Immunology and mathematics: crossing the divide

Cancer immune control dynamics: a clinical data driven model of systemic immunity in patients with metastatic melanoma

Background

Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This interdisciplinary effort exploits engineering analysis methods utilized to investigate anomalous physical system behaviors to explore immune system behaviors. Cancer Immune Control Dynamics (CICD), a systems analysis approach, attempts to identify differences between systemic immune homeostasis of 27 healthy volunteers versus 14 patients with metastatic malignant melanoma based on daily serial measurements of conventional peripheral blood biomarkers (15 cell subsets, 35 cytokines). The modeling strategy applies engineering control theory to analyze an individual’s immune system based on the biomarkers’ dynamic non-linear oscillatory behaviors. The reverse engineering analysis uses a Singular Value Decomposition (SVD) algorithm to solve the inverse problem and identify a solution profile of the active biomarker relationships. Herein, 28,605 biologically possible biomarker interactions are modeled by a set of matrix equations creating a system interaction model. CICD quantifies the model with a participant’s biomarker data then computationally solves it to measure each relationship’s activity allowing a visualization of the individual’s current state of immunity.

Results

CICD results provide initial evidence that this model-based analysis is consistent with identified roles of biomarkers in systemic immunity of cancer patients versus that of healthy volunteers. The mathematical computations alone identified a plausible network of immune cells, including T cells, natural killer (NK) cells, monocytes, and dendritic cells (DC) with cytokines MCP-1 [CXCL2], IP-10 [CXCL10], and IL-8 that play a role in sustaining the state of immunity in advanced cancer.

Conclusions

With CICD modeling capabilities, the complexity of the immune system is mathematically quantified through thousands of possible interactions between multiple biomarkers. Therefore, the overall state of an individual’s immune system regardless of clinical status, is modeled as reflected in their blood samples. It is anticipated that CICD-based capabilities will provide tools to specifically address cancer and treatment modulated (immune checkpoint inhibitors) parameters of human immunity, revealing clinically relevant biological interactions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04025-7.

A primer on cytokines

Cytokines are pleiotropic polypeptides that control the development of and responses mediated by immune cells. Cytokine classification predominantly relies on [1] the target receptor(s), [2] the primary structural features of the extracellular domains of their receptors, and [3] their receptor composition. Functionally, cytokines are either pro-inflammatory or anti-inflammatory, hematopoietic colony-stimulating factors, developmental and would healing maintaining immune homeostasis. When the balance in C can form complex networks amongst themselves that may affect the homeostasis and diseases. Cytokines can affect resistance and susceptibility for many diseases and their availability in the host cytokine production and interaction is disturbed, immunopathogenesis sets in. Therefore, cytokine-targeting bispecific, and chimeric antibodies form a significant mode of immnuo-therapeutics Although the field has grown deep and wide, many areas of cytokine biology remain unknown. Here, we have reviewed these cytokines along with the organization, signaling, and functions through respective cytokine-receptor-families. Being part of the special issue on the Role of Cytokines in Leishmaniasis, this review is intended to be used as an organized primer on cytokines and not a resource for detailed discussion- for which a two-volume Handbook of cytokines is available- on each of the cytokines. Priming the readers on cytokines, we next brief the role of cytokines in Leishmaniasis. In the brief, we do not provide an account of each of the involved cytokines known to date, instead, we offer a temporal relationship between the cytokines and the progress of the infection towards the alternate outcomes- healing or non-healing- of the infection.

Combination anti-coronavirus therapies based on nonlinear mathematical models

Using nonlinear mathematical models and experimental data from laboratory and clinical studies, we have designed new combination therapies against COVID-19.

