Scalability

The scalability of the methodology depends on the size and representation of the modelled system. An increase in the size of the system will negatively impact the model simulation time directly, and the spatio-temporal analysis and the evaluation of logic properties indirectly. The rate at which the model simulation time changes, with respect to the system size, can vary considerably depending on the employed model representation and simulation algorithm. For instance the systems considered by the phase variation and chemotaxis case studies were of similar size (discretised space of size 101×101 for phase variation, and 100×100 for chemotaxis) and complexity, but their simulation time was significantly different (average model simulation time was 50 minutes for phase variation, and 5 seconds for chemotaxis). In contrast both the spatio-temporal analysis and evaluation of logic properties depend on the size of the simulation traces, and not the models used to produce them. Therefore they are expected to scale well (polynomially) with respect to the size of the system. To conclude, one potential bottleneck, if any, for the scalability of the methodology is the model representation and/or simulation algorithm, and not the model validation.

4.4.6

Limitations

In spite of the above described features our approach has the following limitations, which will be addressed in Chapter 5.

First of all the collections of spatial entity types (e.g. regions) and properties (e.g. area) considered are hardcoded into the methodology and the model checker Mudi. Therefore the current version of the model checker cannot be employed for validating models which correspond to other spatial entity types (e.g. 3D structure) and properties (e.g. volume, 3D shape). For instance adapting the methodology to the full 3D scenario would require changing the definition of SSpDESs such that spatial state variables are evaluated to three- instead of two-dimensional arrays of real values, defining a set of 3D specific spatial properties, including them in the logic PBLSTL and developing algorithms for automatically extracting such spatial properties from 3D images.

Secondly the presented methodology is limited to spatio-temporal uniscale models i.e. it assumes that all spatial properties correspond to the same spatial

scale. However for real-life applications there is a need to build and integrate models across multiple temporal and/or spatial scales which are not covered here. Multiple spatial scale models are not currently supported because the methodology does not include a mechanism to explicitly distinguish between spatial patterns from different scales.

Finally our approach has been validated only on simulated data but it should be applicable to real-life datasets as well. Moreover the usefulness of our methodology was illustrated only on biological case studies. However there is nothing inherent to the methodology which limits it to the biological and/or medical scenarios. Therefore one potential direction for future work is to apply this approach to non-biological case studies as well in an attempt to test its applicability limits and/or discover new features which should be included.

Summary

In this chapter the efficiency and applicability of the multidimensional spatio- temporal model checking methodology was assessed against two biological case studies encoding phase variation in bacterial colony growth and the chemotactic aggregation of cells. The conclusions drawn were that the methodology is general because it can be employed for computational models encoded using various high-level modelling formalisms, it employs spatio-temporal analysis methods which can be applied to time series data potentially originating from outside the in silico environment, and it supports both Bayesian and frequentist model checking approaches. Conversely one of the main limitations of the methodology is that the collections of spatial entity types and properties considered are fixed. Therefore the methodology cannot be employed in its current form for case studies in which other spatial entity types (e.g. 3D structure) and properties (e.g. volume) are relevant. Moreover the methodology is currently limited to uniscale computational models and therefore does not enable reasoning about how properties corresponding to biological subsystems from different scales relate to each other. Both of these limitations are addressed in Chapter 5.

CHAPTER

5

Multiscale multidimensional

spatio-temporal meta model

checking

Introduction

In this chapter the multidimensional spatio-temporal model checking methodology and implementation are extended such that they can be employed to validate both uniscale and multiscale computational models of biological systems with respect to case study specific spatial entity types and measures. The resulting approach is called multiscale multidimensional spatio-temporal meta model checking and is described and compared with the multidimensional model checking approach throughout the chapter. Related approaches for reasoning about how systems evolve over space, time and across multiple scales are described in the end.

5.1

Multiscale computational models of

biological systems

Most of the existing computational models of biological systems are uniscale and therefore abstract away all biologically relevant details from more fine- and/or coarse-grained levels of organization (Sloot and Hoekstra, 2010). The main reason for this is that by minimizing the amount of details included in the model its complexity, simulation and analysis times are reduced. However the main disadvantage of uniscale computational models is that they do not enable gaining a truly systems level understanding of how biological systems function (Dada 114

and Mendes, 2011) i.e. how changes at fine-grained scales are responsible for the behaviours observed at coarse-grained scales and vice versa, which is one of the main aims of systems biology.

To overcome this limitation multiscale computational models of biological systems need to be developed instead (Schnell et al., 2007).

