A computational and experimental study of the regulatory mechanisms of the complement system

Revised model of the tissue factor pathway of thrombin generation: Role of the feedback activation of FXI

BACKGROUND

Biochemical reaction networks are self-regulated in part due to feedback activation mechanisms. The tissue factor (TF) pathway of blood coagulation is a complex reaction network controlled by multiple feedback loops that coalesce around the serine protease thrombin.

OBJECTIVES

Our goal was to evaluate the relative contribution of the feedback activation of coagulation factor XI (FXI) in TF-mediated thrombin generation using a comprehensive systems-based analysis.

MATERIALS AND METHODS

We developed a systems biology model that improves the existing Hockin-Mann (HM) model through an integrative approach of mathematical modeling and in vitro experiments. Thrombin generation measured using in vitro assays revealed that the feedback activation of FXI contributes to the propagation of thrombin generation based on the initial concentrations of TF or activated coagulation factor X (FXa). We utilized experimental data to improve the robustness of the HM model to capture thrombin generation kinetics without a role for FXI before including the feedback activation of FXI by thrombin to construct the extended (ext.) HM model.

RESULTS AND CONCLUSIONS

Using the ext.HM model, we predicted that the contribution of positive feedback of FXI activation by thrombin can be abolished by selectively eliminating the inhibitory function of tissue factor pathway inhibitor (TFPI), a serine protease inhibitor of FXa and TF-activated factor VII (FVIIa) complex. This prediction from the ext.HM model was experimentally validated using thrombin generation assays with function blocking antibodies against TFPI and plasmas depleted of FXI. Together, our results demonstrate the applications of combining experimental and modeling techniques in predicting complex biochemical reaction systems.

Systems Biology Modeling of the Complement System Under Immune Susceptible Pathogens

The complement system is assembled from a network of proteins that function to bring about the first line of defense of the body against invading pathogens. However, complement deficiencies or invasive pathogens can hijack complement to subsequently increase susceptibility of the body to infections. Moreover, invasive pathogens are increasingly becoming resistant to the currently available therapies. Hence, it is important to gain insights into the highly dynamic interaction between complement and invading microbes in the frontlines of immunity. Here, we developed a mathematical model of the complement system composed of 670 ordinary differential equations with 328 kinetic parameters, which describes all three complement pathways (alternative, classical, and lectin) and includes description of mannose-binding lectin, collectins, ficolins, factor H-related proteins, immunoglobulin M, and pentraxins. Additionally, we incorporate two pathogens: (type 1) complement susceptible pathogen and (type 2) Neisseria meningitidis located in either nasopharynx or bloodstream. In both cases, we generate time profiles of the pathogen surface occupied by complement components and the membrane attack complex (MAC). Our model shows both pathogen types in bloodstream are saturated by complement proteins, whereas MACs occupy <<1.0% of the pathogen surface. Conversely, the MAC production in nasopharynx occupies about 1.5-10% of the total N. meningitidis surface, thus making nasal MAC levels at least about eight orders of magnitude higher. Altogether, we predict complement-imbalance, favoring overactivation, is associated with nasopharynx homeostasis. Conversely, orientating toward complement-balance may cause disruption to the nasopharynx homeostasis. Thus, for sporadic meningococcal disease, our model predicts rising nasal levels of complement regulators as early infection biomarkers.

The Adjuvant Effects on Vaccine and the Immunomodulatory Mechanisms of Polysaccharides From Traditional Chinese Medicine

Vaccination is still the most successful strategy to prevent and control the spread of infectious diseases by generating an adequate protective immune response. However, vaccines composed of antigens alone can only stimulate weak immunogenicity to prevent infection in many cases. Adjuvant can enhance the immunogenicity of the antigens. Therefore, adjuvant is urgently needed to strengthen the immune response of the vaccines. An ideal adjuvant should be safe, cheap, biodegradable and biologically inert. In addition to having a long shelf life, it can also promote cellular and humoral immune responses. Traditional Chinese medicine (TCM) has many different ingredients, such as glycosides, polysaccharides, acids, terpenes, polyphenols, flavonoids, alkaloids, and so on. TCM polysaccharides are one of the main types of biologically active substances. They have a large range of pharmacological activities, especially immunomodulatory. TCM polysaccharides can regulate the immune system of animals by binding to multiple receptors on the surface of immune cells and activating different signal pathways. This review focuses on a comprehensive summary of the most recent developments in vaccine adjuvant effects of polysaccharides from many important TCM, such as Artemisia rupestris L., Cistanche deserticola, Pinus massoniana, Chuanminshen violaceum, Astragalus, Ganoderma lucidum, Codonopsis pilosula, Lycium barbarum, Angelica, Epimedium, and Achyranthes bidentata. Moreover, this review also introduces their immunomodulatory effects and the molecular mechanisms of action on animal bodies, which showed that TCM polysaccharides can activate macrophages, the signal pathway of T/B lymphocytes, regulate the signal pathway of natural killer cells, activate the complement system, and so on.