Modeling population heterogeneity from microbial communities to immune response in cells

Heterogeneity is universally observed in all natural systems and across multiple scales. Understanding population heterogeneity is an intriguing and attractive topic of research in different disciplines, including microbiology and immunology. Microbes and mammalian immune cells present obviously rather different system-specific biological features. Nevertheless, as typically occurs in science, similar methods can be used to study both types of cells. This is particularly true for mathematical modeling, in which key features of a system are translated into algorithms to challenge our mechanistic understanding of the underlying biology. In this review, we first present a broad overview of the experimental developments that allowed observing heterogeneity at the single cell level. We then highlight how this "data revolution" requires the parallel advancement of algorithms and computing infrastructure for data processing and analysis, and finally present representative examples of computational models of population heterogeneity, from microbial communities to immune response in cells.

Model-Driven Experimentation: A New Approach to Understand Mechanisms of Tertiary Lymphoid Tissue Formation, Function, and Therapeutic Resolution

The molecular and cellular processes driving the formation of secondary lymphoid tissues have been extensively studied using a combination of mouse knockouts, lineage-specific reporter mice, gene expression analysis, immunohistochemistry, and flow cytometry. However, the mechanisms driving the formation and function of tertiary lymphoid tissue (TLT) experimental techniques have proven to be more enigmatic and controversial due to differences between experimental models and human disease pathology. Systems-based approaches including data-driven biological network analysis (gene interaction network, metabolic pathway network, cell-cell signaling, and cascade networks) and mechanistic modeling afford a novel perspective from which to understand TLT formation and identify mechanisms that may lead to the resolution of tissue pathology. In this perspective, we make the case for applying model-driven experimentation using two case studies, which combined simulations with experiments to identify mechanisms driving lymphoid tissue formation and function, and then discuss potential applications of this experimental paradigm to identify novel therapeutic targets for TLT pathology.

Mathematical modeling and its analysis for instability of the immune system induced by chemotaxis

In this paper, we study how chemotaxis affects the immune system by proposing a minimal mathematical model, a reaction-diffusion-advection system, describing a cross-talk between antigens and immune cells via chemokines. We analyze the stability and instability arising in our chemotaxis model and find their conditions for different chemotactic strengths by using energy estimates, spectral analysis, and bootstrap argument. Numerical simulations are also performed to the model, by using the finite volume method in order to deal with the chemotaxis term, and the fractional step methods are used to solve the whole system. From the analytical and numerical results for our model, we explain not only the effective attraction of immune cells toward the site of infection but also hypersensitivity when chemotactic strength is greater than some threshold.

A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination

The immune system must discriminate between agents of disease and an organism's healthy cells. While the identification of an antigen as self/non-self is critically important, the dynamic features of antigen presentation may also determine the immune system's response. Here, we use a simple mathematical model of immune activation to explore the idea of antigen discrimination through dynamics. We propose that antigen presentation is coupled to two nodes, one regulatory and one effecting the immune response, through an incoherent feedforward loop and repressive feedback. This circuit would allow the immune system to effectively estimate the increase of antigens with respect to time, a key determinant of immune reactivity in vivo. Our model makes the prediction that tumors growing at specific rates evade the immune system despite the continuous presence of antigens indicating disease, a phenomenon closely related to clinically observed "two-zone tolerance." Finally, we discuss a plausible biological instantiation of our circuit using combinations of regulatory and effector T cells.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.

Mathematics in modern immunology

Mathematical and statistical methods enable multidisciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. We collect a representative sample of studies in T-cell biology that illustrate the benefits of modelling–experimental collaborations and that have proven valuable or even groundbreaking. We conclude that it is possible to find excellent examples of synergy between mathematical modelling and experiment in immunology, which have brought significant insight that would not be available without these collaborations, but that much remains to be discovered.

