Pairwise agonist scanning predicts cellular signaling responses to combinatorial stimuli

Decoding thrombosis through code: a review of computational models

From the molecular level up to a blood vessel, thrombosis and hemostasis involves many interconnected biochemical and biophysical processes over a wide range of length and time scales. Computational modeling has gained eminence in offering insights into these processes beyond what can be obtained from in vitro or in vivo experiments, or clinical measurements. The multiscale and multiphysics nature of thrombosis has inspired a wide range of modeling approaches that aim to address how a thrombus forms and dismantles. Here, we review recent advances in computational modeling with a focus on platelet-based thrombosis. We attempt to summarize the diverse range of modeling efforts straddling the wide-spectrum of physical phenomena, length scales, and time scales; highlighting key advancements and insights from existing studies. Potential information gleaned from models is discussed, ranging from identification of thrombus-prone regions in patient-specific vasculature to modeling thrombus deformation and embolization in response to fluid forces. Furthermore, we highlight several limitations of current models, future directions in the field, and opportunities for clinical translation, to illustrate the state-of-the-art. There are a plethora of opportunity areas for which models can be expanded, ranging from topics of thromboinflammation to platelet production and clearance. Through successes demonstrated in existing studies described here, as well as continued advancements in computational methodologies and computer processing speeds and memory, in silico investigations in thrombosis are poised to bring about significant knowledge growth in the years to come.

High-throughput assessment identifying major platelet Ca2+ entry pathways via tyrosine kinase-linked and G protein-coupled receptors

In platelets, elevated cytosolic Ca²⁺ is a crucial second messenger, involved in most functional responses, including shape change, secretion, aggregation and procoagulant activity. The platelet Ca²⁺ response consists of Ca²⁺ mobilization from endoplasmic reticulum stores, complemented with store-operated or receptor-operated Ca²⁺ entry pathways. Several channels can contribute to the Ca²⁺ entry, but their relative contribution is unclear upon stimulation of ITAM-linked receptors such as glycoprotein VI (GPVI) and G-protein coupled receptors such as the protease-activated receptors (PAR) for thrombin. We employed a 96-well plate high-throughput assay with Fura-2-loaded human platelets to perform parallel [Ca²⁺]_i measurements in the presence of EGTA or CaCl₂. Per agonist condition, this resulted in sets of EGTA, CaCl₂ and Ca²⁺ entry ratio curves, defined by six parameters, reflecting different Ca²⁺ ion fluxes. We report that threshold stimulation of GPVI or PAR, with a variable contribution of secondary mediators, induces a maximal Ca²⁺ entry ratio of 3-7. Strikingly, in combination with Ca²⁺-ATPase inhibition by thapsigargin, the maximal Ca²⁺ entry ratio increased to 400 (GPVI) or 40 (PAR), pointing to a strong receptor-dependent enhancement of store-operated Ca²⁺ entry. By pharmacological blockage of specific Ca²⁺ channels in platelets, we found that, regardless of GPVI or PAR stimulation, the Ca²⁺ entry ratio was strongest affected by inhibition of ORAI1 (2-APB, Synta66) > Na⁺/Ca²⁺ exchange (NCE) > P2×₁ (only initial). In contrast, inhibition of TRPC6, Piezo1/2 or STIM1 was without effect. Together, these data reveal ORAI1 and NCE as dominating Ca²⁺ carriers regulating GPVI- and PAR-induced Ca²⁺ entry in human platelets.

Computational modeling of hypercoagulability in COVID-19

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected more than 100 million people worldwide and claimed millions of lives. While the leading cause of mortality in COVID-19 patients is the hypoxic respiratory failure from acute respiratory distress syndrome, there is accumulating evidence that shows excessive coagulation also increases the fatalities in COVID-19. Thus, there is a pressing demand to understand the association between COVID-19-induced hypercoagulability and the extent of formation of undesired blood clots. Mathematical modeling of coagulation has been used as an important tool to identify novel reaction mechanisms and to identify targets for new drugs. Here, we employ the coagulation factor data of COVID-19 patients reported from published studies as inputs for two mathematical models of coagulation to identify how the concentrations of coagulation factors change in these patients. Our simulation results show that while the levels of many of the abnormal coagulation factors measured in COVID-19 patients promote the generation of thrombin and fibrin, two key components of blood clots, the increased level of fibrinogen and then the reduced level of antithrombin are the factors most responsible for boosting the level of fibrin and thrombin, respectively. Altogether, our study demonstrates the potential of mathematical modeling to identify coagulation factors responsible for the increased clot formation in COVID-19 patients where clinical data is scarce.