Modeling the activation of the alternative complement pathway and its effects on hemolysis in health and disease

The complement system is a powerful mechanism of innate immunity poised to eliminate foreign cells and pathogens. It is an intricate network of >35 proteins, which, once activated, leads to the tagging of the surface to be eliminated, produces potent chemoattractants to recruit immune cells, and inserts cytotoxic pores into nearby lipid surfaces. Although it can be triggered via different pathways, its net output is largely based on the direct or indirect activation of the alternative pathway. Complement dysregulation or deficiencies may cause severe pathologies, such as paroxysmal nocturnal hemoglobinuria (PNH), where a lack of complement control proteins leads to hemolysis and life-threatening anemia. The complexity of the system poses a challenge for the interpretation of experimental data and the design of effective pharmacological therapies. To address this issue, we developed a mathematical model of the alternative complement pathway building on previous modelling efforts. The model links complement activation to the hemolytic activity of the terminal alternative pathway, providing an accurate description of pathway activity as observed in vitro and in vivo, in health and disease. Through adjustment of the parameters describing experimental conditions, the model was capable of reproducing the results of an array of standard assays used in complement research. To demonstrate its clinical applicability, we compared model predictions with clinical observations of the recovery of hematological biomarkers in PNH patients treated with the complement inhibiting anti-C5 antibody eculizumab. In conclusion, the model can enhance the understanding of complement biology and its role in disease pathogenesis, help identifying promising targets for pharmacological intervention, and predict the outcome of complement-targeting pharmacological interventions.

Quantification of Factor H Mediated Self vs. Non-self Discrimination by Mathematical Modeling

The complement system is part of the innate immune system and plays an important role in the host defense against infectious pathogens. One of the main effects is the opsonization of foreign invaders and subsequent uptake by phagocytosis. Due to the continuous default basal level of active complement molecules, a tight regulation is required to protect the body's own cells (self cells) from opsonization and from complement damage. A major complement regulator is Factor H, which is recruited from the fluid phase and attaches to cell surfaces where it effectively controls complement activation. Besides self cells, pathogens also have the ability to bind Factor H; they can thus escape opsonization and phagocytosis causing severe infections. In order to advance our understanding of the opsonization process at a quantitative level, we developed a mathematical model for the dynamics of the complement system-termed DynaCoSys model-that is based on ordinary differential equations for cell surface-bound molecules and on partial differential equations for concentration profiles of the fluid phase molecules in the environment of cells. This hybrid differential equation approach allows to model the complement cascade focusing on the role of active C3b in the fluid phase and on the cell surface as well as on its inactivation on the cell surface. The DynaCoSys model enables us to quantitatively predict the conditions under which Factor H mediated complement evasion occurs. Furthermore, investigating the quantitative impact of model parameters by a sensitivity analysis, we identify the driving processes of complement activation and regulation in both the self and non-self regime. The two regimes are defined by a critical Factor H concentration on the cell surface and we use the model to investigate the differential impact of complement model parameters on this threshold value. The dynamic modeling on the surface of pathogens are further relevant to understand pathophysiological situations where Factor H mutants and defective Factor H binding to target surfaces results in pathophysiology such as renal and retinal disease. In the future, this DynaCoSys model will be extended to also enable evaluating treatment strategies of complement-related diseases.

