Computational Biology Modeling Research Articles

Systems biology insights into the meaning of the platelet's dual‐receptor thrombin signaling

Background Activation of human platelets with thrombin proceeds via two protease-activated receptors (PARs), PAR1 and PAR4, that have identical main intracellular signaling responses. Although there is evidence that they have different cleavage/inactivation kinetics (and some secondary variations in signaling), the reason for such redundancy is not clear. Methods We developed a multicompartmental stochastic computational systems biology model of dual-receptor thrombin signaling in platelets to gain insight into the mechanisms and roles of PAR1 and PAR4 functioning. Experiments employing continuous flow cytometry of washed human platelets were used to validate the model and test its predictions. Activity of PAR receptors in donors was evaluated by mRNA measurement and by polymorphism sequencing. Results Although PAR1 activation produced rapid and short-lived response, signaling via PAR4 developed slowly and propagated in time. Response of the dual-receptor system was both rapid and prolonged in time. Inclusion of PAR1/PAR4 heterodimer formation promoted PAR4 signaling in the medium range of thrombin concentration (about 10 nm), with little contribution at high and low thrombin. Different dynamics and dose-dependence of procoagulant platelet formation in healthy donors was associated with individual variations in PAR1 and PAR4 activities and particularly by the Ala120Thr polymorphism in the F2RL3 gene encoding PAR4. Conclusions The dual-receptor combination is critical to produce a response combining three critical advantages: sensitivity to thrombin concentration, rapid onset and steady propagation; specific features of the protease-activated receptors do not allow combination of all three in a single receptor.

Redox status in a model of cancer stem cells

Reversible oxidation of Cys residues is a crucial element of redox homeostasis and signaling. According to a popular concept in oxidative stress signaling, the oxidation of targets of signals can only take place following an overwhelming of the cellular antioxidant capacity. This concept, however, ignores the activation of feedback mechanisms possibly leading to a paradoxical effect. In a model of cancer stem cells (CSC), stably overexpressing the TAZ oncogene, we observed that the increased formation of oxidants is associated with a globally more reduced state of proteins. Redox proteomics revealed that several proteins, capable of undergoing reversible redox transitions, are indeed more reduced while just few are more oxidized. Among the proteins more oxidized, G6PDH emerges as both more expressed and activated by oxidation. This accounts for the observed more reduced state of the NADPH/NADP+ couple. The dynamic redox flux generating this apparently paradoxical effect is rationalized in a computational system biology model highlighting the crucial role of G6PDH activity on the rate of redox transitions eventually leading to the reduction of reversible redox switches.

Dynamics of calcium spiking, mitochondrial collapse and phosphatidylserine exposure in platelet subpopulations during activation

Essentials The sequence and logic of events leading to platelet procoagulant activity are poorly understood. Confocal time-lapse microscopy was used to investigate activation of single adherent platelets. Platelet transition to the procoagulant state followed cytosolic calcium oscillations. Mitochondria did not collapse simultaneously and membrane potential loss could be reversible. Background Activated platelets form two subpopulations, one of which is able to efficiently aggregate, and another that externalizes phosphatidylserine (PS) and thus accelerates membrane-dependent reactions of blood coagulation. The latter, procoagulant subpopulation is characterized by a high cytosolic calcium level and the loss of inner mitochondrial membrane potential, and there are conflicting opinions on their roles in its formation. Methods We used confocal microscopy to investigate the dynamics of subpopulation formation by imaging single, fibrinogen-bound platelets with individual mitochondria in them upon loading with calcium-sensitive and mitochondrial potential-sensitive dyes. Stimulation was performed with thrombin or the protease-activated receptor (PAR) 1 agonist SFLLRN. Stochastic simulations with a computational systems biology model of PAR1 calcium signaling were employed for analysis. Results Platelet activation resulted in a series of cytosolic calcium spikes and mitochondrial calcium uptake in all platelets. The frequency of spikes decreased with time for SFLLRN stimulation, but remained high for a long period of time for thrombin. In some platelets, uptake of calcium by mitochondria led to the mitochondrial permeability transition pore opening and inner mitochondrial membrane potential loss, which could be either reversible or irreversible. The latter resulted in an increase in the cytosolic calcium level and PS exposure. These platelets had higher cytosolic calcium levels before activation, and their mitochondria collapsed not simultaneously but one after another. Conclusions These results support a model of procoagulant subpopulation development following a series of stochastic cytosolic calcium spikes that are accumulated by mitochondria, leading to a collapse, and suggest important roles of individual platelet reactivity and signal exchange between different mitochondria of a platelet.