The importance of multiscale computational models has been recognized at an international level as shown by the large number of active multiscale modelling projects in the European Union and the United States alone, which has reached at least a few hundred in 2014 and is currently following an increasing trend (Groen et al., 2014), and by the 2013 Nobel prize in chemistry awarded to Martin Karplus, Michael Levitt and Arieh Warshel for their contributions to the development of multiscale computational models of complex chemical systems (Thiel and Hummer, 2013).

The minimum requirements for a model to be considered multiscale are (Bernard, 2013):

• The model covers two or more spatial and/or temporal scales; • There is interaction between scales.

When studying biological systems the spatial and/or temporal scales considered usually correspond to a subset of the following ten levels of biological organiza- tion (Southern et al., 2008) (ordered from fine- to coarse-grained):

1. Quantum: Modelling electron-electron interactions.

2. Molecular: Representing the interactions between atoms (and ions) of interest.

3. Macro-molecular: Considering the interactions between several molecules. 4. Subcellular: When the number of molecules/particles considered is large

it is too computationally expensive to simulate the interactions between them explicitly. The entire process can be modelled as a single continuum capturing how the average number of molecules/particles changes over time. The natural upper bound of this continuum is the cell membrane.

5. Cellular: Cells are the basic structural and functional component of an organism and lie at the interface between most micro- and macro-scale biological processes. Therefore this is the level from which most middle-out multiscale modelling integration procedures start.

6. Tissue: Modelling how large groups of connected cells of the same type perform a specific function (e.g. myocardium).

7. Organ: Integrating multiple tissue models of potentially different types into a discrete entity performing a function or group of functions (e.g. heart). Such models usually account for the explicit geometry of the organ.

8. Organ system: Representing a group of organs which perform a common function together (e.g. cardiovascular system).

9. Organism: Modelling an individual life form (e.g. human).

10. Environment: Considering the external factors (e.g. temperature, humid- ity) and their influence on the development of the organism.

Depending on the organism considered and its inherent complexity (e.g. lower vs. higher organisms) some of these levels might not be present. For instance lower organisms such as bacteria do not have organs, whereas higher organisms such as primates do. Moreover the spatial and temporal scales corresponding to each level of biological organization can vary significantly depending on the biological system considered. For instance the spatial and temporal scales associated with the organism level of organization are much smaller for a mayfly (i.e. u 105_s, u 10−3m) than for a human (i.e. u 109s, u 100m).

The construction of a multiscale computational model usually starts from the level of biological organization where the most data and knowledge are available. Once a single scale model is built and validated, the construction continues with the integration of models from the subsequent levels of organization, which are either above or below depending on the chosen model construction strategy (i.e. top-down, bottom-up or middle-out). Integrating computational models across scales represents one of the biggest challenges of multiscale modelling (Dada and Mendes, 2011; Groen et al., 2014). Several reasons for this are:

• The uniscale computational models considered have been potentially encoded using different formalisms and their integration is not straightforward; • The complexity of the multiscale model could increase nonlinearly when

integrating multiple uniscale models to the point where model simulations and/or parameterizations cannot be executed in reasonable time;

• Quantifying the magnitude of errors in the multiscale model can prove challenging especially when the submodels have been developed considering different levels of approximation (Yang, 2013).

In order to tackle these challenges and enable the systematic construction of multiscale models there is a need to develop a generic multiscale modelling methodology which will be adopted by most of the scientific community (Hoekstra et al., 2014).

Although such a generic community-wide adopted approach does not yet exist various multiscale modelling approaches have been developed for computational models of biological systems. They are either tailored to a particular biological problem (i.e. problem specific) or generic (i.e. problem independent). An example of a problem specific modelling approach for cancer systems biology is described by Chaudhary et al. (Chaudhary et al., 2013). Conversely some of the most employed problem independent multiscale modelling approaches are described in Table 5.1.

Table 5.1: Several of the most employed problem independent multiscale modelling approaches for constructing computational models of biological systems. For each problem independent multiscale modelling approach considered the table columns record (from left to right) the name of a corresponding software tool, description, supported model types and references.

Software Description Model types Ref.

Chaste

A generic open source multiscale modelling and simulation framework for biological and physiological problems. The current version of the framework contains two modules, namely the Cardiac and the Cell-based module.