Modeling Variability in Populations of Cells Using Approximated Multivariate Distributions

We are interested in studying the evolution of large homogeneous populations of cells, where each cell is assumed to be composed of a group of biological players (species) whose dynamics is governed by a complex biological pathway, identical for all cells. Modeling the inherent variability of the species concentrations in different cells is crucial to understand the dynamics of the population. In this work, we focus on handling this variability by modeling each species by a random variable that evolves over time. This appealing approach runs into the curse of dimensionality since exactly representing a joint probability distribution involving a large set of random variables quickly becomes intractable as the number of variables grows. To make this approach amenable to biopathways, we explore different techniques to (i) approximate the exact joint distribution at a given time point, and (ii) to track its evolution as time elapses. We start with the problem of approximating the probability distribution of biological species in a population of cells at some given time point. Data come from different fine-grained models of biological pathways of increasing complexities, such as (perturbed) Ordinary Differential Equations (ODEs). Classical approximations rely on the strong and unrealistic assumption that variables/species are independent, or that they can be grouped into small independent clusters. We propose instead to use the Chow-Liu tree representation, based on overlapping clusters of two variables, which better captures correlations between variables. Our experiments show that the proposed approximation scheme is more accurate than existing ones to model probability distributions deriving from biopathways. Then we address the problem of tracking the dynamics of a population of cells, that is computing from an initial distribution the evolution of the (approximate) joint distribution of species over time, called the inference problem. We evaluate several approximate inference algorithms (e.g., [14] , [17] ) for coarse-grained abstractions [12], [16] of biological pathways. Using the Chow-Liu tree approximation, we develop a new inference algorithm which is very accurate according to the experiments we report, for a minimal computation overhead. Our implementation is available at https://codeocean.com/capsule/6491669/tree.

A Reflective Account of a Research Ethics Course for an Interdisciplinary Cohort of Graduate Students

The graduate course in research ethics in the Graduate School for Integrative Sciences and Engineering at the National University of Singapore consists of a semester long mandatory course titled: "Research Ethics and Scientific Integrity." The course provides students with guiding principles for appropriate conduct in the professional and social settings of scientific research and in making morally weighted and ethically sound decisions when confronted with moral dilemmas. It seeks to enhance understanding and appreciation of the moral reasoning underpinning various rules and legislative constraints associated with research subjects and procedures. Further, students are trained to critically analyse cases and issues associated with scientific misconduct preparing them to act in a responsible and effective manner should they encounter such cases. The diverse background and training of the cohort also provide a unique setting and opportunity for student-initiated collaborative interdisciplinary learning. This article offers a reflective account of the course and some preliminary insights into learning outcomes.

Molecular crypsis by pathogenic fungi using human factor H. A numerical model

Molecular mimicry is the formation of specific molecules by microbial pathogens to avoid recognition and attack by the immune system of the host. Several pathogenic Ascomycota and Zygomycota show such a behaviour by utilizing human complement factor H to hide in the blood stream. We call this type of mimicry molecular crypsis. Such a crypsis can reach a point where the immune system can no longer clearly distinguish between self and non-self cells. Thus, a trade-off between attacking disguised pathogens and erroneously attacking host cells has to be made. Based on signalling theory and protein-interaction modelling, we here present a mathematical model of molecular crypsis of pathogenic fungi using the example of Candida albicans. We tackle the question whether perfect crypsis is feasible, which would imply that protection of human cells by complement factors would be useless. The model identifies pathogen abundance relative to host cell abundance as the predominant factor influencing successful or unsuccessful molecular crypsis. If pathogen cells gain a (locally) quantitative advantage over host cells, even autoreactivity may occur. Our new model enables insights into the mechanisms of candidiasis-induced sepsis and complement-associated autoimmune diseases.

A computational model for the evaluation of complement system regulation under homeostasis, disease, and drug intervention

