Identification of periodic attractors in Boolean networks using a priori information

Boolean networks (BNs) have been developed to describe various biological processes, which requires analysis of attractors, the long-term stable states. While many methods have been proposed to detection and enumeration of attractors, there are no methods which have been demonstrated to be theoretically better than the naive method and be practically used for large biological BNs. Here, we present a novel method to calculate attractors based on a priori information, which works much and verifiably faster than the naive method. We apply the method to two BNs which differ in size, modeling formalism, and biological scope. Despite these differences, the method presented here provides a powerful tool for the analysis of both networks. First, our analysis of a BN studying the effect of the microenvironment during angiogenesis shows that the previously defined microenvironments inducing the specialized phalanx behavior in endothelial cells (ECs) additionally induce stalk behavior. We obtain this result from an extended network version which was previously not analyzed. Second, we were able to heuristically detect attractors in a cell cycle control network formalized as a bipartite Boolean model (bBM) with 3158 nodes. These attractors are directly interpretable in terms of genotype-to-phenotype relationships, allowing network validation equivalent to an in silico mutagenesis screen. Our approach contributes to the development of scalable analysis methods required for whole-cell modeling efforts.

Conjoining Wymore’s Systems Theoretic Framework and the DEVS Modeling Formalism: Toward Scientific Foundations for MBSE

The objective of this research article is to re-introduce some of the concepts provided by A. Wayne Wymore in his mathematical theory of Model-Based Systems Engineering, discuss why his framework might have not been adopted, and define a potential path to modernize the framework for practical application in the digital age. The dense mathematical theory has never been converted to a practical form. We propose a path to modernization by creating a metamodel of Wymore’s mathematical theory of MBSE. This enables explaining the concepts in simple to understand terms and shows the internal consistency provided by the theory. Furthermore, the metamodel allows for conversion of the theory into software application, for which we show some initial results that open the research to the art of the possible. In recognition of limitation of the theory, we make the case for a merger of the theoretical framework with the enhanced formalism of Discrete Event System Specification (DEVS). This will establish a path toward the scientific foundations for MBSE to enable future implementations of the complementary pairing and their empirical results.

Translating Workflow Nets to Process Trees: An Algorithmic Approach

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound workflow net is trivial. However, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability.

Translating Workflow Nets to Process Trees: An Algorithmic Approach

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound Workflow net is trivial; however, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, when the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree re-discoverability.

A DEVS-based pivotal modeling formalism and its verification and validation framework

System verification is an ever-lasting system engineering challenge. The increasing complexity in system simulation requires some level of expertise in handling the idioms of logic and discrete mathematics to correctly drive a full verification process. It is recognized that visual modeling can help to fill the knowledge gap between system experts and analysis experts. However, such an approach has been used on the one hand to specify the behavior of complex systems, and on the other hand to specify complex requirement properties, but not simultaneously. This paper proposes a framework that is unique in supporting a full system verification process based on the graphical modeling of both the system of interest and the requirements to be checked. Patterns are defined to transform the resulting models to formal specifications that a model checker can manipulate. A real-time crossing system is used to illustrate the proposed framework.

The environment selects: Modeling energy allocation in microbial communities under dynamic environments

ABSTRACTWhat will be the best metabolic strategy in a competitive environment where oxygen is periodically unavailable? A few decades ago, an accidental, man-made cyclic anaerobic/aerobic environment selected for Polyphosphate Accumulating Organisms (PAOs) and this strategy is now widely used to allow for Enhanced Biological Phosphorus Removal (EBPR) of wastewater. But could it have been predicted? Here, a dynamic resource allocation modeling formalism was used to analyze the impact of selection pressures on metabolic function. With the same meta-network but modified selective pressures, different successful strategies can be predicted: Polyphosphate-AOs, Glycogen-AOs, Polyhydroxyalkanoate-AOs, and regular aerobic heterotrophs. The results demonstrate how storage metabolism allows for different trade-offs between growth yield, robustness, and competitiveness, and highlight how each metabolic function is an important determining factor for a selective advantage in a given environment. This can be seen as an example of when “Unity in biochemistry” by A.Kluyver meets “Everything is everywhere, but the environment selects” by B.Becking and how microbial ecosystems may be described by the energy allocation phenotype instead of a detailed description of each organism.