Modeling the role of TGF-β in regulation of the Th17 phenotype in the LPS-driven immune system

Airway exposure levels of lipopolysaccharide (LPS) are known to determine type I versus type II helper T cell induced experimental asthma. While low doses of LPS derive Th2 inflammatory responses, high (and/or intermediate) LPS levels induce Th1- or Th17-dominant responses. The present paper develops a mathematical model of the phenotypic switches among three Th phenotypes (Th1, Th2, and Th17) in response to various LPS levels. In the present work, we simplify the complex network of the interactions between cells and regulatory molecules. The model describes the nonlinear cross-talks between the IL-4/Th2 activities and a key regulatory molecule, transforming growth factor β (TGF-β), in response to high, intermediate, and low levels of LPS. The model characterizes development of three phenotypes (Th1, Th2, and Th17) and predicts the onset of a new phenotype, Th17, under the tight control of TGF-β. Analysis of the model illustrates the mono-, bi-, and oneway-switches in the key regulatory parameter sets in the absence or presence of time delays. The model also predicts coexistence of those phenotypes and Th1- or Th2-dominant immune responses in a spatial domain under various biochemical and bio-mechanical conditions in the microenvironment.

Simulating the decentralized processes of the human immune system in a virtual anatomy model

Background

Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spacial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation.

Results

Using agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project.

Conclusions

Our current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics.

TH17 functional study in severe asthma using agent based model

TH17 is a subset of CD4+T cells. Comparing to common asthma patients, there are more TH17 cells in the respiratory systems of the patients with severe asthma. TH17 cells are mainly adjusted by IL23 to produce IL17A and IL17F, which act on the epithelial cells and cause severe asthma. However, the TH17 function in severe asthma as a driving mechanism of neutrophilic inflammation is not yet fully understood and deserves further study. However, it is very difficult to describe the interactions between TH17 and other cells using mathematics equations due to the high complexity of immunity system. In order to explore the TH17 function in severe asthma, we used BIS (Basic Immune Simulator) platform to simulate TH17 models, and compared DC (Dendritic Cell) models with TH17 models. We studied the interaction between innate immune and adaptive immune cells, which was resulted from TH17 cells. The simulation results for the TH17 models are consistent with clinical data, which suggests that DC-IL23-TH17 axis might be the path of causing severe asthma. Our simulation studies support a role for TH17 in severe asthma, and hence it could be taken as a new target candidate for clinical treatment of severe asthma.

Safety of higher dosages of Viscum album L. in animals and humans--systematic review of immune changes and safety parameters

BACKGROUND

Viscum album L extracts (VAE, mistletoe) and isolated mistletoe lectins (ML) have immunostimulating properties and a strong dose-dependent cytotoxic activity. They are frequently used in complementary cancer treatment, mainly to improve quality of life, but partly also to influence tumour growth, especially by injecting VAE locally and in high dosage. The question is raised whether these higher dosages can induce any harm or immunosuppressive effects.

METHODS

Systematic review of all experiments and clinical studies investigating higher dosages of VAE in animals and humans (Viscum album > 1 mg in humans corresponding to > 0.02 mg/kg in animals or ML > 1 ng/kg) and assessing immune parameters or infections or adverse drug reactions.

RESULTS

69 clinical studies and 48 animal experiments reported application of higher doses of VAE or ML and had assessed immune changes and/or harm. In these studies, Viscum album was applied in dosages up to 1500 mg in humans and 1400 mg/kg in animals, ML was applied up to 6.4 μg/kg in humans and in animals up to 14 μg/kg subcutaneously, 50 μg/kg nasally and 500 μg/kg orally. A variety of immune parameters showed fluctuating or rising outcomes, but no immunosuppressive effect. Side effects consisted mainly of dose-dependent flu-like symptoms (FLS), fever, local reactions at the injection site and various mild unspecific effects. Occasionally, allergic reactions were reported. After application of high doses of recombinant ML, reversible hepatotoxicity was observed in some cases.

CONCLUSIONS

Application of higher dosages of VAE or ML is not accompanied by immunosuppression; altogether VAE seems to exhibit low risk but should be monitored by clinicians when applied in high dosages.