The complement system is an intricate defense network that rapidly removes invading pathogens. Although many complement regulators are present to protect host cells under homeostasis, the impairment of Factor H (FH) regulatory mechanism has been associated with several autoimmune and inflammatory diseases. To understand the dynamics involved in the pivotal balance between activation and regulation, we have developed a comprehensive computational model of the alternative and classical pathways of the complement system. The model is composed of 290 ordinary differential equations with 142 kinetic parameters that describe the state of complement system under homeostasis and disorder through FH impairment. We have evaluated the state of the system by generating concentration-time profiles for the biomarkers C3, C3a-desArg, C5, C5a-desArg, Factor B (FB), Ba, Bb, and fC5b-9 that are influenced by complement dysregulation. We show that FH-mediated disorder induces substantial levels of complement activation compared to homeostasis, by generating reduced levels of C3 and FB, and to a lesser extent C5, and elevated levels of C3a-desArg, Ba, Bb, C5a-desArg, and fC5b-9. These trends are consistent with clinically observed biomarkers associated with complement-mediated diseases. Furthermore, we introduced therapy states by modeling known inhibitors of the complement system, a compstatin variant (C3 inhibitor) and eculizumab (C5 inhibitor). Compstatin demonstrates strong restorative effects for early-stage biomarkers, such as C3a-desArg, FB, Ba, and Bb, and milder restorative effects for late-stage biomarkers, such as C5a-desArg and fC5b-9, whereas eculizumab has strong restorative effects on late-stage biomarkers, and negligible effects on early-stage biomarkers. These results highlight the need for patient-tailored therapies that target early complement activation at the C3 level, or late-stage propagation of the terminal cascade at the C5 level, depending on the specific FH-mediated disease and the manifestations of a patient's genetic profile in complement regulatory function.

Abstracting the dynamics of biological pathways using information theory: a case study of apoptosis pathway

Motivation

Quantitative models are increasingly used in systems biology. Usually, these quantitative models involve many molecular species and their associated reactions. When simulating a tissue with thousands of cells, using these large models becomes computationally and time limiting.

Results

In this paper, we propose to construct abstractions using information theory notions. Entropy is used to discretize the state space and mutual information is used to select a subset of all original variables and their mutual dependencies. We apply our method to an hybrid model of TRAIL-induced apoptosis in HeLa cell. Our abstraction, represented as a Dynamic Bayesian Network (DBN), reduces the number of variables from 92 to 10, and accelerates numerical simulation by an order of magnitude, yet preserving essential features of cell death time distributions.

Availability and Implementation

This approach is implemented in the tool DBNizer, freely available at http://perso.crans.org/genest/DBNizer .

Contact

gregory.batt@inria.fr or bgenest@irisa.fr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Reduced order modeling and analysis of the human complement system

Complement is an important pathway in innate immunity, inflammation, and many disease processes. However, despite its importance, there are few validated mathematical models of complement activation. In this study, we developed an ensemble of experimentally validated reduced order complement models. We combined ordinary differential equations with logical rules to produce a compact yet predictive model of complement activation. The model, which described the lectin and alternative pathways, was an order of magnitude smaller than comparable models in the literature. We estimated an ensemble of model parameters from in vitro dynamic measurements of the C3a and C5a complement proteins. Subsequently, we validated the model on unseen C3a and C5a measurements not used for model training. Despite its small size, the model was surprisingly predictive. Global sensitivity and robustness analysis suggested complement was robust to any single therapeutic intervention. Only the simultaneous knockdown of both C3 and C5 consistently reduced C3a and C5a formation from all pathways. Taken together, we developed a validated mathematical model of complement activation that was computationally inexpensive, and could easily be incorporated into pre-existing or new pharmacokinetic models of immune system function. The model described experimental data, and predicted the need for multiple points of therapeutic intervention to fully disrupt complement activation.

A mathematical model of cellular swelling in Neuromyelitis optica

Neuromyelitis Optica (NMO) is a severe neuro-inflammatory disease of the central nervous system characterized by predominant damage to the optic nerve and of the spinal cord. The pathogenic antibody found in the majority of patients targets the AQP4 channels on astrocytic endfeet and causes the cells to swell. Although, the pathophysiology of the disease is broadly known, there are no specific targeted treatments for this process clinically available nor accurate prognostic markers both during attacks and for predicting long term neuronal damage. This lack is, in part, due to the rarity of the disease and its relatively recent pathogenic clarity. Hence, the ability to mathematically model the progress of the condition to test prospective therapies in silico would be a step forward. This paper combines state of the art models of cellular metabolism and cytotoxic oedema in neurons and astrocytes and augments it with a detailed characterization of water transport across the cellular membrane. In particular, we capture the process of perforation of the cell through the human complement cascade and resulting water and ionic fluxes. Simulating NMO by injecting its antibody and human complement into the extracellular space showed a 25% increase of the astrocytic volume after 12 h from onset. Most of the volume change occurred during the first 30 min of simulation with a peak volume change of 38%. The model was further adapted to simulate the therapeutic potential of CD59. It was found that there is a threshold of CD59 concentration that can prevent the swelling of astrocytes. Since the astrocyte volume changes mostly during the first hour, further experimental work should focus on this time scale to provide data for further model refinement and validation.

Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways

Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs) and stimulate the innate immune response through the production of cytokines. The innate immune response depends on the timing of encountering PAMPs, suggesting a short-term "memory." In particular, activation of TLR3 appears to prime macrophages for the subsequent activation of TLR7, which leads to synergistically increased production of cytokines. By developing a calibrated mathematical model for the kinetics of TLR3 and TLR7 pathway crosstalk and providing experimental validation, we demonstrated the involvement of the Janus-activated kinase (JAK)-signal transducer and activator of transcription (STAT) pathway in controlling the synergistic production of cytokines. Signaling through this pathway played a dual role: It mediated the synergistic production of cytokines, thus boosting the immune response, and it also maintained homeostasis to avoid an excessive inflammatory response. Thus, we propose that the JAK-STAT pathway provides a cytokine rheostat mechanism, which enables macrophages to fine-tune their responses to multiple, temporally separated infection events involving the TLR3 and TLR7 pathways.

Quantitative Modeling of the Alternative Pathway of the Complement System

The complement system is an integral part of innate immunity that detects and eliminates invading pathogens through a cascade of reactions. The destructive effects of the complement activation on host cells are inhibited through versatile regulators that are present in plasma and bound to membranes. Impairment in the capacity of these regulators to function in the proper manner results in autoimmune diseases. To better understand the delicate balance between complement activation and regulation, we have developed a comprehensive quantitative model of the alternative pathway. Our model incorporates a system of ordinary differential equations that describes the dynamics of the four steps of the alternative pathway under physiological conditions: (i) initiation (fluid phase), (ii) amplification (surfaces), (iii) termination (pathogen), and (iv) regulation (host cell and fluid phase). We have examined complement activation and regulation on different surfaces, using the cellular dimensions of a characteristic bacterium (E. coli) and host cell (human erythrocyte). In addition, we have incorporated neutrophil-secreted properdin into the model highlighting the cross talk of neutrophils with the alternative pathway in coordinating innate immunity. Our study yields a series of time-dependent response data for all alternative pathway proteins, fragments, and complexes. We demonstrate the robustness of alternative pathway on the surface of pathogens in which complement components were able to saturate the entire region in about 54 minutes, while occupying less than one percent on host cells at the same time period. Our model reveals that tight regulation of complement starts in fluid phase in which propagation of the alternative pathway was inhibited through the dismantlement of fluid phase convertases. Our model also depicts the intricate role that properdin released from neutrophils plays in initiating and propagating the alternative pathway during bacterial infection.

Complex pattern of interleukin-11-induced inflammation revealed by mathematically modeling the dynamics of C-reactive protein

Inflammation underlies many diseases and is an undesired effect of several therapy modalities. Biomathematical modeling can help unravel the complex inflammatory processes and the mechanisms triggering their emergence. We developed a model for induction of C-reactive protein (CRP), a clinically reliable marker of inflammation, by interleukin (IL)-11, an approved cytokine for treatment of chemotherapy-induced thrombocytopenia. Due to paucity of information on the mechanisms underlying inflammation-induced CRP dynamics, our model was developed by systematically evaluating several models for their ability to retrieve variable CRP profiles observed in IL-11-treated breast cancer patients. The preliminary semi-mechanistic models were designed by non-linear mixed-effects modeling, and were evaluated by various performance criteria, which test goodness-of-fit, parsimony and uniqueness. The best-performing model, a robust population model with minimal inter-individual variability, uncovers new aspects of inflammation dynamics. It shows that CRP clearance is a nonlinear self-controlled process, indicating an adaptive anti-inflammatory reaction in humans. The model also reveals a dual IL-11 effect on CRP elevation, whereby the drug has not only a potent immediate influence on CRP incline, but also a long-term influence inducing elevated CRP levels for several months. Consistent with this, model simulations suggest that periodic IL-11 therapy may result in prolonged low-grade (chronic) inflammation post treatment. Future application of the model can therefore help design improved IL-11 regimens with minimized long-term CRP toxicity. Our study illuminates the dynamics of inflammation and its control, and provides a prototype for progressive modeling of complex biological processes in the medical realm and beyond.