A three-dimensional multiscale model for the prediction of thrombus growth under flow with single-platelet resolution

Modeling thrombus growth in pathological flows allows evaluation of risk under patient-specific pharmacological, hematological, and hemodynamical conditions. We have developed a 3D multiscale framework for the prediction of thrombus growth under flow on a spatially resolved surface presenting collagen and tissue factor (TF). The multiscale framework is composed of four coupled modules: a Neural Network (NN) that accounts for platelet signaling, a Lattice Kinetic Monte Carlo (LKMC) simulation for tracking platelet positions, a Finite Volume Method (FVM) simulator for solving convection-diffusion-reaction equations describing agonist release and transport, and a Lattice Boltzmann (LB) flow solver for computing the blood flow field over the growing thrombus. A reduced model of the coagulation cascade was embedded into the framework to account for TF-driven thrombin production. The 3D model was first tested against in vitro microfluidics experiments of whole blood perfusion with various antiplatelet agents targeting COX-1, P₂Y₁, or the IP receptor. The model was able to accurately capture the evolution and morphology of the growing thrombus. Certain problems of 2D models for thrombus growth (artifactual dendritic growth) were naturally avoided with realistic trajectories of platelets in 3D flow. The generalizability of the 3D multiscale solver enabled simulations of important clinical situations, such as cylindrical blood vessels and acute flow narrowing (stenosis). Enhanced platelet-platelet bonding at pathologically high shear rates (e.g., von Willebrand factor unfolding) was required for accurately describing thrombus growth in stenotic flows. Overall, the approach allows consideration of patient-specific platelet signaling and vascular geometry for the prediction of thrombotic episodes.

The excessive formation of blood clots under flow within the circulatory system (thrombosis) is known to initiate heart attacks and strokes. Therefore, obtaining insights into the formation and progression of these clots will be useful in evaluating pharmacological options. To this end, we have developed a 3D computational model that tracks the growth of a blood clot under flow from initial platelet deposition to full vessel occlusion in the presence of soluble platelet agonists. We first validated the model against experimental predictions of blood clots formed in vitro. Due to the construction of the model in 3D, we were able to carry out simulations of clot formation under important clinical situations, namely cylindrical blood vessels and acute flow narrowings (stenoses). To our knowledge, our model is the first of its kind that can account for patient-specific platelet phenotypes to perform robust 3D simulations of thrombus growth in geometries of clinical relevance.

Determinants of Ligand Specificity and Functional Plasticity in Type I Interferon Signaling

The Type I Interferon family of cytokines all act through the same cell surface receptor and induce phosphorylation of the same subset of response regulators of the STAT family. Despite their shared receptor, different Type I Interferons have different functions during immune response to infection. In particular, they differ in the potency of their induced anti-viral and anti-proliferative responses in target cells. It remains not fully understood how these functional differences can arise in a ligand-specific manner both at the level of STAT phosphorylation and the downstream function. We use a minimal computational model of Type I Interferon signaling, focusing on Interferon-α and Interferon-β. We validate the model with quantitative experimental data to identify the key determinants of specificity and functional plasticity in Type I Interferon signaling. We investigate different mechanisms of signal discrimination, and how multiple system components such as binding affinity, receptor expression levels and their variability, receptor internalization, short-term negative feedback by SOCS1 protein, and differential receptor expression play together to ensure ligand specificity on the level of STAT phosphorylation. Based on these results, we propose phenomenological functional mappings from STAT activation to downstream anti-viral and anti-proliferative activity to investigate differential signal processing steps downstream of STAT phosphorylation. We find that the negative feedback by the protein USP18, which enhances differences in signaling between Interferons via ligand-dependent refractoriness, can give rise to functional plasticity in Interferon-α and Interferon-β signaling, and explore other factors that control functional plasticity. Beyond Type I Interferon signaling, our results have a broad applicability to questions of signaling specificity and functional plasticity in signaling systems with multiple ligands acting through a bottleneck of a small number of shared receptors.

Multi-omics approaches for revealing the complexity of cardiovascular disease

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Pleiotropy enables specific and accurate signaling in the presence of ligand cross talk

Living cells sense their environment through the binding of extracellular molecular ligands to cell surface receptors. Puzzlingly, vast numbers of signaling pathways exhibit a high degree of cross talk between different signals whereby different ligands act through the same receptor or shared components downstream. It remains unclear how a cell can accurately process information from the environment in such cross-wired pathways. We show that a feature which commonly accompanies cross talk-signaling pleiotropy (the ability of a receptor to produce multiple outputs)-offers a solution to the cross-talk problem. In a minimal model we show that a single pleiotropic receptor can simultaneously identify and accurately sense the concentrations of arbitrary unknown ligands present individually or in a mixture. We calculate the fundamental limits of the signaling specificity and accuracy of such signaling schemes. The model serves as an elementary "building block" toward understanding more complex cross-wired receptor-ligand signaling networks.

Refining a numerical model for device-induced thrombosis and investigating the effects of non-Newtonian blood models

Thrombosis is one of the main causes of failure in device implantation. Computational thrombosis simulation is a convenient approach to evaluate the risk of thrombosis for a device. However, thrombosis is a complicated process involving multiple species and reactions. Application of a macroscopic, single-scale computational model for device-induced thrombosis is a cost-effective approach. The current study has refined an existing thrombosis model, which simulated thrombosis by tracing four species in blood: non-activated platelets, activated platelets, surface adherent platelets, and ADP. Platelets are activated mechanically by shear stress, and chemically by ADP. Platelet adhesion occurs on surfaces with low wall shear stress with platelet aggregation inhibited in regions of high shear stress. The study improves the existing thrombosis model by: 1) Modifying the chemical platelet activation function so that ADP activates platelets; 2) Modifying the function describing thrombus deposition and growth to distinguish between thrombus deposition on wall surfaces and thrombus growth on existing thrombus surfaces; 3) Modifying the thrombus breakdown function to allow for thrombus breakdown by shear stress; 4) Modeling blood flow as non-Newtonian. The results show that the inclusion of ADP and the use of a non-Newtonian model improve agreement with experimental data.

