BioModel engineering for multiscale Systems Biology

Design patterns for the construction of computational biological models

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

Protocol for biomodel engineering of unilevel to multilevel biological models using colored Petri nets

Biological systems inherently span multiple levels, which can pose challenges in spatial representation for modelers. We present a protocol that utilizes colored Petri nets to construct and analyze biological models of systems, encompassing both unilevel and multilevel scenarios. We detail a modeling workflow exploiting the PetriNuts platform comprising a set of tools linked together via common file formats. We describe steps for modeling preparation, component-level modeling and analysis, followed by system-level modeling and analysis, and model use.

A Variational Synthesis of Evolutionary and Developmental Dynamics

This paper introduces a variational formulation of natural selection, paying special attention to the nature of 'things' and the way that different 'kinds' of 'things' are individuated from-and influence-each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain-and are constrained by-fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses-and identify points of contact with related mathematical formulations of evolution.

Formal verification confirms the role of p53 protein in cell fate decision mechanism

The bio-cell cycle is controlled by a complex biochemical network of signaling pathways. Modeling such challenging networks accurately is imperative for the understanding of their detailed dynamical behavior. In this paper, we construct, analyze, and verify a hybrid Petri net (HPN) model of a complex biochemical network that captures the role of an important protein (namely p53) in deciding the fate of the cell. We model the behavior of the cell nucleus and cytoplasm as two stochastic and continuous Petri nets, respectively, combined together into a single HPN. We use simulative model checking to verify three different properties that capture the dynamical behavior of p53 protein with respect to the intensity of the ionizing radiation (IR) to which the cell is exposed. For each IR dose, 1000 simulation runs are carried out to verify each property. Our verification results showed that the fluctuations in p53, which relies on IR intensity, are compatible with the findings of the preceding simulation studies that have previously examined the role of p53 in cell fate decision.

High-resolution computational modeling of immune responses in the gut

Background

Helicobacter pylori causes gastric cancer in 1–2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori–colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing–driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations.

Results

The outputs were analyzed using 2 sequential stages: the first used a partial rank correlation coefficient regression–based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells.

Conclusions

The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.

Coloured Petri nets for multilevel, multiscale and multidimensional modelling of biological systems

Owing to the availability of data of one biological phenomenon at different levels/scales, modelling of biological systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets (ColPNs) have been successfully applied to multilevel, multiscale and multidimensional modelling of some biological systems, addressing many of these challenges. In this article, we first review the basics of ColPNs and some popular extensions, and then their applications for multilevel, multiscale and multidimensional modelling of biological systems. This understanding of how to use ColPNs for modelling biological systems will assist readers in selecting appropriate ColPN classes for specific modelling circumstances.

Coloured Hybrid Petri Nets: An adaptable modelling approach for multi-scale biological networks

Coloured Petri nets are an excellent choice for exploring large biological models, particularly when there are repetitions of components. Such models can be easily adapted by slight modifications of parameter values related to colours. Similarly, multi-scale models could involve multiple spatial scales in addition to multiple time scales. Thus, they require the full interplay between stochastic as well as deterministic processes. In this paper we take these two aspects into account and present a modelling and simulation approach for multi-scale biochemical networks using Coloured Generalised Hybrid Petri Nets (GHPN^C). GHPN^C are a Petri net class that associates colours to Generalised Hybrid Petri Nets (GHPN), which incorporate discrete and continuous places in addition to stochastic and continuous transitions. Moreover, we present two case studies to illustrate typical applications taking advantage of such a Petri net class.

Petri-net-based 2D design of DNA walker circuits

We consider localised DNA computation, where a DNA strand walks along a binary decision graph to compute a binary function. One of the challenges for the design of reliable walker circuits consists in leakage transitions, which occur when a walker jumps into another branch of the decision graph. We automatically identify leakage transitions, which allows for a detailed qualitative and quantitative assessment of circuit designs, design comparison, and design optimisation. The ability to identify leakage transitions is an important step in the process of optimising DNA circuit layouts where the aim is to minimise the computational error inherent in a circuit while minimising the area of the circuit. Our 2D modelling approach of DNA walker circuits relies on coloured stochastic Petri nets which enable functionality, topology and dimensionality all to be integrated in one two-dimensional model. Our modelling and analysis approach can be easily extended to 3-dimensional walker systems.

MODELING AND ANALYZING BIOLOGICAL SYSTEMS USING COLORED HIERARCHICAL PETRI NETS ILLUSTRATED BYC. ELEGANSVULVAL DEVELOPMENT

Colored Petri nets allow compact, parameterizable and scalable representations of large-scale biological models by encoding, e.g., species as colored tokens, and offer a variety of analysis techniques, e.g., structural analysis, simulation and model checking to analyze biological models. However, so far colored Petri nets have not been widely used and well explored in systems biology. In this paper, we aim to present a systematic approach to modeling and analyzing complex biological systems using colored Petri nets in order to help biologists to easily use them. We first describe a framework comprising a family of related colored Petri nets: colored qualitative Petri net (𝒬𝒫𝒩𝒞), colored stochastic Petri net (𝒮𝒫𝒩𝒞) and colored continuous Petri net (𝒞𝒫𝒩𝒞). They share structure, but are specialized by their kinetic information. Based on this framework, we present our colored Petri net approach to modeling and analyzing complex biological systems. First a biological system is modeled as a hierarchical 𝒬𝒫𝒩𝒞model, animated and analyzed by structural analysis; then it is converted into a 𝒮𝒫𝒩𝒞or 𝒞𝒫𝒩𝒞model, to be further analyzed using stochastic or continuous simulation, and simulative or numerical model checking. We demonstrate this approach using a nontrivial example, Caenorhabditis elegans vulval development.

Multiscale modelling of coupled Ca2+ channels using coloured stochastic Petri nets

Stochastic modelling of coupled Ca2+ channels is a challenge, especially when the coupling of the channels, as determined by their spatial arrangement relative to each other, has to be considered at multiple spatial scales. In this study, the authors address this problem using coloured stochastic Petri nets (SPNc) as high-level description to generate continuous-time Markov chains. The authors develop several models with increasing complexity. They first apply SPNc to model single clusters of coupled Ca2+ channels arranged in a regular or irregular lattice, where they describe how to represent the geometrical arrangement of Ca2+ channels relative to each other using colours. They then apply this modelling idea to construct more complex models by modelling spatially arranged clusters of channels. The authors' models can be easily reproduced and adapted to different scenarios.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

On some recent insights in Integral Biomathics

This paper summarizes the results in Integral Biomathics obtained to this moment and provides an outlook for future research in the field.