Natural IgG antibodies provide innate protection against ficolin-opsonized bacteria

For nearly five decades since its discovery, the role of natural IgG, which pre-exists in neonates and uninfected individuals, has remained unclear due to the general perception that natural antibodies lack affinity for pathogens. Here, we show for the first time that natural IgG recognizes a spectrum of bacteria through lectins like ficolin and mannose binding lectin (MBL). Infection-inflammation condition markedly increased the affinity of natural IgG for bacteria associated with ficolins. After opsonization with IgG:ficolin complex, the bacteria were phagocytosed by monocytes via FcγRI. Infection of C3(-/-) mice indicated that the natural IgG-mediated immune complex was formed independently of C3. AID(-/-) mice lacking IgG were susceptible to infection, unless reconstituted with natural IgG. Thus, we have proven that natural IgG is not quiescent; rather, it plays a vital and immediate role in immune defense. Our findings provide a fresh perspective on natural antibodies, opening new avenues to explore host-microbe interaction.

Advanced systems biology methods in drug discovery and translational biomedicine

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

Association between lectin complement pathway initiators, C-reactive protein and left ventricular remodeling in myocardial infarction-a magnetic resonance study

BACKGROUND

Lectin complement pathway (LP) activation is an important mechanism in myocardial ischemia reperfusion injury (IRI). LP is activated via the recognition molecules mannose-binding lectin (MBL), ficolins-2 and-3 and is regulated by MBL/Ficolin-associated Protein-1 (MAP-1). Also, C-reactive protein (CRP) and ficolin-2 interact in vitro, but the role of the ficolins in IRI is unknown.

METHODS AND RESULTS

In 55 patients with ST segment elevation myocardial infarction, we investigated the association of LP components and CRP in plasma samples with left ventricular (LV) end systolic and diastolic volumes (ESV and EDV) and infarct size, assessed by cardiac magnetic resonance early at 1-3 days after primary percutaneous coronary intervention and at 6 months follow-up. Opposed to MBL, ficolin-3 and MAP-1, ficolin-2 levels were low at baseline. At baseline, ficolin-2>median was associated with ESV and EDV increases by 7.83 ml/m(2) (p=0.004) and 14.04 ml/m(2) (p<0.001). MBL and MAP-1 were not associated with LV dilatation, yet ficolin-2 and MBL worked synergistically and the combination of their levels>median was associated with ESV (11.21 ml/m(2); p=0.017) and EDV increases (14.72 ml/m(2); p=0.006). MAP-1median had the greatest LV dilatation (17.61 ml/m(2)). The ficolin-2 × CRP interaction variable was positively associated with infarct size and inversely associated with EDV change over 6 months (p=0.006). There was no interaction between CRP and the other LP molecules.

CONCLUSION

The LP initiator molecule ficolin-2 and combinations of ficolin-2, MBL and MAP-1 are associated with LV dilatation after myocardial infarction. Interaction of ficolin-2 and CRP was associated with infarct size and LV remodeling, indicating a potential role for LP and LP-pentraxin cross-activation in IRI and LV remodeling.

Spatiotemporal dynamics of complement C5a production within bacterial extracellular polymeric substance

Opsonization and anaphylatoxin production are early events in the innate response to bacterial pathogens. Opsonization alone is frequently not lethal and production of anaphy-latoxins, especially C5a, allows for recruitment of cellular defenses. Complement biochemistry is extensively studied and computational models have been reported previously. However, a critical feature of complement-mediated attack is its spatial dependence: diffusion of mediators into and away from a bacterium is central to understanding C5a generation. Spatial dependence is especially important in biofilms, where diffusion limitation is crucial to bacterial counterdefense. Here we develop a model of opsonization and C5a production in the presence of a common blood-borne pathogen, Staphylococcus epidermidis. Our results indicate that when complement attacks a single cell, diffusion into the extracellular polymeric substance (EPS) is complete within 10 ms and that production of C5a peaks over the next 15 min. When longer diffusion lengths (as in an EPS-rich biofilm) are incorporated, diffusion limitation appears such that the intensity and duration of C5a production is increased. However, the amount of C5a produced under several likely clinical scenarios where single cells or sparse biofilms are present is below the kD of the C5a receptor suggesting that complement activation by a single bacterium may be difficult to detect when diffusion is taken into account.