D-Dimer and Fibrin Degradation Products Impair Platelet Signaling: Plasma D-Dimer Is a Predictor and Mediator of Platelet Dysfunction During Trauma

BACKGROUND

Platelet dysfunction often accompanies trauma-induced coagulopathy. Because soluble fibrin impairs platelet glycoprotein VI (GPVI) signaling and platelets of trauma patients can display impaired calcium mobilization, we explored the role of fibrinolysis on platelet dysfunction during trauma.

METHODS

Convulxin-induced GPVI calcium mobilization was investigated in healthy platelet-rich plasma (PRP) pretreated with thrombin and tissue plasminogen activator (tPA). Blood samples from healthy participants (n = 7) and trauma patients (n = 22) were tested for platelet calcium mobilization, plasma D-dimer, platelet D-dimer binding (via flow cytometry), and platelet lumi-aggregometry.

RESULTS

For healthy platelets, maximal platelet dysfunction was observed when cross-linked soluble fibrin (no tPA) or cross-linked fibrin degradation products (FDPs) were generated in suspension before convulxin stimulation. Lack of fibrin polymerization (inhibited by Gly-Pro-Arg-Pro [GPRP]) or lack of factor XIIIa cross-linking (T101-inhibited) restored GPVI signaling, whereas non-cross-linked FDPs only partially blocked signaling induced by convulxin. In addition, D-dimer added to healthy PRP impaired platelet aggregation and dense granule release induced by various agonists. Plasma D-dimer level was strongly correlated (R = 0.8236) with platelet dysfunction as measured by platelet calcium mobilization induced with various agonists. By 48 to 120 h after trauma, plasma D-dimer levels declined, and platelet function increased significantly but not to healthy levels. Trauma platelets displayed elevated D-dimer binding that was only partially reduced by αIIbβ3-inhibitor GR144053. After 60-minute incubation, washed healthy platelets resuspended in plasma from trauma patients captured approximately 10 000 D-dimer equivalents per platelet.

CONCLUSIONS

During trauma, D-dimer and FDPs inhibit platelets, potentially via GPVI and integrin αIIbβ3 engagement, contributing to a fibrinolysis-dependent platelet loss-of-function phenotype.

Computationally Driven Discovery in Coagulation

Bleeding frequency and severity within clinical categories of hemophilia A are highly variable and the origin of this variation is unknown. Solving this mystery in coagulation requires the generation and analysis of large data sets comprised of experimental outputs or patient samples, both of which are subject to limited availability. In this review, we describe how a computationally driven approach bypasses such limitations by generating large synthetic patient data sets. These data sets were created with a mechanistic mathematical model, by varying the model inputs, clotting factor, and inhibitor concentrations, within normal physiological ranges. Specific mathematical metrics were chosen from the model output, used as a surrogate measure for bleeding severity, and statistically analyzed for further exploration and hypothesis generation. We highlight results from our recent study that employed this computationally driven approach to identify FV (factor V) as a key modifier of thrombin generation in mild to moderate hemophilia A, which was confirmed with complementary experimental assays. The mathematical model was used further to propose a potential mechanism for these observations whereby thrombin generation is rescued in FVIII-deficient plasma due to reduced substrate competition between FV and FVIII for FXa (activated factor X).

Pairwise Stimulations of Pathogen-Sensing Pathways Predict Immune Responses to Multi-adjuvant Combinations

The immune system makes decisions in response to combinations of multiple microbial inputs. We do not understand the combinatorial logic governing how higher-order combinations of microbial signals shape immune responses. Here, using coculture experiments and statistical analyses, we discover a general property for the combinatorial sensing of microbial signals, whereby the effects of triplet combinations of microbial signals on immune responses can be predicted by combining the effects of single and pairs. Mechanistically, we find that singles and pairs dictate the information signaled by triplets in mouse and human DCs at the levels of transcription, chromatin, and protein secretion. We exploit this simplifying property to develop cell-based immunotherapies prepared with adjuvant combinations that trigger protective responses in mouse models of cancer. We conclude that the processing of multiple input signals by innate immune cells is governed by pairwise effects, which will inform the rationale combination of adjuvants to manipulate immunity.

Platelet dysfunction during trauma involves diverse signaling pathways and an inhibitory activity in patient-derived plasma

BACKGROUND

Trauma-induced coagulopathy occurs in about 25% of injured patients and accounts for about 10% of deaths worldwide. Upon injury, hemostatic function may decline due to vascular dysfunction, clotting factor deficiencies, hyperfibrinolysis, and/or platelet dysfunction. We investigated agonist-induced calcium signaling in platelets obtained over time from trauma patients.

METHODS

Platelets from trauma patients and healthy donors were monitored via intracellular calcium mobilization and flow cytometry markers (α2bβ3 activation, P-selectin display, and phosphatidylserine exposure) following stimulation with a panel of agonists (adenosine 5'-diphosphate sodium salt, U46619, convulxin, PAR-1/4 activating peptides, iloprost) used in isolation or in pairwise tests. Furthermore, healthy donor platelets were tested in heterologous plasma isolated from healthy subjects and trauma patients.