A systems model for immune cell interactions unravels the mechanism of inflammation in human skin

Inflammation is characterized by altered cytokine levels produced by cell populations in a highly interdependent manner. To elucidate the mechanism of an inflammatory reaction, we have developed a mathematical model for immune cell interactions via the specific, dose-dependent cytokine production rates of cell populations. The model describes the criteria required for normal and pathological immune system responses and suggests that alterations in the cytokine production rates can lead to various stable levels which manifest themselves in different disease phenotypes. The model predicts that pairs of interacting immune cell populations can maintain homeostatic and elevated extracellular cytokine concentration levels, enabling them to operate as an immune system switch. The concept described here is developed in the context of psoriasis, an immune-mediated disease, but it can also offer mechanistic insights into other inflammatory pathologies as it explains how interactions between immune cell populations can lead to disease phenotypes.

A functional immune system requires complex interactions among diverse cell types, mediated by a variety of cytokines. These interactions include phenomena such as positive and negative feedback loops that can be experimentally characterized by dose-dependent cytokine production measurements. However, any experimental approach is not only limited with regard to the number of cell-cell interactions that can be studied at a given time, but also does not have the capacity to assess or predict the overall immune response which is the result of complex interdependent immune cell interactions. Therefore, experimental data need to be viewed from a theoretical perspective allowing the quantitative modeling of immune cell interactions. Here, we propose a strategy for a quantitative description of multiple interactions between immune cell populations based on their cytokine production profiles. The model predicts that the modified feedback loop interactions can result in the appearance of alternative steady-states causing the switch-like immune system effect that is experimentally observed in pathologic phenotypes. Overall, the quantitative description of immune cell interactions via cytokine signaling reported here offers new insights into understanding and predicting normal and pathological immune system responses.

Network, degeneracy and bow tie. Integrating paradigms and architectures to grasp the complexity of the immune system

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information.

Applying predator-prey theory to modelling immune-mediated, within-host interspecific parasite interactions

Predator-prey models are often applied to the interactions between host immunity and parasite growth. A key component of these models is the immune system's functional response, the relationship between immune activity and parasite load. Typically, models assume a simple, linear functional response. However, based on the mechanistic interactions between parasites and immunity we argue that alternative forms are more likely, resulting in very different predictions, ranging from parasite exclusion to chronic infection. By extending this framework to consider multiple infections we show that combinations of parasites eliciting different functional responses greatly affect community stability. Indeed, some parasites may stabilize other species that would be unstable if infecting alone. Therefore hosts' immune systems may have adapted to tolerate certain parasites, rather than clear them and risk erratic parasite dynamics. We urge for more detailed empirical information relating immune activity to parasite load to enable better predictions of the dynamic consequences of immune-mediated interspecific interactions within parasite communities.

Integrating extrinsic and intrinsic cues into a minimal model of lineage commitment for hematopoietic progenitors

Autoregulation of transcription factors and cross-antagonism between lineage-specific transcription factors are a recurrent theme in cell differentiation. An equally prevalent event that is frequently overlooked in lineage commitment models is the upregulation of lineage-specific receptors, often through lineage-specific transcription factors. Here, we use a minimal model that combines cell-extrinsic and cell-intrinsic elements of regulation in order to understand how both instructive and stochastic events can inform cell commitment decisions in hematopoiesis. Our results suggest that cytokine-mediated positive receptor feedback can induce a “switch-like” response to external stimuli during multilineage differentiation by providing robustness to both bipotent and committed states while protecting progenitors from noise-induced differentiation or decommitment. Our model provides support to both the instructive and stochastic theories of commitment: cell fates are ultimately driven by lineage-specific transcription factors, but cytokine signaling can strongly bias lineage commitment by regulating these inherently noisy cell-fate decisions with complex, pertinent behaviors such as ligand-mediated ultrasensitivity and robust multistability. The simulations further suggest that the kinetics of differentiation to a mature cell state can depend on the starting progenitor state as well as on the route of commitment that is chosen. Lastly, our model shows good agreement with lineage-specific receptor expression kinetics from microarray experiments and provides a computational framework that can integrate both classical and alternative commitment paths in hematopoiesis that have been observed experimentally.