Bench-to-bedside review: Immunoglobulin therapy for sepsis - biological plausibility from a critical care perspective

Sepsis represents a dysregulated host response to infection, the extent of which determines the severity of organ dysfunction and subsequent outcome. All trialled immunomodulatory strategies to date have resulted in either outright failure or inconsistent degrees of success. Intravenous immunoglobulin (IVIg) therapy falls into the latter category with opinion still divided as to its utility. This article provides a narrative review of the biological rationale for using IVIg in sepsis. A literature search was conducted using the PubMed database (1966 to February 2011). The strategy included the following text terms and combinations of these: IVIg, intravenous immune globulin, intravenous immunoglobulin, immunoglobulin, immunoglobulin therapy, pentaglobin, sepsis, inflammation, immune modulation, apoptosis. Preclinical and extrapolated clinical data of IVIg therapy in sepsis suggests improved bacterial clearance, inhibitory effects upon upstream mediators of the host response (for example, the nuclear factor kappa B (NF-κB) transcription factor), scavenging of downstream inflammatory mediators (for example, cytokines), direct anti-inflammatory effects mediated via Fcγ receptors, and a potential ability to attenuate lymphocyte apoptosis and thus sepsis-related immunosuppression. Characterizing the trajectory of change in immunoglobulin levels during sepsis, understanding mechanisms contributing to these changes, and undertaking IVIg dose-finding studies should be performed prior to further large-scale interventional trials to enhance the likelihood of a successful outcome.

Understanding cancer mechanisms through network dynamics

Cancer is a complex, multifaceted disease. Cellular systems are perturbed both during the onset and development of cancer, and the behavioural change of tumour cells usually involves a broad range of dynamic variations. To an extent, the difficulty of monitoring the systemic change has been alleviated by recent developments in the high-throughput technologies. At both the genomic as well as proteomic levels, the technological advances in microarray and mass spectrometry, in conjunction with computational simulations and the construction of human interactome maps have facilitated the progress of identifying disease-associated genes. On a systems level, computational approaches developed for network analysis are becoming especially useful for providing insights into the mechanism behind tumour development and metastasis. This review emphasizes network approaches that have been developed to study cancer and provides an overview of our current knowledge of protein-protein interaction networks, and how their systemic perturbation can be analysed by two popular network simulation methods: Boolean network and ordinary differential equations.

A hybrid Factored Frontier algorithm for Dynamic Bayesian Networks with a biopathways application

Dynamic Bayesian Networks (DBNs) can serve as succinct probabilistic dynamic models of biochemical networks. To analyze these models, one must compute the probability distribution over system states at a given time point. Doing this exactly is infeasible for large models; hence one must use approximate algorithms. The Factored Frontier algorithm (FF) is one such algorithm. However FF as well as the earlier Boyen-Koller (BK) algorithm can incur large errors. To address this, we present a new approximate algorithm called the Hybrid Factored Frontier (HFF) algorithm. At each time slice, in addition to maintaining probability distributions over local states-as FF does-HFF explicitly maintains the probabilities of a number of global states called spikes. When the number of spikes is 0, we get FF and with all global states as spikes, we get the exact inference algorithm. We show that by increasing the number of spikes one can reduce errors while the additional computational effort required is only quadratic in the number of spikes. We validated the performance of HFF on large DBN models of biopathways. Each pathway has more than 30 species and the corresponding DBN has more than 3,000 nodes. Comparisons with FF and BK show that HFF is a useful and powerful approximate inferencing algorithm for DBNs.

Sensitivity analysis approaches applied to systems biology models

With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

Blurred line between chemotactic chase and phagocytic consumption: an immunophysical single-cell perspective

An innate immune cell can sense a pathogen, either from a distance by recognizing chemoattractant stimuli or by direct physical contact. The pathogen is subsequently neutralized, which usually occurs through its phagocytic internalization. By investigating chemotaxis and phagocytosis from an immunophysical single-cell perspective, it now appears that the demarcation between these two processes is less distinct than originally thought. Several lines of evidence support this notion. First, chemotactic stimulation does not cease at the moment of initial contact between the cell and the pathogenic target. Second, even when classical chemotaxis of neutrophils is suppressed, the early cell response to contact with typical chemoattractant targets, such as zymosan, fungal spores or chemokine-coated particles, can still involve morphological attributes of chemotaxis. Recognizing that the changing morphology of motile cells is inextricably linked to physical cell behavior, this Commentary focuses on the mechanical aspects of the early response of innate immune cells to chemotactic and phagocytic stimuli. On the basis of this perspective, we propose that the combined study of chemotaxis and phagocytosis will, potentially, not only advance our grasp of the mechanisms underlying immune-cell motility but also open new lines of research that will promote a deeper understanding of the innate recognition of pathogens.