RESULTS

When exposed to agonists over the first 24 hours postinjury, trauma patient platelets mobilized less calcium in comparison to healthy platelets. Partial recovery of platelet activity was observed in about a third of patients after 120 hours, although not fully obtaining healthy baseline function. Flow cytometry markers of trauma platelets were similar to healthy platelets prior to stimulation, but were depressed in trauma platelets stimulated with adenosine 5'-diphosphate sodium salt or convulxin. Also, washed healthy platelets showed a significant reduction in calcium mobilization when reconstituted in plasma from trauma patients, relative to healthy plasma, at all plasma doses tested.

CONCLUSION

Platelet dysfunction in trauma patients included poor response to multiple agonists relevant to hemostatic function. Furthermore, the inhibitor effect of patient plasma on healthy platelets suggests that soluble plasma species may downregulate endogenous or transfused platelets during trauma.

Cytokine-mediated communication: a quantitative appraisal of immune complexity

Intercellular communication mediated by cytokines is the main mechanism by which cells of the immune system talk to each other. Many aspects of cytokine signalling in the immune system have been explored in great detail at the structural, biophysical, biochemical and cellular levels. However, a systematic understanding of the quantitative rules that govern cytokine-mediated cell-to-cell communication is still lacking. Here, we discuss recent efforts in the field of systems immunology to bring about a quantitative understanding of cytokine-mediated communication between leukocytes and to provide novel insights into the orchestration of immune responses and inflammation.

Effect of leukoreduction and pathogen reduction on the hemostatic function of whole blood

BACKGROUND

There is renewed interest in the use of whole blood (WB) for resuscitation of patients in hemorrhagic shock. Leukoreduction with platelet-sparing filters and pathogen reduction may be used to improve the safety profile of WB, yet the effects of leukoreduction and pathogen reduction on WB hemostatic function are not well characterized.

STUDY DESIGN AND METHODS

Blood from 32 healthy group O donors was divided into treatment groups (n = 8 for each group): untreated, pathogen reduced (PR⁺ ), leukoreduced using an in-line filter (LR⁺ ), or PR⁺ LR⁺ . Units were stored without agitation for 21 days between 1° and 6°C, with sampling on days 0 (pre- and post-treatments), 1, 3, 5, 10, 15, and 21 for hemostatic function as assessed by thromboelastometry, thrombin generation, platelet activation factors, and platelet impedance aggregometry.

RESULTS

From day 3 (D3) to D15 of storage, platelet count was reduced in PR⁺ /LR⁺ units compared to PR^- /LR^- units. From D10 to D21 of storage, maximum clot firmness (MCF) was reduced in PR⁺ /LR⁺ units compared to PR^- /LR^- units. From D3 to D21 of storage, platelet aggregation was reduced in PR⁺ /LR⁺ units compared to PR^- /LR^- units. Total thrombin generation was similar in all groups from D0 to D21.

CONCLUSIONS

The combination of LR with a platelet-sparing filter and PR significantly reduces hemostatic function compared to either treatment alone or untreated WB. The clinical consequences of LR and PR of WB in patients with severe bleeding should be examined in trials before both are used in combination in patients.

In vivo measurement of blood clot mechanics from computational fluid dynamics based on intravital microscopy images

Ischemia which leads to heart attacks and strokes is one of the major causes of death in the world. Whether an occlusion occurs or not depends on the ability of a growing thrombus to resist flow forces exerted on its structure. This manuscript provides the first known in vivo measurement of how much stress a clot can withstand, before yielding to the surrounding blood flow. Namely, Lattice-Boltzmann Method flow simulations are performed based on 3D clot geometries, which are estimated from intravital microscopy images of laser-induced injuries in cremaster microvasculature of live mice. In addition to reporting the blood clot yield stresses, we also show that the thrombus "core" does not experience significant deformation, while its "shell" does. This indicates that the shell is more prone to embolization. Therefore, drugs should be designed to target the shell selectively, while leaving the core intact to minimize excessive bleeding. Finally, we laid down a foundation for a nondimensionalization procedure which unraveled a relationship between clot mechanics and biology. Hence, the proposed framework could ultimately lead to a unified theory of thrombogenesis, capable of explaining all clotting events. Thus, the findings presented herein will be beneficial to the understanding and treatment of heart attacks, strokes and hemophilia.

Multiscale simulation of thrombus growth and vessel occlusion triggered by collagen/tissue factor using a data-driven model of combinatorial platelet signalling

During clotting under flow, platelets bind and activate on collagen and release autocrinic factors such as ADP and thromboxane, while tissue factor (TF) on the damaged wall leads to localized thrombin generation. Towards patient-specific simulation of thrombosis, a multiscale approach was developed to account for: platelet signalling [neural network (NN) trained by pairwise agonist scanning (PAS), PAS-NN], platelet positions (lattice kinetic Monte Carlo, LKMC), wall-generated thrombin and platelet-released ADP/thromboxane convection–diffusion (partial differential equation, PDE) and flow over a growing clot (lattice Boltzmann). LKMC included shear-driven platelet aggregate restructuring. The PDEs for thrombin, ADP and thromboxane were solved by finite element method using cell activation-driven adaptive triangular meshing. At all times, intracellular calcium was known for each platelet by PAS-NN in response to its unique exposure to local collagen, ADP, thromboxane and thrombin. When compared with microfluidic experiments of human blood clotting on collagen/TF driven by constant pressure drop, the model accurately predicted clot morphology and growth with time. In experiments and simulations at TF at 0.1 and 10 molecule-TF/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mu$\end{document}m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^{2}$\end{document} and initial wall shear rate of 200 s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^{-1}$\end{document}, the occlusive blockade of flow for a 60-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mu$\end{document}m channel occurred relatively abruptly at 600 and 400 s, respectively (with no occlusion at zero TF). Prior to occlusion, intrathrombus concentrations reached 50 nM thrombin, ~ 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mu$\end{document}M thromboxane and ~ 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mu$\end{document}M ADP, while the wall shear rate on the rough clot peaked at ~ 1000–2000 s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^{-1}$\end{document}. Additionally, clotting on TF/collagen was accurately simulated for modulators of platelet cyclooxygenase-1, P2Y\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{1}$\end{document} and IP-receptor. This multiscale approach facilitates patient-specific simulation of thrombosis under hemodynamic and pharmacological conditions.