Geometric analysis of pathways dynamics: Application to versatility of TGF-β receptors

We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. These metastable regimes depend on the network topology and on the orders of magnitude of the kinetic parameters. Benchmarking of the method on a computational biology model repository suggests that the number of metastable regimes is sub-exponential in the number of variables and equations. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed connectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional capacities of signalling pathways, as well as parameters responsible for specificity of the pathway response. In particular, for a model of TGFβ signalling, we find that the ratio of TGFBR2 to TGFBR1 receptors concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptors of the two types.

COMODI: an ontology to characterise differences in versions of computational models in biology.

BackgroundOpen model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to different model versions. Taken together, the underlying changes reflect a model’s provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context.MethodsWe manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology.ResultsWe present COMODI, an ontology needed because COmputational MOdels DIffer. It empowers users and software to describe changes in a model on the semantic level. COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is a step towards predicting how a change in a model influences the simulation results.ConclusionCOMODI, coupled with our algorithm for difference detection, ensures the transparency of a model’s evolution, and it enhances the traceability of updates and error corrections. COMODI is encoded in OWL. It is openly available at http://comodi.sems.uni-rostock.de/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0080-2) contains supplementary material, which is available to authorized users.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0080-2) contains supplementary material, which is available to authorized users.

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

The thioredoxin system and not the Michaelis–Menten equation should be fitted to substrate saturation datasets from the thioredoxin insulin assay

Introduction: The thioredoxin system, consisting of thioredoxin reductase, thioredoxin and NADPH, is present in most living organisms and reduces a large array of target protein disulfides.Objective: The insulin reduction assay is commonly used to characterise thioredoxin activity in vitro, but it is not clear whether substrate saturation datasets from this assay should be fitted and modeled with the Michaelis-Menten equation (thioredoxin enzyme model), or fitted to the thioredoxin system with insulin reduction described by mass-action kinetics (redox couple model).Methods: We utilized computational modeling and in vitro assays to determine which of these approaches yield consistent and accurate kinetic parameter sets for insulin reduction.Results: Using computational modeling, we found that fitting to the redox couple model, rather than to the thioredoxin enzyme model, resulted in consistent parameter sets over a range of thioredoxin reductase concentrations. Furthermore, we established that substrate saturation in this assay was due to the progressive redistribution of the thioredoxin moiety into its oxidised form. We then confirmed these results in vitro using the yeast thioredoxin system.Discussion: This study shows how consistent parameter sets for thioredoxin activity can be obtained regardless of the thioredoxin reductase concentration used in the insulin reduction assay, and validates computational systems biology modeling studies that have described the thioredoxin system with the redox couple modeling approach.

Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive.

The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.

Study of Functional and Allosteric Sites in Protein Superfamilies

The interaction of proteins (enzymes) with a variety of low-molecular-weight compounds, as well as protein-protein interactions, is the most important factor in the regulation of their functional properties. To date, research effort has routinely focused on studying ligand binding to the functional sites of proteins (active sites of enzymes), whereas the molecular mechanisms of allosteric regulation, as well as binding to other pockets and cavities in protein structures, remained poorly understood. Recent studies have shown that allostery may be an intrinsic property of virtually all proteins. Novel approaches are needed to systematically analyze the architecture and role of various binding sites and establish the relationship between structure, function, and regulation. Computational biology, bioinformatics, and molecular modeling can be used to search for new regulatory centers, characterize their structural peculiarities, as well as compare different pockets in homologous proteins, study the molecular mechanisms of allostery, and understand the communication between topologically independent binding sites in protein structures. The establishment of an evolutionary relationship between different binding centers within protein superfamilies and the discovery of new functional and allosteric (regulatory) sites using computational approaches can improve our understanding of the structure-function relationship in proteins and provide new opportunities for drug design and enzyme engineering.