Complex biomolecular interaction pathways in signaling networks can lead to non-intuitive behaviors that can prove critical for the regulation and robustness of biological processes. In this work, we present a signaling topology that can generate dynamic responses that are particularly pertinent to cell commitment in hematopoiesis. Our minimal model explores fundamental questions of instructive signaling that have persisted in cell-fate decisions. We show that even when lineage commitment decisions are inherently noisy, external cytokine signals, amplified by receptor upregulation, can bias the lineage choices of a progenitor cell. The multipotent progenitor, based on its differentiation potential, can exhibit several layers of memory to provide stability to both intermediate and mature states and can potentially bypass canonical intermediate states in generating mature cell types. Thus, our model provides a computational framework that can accommodate both classical and non-classical commitment paths in hematopoiesis.

Positive receptor feedback during lineage commitment can generate ultrasensitivity to ligand and confer robustness to a bistable switch

Cytokines and lineage-specific transcription factors are critical molecular effectors for terminal differentiation during hematopoiesis. Intrinsic transcription factor activity is often believed to drive commitment and differentiation, whereas cytokine receptor signals have been implicated in the regulation of cell proliferation, survival, and differentiation. In erythropoiesis, recent experimental findings provide direct evidence that erythropoietin (Epo) can generate commitment cues via the erythropoietin receptor (EpoR); specifically, EpoR signaling leads to activation of the transcription factor GATA-1, which then triggers transcription of erythrocyte-specific genes. In particular, activated GATA-1 induces two positive feedback loops in the system through the enhanced expression of both inactive GATA-1 and EpoR, the latter of which is externally regulatable by Epo. Based upon this network architecture, we present a mathematical model of GATA-1 activation by EpoR, which bidirectionally links a lineage-specific receptor and transcription factor. Our deterministic model offers insight into stimulus-response relationships between Epo and several downstream effectors. In addition to the survival signals that EpoR provides, steady-state analysis of our model suggests that receptor upregulation during lineage commitment can also generate ultrasensitivity to Epo and bistability in GATA-1 activity. These system-level properties can induce a switch-like characteristic during differentiation and provide robustness to the mature state. The topology also suggests a novel mechanism for achieving robust bistability in a purely deterministic manner without molecular cooperativity. The analytical solution of a generalized, minimal model is provided and the significance of each of the two positive feedback loops is elucidated through bifurcation analysis. This network topology, or variations thereof, may link other receptor-transcription factor pairs and may therefore be of general relevance in cellular decision-making.

A simple immune system simulation reveals optimal movement and cell density parameters for successful target clearance

We report here a simple simulation of the immune system in which we analysed the behaviour of responder cells in the presence of target cells. Variable parameters determined the behaviour of the cells within the simulation, and many simulations using the same parameters ensured that statistical variability was achieved. The model demonstrated that high mobility of the target or responder cells produced a more robust response, and that clearance by the immune system was favoured when effector cells moved rapidly compared with the target cells. Therefore, the high motility coefficients exhibited by T cells studied in vivo may play a role in optimizing the effector response to pathogens. Surprisingly, when the number density of responding cells was increased, target cell numbers were limited more effectively, but there was an increased likelihood of a prolonged response.

Coupled feedback loops form dynamic motifs of cellular networks

Cellular networks are composed of complicated interconnections among components, and some subnetworks of particular functioning are often identified as network motifs. Among such network motifs, feedback loops are thought to play important dynamical roles. Intriguingly, such feedback loops are very often found as a coupled structure in cellular circuits. Therefore, we integrated all the scattered information regarding the coupled feedbacks in various cellular circuits and investigated the dynamical role of each coupled structure. Finally, we discovered that coupled positive feedbacks enhance signal amplification and bistable characteristics; coupled negative feedbacks realize enhanced homeostasis; coupled positive and negative feedbacks enable reliable decision-making by properly modulating signal responses and effectively dealing with noise.

The Basic Immune Simulator: an agent-based model to study the interactions between innate and adaptive immunity

BACKGROUND

We introduce the Basic Immune Simulator (BIS), an agent-based model created to study the interactions between the cells of the innate and adaptive immune system. Innate immunity, the initial host response to a pathogen, generally precedes adaptive immunity, which generates immune memory for an antigen. The BIS simulates basic cell types, mediators and antibodies, and consists of three virtual spaces representing parenchymal tissue, secondary lymphoid tissue and the lymphatic/humoral circulation. The BIS includes a Graphical User Interface (GUI) to facilitate its use as an educational and research tool.

