CSB modelling principles

Quantitative Systems Models: Principles

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Developing predictive, quantitative systems models can be considered the holy grail of the field, yet it is a formidable challenge. Not only do we require that models are quantitatively correct, which is a difficult task given that our knowledge of kinetic parameters and time-varying concentrations is incomplete, we also need to integrate models over several spatial and temporal orders of magnitude - from molecular-scale reactions to organ- and organism-scale phenotypes.

 

Introductory reading

Blinov & Moraru (2012) Logic modeling and the ridiculome under the rug. BMC Biol 10:92. (pmid: 23171629)

[ PubMed ] [ DOI ] Logic-derived modeling has been used to map biological networks and to study arbitrary functional interactions, and fine-grained kinetic modeling can accurately predict the detailed behavior of well-characterized molecular systems; at present, however, neither approach comes close to unraveling the full complexity of a cell. The current data revolution offers significant promises and challenges to both approaches - and could bring them together as it has spurred the development of new methods and tools that may help to bridge the many gaps between data, models, and mechanistic understanding.

Tenazinha & Vinga (2011) A survey on methods for modeling and analyzing integrated biological networks. IEEE/ACM Trans Comput Biol Bioinform 8:943-58. (pmid: 21116043)

[ PubMed ] [ DOI ] Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.

Santos et al. (2011) A practical guide to genome-scale metabolic models and their analysis. Meth Enzymol 500:509-32. (pmid: 21943912)

[ PubMed ] [ DOI ] Genome-scale metabolic reconstructions and their analysis with constraint-based modeling techniques have gained enormous momentum. It is a natural next step after sequencing of a genome, as a technique that links top-down systems biology analyses at genome scale with bottom-up systems biology modeling scrutiny. This chapter aims at (systems) biologists that have an interest in, but no extensive knowledge of, applying genome-scale metabolic reconstruction and modeling to their organism. Rather than being comprehensive--excellent and extensive reviews exist on every aspect of this field--we give a rather personal account on our experience with the process of reconstruction and modeling. First, we place genome-scale metabolic models in the spectrum of modeling approaches, and rather extensively discuss, for nonexperts, the central concept in constraint-based modeling: the solution space that is bounded through constraints on fluxes. We subsequently provide an overview of the different steps involved in metabolic reconstruction and modeling, pointing to aspects that we found difficult, important, not well enough addressed in the current reviews, or any combination thereof. In this way, we hope that this chapter serves as a practical guide through the field.

 

Contents

• Principles
• Applications

   

Further reading and resources

Concepts

Kirk et al. (2013) Model selection in systems and synthetic biology. Curr Opin Biotechnol 24:767-74. (pmid: 23578462)

[ PubMed ] [ DOI ] Developing mechanistic models has become an integral aspect of systems biology, as has the need to differentiate between alternative models. Parameterizing mathematical models has been widely perceived as a formidable challenge, which has spurred the development of statistical and optimisation routines for parameter inference. But now focus is increasingly shifting to problems that require us to choose from among a set of different models to determine which one offers the best description of a given biological system. We will here provide an overview of recent developments in the area of model selection. We will focus on approaches that are both practical as well as build on solid statistical principles and outline the conceptual foundations and the scope for application of such methods in systems biology.

Bradley et al. (2011) OpenCMISS: a multi-physics & multi-scale computational infrastructure for the VPH/Physiome project. Prog Biophys Mol Biol 107:32-47. (pmid: 21762717)

[ PubMed ] [ DOI ] The VPH/Physiome Project is developing the model encoding standards CellML (cellml.org) and FieldML (fieldml.org) as well as web-accessible model repositories based on these standards (models.physiome.org). Freely available open source computational modelling software is also being developed to solve the partial differential equations described by the models and to visualise results. The OpenCMISS code (opencmiss.org), described here, has been developed by the authors over the last six years to replace the CMISS code that has supported a number of organ system Physiome projects. OpenCMISS is designed to encompass multiple sets of physical equations and to link subcellular and tissue-level biophysical processes into organ-level processes. In the Heart Physiome project, for example, the large deformation mechanics of the myocardial wall need to be coupled to both ventricular flow and embedded coronary flow, and the reaction-diffusion equations that govern the propagation of electrical waves through myocardial tissue need to be coupled with equations that describe the ion channel currents that flow through the cardiac cell membranes. In this paper we discuss the design principles and distributed memory architecture behind the OpenCMISS code. We also discuss the design of the interfaces that link the sets of physical equations across common boundaries (such as fluid-structure coupling), or between spatial fields over the same domain (such as coupled electromechanics), and the concepts behind CellML and FieldML that are embodied in the OpenCMISS data structures. We show how all of these provide a flexible infrastructure for combining models developed across the VPH/Physiome community.