Abstract B1-19: Computable cancer hallmarks - The construction of novel computable biological network models reflecting causal mechanisms of cancer hallmarks

Abstract Over the past few years, we constructed a number of biological network models representing comprehensive connectivity maps of fundamental molecular mechanisms regulating cell proliferation, cellular stress, and cell fate in the healthy and inflamed lung and cardiovascular system [1, 2, 3, 4]. These network models are based on causal and correlative biological relationships expressed in Biological Expression Language (BEL). We further developed a method that quantifies network response as a whole in an interpretable manner by integrating these causal networks with systems biology data (e.g. transcriptomics) and could show that quantitative network perturbation was in agreement with experimental endpoint data for many of the mechanistic effects of interest [5]. Recently, we extended our efforts in an attempt to build a comprehensive set of computational models reflecting the biology of cancer hallmarks as described by Hanahan and Weinberg [6] with specific attention to mechanisms occurring in the early stages of non-small cell lung cancer (NSCLC) development and progression. Using a dual approach of curating relevant literature and supplementing this information with experimental data sets from a variety of NSCLC microarray studies, we thus far completed the construction of a “Sustaining Proliferative Signaling/Evading Growth Suppressors” hallmark model that describes multiple autocrine and paracrine signaling pathways responsible for driving continuous growth of tumor cells (e.g. growth factor/growth factor receptor, MAPK, JAK/STAT signaling etc.) and deregulating cell cycle check points, as well as a “Resisting Cell Death” hallmark model combining biological mechanisms indicative of intrinsic and extrinsic apoptosis pathways, necroptosis and autophagy that ensure (lung) tumor maintenance and survival. Processes related to VEGF- and other growth factor-driven angiogenesis, vascular sprouting and tubulogenesis, HIF1A signaling and endothelial cell activation were included in an “Inducing Angiogenesis” hallmark model. The latter is closely connected to the hallmark model “Activating Invasion and Metastasis” which also considers the mechanisms associated with the acquisition of invasive capabilities by e.g. epithelial-mesenchymal transition, the degradation of extracellular matrix, and epithelial and endothelial permeability permitting tumor cell dissemination. We further built a “Deregulating Cellular Energetics” hallmark model reflecting the metabolic switch in tumor cells to aerobic glycolysis including various aspects of hypoxia and autophagy. Our focus at the current time is to address the features associated with tumor immune surveillance as exemplified by the complex interplay between tumor cells and tumor-infiltrating lymphocytes, macrophages, dendritic cells and natural killer cells which could be reflected in an “Avoiding Immune Destruction” hallmark model. As a next step we wish to integrate specific mechanisms that contribute to persistent pro-inflammatory signaling into a “Tumor-promoting Inflammation” hallmark network model. This will be followed by a comprehensive review of the newly constructed models and, if necessary, further augmentation by literature and validation with molecular data. Ultimately, we will employ our previously developed network quantification approach together with a number of publicly available lung cancer data sets to objectively evaluate the predictability of disease mechanisms in silico using transcriptomics data, and we hope that, if successful in this endeavor, various applications from drug development to environmental impact analysis could benefit from employing this portfolio of network models in unraveling disease-specific mechanisms and identifying new therapeutic targets. [1] Westra JW, Schlage WK, Frushour BP et al. (2011). Construction of a computable cell proliferation network focused on non-diseased lung cells. BMC Syst Biol. 5, 105. [2] Gebel S, Lichtner RB, Frushour B et al. (2013). Construction of a computable network model for DNA damage, autophagy, cell death, and senescence. Bioinform Biol Insights 7, 97-117. [3] Westra JW, Schlage WK, Hengstermann A et al. (2013). A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue. Bioinform Biol Insights 7, 167-192. [4] De León H, Boué S, Schlage WK et al. (2014). A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability. J Transl Med. 12, 185. [5] Thomson TM, Sewer A, Martin F et al. (2013). Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicol Appl Pharmacol. 272(3), 863-878. [6] Hanahan D & Weinberg RA (2011). Hallmarks of Cancer: The Next Generation. Cell 144(5), 646–674. Citation Format: Karsta Luettich, Marja Talikka, Anita Iskandar, Justyna Szostak, Ulrike Kogel, Walter Schlage, Yang Xiang, Vered Katz Ben-Yair, Shay Rotkopf, Brett Fields, Jennifer Park, Julia Hoeng, Manuel Peitsch. Computable cancer hallmarks - The construction of novel computable biological network models reflecting causal mechanisms of cancer hallmarks. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-19.

