturen für Medikamente. Experimentelle Ansätze zur Identifizierung möglicher Kontrollziele sind in der Regel kostspielig und zeitaufwändig. Die mathematische Modellierung bietet einen formalen Rahmen zur Untersuchung biologischer Systeme und zur Vorhersage potenziell erfolgreicher Interventionskandidaten. Ein etablierter Formalismus ist die boolesche Modellierung, die sich durch ihre Fähigkeit auszeichnet, das qualitative Verhalten des Systems mit Hilfe grober Darstellungen der Wechselwirkungen zwischen den Komponenten zu erfassen und so das übliche Parametrisierungsproblem zu überwinden. Das Hauptziel dieser Arbeit ist die Untersuchung der Kontrollprobleme in biologischen Systemen und die Entwicklung von effizienten und vollständigen Ansätzen zur Identifikation von Kontrollstrategien. Insbesondere geht es um die Entwicklung von Methoden zur Identifizierung von Mengen minimaler Steuerungen, die in der Lage sind, die gewünschten Zustände in biologischen, durch boolesche Netzwerke modellierten Systemen zu induzieren. Um unsere Ansätze für die Anwendung attraktiv zu machen, legen wir zwei Schlüsselfaktoren fest: Effizienz und Vielfalt. Unsere Methoden sollen in der Lage sein, biologische Netzwerke von aktuellem Interesse in angemessener Zeit zu bearbeiten und dabei so viele verschiedene optimale Kontrollsätze wie möglich bereitzustellen. Mit Blick auf diese Faktoren haben wir zwei verschiedene Ansätze entwickelt. Unsere erste Methode basiert auf der Wertperkolation, einem der einfachsten und effizientesten Ansätze zur Berechnung von Steuerungen boolescher Netze. Auf Perkolation basierende Methoden können zwar effizient implementiert werden, lassen aber möglicherweise viele Kontrollstrategien außer Acht. Unser Ansatz führt die Verwendung von Trap-Spaces ein, d.h. Regionen des Zustandsraums, die unter der Dynamik abgeschlossen sind. Dadurch können wir die Anzahl der identifizierten Kontrollstrategien erhöhen und gleichzeitig von einer effizienten Implementierung profitieren. Unser zweiter Ansatz konzentriert sich auf Vollständigkeit und Flexibilität. Auf der Grundlage von Modellprüfungstechniken können wir alle minimalen Kontrollstrategien für ein bestimmtes Ziel identifizieren. Dieser Ansatz ist auch in der Lage, komplexere Steuerungsprobleme zu behandeln, da er mit jeder Art von Ziel umgehen kann. Um die mit der Vollständigkeit der Methode verbundenen höheren Rechenkosten zu überwinden, führen wir mehrere leistungsverbessernde Reduktionstechniken ein. Im letzten Kapitel zeigen wir die Anwendbarkeit unserer Ansätze auf verschiedene biologische Systeme. Wir untersuchen die Kontrollstrategien, die wir für ein Netzwerk erhalten, das den Übergang von Epithel- zu Mesenchymzellen modelliert, wobei wir verschiedene Kontrollziele und Arten von Eingriffen berücksichtigen. Wir untersuchen auch die Relevanz der ermittelten Interventionsstrategien im biologischen Kontext. Schließlich vergleichen wir unsere Ansätze mit anderen aktuellen Kontrollmethoden angewandt auf verschiedene boolesche Netzwerke

Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks

Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to efficiently identify a network\u27s set of attractors and to predict the likelihood with which an arbitrary state leads to each of the network’s attractors. These probabilities will be represented using a fuzzy membership vector. Predicting fuzzy membership vectors using machine learning techniques may address the intractability posed by networks of realistic size and complexity. Modeling and simulation can be used to provide the necessary training sets for machine learning methods to predict fuzzy membership vectors. The experiments comprise several GRNs, each represented by a set of output classes. These classes consist of thresholds τ and ¬τ, where τ = [τlaw,τhigh]; state s belongs to class τ if the probability of its transitioning to attractor belongs to the range [τlaw,τhigh]; otherwise it belongs to class ¬τ. Finally, each machine learning classifier was trained with the training sets that was previously collected. The objective is to explore methods to discover patterns for meaningful classification of states in realistically complex regulatory networks. The research design took a GRN and a machine learning method as input and produced output class \u3c Ατ \u3e and its negation ¬ \u3c Ατ \u3e. For each GRN, attractors were identified, data was collected by sampling each state to create fuzzy membership vectors, and machine learning methods were trained to predict whether a state is in a healthy attractor or not. For T-LGL, SVMs had the highest accuracy in predictions (between 93.6% and 96.9%) and precision (between 94.59% and 97.87%). However, naive Bayesian classifiers had the highest recall (between 94.71% and 97.78%). This study showed that all experiments have extreme significance with pvalue \u3c 0.0001. The contribution this research offers helps clinical biologist to submit genetic states to get an initial result on their outcomes. For future work, this implementation could use other machine learning classifiers such as xgboost or deep learning methods. Other suggestions offered are developing methods that improves the performance of state transition that allow for larger training sets to be sampled

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

Optimization of logical networks for the modelling of cancer signalling pathways

Cancer is one of the main causes of death throughout the world. The survival of patients diagnosed with various cancer types remains low despite the numerous progresses of the last decades. Some of the reasons for this unmet clinical need are the high heterogeneity between patients, the differentiation of cancer cells within a single tumor, the persistence of cancer stem cells, and the high number of possible clinical phenotypes arising from the combination of the genetic and epigenetic insults that confer to cells the functional characteristics enabling them to proliferate, evade the immune system and programmed cell death, and give rise to neoplasms. To identify new therapeutic options, a better understanding of the mechanisms that generate and maintain these functional characteristics is needed. As many of the alterations that characterize cancerous lesions relate to the signaling pathways that ensure the adequacy of cellular behavior in a specific micro-environment and in response to molecular cues, it is likely that increased knowledge about these signaling pathways will result in the identification of new pharmacological targets towards which new drugs can be designed. As such, the modeling of the cellular regulatory networks can play a prominent role in this understanding, as computational modeling allows the integration of large quantities of data and the simulation of large systems. Logical modeling is well adapted to the large-scale modeling of regulatory networks. Different types of logical network modeling have been used successfully to study cancer signaling pathways and investigate specific hypotheses. In this work we propose a Dynamic Bayesian Network framework to contextualize network models of signaling pathways. We implemented FALCON, a Matlab toolbox to formulate the parametrization of a prior-knowledge interaction network given a set of biological measurements under different experimental conditions. The FALCON toolbox allows a systems-level analysis of the model with the aim of identifying the most sensitive nodes and interactions of the inferred regulatory network and point to possible ways to modify its functional properties. The resulting hypotheses can be tested in the form of virtual knock-out experiments. We also propose a series of regularization schemes, materializing biological assumptions, to incorporate relevant research questions in the optimization procedure. These questions include the detection of the active signaling pathways in a specific context, the identification of the most important differences within a group of cell lines, or the time-frame of network rewiring. We used the toolbox and its extensions on a series of toy models and biological examples. We showed that our pipeline is able to identify cell type-specific parameters that are predictive of drug sensitivity, using a regularization scheme based on local parameter densities in the parameter space. We applied FALCON to the analysis of the resistance mechanism in A375 melanoma cells adapted to low doses of a TNFR agonist, and we accurately predict the re-sensitization and successful induction of apoptosis in the adapted cells via the silencing of XIAP and the down-regulation of NFkB. We further point to specific drug combinations that could be applied in the clinics. Overall, we demonstrate that our approach is able to identify the most relevant changes between sensitive and resistant cancer clones

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Processing hidden Markov models using recurrent neural networks for biological applications

Philosophiae Doctor - PhDIn this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems. In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good. It should be noted that in the past, methods for applying HMMs into RNNs have been developed by other researchers. However, to the best of our knowledge, no algorithm for processing HMMs through learning has been given. Taking advantage of the structural similarities of the architectural dynamics of the RNNs and HMMs, in this work we analyze the combination of these two systems into the hybrid architecture. To this end, the main objective of this study is to improve the sequence recognition/classi_cation performance by applying a hybrid neural/symbolic approach. In particular, trained HMMs are used as the initial symbolic domain theory and directly encoded into appropriate RNN architecture, meaning that the prior knowledge is processed through the training of RNNs. Proposed algorithm is then implemented on sample test beds and other real time biological applications