Dual antiplatelet and anticoagulant (APAC) heparin proteoglycan mimetic with shear-dependent effects on platelet-collagen binding and thrombin generation

Heparin proteoglycans (HEP-PGs) carry standard heparin-mediated anticoagulant properties as well as novel antiplatelet functions, a combination that may be significant for targeting multiple pathways in a single therapy. Recent work developing semisynthetic HEP-PG mimetics has shown promising results also in vivo, however flow conditions in vitro that replicate in vivo hemodynamics have not been reported. In this work, we present several assays (platelet calcium mobilization, aggregometry, microfluidic tests at venous and arterial hemodynamics) to characterize specific mechanistic effects of dual antiplatelet and anticoagulant (APAC) constructs as mimetics of HEP-PGs. Three APACs with different conjugation levels of heparin chains (CL10, CL18, HICL) were shown to decrease platelet deposition to collagen surfaces in PPACK-treated whole blood at venous shear rate (200 s^-1). FXIIa-inhibited whole blood (CTI: corn trypsin inhibitor, 40 μg/mL) perfused over collagen/tissue factor showed reduced both platelet and fibrin deposition when treated with APACs. IC50 values for platelet and fibrin inhibition were calculated for each molecule at venous shear rate. Increasing the shear rate to arterial flows (1000 s^-1) and using APAC as the sole anticoagulant, resulted in a more potent antiplatelet effect of APAC, suggesting an added effect on von Willebrand Factor (vWF) function. Additionally, APAC caused an inhibition of calcium mobilization specific to thrombin and collagen stimulation and a dose-dependent reduction in collagen-mediated platelet aggregation. Understanding the sensitivity of APAC activity to shear rate, platelet signaling and procoagulant pathways is important for applications in which APAC administration may have beneficial therapeutic effects.

Thrombosis in Cerebral Aneurysms and the Computational Modeling Thereof: A Review

Thrombosis is a condition closely related to cerebral aneurysms and controlled thrombosis is the main purpose of endovascular embolization treatment. The mechanisms governing thrombus initiation and evolution in cerebral aneurysms have not been fully elucidated and this presents challenges for interventional planning. Significant effort has been directed towards developing computational methods aimed at streamlining the interventional planning process for unruptured cerebral aneurysm treatment. Included in these methods are computational models of thrombus development following endovascular device placement. The main challenge with developing computational models for thrombosis in disease cases is that there exists a wide body of literature that addresses various aspects of the clotting process, but it may not be obvious what information is of direct consequence for what modeling purpose (e.g., for understanding the effect of endovascular therapies). The aim of this review is to present the information so it will be of benefit to the community attempting to model cerebral aneurysm thrombosis for interventional planning purposes, in a simplified yet appropriate manner. The paper begins by explaining current understanding of physiological coagulation and highlights the documented distinctions between the physiological process and cerebral aneurysm thrombosis. Clinical observations of thrombosis following endovascular device placement are then presented. This is followed by a section detailing the demands placed on computational models developed for interventional planning. Finally, existing computational models of thrombosis are presented. This last section begins with description and discussion of physiological computational clotting models, as they are of immense value in understanding how to construct a general computational model of clotting. This is then followed by a review of computational models of clotting in cerebral aneurysms, specifically. Even though some progress has been made towards computational predictions of thrombosis following device placement in cerebral aneurysms, many gaps still remain. Answering the key questions will require the combined efforts of the clinical, experimental and computational communities.

Multiscale systems biology of trauma-induced coagulopathy

Trauma with hypovolemic shock is an extreme pathological state that challenges the body to maintain blood pressure and oxygenation in the face of hemorrhagic blood loss. In conjunction with surgical actions and transfusion therapy, survival requires the patient's blood to maintain hemostasis to stop bleeding. The physics of the problem are multiscale: (a) the systemic circulation sets the global blood pressure in response to blood loss and resuscitation therapy, (b) local tissue perfusion is altered by localized vasoregulatory mechanisms and bleeding, and (c) altered blood and vessel biology resulting from the trauma as well as local hemodynamics control the assembly of clotting components at the site of injury. Building upon ongoing modeling efforts to simulate arterial or venous thrombosis in a diseased vasculature, computer simulation of trauma-induced coagulopathy is an emerging approach to understand patient risk and predict response. Despite uncertainties in quantifying the patient's dynamic injury burden, multiscale systems biology may help link blood biochemistry at the molecular level to multiorgan responses in the bleeding patient. As an important goal of systems modeling, establishing early metrics of a patient's high-dimensional trajectory may help guide transfusion therapy or warn of subsequent later stage bleeding or thrombotic risks. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Regulatory Biology Models of Systems Properties and Processes > Mechanistic Models.