Qu et al. (2011) Multi-scale modeling in biology: how to bridge the gaps between scales?. Prog Biophys Mol Biol 107:21-31. (pmid: 21704063)

[ PubMed ] [ DOI ] Human physiological functions are regulated across many orders of magnitude in space and time. Integrating the information and dynamics from one scale to another is critical for the understanding of human physiology and the treatment of diseases. Multi-scale modeling, as a computational approach, has been widely adopted by researchers in computational and systems biology. A key unsolved issue is how to represent appropriately the dynamical behaviors of a high-dimensional model of a lower scale by a low-dimensional model of a higher scale, so that it can be used to investigate complex dynamical behaviors at even higher scales of integration. In the article, we first review the widely-used different modeling methodologies and their applications at different scales. We then discuss the gaps between different modeling methodologies and between scales, and discuss potential methods for bridging the gaps between scales.

Vallabhajosyula & Raval (2010) Computational modeling in systems biology. Methods Mol Biol 662:97-120. (pmid: 20824468)

[ PubMed ] [ DOI ] Interactions among cellular constituents play a crucial role in overall cellular function and organization. These interactions can be viewed as being complementary to the usual "parts list" of genes and proteins and, in conjunction with the expression states of these parts, are key to a systems level understanding of the cell. Here, we review computational approaches to the understanding of the functional roles of cellular networks, ranging from "static" models of network topology to dynamical and stochastic simulations.

Frazier et al. (2009) Computational representation of biological systems. Methods Mol Biol 541:535-49. (pmid: 19381532)

[ PubMed ] [ DOI ] Integration of large and diverse biological data sets is a daunting problem facing systems biology researchers. Exploring the complex issues of data validation, integration, and representation, we present a systematic approach for the management and analysis of large biological data sets based on data warehouses. Our system has been implemented in the Bioverse, a framework combining diverse protein information from a variety of knowledge areas such as molecular interactions, pathway localization, protein structure, and protein function.

Kestler et al. (2008) Network modeling of signal transduction: establishing the global view. Bioessays 30:1110-25. (pmid: 18937364)

[ PubMed ] [ DOI ] Embryonic development and adult tissue homeostasis are controlled through activation of intracellular signal transduction pathways by extracellular growth factors. In the past, signal transduction has largely been regarded as a linear process. However, more recent data from large-scale and high-throughput experiments indicate that there is extensive cross-talk between individual signaling cascades leading to the notion of a signaling network. The behavior of such complex networks cannot be predicted by simple intuitive approaches but requires sophisticated models and computational simulations. The purpose of such models is to generate experimentally testable hypotheses and to find explanations for unexpected experimental results. Here, we discuss the need for, and the future impact of, mathematical models for exploring signal transduction in different biological contexts such as for example development.

Ridgway et al. (2006) Accommodating space, time and randomness in network simulation. Curr Opin Biotechnol 17:493-8. (pmid: 16962764)

[ PubMed ] [ DOI ] Interest in the possibility of dynamically simulating complex cellular processes has escalated markedly in recent years. This interest has been fuelled by three factors: the generally accepted value in understanding living processes as integrated systems; the dramatic increase in computational capability; and the availability of new or improved technology for making the quantitative measurements that are needed to drive and validate cellular simulations. Between the extremes of atom-scale and organism-scale simulation is a vast middle-ground requiring simulation strategies that are capable of dealing with a range of spatial, temporal and molecular abundance scales that are crucial for a comprehensive understanding of integrative cell biology. Although at an early stage, methodological improvements and the development of computational platforms provide some hope that simulations will emerge that can bridge the gap between network models and the true operation of the cell as a complex machine.

Coveney & Fowler (2005) Modelling biological complexity: a physical scientist's perspective. J R Soc Interface 2:267-80. (pmid: 16849185)