There Is a "U" in Clutter: Evidence for Robust Sparse Codes Underlying Clutter Tolerance in Human Vision.

The neural mechanisms underlying object recognition in cluttered scenes (i.e., containing more than one object) remain poorly understood. Studies have suggested that neural responses to multiple objects correspond to an average of the responses to the constituent objects. Yet, it is unclear how the identities of an unknown number of objects could be disentangled from a confounded average response. Here, we use a popular computational biological vision model to show that averaging-like responses can result from responses of clutter-tolerant neurons to suboptimal stimuli. The model also provides a novel prediction, that human detection ability should show a U-shaped dependency on target-clutter similarity, which is confirmed experimentally, supporting a simple, unifying account of how the brain performs object recognition in clutter.

Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities.

Estimating model parameters from experimental data is a crucial technique for working with computational models in systems biology. Since stochastic models are increasingly important, parameter estimation methods for stochastic modelling are also of increasing interest. This study presents an extension to the ‘multiple shooting for stochastic systems (MSS)’ method for parameter estimation. The transition probabilities of the likelihood function are approximated with normal distributions. Means and variances are calculated with a linear noise approximation on the interval between succeeding measurements. The fact that the system is only approximated on intervals which are short in comparison with the total observation horizon allows to deal with effects of the intrinsic stochasticity. The study presents scenarios in which the extension is essential for successfully estimating the parameters and scenarios in which the extension is of modest benefit. Furthermore, it compares the estimation results with reversible jump techniques showing that the approximation does not lead to a loss of accuracy. Since the method is not based on stochastic simulations or approximative sampling of distributions, its computational speed is comparable with conventional least‐squares parameter estimation methods.

Specifications of Standards in Systems and Synthetic Biology

Summary Standards shape our everyday life. From nuts and bolts to electronic devices and technological processes, standardised products and processes are all around us. Standards have technological and economic benefits, such as making information exchange, production, and services more efficient. However, novel, innovative areas often either lack proper standards, or documents about standards in these areas are not available from a centralised platform or formal body (such as the International Standardisation Organisation).Systems and synthetic biology is a relatively novel area, and it is only in the last decade that the standardisation of data, information, and models related to systems and synthetic biology has become a community-wide effort. Several open standards have been established and are under continuous development as a community initiative. COMBINE, the ‘COmputational Modeling in BIology’ NEtwork [1] has been established as an umbrella initiative to coordinate and promote the development of the various community standards and formats for computational models. There are yearly two meeting, HARMONY (Hackathons on Resources for Modeling in Biology), Hackathon-type meetings with a focus on development of the support for standards, and COMBINE forums, workshop-style events with oral presentations, discussion, poster, and breakout sessions for further developing the standards. For more information see http://co.mbine.org/.So far the different standards were published and made accessible through the standards’ web-pages or preprint services. The aim of this special issue is to provide a single, easily accessible and citable platform for the publication of standards in systems and synthetic biology. This special issue is intended to serve as a central access point to standards and related initiatives in systems and synthetic biology, it will be published annually to provide an opportunity for standard development groups to communicate updated specifications.

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative.

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE’s aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.

Mathematical and computational modeling in biology at multiple scales.

A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.

Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing

Wound healing is a synchronized cascade of chemical, biological, and mechanical phenomena, which act in concert to restore the damaged tissue. An imbalance between these events can induce painful scarring. Despite intense efforts to decipher the mechanisms of wound healing, the role of mechanics remains poorly understood. Here, we establish a computational systems biology model to identify the chemical, biological, and mechanical mechanisms of scar formation. First, we introduce the generic problem of coupled chemo-bio-mechanics. Then, we introduce the model problem of wound healing in terms of a particular chemical signal, inflammation, a particular biological cell type, fibroblasts, and a particular mechanical model, isotropic hyperelasticity. We explore the cross-talk between chemical, biological, and mechanical signals and show that all three fields have a significant impact on scar formation. Our model is the first step toward rigorous multiscale, multifield modeling in wound healing. Our formulation has the potential to improve effective wound management and optimize treatment on an individualized patient-specific basis.

Multiscale computational models in physical systems biology of intracellular trafficking.