Understanding non-linear effects from Hill-type dynamics with application to decoding of p53 signaling

Analytical equations are derived depicting four possible scenarios resulting from pulsed signaling of a system subject to Hill-type dynamics. Pulsed Hill-type dynamics involves the binding of multiple signal molecules to a receptor and occurs e.g., when transcription factor p53 orchestrates cancer prevention, during calcium signaling, and during circadian rhythms. The scenarios involve: (i) enhancement of high-affinity binders compared to low-affinity ones, (ii) slowing reactions involving high-affinity binders, (iii) transfer of the clocking of low-affinity binders from the signal molecule to the products, and (iv) a unique clocking process that produces incremental increases in the activity of high-affinity binders with each signal pulse. In principle, these mostly non-linear effects could control cellular outcomes. An applications to p53 signaling is developed, with binding to most gene promoters identified as category (iii) responses. However, currently unexplained enhancement of high-affinity promoters such as CDKN1a (p21) by pulsed signaling could be an example of (i). In general, provision for all possible scenarios is required in the design of mathematical models incorporating pulsed Hill-type signaling as some aspect.

Mathematical Techniques for Understanding Platelet Regulation and the Development of New Pharmacological Approaches

Mathematical and computational modeling is currently in the process of becoming an accepted tool in the arsenal of methods utilized for the investigation of complex biological systems. For some problems in the field, like cellular metabolic regulation, neural impulse propagation, or cell cycle, progress is already unthinkable without use of such methods. Mathematical models of platelet signaling, function, and metabolism during the last years have not only been steadily increasing in their number, but have also been providing more in-depth insights, generating hypotheses, and allowing predictions to be made leading to new experimental designs and data. Here we describe the basic approaches to platelet mathematical model development and validation, highlighting the challenges involved. We then review the current theoretical models in the literature and how these are being utilized to increase our understanding of these complex cells.

Systems Analysis of Thrombus Formation

The systems analysis of thrombosis seeks to quantitatively predict blood function in a given vascular wall and hemodynamic context. Relevant to both venous and arterial thrombosis, a Blood Systems Biology approach should provide metrics for rate and molecular mechanisms of clot growth, thrombotic risk, pharmacological response, and utility of new therapeutic targets. As a rapidly created multicellular aggregate with a polymerized fibrin matrix, blood clots result from hundreds of unique reactions within and around platelets propagating in space and time under hemodynamic conditions. Coronary artery thrombosis is dominated by atherosclerotic plaque rupture, complex pulsatile flows through stenotic regions producing high wall shear stresses, and plaque-derived tissue factor driving thrombin production. In contrast, venous thrombosis is dominated by stasis or depressed flows, endothelial inflammation, white blood cell-derived tissue factor, and ample red blood cell incorporation. By imaging vessels, patient-specific assessment using computational fluid dynamics provides an estimate of local hemodynamics and fractional flow reserve. High-dimensional ex vivo phenotyping of platelet and coagulation can now power multiscale computer simulations at the subcellular to cellular to whole vessel scale of heart attacks or strokes. In addition, an integrated systems biology approach can rank safety and efficacy metrics of various pharmacological interventions or clinical trial designs.

Platelets and hemostasis: a new perspective on an old subject

Publisher's Note: This article has a companion Counterpoint by Kapur and Semple. Publisher's Note: Join in the discussion of these articles at Blood Advances Community Conversations.

In microfluidico: Recreating in vivo hemodynamics using miniaturized devices

Microfluidic devices create precisely controlled reactive blood flows and typically involve: (i) validated anticoagulation/pharmacology protocols, (ii) defined reactive surfaces, (iii) defined flow-transport regimes, and (iv) optical imaging. An 8-channel device can be run at constant flow rate or constant pressure drop for blood perfusion over a patterned collagen, collagen/kaolin, or collagen/tissue factor (TF) to measure platelet, thrombin, and fibrin dynamics during clot growth. A membrane-flow device delivers a constant flux of platelet agonists or coagulation enzymes into flowing blood. A trifurcated device sheaths a central blood flow on both sides with buffer, an ideal approach for on-chip recalcification of citrated blood or drug delivery. A side-view device allows clotting on a porous collagen/TF plug at constant pressure differential across the developing clot. The core-shell architecture of clots made in mouse models can be replicated in this device using human blood. For pathological flows, a stenosis device achieves shear rates of >100,000 s(-1) to drive plasma von Willebrand factor (VWF) to form thick long fibers on collagen. Similarly, a micropost-impingement device creates extreme elongational and shear flows for VWF fiber formation without collagen. Overall, microfluidics are ideal for studies of clotting, bleeding, fibrin polymerization/fibrinolysis, cell/clot mechanics, adhesion, mechanobiology, and reaction-transport dynamics.

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, AKT-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the AKT-repressed signal as glycogen synthase kinase 3 (GSK3)-catalyzed phosphorylation of Ser(37) on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser(37) promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.