[ PubMed ] [ DOI ] We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical examples of systems biology, are modelled by sets of nonlinear coupled differential equations, which have to be solved numerically. A major difficulty with this approach is that all the parameters within these equations are not usually known. Coupled models that include biomolecular detail could help solve this problem. Coupling models across large ranges of length- and time-scales is central to describing complex systems and therefore to biology. Such coupling may be performed in at least two different ways, which we refer to as hierarchical and hybrid multiscale modelling. While limited progress has been made in the former case, the latter is only beginning to be addressed systematically. These modelling methods are expected to bring numerous benefits to biology, for example, the properties of a system could be studied over a wider range of length- and time-scales, a key aim of systems biology. Multiscale models couple behaviour at the molecular biological level to that at the cellular level, thereby providing a route for calculating many unknown parameters as well as investigating the effects at, for example, the cellular level, of small changes at the biomolecular level, such as a genetic mutation or the presence of a drug. The modelling and simulation of biomolecular systems is itself very computationally intensive; we describe a recently developed hybrid continuum-molecular model, HybridMD, and its associated molecular insertion algorithm, which point the way towards the integration of molecular and more coarse-grained representations of matter. The scope of such integrative approaches to complex systems research is circumscribed by the computational resources available. Computational grids should provide a step jump in the scale of these resources; we describe the tools that RealityGrid, a major UK e-Science project, has developed together with our experience of deploying complex models on nascent grids. We also discuss the prospects for mathematical approaches to reducing the dimensionality of complex networks in the search for universal systems-level properties, illustrating our approach with a description of the origin of life according to the RNA world view.

Takahashi et al. (2005) Space in systems biology of signaling pathways--towards intracellular molecular crowding in silico. FEBS Lett 579:1783-8. (pmid: 15763552)

[ PubMed ] [ DOI ] How cells utilize intracellular spatial features to optimize their signaling characteristics is still not clearly understood. The physical distance between the cell-surface receptor and the gene expression machinery, fast reactions, and slow protein diffusion coefficients are some of the properties that contribute to their intricacy. This article reviews computational frameworks that can help biologists to elucidate the implications of space in signaling pathways. We argue that intracellular macromolecular crowding is an important modeling issue, and describe how recent simulation methods can reproduce this phenomenon in either implicit, semi-explicit or fully explicit representation.

Applications

Nookaew et al. (2011) Genome-scale metabolic models of Saccharomyces cerevisiae. Methods Mol Biol 759:445-63. (pmid: 21863502)

[ PubMed ] [ DOI ] Systematic analysis of Saccharomyces cerevisiae metabolic functions and pathways has been the subject of extensive studies and established in many aspects. With the reconstruction of the yeast genome-scale metabolic (GSM) network and in silico simulation of the GSM model, the nature of the underlying cellular processes can be tested and validated with the increasing metabolic knowledge. GSM models are also being exploited in fundamental research studies and industrial applications. In this chapter, the principle concepts for construction, simulation and validation of GSM models, progressive applications of the yeast GSM models, and future perspectives are described. This will support and encourage researchers who are interested in systemic analysis of yeast metabolism and systems biology.

Kriete et al. (2011) Computational systems biology of aging. Wiley Interdiscip Rev Syst Biol Med 3:414-28. (pmid: 21197651)

[ PubMed ] [ DOI ] Computational systems biology is expected to make major contributions to unravel the complex molecular mechanisms underlying the progression of aging in cells, tissues, and organisms. The development of computational approaches is, however, challenged by a wide spectrum of aging mechanisms participating on different levels of biological organization. The tight connectivity between the molecular constituents, functions, and cell states requires frameworks and strategies that extend beyond current practice to model, simulate, and predict the progression of aging and the emerging aging phenotypes. We provide a general overview of the specific computational tasks and opportunities in aging research, and discuss some illustrative systems level concepts in more detail. One example provided here is the assembly of a conceptual whole cell model that considers the temporal dynamics of the aging process grounded on molecular mechanisms. Another application is the assembly of interactomes, such as protein networks that allow us to analyze changes in network topology and interaction of proteins that have been implicated in aging with other cellular constituents and processes. We introduce the necessary key steps to build these applications and discuss their merits and future extensions for aging research. WIREs Syst Biol Med 2011 3 414-428 DOI: 10.1002/wsbm.126

Bhattacharya et al. (2010) Toward failure analyses in systems biology. Wiley Interdiscip Rev Syst Biol Med 2:507-517. (pmid: 20836044)

[ PubMed ] [ DOI ] Parallels between designed and biological systems with respect to formal failure analyses have been presented. Failure analysis in designed systems depends on an identified, limited set of parameters or operation variables with high predictive value. In contrast, the biological systems pose problems in identification of operation variables and the identified variables may not be accurate predictors of failure. The difficulty in parameter identification is because of large numbers of components and the inability to envelope variables at each compartment or contour level. Contour level maps for biological systems are currently non-existent, and most failure models are based on very limited, unilateral operation variables (a mutant gene). Operation variable identification within each contour level will enhance failure analyses of complex biological systems.