In intracellular trafficking, a definitive understanding of the interplay between protein binding and membrane morphology remains incomplete. The authors describe a computational approach by integrating coarse-grained molecular dynamics (CGMD) simulations with continuum Monte Carlo (CM) simulations of the membrane to study protein-membrane interactions and the ensuing membrane curvature. They relate the curvature field strength discerned from the molecular level to its effect at the cellular length-scale. They perform thermodynamic integration on the CM model to describe the free energy landscape of vesiculation in clathrin-mediated endocytosis. The method presented here delineates membrane morphologies and maps out the free energy changes associated with membrane remodeling due to varying coat sizes, coat curvature strengths, membrane bending rigidities, and tensions; furthermore several constraints on mechanisms underlying clathrin-mediated endocytosis have also been identified, Their CGMD simulations have revealed the importance of PIP2 for stable binding of proteins essential for curvature induction in the bilayer and have provided a molecular basis for the positive curvature induction by the epsin N-terminal homology (EIMTH) domain. Calculation of the free energy landscape for vesicle budding has identified the critical size and curvature strength of a clathrin coat required for nucleation and stabilisation of a mature vesicle.

1D sweep-and-prune self-collision detection for deforming cables

Detecting self-collision for cables and similar objects is an important part of numerous models in computational biology (protein chains), robotics (electric cables), hair modeling, computer graphics, etc. In this paper the 1D sweep-and-prune algorithm for detecting self-collisions of a deforming cable comprising linear segments is investigated. The sweep-and-prune algorithm is compared with other state-of-the-art self-collision detection algorithms for deforming cables and is shown to be up to an order of magnitude faster than existing algorithms for cables with a high proportion of segments moving. We also present a multi-threaded version of the algorithm and investigate its performance. In addition, we present worst-case bounds for 1D sweep-and-prune algorithms whereby the colliding objects do not exceed a certain object density, and apply these results to deforming cables.

Morphogenesis, morphology and men: pattern formation from embryo to mind

In 1952, Alan Turing published his last work on the concept of embryonic morphogenesis, propounding a computational framework for pattern formation within the developing embryo. This concept of morphogenesis and the concept of embryo pattern formation based on chemical diffusion patterns were corroborated with the discovery of the Homeobox or Hox genes. In the following decades, Hox gene research has expanded and is now shown to underlie the variety of morphological novelties that we experience in nature, the patterning of structural aspects of different organs including the brain and also mutant animals that may in the future give rise to novel speciation. Turing had the foresight and vision and with his work created the field of computational biology and mathematical modeling in biological systems. In this paper, we will discuss the concept of Hox genes, their role in patterning the embryo, how it relates to Turing’s concept of morphogenesis and what further insights they may provide.

Sparse Adaptive Approximation of High Dimensional Parametric Initial Value Problems

We consider non-linear systems of ordinary differential equations (ODEs) on a state space $\mathcal{S}$ . We consider the general setting when $\mathcal{S}$ is a Banach space over ℝ or ℂ. We assume the right-hand side depends in an affine fashion on a sequence y=(y j ) j≥1 of possibly countably many parameters, normalized such that |y j |≤1. Under suitable analyticity assumptions on the ODEs, we prove that the parametric solution $\{X(t;y): 0\leq t\leq T\}\subset\mathcal{S}$ of the corresponding IVP depends holomorphically on the parameter vector y, as a mapping from the infinite-dimensional parameter domain U=[−1,1]ℕ into a suitable function space on $[0,T]\times \mathcal{S}$ . While such affine parameter dependence of the ODE seems quite special it is of great importance for applications as it arises, among others, in mass–action models in computational biology (see, e.g., Horn and Jackson in Arch. Ration. Mech. Anal. 47:81–116, 1972) and in stoichiometry with uncertain reaction rate constants. Using our analytic regularity result, we prove summability theorems for coefficient sequences of generalized polynomial chaos (gpc) expansions of the parametric solutions {X(⋅;y)} y∈U with respect to tensor product orthogonal polynomial bases of L 2(U). We give sufficient conditions on the ODEs for N-term truncations of these expansions to converge on the entire parameter space with efficiency (i.e., accuracy versus complexity) being independent of the number of parameters viz. the dimension of the parameter space U. Computational aspects of such approximations will be addressed in Hansen et al. (Proceedings of the 5th International Conference on High Performance Scientific Computing, 2013).