Systems Pharmacology and Rational Polypharmacy: Nitric Oxide-Cyclic GMP Signaling Pathway as an Illustrative Example and Derivation of the General Case

Impaired nitric oxide (NO˙)-cyclic guanosine 3', 5'-monophosphate (cGMP) signaling has been observed in many cardiovascular disorders, including heart failure and pulmonary arterial hypertension. There are several enzymatic determinants of cGMP levels in this pathway, including soluble guanylyl cyclase (sGC) itself, the NO˙-activated form of sGC, and phosphodiesterase(s) (PDE). Therapies for some of these disorders with PDE inhibitors have been successful at increasing cGMP levels in both cardiac and vascular tissues. However, at the systems level, it is not clear whether perturbation of PDE alone, under oxidative stress, is the best approach for increasing cGMP levels as compared with perturbation of other potential pathway targets, either alone or in combination. Here, we develop a model-based approach to perturbing this pathway, focusing on single reactions, pairs of reactions, or trios of reactions as targets, then monitoring the theoretical effects of these interventions on cGMP levels. Single perturbations of all reaction steps within this pathway demonstrated that three reaction steps, including the oxidation of sGC, NO˙ dissociation from sGC, and cGMP degradation by PDE, exerted a dominant influence on cGMP accumulation relative to other reaction steps. Furthermore, among all possible single, paired, and triple perturbations of this pathway, the combined perturbations of these three reaction steps had the greatest impact on cGMP accumulation. These computational findings were confirmed in cell-based experiments. We conclude that a combined perturbation of the oxidatively-impaired NO˙-cGMP signaling pathway is a better approach to the restoration of cGMP levels as compared with corresponding individual perturbations. This approach may also yield improved therapeutic responses in other complex pharmacologically amenable pathways.

Developing drugs for a well-defined biochemical or molecular pathway has conventionally been approached by optimizing the inhibition (or activation) of a single target by a single pharmacologic agent. On occasion, drug combinations have been used that generally target multiple pathways affecting a common phenotype, again by optimizing the extent of inhibition of individual targets, semi-empirically adjusting their doses to minimize toxicities as they are manifest. Here, we present a computational approach for identifying optimal combinations of agents that can affect (inhibit) a well-defined biochemical pathway, doing so at minimal combined concentrations, thereby potentially minimizing dose-dependent toxicities. This approach is illustrated computationally and experimentally with a well-known pathway, the nitric oxide-cyclic GMP pathway, but is readily generalizable to rational polypharmacy.

Computational modelling of clot development in patient-specific cerebral aneurysm cases

ESSENTIALS: Clotting in cerebral aneurysms is a process that can either stabilize the aneurysm or lead to rupture. A patient-specific computational model capable of predicting cerebral aneurysm thrombosis is presented. The different clotting outcomes highlight the importance of personalization of treatment. Once validated, the model can be used to tailor treatment and to clarify clotting processes in aneurysms.

BACKGROUND

In cerebral aneurysms, clotting can either stabilize the aneurysm sac via aneurysm occlusion, or it can have a detrimental effect by giving rise to embolic occlusion.

OBJECTIVE

The work presented in this study details the development of an in silico model that combines all the salient, clinically relevant features of cerebral aneurysm clotting. A comprehensive computational model of clotting that accounts for biochemical complexity coupled with three-dimensional hemodynamics in image-derived patient aneurysms and in the presence of virtually implanted interventional devices is presented.

METHODS

The model is developed and presented in two stages. First, a two-dimensional computational model of clotting is presented for an idealized geometry. This enables verification of the methods with existing, physiological data before the pathological state is considered. This model is used to compare the results predicted by two different underlying biochemical cascades. The two-dimensional model is then extended to image-derived, three-dimensional aneurysmal topologies by incorporating level set methods, demonstrating the potential use of this model.

RESULTS AND CONCLUSION

As a proof of concept, comparisons are then made between treated and untreated aneurysms. The prediction of different clotting outcomes for different patients demonstrates that with further development, refinement and validation, this methodology could be used for patient-specific interventional planning.

Regulation of Early Steps of GPVI Signal Transduction by Phosphatases: A Systems Biology Approach

We present a data-driven mathematical model of a key initiating step in platelet activation, a central process in the prevention of bleeding following Injury. In vascular disease, this process is activated inappropriately and causes thrombosis, heart attacks and stroke. The collagen receptor GPVI is the primary trigger for platelet activation at sites of injury. Understanding the complex molecular mechanisms initiated by this receptor is important for development of more effective antithrombotic medicines. In this work we developed a series of nonlinear ordinary differential equation models that are direct representations of biological hypotheses surrounding the initial steps in GPVI-stimulated signal transduction. At each stage model simulations were compared to our own quantitative, high-temporal experimental data that guides further experimental design, data collection and model refinement. Much is known about the linear forward reactions within platelet signalling pathways but knowledge of the roles of putative reverse reactions are poorly understood. An initial model, that includes a simple constitutively active phosphatase, was unable to explain experimental data. Model revisions, incorporating a complex pathway of interactions (and specifically the phosphatase TULA-2), provided a good description of the experimental data both based on observations of phosphorylation in samples from one donor and in those of a wider population. Our model was used to investigate the levels of proteins involved in regulating the pathway and the effect of low GPVI levels that have been associated with disease. Results indicate a clear separation in healthy and GPVI deficient states in respect of the signalling cascade dynamics associated with Syk tyrosine phosphorylation and activation. Our approach reveals the central importance of this negative feedback pathway that results in the temporal regulation of a specific class of protein tyrosine phosphatases in controlling the rate, and therefore extent, of GPVI-stimulated platelet activation.

