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

A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function

Revision of Boolean Logical Models of Biological Regulatory Networks using Answer-Set Programming

Biological regulatory networks are one of the most prominent tools used to represent complex, regulatory cellular processes. Creating computational models of these networks is key to better comprehend the corresponding cellular processes, as they allow for the reproduction of known behaviors, the testing of hypotheses, and the identification of predictions in silico. However, given that the process of constructing and revising such models is mainly a manual one, it is prone to error, and would therefore benefit from automation. An attempt at solving this problem has already been made using a mixture of Answer Set Programming (ASP) and C++. The previous attempt automated the process of revising these models, by using ASP to verify whether a Boolean logical model of a biological regulatory network was consistent with a given set of experimental observations and, in case of inconsistencies, used C++ to implement an algorithm capable of searching for possible sets of repair operations to render the model consistent. In our work we propose an alternative solution for this problem, a solution that fully leverages ASP which, being a declarative language tailored for this type of difficult search problems, has demonstrated to be a great tool to use both for consistency checking as well as model repair. This is in view of the fact that ASP offers a more intuitive and elaboration-tolerant programming style, which facilitates the processes of understanding, and modifying the code behind the model revision process. This, coupled with the powerful and exhaustively optimized solving capabilities provided by the state of the art ASP system clingo, has shown that there is great potential in adopting a fully ASP-based approach to aid in the automation of the revision of Boolean logical models. In this thesis we present the tool that we have developed to automate the process of revising Boolean logical models of Biological Regulatory Network(s) (BRN), which uses ASP to search for inconsistencies and perform repairs on these models.As redes reguladoras biológicas são das ferramentas mais proeminentes usadas para representar processos celulares regulatórios complexos. A criação de modelos computacionais destas redes é fundamental para entender melhor os processos celulares correspondentes, pois permitem reproduzir comportamentos conhecidos, testar hipóteses e identificar previsões in silico. Porém, dado que o processo de construção e revisão destes modelos é principalmente manual, torna-se propenso a erros e, logo, beneficiaria de automação. Já foi feita uma tentativa de resolução deste problema usando uma mistura de Programação por Conjuntos de Resposta (ASP) com C++. A tentativa anterior automatizou o processo de revisão destes modelos, usando ASP para verificar se um modelo lógico booleano de uma rede regulatória é consistente com um determinado conjunto de observações experimentais e, caso inconsistências se verifiquem, é utilizado um algoritmo desenvolvido em C++ capaz de encontrar possíveis conjuntos de operações de reparo para tornar o modelo consistente. No nosso trabalho, propomos uma solução alternativa para este problema, que tira completo partido da utilização ASP que, sendo uma linguagem declarativa adaptada a este tipo de problemas de busca difíceis, demonstrou ser uma excelente ferramenta a utilizar tanto para a verificação da consistência como para a reparação de modelos. Tal deve-se ao facto de ASP oferecer um estilo de programação mais intuitivo e tolerante à elaboração, o que facilita os processos de compreensão, e a modificação do código por detrás do processo de revisão de modelos. Isto, juntamente com as poderosas e otimizadas capacidades de resolução de problemas de busca oferecidas pelo sistema ASP de última geração clingo, demonstrou que existe um grande potencial na adopção de um sistema totalmente baseado em ASP para ajudar na automatização da revisão destes modelos. Nesta tese apresentamos a ferramenta que desenvolvemos para automatizar o processo de revisão de modelos lógicos booleanos de redes reguladoras biológicas (BRN), que utiliza ASP para procurar inconsistências e efectuar reparações nestes modelos

Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis

Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on their fixed points and attractors. We consider marker properties, which specify that some components are fixed to a specific value. We study 4 variants of the marker reprogramming problem: the reprogramming of fixed points, of minimal trap spaces, and of fixed points and minimal trap spaces reachable from a given initial configuration with the most permissive update mode. The perturbations consist of fixing a set of components to a fixed value. They can destroy and create new attractors. In each case, we give an upper bound on their theoretical computational complexity, and give an implementation of the resolution using the BoNesis Python framework. Finally, we lift the reprogramming problems to ensembles of BNs, as supported by BoNesis, bringing insight on possible and universal reprogramming strategies. This paper can be executed and modified interactively.Comment: Notebook available at https://nbviewer.org/github/bnediction/reprogramming-with-bonesis/blob/release/paper.ipyn

Approximating attractors of Boolean networks by iterative CTL model checking

This paper introduces the notion of approximating asynchronous attractors of Boolean networks by minimal trap spaces. We define three criteria for determining the quality of an approximation: “faithfulness” which requires that the oscillating variables of all attractors in a trap space correspond to their dimensions, “univocality” which requires that there is a unique attractor in each trap space, and “completeness” which requires that there are no attractors outside of a given set of trap spaces. Each is a reachability property for which we give equivalent model checking queries. Whereas faithfulness and univocality can be decided by model checking the corresponding subnetworks, the naive query for completeness must be evaluated on the full state space. Our main result is an alternative approach which is based on the iterative refinement of an initially poor approximation. The algorithm detects so-called autonomous sets in the interaction graph, variables that contain all their regulators, and considers their intersection and extension in order to perform model checking on the smallest possible state spaces. A benchmark, in which we apply the algorithm to 18 published Boolean networks, is given. In each case, the minimal trap spaces are faithful, univocal, and complete, which suggests that they are in general good approximations for the asymptotics of Boolean networks

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