Ordinary/partial differential equations (ODE/PDE), rule-based models, Cellular Potts models (CPM), cellular automata, lattice-free models (Mirams et al., 2013) Coloured Stochastic Multilevel Multiset Rewriting (CSMMR) model simulator

A multilevel multiset rewriting modelling approach which enables the construction of computational models with parameters, dynamic compartments and multilevel compartmental structures. CSMMR models (Oury and Plotkin, 2011) Compu- Cell3D

A multiscale modelling framework for representing cellular (using Cellular Potts models) and subcellular behaviours (when interfaced with numerical solvers such as BionetSolver).

CPMs, model types (e.g. ODE) supported by various numerical solvers (Swat et al., 2012) FLAME

A generic multi-agent modelling platform employed, amongst others, to construct a multiscale 3D model of the epidermis (Adra et al., 2010). Agent-based models (ABM) (Kiran et al., 2010)

Software Description Model types Ref.

JAMES II

A multilevel rule-based modelling framework developed to support the construction of computational models of cell biological systems which span multiple levels of organization (Helms et al., 2014).

ML-Rules models

(Maus et al., 2011)

ManyCell

A multiscale cellular modelling environment encoding cells as agents, and subcellular processes as systems of ODEs that are solved using COPASI Web Services (Hoops et al., 2006). ABMs, ODEs (Dada and Mendes, 2012) MOBI and PK-Sim

Commercial multiscale modelling tools employed for developing physiologically-based pharmacokinetic whole-body models.

ODEs, metabolic network models simulated using Dynamic Flux Balance Analysis (Krauss et al., 2012) Morpheus

A multiscale cell-based modelling and simulation environment which enables integrating Cellular Potts (for cell behaviour) with ordinary, stochastic and delay differential equation (DDE) based models.

Reaction-diffusion systems are also supported and encoded using PDEs.

CPMs, ODEs, DDEs, PDEs (Starruß et al., 2014) Multiscale Modelling and Simulation Framework

A domain-independent multiscale modelling framework based on complex

automata (Hoekstra et al., 2007), validated against case studies from different domains of science (Borgdorff et al., 2014a). Single scale models can be encoded using different

modelling formalisms, are integrated according to the Multiscale Modelling Language (Falcone et al., 2010) specification, and simulated (in a distributed fashion) using the MUSCLE 2 (Borgdorff et al., 2014b) model coupling and simulation library; see (Caiazzo et al., 2011) for an illustrative biomedical application.

Modelling formalism independent (Chopard et al., 2014) NetLogo

A multi-agent modelling environment which can be integrated with deterministic continuous (ODE) models via a Matlab extension called MatNet. Illustrative

biomedical examples include a model of acute inflammation (An, 2008) and Pseudomonas aeruginosa biofilm formation (Biggs and Papin, 2013).

ABMs, ODEs

(Wilen- sky, 2015)

Software Description Model types Ref.

Open- CMISS

An open source multiscale modelling framework implemented in Fortran which supports models encoded in Cell Markup Language (CellML) (Lloyd et al., 2004) and Field Markup Language (FieldML) (Christie et al., 2009), standard model representations developed within the VPH and Physiome projects. CellML models, FieldML models (Bradley et al., 2011) PhysioDe- signer

A multilevel modelling framework based on the Physiological Hierarchy Markup Language (PHML), a standard modelling language for

the integration of single scale models.

PHML models

(Asai et al., 2014)

Snoopy

A unified Petri nets based modelling framework employed to construct both uniscale (Pˆarvu et al., 2015) and

multiscale(Gao et al., 2013; Liu and Heiner, 2013) computational models of biological systems. Qualitative, deterministic, stochastic and hybrid (coloured) Petri nets (Heiner et al., 2012)

Using such modelling approaches various multiscale computational models of biological systems have been constructed, covering different organisms (e.g. microorganisms (Biggs and Papin, 2013), plants (Grafahrend-Belau et al., 2013), humans (Krauss et al., 2012)), organ systems (e.g. cardiovascular system (Caiazzo et al., 2011; Formaggia et al., 1999; Lagan`a et al., 2005), digestive system (Du et al., 2013a; Graudenzi et al., 2014), nervous system (Bouteiller et al., 2011)) and diseases (e.g. thrombus formation (Xu et al., 2010), cancer (Deisboeck et al., 2011; Masoudi-Nejad et al., 2014), Crohn’s disease (Dwivedi et al., 2014)).

To use results generated by multiscale computational models outside the in silico environment the models need to be validated first. However generic methodologies for multiscale model validation and error quantification have not yet been developed (Hoekstra et al., 2014). Moreover validating multiscale computational models against real-life data is often not possible due to the lack of relevant information from and between all relevant levels of organization (Walpole et al., 2013).