RESULTS

The BIS was used to qualitatively examine the innate and adaptive interactions of the immune response to a viral infection. Calibration was accomplished via a parameter sweep of initial agent population size, and comparison of simulation patterns to those reported in the basic science literature. The BIS demonstrated that the degree of the initial innate response was a crucial determinant for an appropriate adaptive response. Deficiency or excess in innate immunity resulted in excessive proliferation of adaptive immune cells. Deficiency in any of the immune system components increased the probability of failure to clear the simulated viral infection.

CONCLUSION

The behavior of the BIS matches both normal and pathological behavior patterns in a generic viral infection scenario. Thus, the BIS effectively translates mechanistic cellular and molecular knowledge regarding the innate and adaptive immune response and reproduces the immune system's complex behavioral patterns. The BIS can be used both as an educational tool to demonstrate the emergence of these patterns and as a research tool to systematically identify potential targets for more effective treatment strategies for diseases processes including hypersensitivity reactions (allergies, asthma), autoimmunity and cancer. We believe that the BIS can be a useful addition to the growing suite of in-silico platforms used as an adjunct to traditional research efforts.

Molecular networks of brain and immunity

Exciting complexity of natural phenomena can be based on rather simple biophysical principles. For example, the genetic code is based on a double-helix of DNA formed by planar geometry of weak hydrogen bounds. On the examples of cytokine networks, immune synapse, psychoneuroimmunology and systems biology, this review paper attempts to show how molecular networks both in brain and immunity can be studied using common principles of protein-protein interactions.

Survival and apoptotic pathways initiated by TNF-alpha: modeling and predictions

We present a mathematical model which includes TNF-alpha initiated survival and apoptotic cascades, as well as nuclear transcription of IkappaB. These pathways play a crucial role in deciding cell fate in response to inflammation and infection. Our model incorporates known specific protein-protein interactions as identified by experiments. Using these biochemical interactions, we develop a mathematical model of the NF-kappaB-mediated survival and caspase-mediated apoptosis pathways. Using mass action kinetics, we follow the formation of the survival and late complexes as well as the dynamics of DNA fragmentation. The effect of TNF-alpha concentration on DNA fragmentation is modeled and compares well with experiment. Nuclear transcription is also modeled phenomenologically by means of time lagged cytosolic concentrations. This results in transcription related concentrations undergoing under-damped oscillations, in qualitative and quantitative agreement with experiment. Using a tumor cell as a hypothetical model, we explore the interplay between the components of the survival and apoptotic pathways. Results are presented which make predictions on the limits of cellular oscillations in terms of time delay, initial concentration ratios and other features of the model. The model also makes clear predictions on cell viability in terms of DNA damage within the framework of TNF-alpha stimulus duration.

Decision‐making by the immune response

Decisions by uncommitted cells to differentiate down one lineage pathway or another is fundamental to developmental biology. In the immune system, lymphocyte precursors commit to T- or B-cell lineages and T-cell precursors to CD4 or CD8 independently of foreign antigen. T and B cells must also decide whether or not to respond to antigen and when a response is initiated, what sort of response to make such as the type of antibody, CD4 or CD8, and CD4 Th1 or Th2. The two basic mechanisms for these decision-making processes are selection and instruction. Selection depends on prior stochastic production of precommitted cells, which are then selected to respond by an appropriate signal; for example, CD8 and CD4 responses selected by peptide presented in association with major histocompatibility complex class I or II. In contrast, instruction occurs when an uncommitted precursor embarks upon a differentiation pathway in response to a particular set of signals; for example, Th1 and Th2 lineage commitment. In this paper, the signals that determine Th1 and Th2 differentiation are examined with a mathematical model and shown to act as a bistable switch permitting either Tbet or Gata3 to be expressed in an individual cell but not both. The model is used to show how the Tbet Gata3 network within an individual cell interacts with cytokine signals between cells and suggests how Th1 and Th2 lineage commitment can become irreversible. These considerations provide an example of how mathematical models can be used to gain a better understanding of lymphocyte differentiation in an immune response.