Systems biology data analysis methodology in pharmacogenomics

Pharmacogenetics aims to elucidate the genetic factors underlying the individual's response to pharmacotherapy. Coupled with the recent (and ongoing) progress in high-throughput genotyping, sequencing and other genomic technologies, pharmacogenetics is rapidly transforming into pharmacogenomics, while pursuing the primary goals of identifying and studying the genetic contribution to drug therapy response and adverse effects, and existing drug characterization and new drug discovery. Accomplishment of both of these goals hinges on gaining a better understanding of the underlying biological systems; however, reverse-engineering biological system models from the massive datasets generated by the large-scale genetic epidemiology studies presents a formidable data analysis challenge. In this article, we review the recent progress made in developing such data analysis methodology within the paradigm of systems biology research that broadly aims to gain a 'holistic', or 'mechanistic' understanding of biological systems by attempting to capture the entirety of interactions between the components (genetic and otherwise) of the system.

Mathematical modeling and sensitivity analysis of the integrated TNFα-mediated apoptotic pathway for identifying key regulators

TNFα-mediated apoptosis is one of the complex and tightly regulated cellular processes as it involves the activation of both pro- and anti-apoptotic signaling pathways. Thus, it is important to elucidate the molecular players of this process and their dynamics in order to gain an in-depth understanding of the mechanisms underlying apoptosis. To this end, we proposed an integrated model of TNFα-mediated apoptosis pathway in Type I cells, formulated based on the principles of mass action kinetics. The model includes major apoptotic modules-the extrinsic and intrinsic pathways, the NFκB survival signaling and various regulatory mechanisms. We performed simulations and sensitivity analyses to study the role of NFκB pathway in regulating apoptosis, and identified IAP as one of the more potent regulators of apoptosis.

Histidine-mediated pH-sensitive regulation of M-ficolin:GlcNAc binding activity in innate immunity examined by molecular dynamics simulations

BACKGROUND

M-ficolin, a pathogen recognition molecule in the innate immune system, binds sugar residues including N-acetyl-D-glucosamine (GlcNAc), which is displayed on invading microbes and on apoptotic cells. The cis and trans Asp282-Cys283 peptide bond in the M-ficolin, which was found to occur at neutral and acidic pH in crystal structures, has been suggested to represent binding and non-binding activity, respectively. A detailed understanding of the pH-dependent conformational changes in M-ficolin and pH-mediated discrimination mechanism of GlcNAc-binding activity are crucial to both immune-surveillance and clearance of apoptotic cells.

METHODOLOGY/PRINCIPAL FINDINGS

By immunodetection analysis, we found that the pH-sensitive binding of GlcNAc is regulated by a conformational equilibrium between the active and inactive states of M-ficolin. We performed constant pH molecular dynamics (MD) simulation at a series of pH values to explore the pH effect on the cis-trans isomerization of the Asp282-Cys283 peptide bond in the M-ficolin fibrinogen-like domain (FBG). Analysis of the hydrogen bond occupancy of wild type FBG compared with three His mutants (H251A, H284A and H297A) corroborates that His284 is indispensible for pH-dependent binding. H251A formed new but weaker hydrogen bonds with GlcNAc. His297, unlike the other two His mutants, is more dependent on the solution pH and also contributes to cis-trans isomerization of the Asp282-Cys283 peptide bond in weak basic solution.

CONCLUSIONS/SIGNIFICANCE

Constant pH MD simulation indicated that the cis active isomer of Asp282-Cys283 peptide bond was predominant around neutral pH while the trans bond gradually prevailed towards acidic environment. The protonation of His284 was found to be associated with the trans-to-cis isomerization of Asp282-Cys283 peptide bond which dominantly regulates the GlcNAc binding. Our MD simulation approach provides an insight into the pH-sensitive proteins and hence, ligand binding activity.