3D computation modelling of the influence of cytokine secretion on Th‐cell development suggests that negative selection (inhibition of Th1 cells) is more effective than positive selection by IL‐4 for Th2 cell dominance

Th-cell development has been suggested to include selective mechanisms in which certain cytokines select either Th1 or Th2 cells to proliferate and grow. The selective theory is based on the observation that Th2 cells secrete IL-4, a cytokine that promotes Th2 development, whereas Th1 cells secrete interferon-gamma (IFN-gamma) that favours Th1 development, and both positive and negative selective influences have been suggested to operate. In this study, we investigate the role of autocrine secretion and utilization of IL-4 by Th2 cells and address the question of whether an activated Th2 cell can be positively selected by IL-4 secreted from other Th2 cells. We present a spatial three dimensional (3D) modelling approach to simulate the interaction between the IL-4 ligand and its IL-4 receptors expressed on discrete IL-4 secreting cells. The simulations, based on existing experimental data on the IL-4 receptor-ligand system, illustrate how Th-cell development is highly dependent on the distance between cells that are communicating. The model suggests that a single Th2 cell is likely to communicate with possible target cells within a range of approximately 100 microm and that an activated Th2 cell manages to fill most of its own IL-4 receptors, even at a low secretion rate. The predictions made by the model suggest that negative selection against Th1 cells is more effective than positive selection by IL-4 for promoting Th2 dominance.

In silico simulations suggest that Th‐cell development is regulated by both selective and instructive mechanisms

Th-cell differentiation is highly influenced by the local cytokine environment. Although cytokines such as IL-12 and IL-4 are known to polarize the Th-cell response towards Th1 or Th2, respectively, it is not known whether these cytokines instruct the developmental fate of uncommitted Th cells or select cells that have already been committed through a stochastic process. We present an individual based model that accommodates both stochastic and deterministic processes to simulate the dynamic behaviour of selective versus instructive Th-cell development. The predictions made by each model show distinct behaviours, which are compared with experimental observations. The simulations show that the instructive model generates an exclusive Th1 or Th2 response in the absence of an external cytokine source, whereas the selective model favours coexistence of the phenotypes. A hybrid model, including both instructive and selective development, shows behaviour similar to either the selective or the instructive model dependent on the strength of activation. The hybrid model shows the closest qualitative agreement with a number of well-established experimental observations. The predictions by each model suggest that neither pure selective nor instructive Th development is likely to be functional as exclusive mechanisms in Th1/Th2 development.

Impact of animal health and welfare research within the CRC for Cattle and Beef Quality on Australian beef production

Research within the health and welfare program of the Cooperative Research Centre for Cattle and Beef Quality has delivered important improvements to the Australian cattle industry. Vaccines to assist with the control of bovine respiratory disease were developed and commercialised from Australian isolates of Mannheimia haemolytica and pestivirus (mucosal disease). Our understanding of the benefits of weaning cattle by confinement and hand feeding in yards (yard weaning) has been consolidated, and yard weaning has been adopted as ‘best practice’ for cattle production in the temperate zones of Australia. The importance of good temperament for improved growth rates and reduced morbidity during feedlot finishing, and for adaptation to stressors such as road transport, has been demonstrated. In response to this knowledge, industry is increasingly measuring flight time for use in breeding programs and feedlot management. The risk to meat quality of stressors such as mixing unfamiliar cattle in the weeks preceding slaughter or acute stress in the last 15 min before slaughter has been described. Adoption of these findings through Quality Assurance schemes will assist in assurance for the community and for export markets of the welfare standards of the Australian cattle and beef industry. This review provides details of the experiments that led to these achievements and to some improved understandings of temperament and behaviour of beef cattle.