Discovering anti-platelet drug combinations with an integrated model of activator-inhibitor relationships, activator-activator synergies and inhibitor-inhibitor synergies

Identifying effective therapeutic drug combinations that modulate complex signaling pathways in platelets is central to the advancement of effective anti-thrombotic therapies. However, there is no systems model of the platelet that predicts responses to different inhibitor combinations. We developed an approach which goes beyond current inhibitor-inhibitor combination screening to efficiently consider other signaling aspects that may give insights into the behaviour of the platelet as a system. We investigated combinations of platelet inhibitors and activators. We evaluated three distinct strands of information, namely: activator-inhibitor combination screens (testing a panel of inhibitors against a panel of activators); inhibitor-inhibitor synergy screens; and activator-activator synergy screens. We demonstrated how these analyses may be efficiently performed, both experimentally and computationally, to identify particular combinations of most interest. Robust tests of activator-activator synergy and of inhibitor-inhibitor synergy required combinations to show significant excesses over the double doses of each component. Modeling identified multiple effects of an inhibitor of the P2Y12 ADP receptor, and complementarity between inhibitor-inhibitor synergy effects and activator-inhibitor combination effects. This approach accelerates the mapping of combination effects of compounds to develop combinations that may be therapeutically beneficial. We integrated the three information sources into a unified model that predicted the benefits of a triple drug combination targeting ADP, thromboxane and thrombin signaling.

Drugs are often used in combinations, but establishing the best combinations is a considerable challenge for basic and clinical research. Anti-platelet therapies reduce thrombosis and heart attacks by lowering the activation of platelet cells. We wanted to find good drug combinations, but a full systems model of the platelet is absent, so we had no good predictions of how particular combinations might behave. Instead, we put together three sources of knowledge. The first concerned what inhibitors act on what activators; the second concerned what pairs of activators synergise together (having a bigger effect than expected); and the third concerned what pairs of inhibitors synergise together. We implemented an efficient experimental approach to collect this information from experiments on platelets. We developed a statistical model that brought these separate results together. This gave us insights into how platelet inhibitors act. For example, an inhibitor of an ADP receptor showed multiple effects. We also worked out from the model what further (triple) combinations of drugs may be most efficient. We predicted, and then tested experimentally, the effects of a triple drug combination. This simultaneously inhibited the platelet’s responses to three stimulants that it encounters during coronary thrombosis, namely ADP, thromboxane and thrombin.

A human platelet calcium calculator trained by pairwise agonist scanning

Since platelet intracellular calcium mobilization [Ca(t)]_i controls granule release, cyclooxygenase-1 and integrin activation, and phosphatidylserine exposure, blood clotting simulations require prediction of platelet [Ca(t)]_i in response to combinatorial agonists. Pairwise Agonist Scanning (PAS) deployed all single and pairwise combinations of six agonists (ADP, convulxin, thrombin, U46619, iloprost and GSNO used at 0.1, 1, and 10xEC₅₀; 154 conditions including a null condition) to stimulate platelet P₂Y₁/P2Y₁₂ GPVI, PAR1/PAR4, TP, IP receptors, and guanylate cyclase, respectively, in Factor Xa-inhibited (250 nM apixaban), diluted platelet rich plasma that had been loaded with the calcium dye Fluo-4 NW. PAS of 10 healthy donors provided [Ca(t)]_i data for training 10 neural networks (NN, 2-layer/12-nodes) per donor. Trinary stimulations were then conducted at all 0.1x and 1xEC₅₀ doses (160 conditions) as was a sampling of 45 higher ordered combinations (four to six agonists). The NN-ensemble average was a calcium calculator that accurately predicted [Ca (t)]_i beyond the single and binary training set for trinary stimulations (R = 0.924). The 160 trinary synergy scores, a normalized metric of signaling crosstalk, were also well predicted (R = 0.850) as were the calcium dynamics (R = 0.871) and high-dimensional synergy scores (R = 0.695) for the 45 higher ordered conditions. The calculator even predicted sequential addition experiments (n = 54 conditions, R = 0.921). NN-ensemble is a fast calcium calculator, ideal for multiscale clotting simulations that include spatiotemporal concentrations of ADP, collagen, thrombin, thromboxane, prostacyclin, and nitric oxide.

Platelets regulate clotting during injury to prevent blood loss. Hyperactive platelets may increase risk of thrombosis, whereas hypoactive platelets may increase risk of bleeding. Platelets are activated during a clotting event by agonists, through different signaling pathways, all of which converge on intracellular calcium mobilization. Calcium mobilization is a global metric of platelet activation. Predicting platelet response to different combinations of agonists is essential to scoring bleeding or clotting risks or drug response. We collected pairwise agonist scanning data, in which platelets are activated by all single and pairwise combinations of six important agonists at low, medium and high doses, from 10 donors and subsequently trained artificial neural networks. The combined trained model was able to predict the dynamic calcium time traces of combinations of three, four, five and six agonists at various dose ranges, as well as conditions where agonists were added sequentially. The data-driven neural network model is computationally fast and is able to capture a significant level of signaling complexity within the human platelet.

Fluid Mechanics of Blood Clot Formation

Intravascular blood clots form in an environment in which hydrodynamic forces dominate and in which fluid-mediated transport is the primary means of moving material. The clotting system has evolved to exploit fluid dynamic mechanisms and to overcome fluid dynamic challenges to ensure that clots that preserve vascular integrity can form over the wide range of flow conditions found in the circulation. Fluid-mediated interactions between the many large deformable red blood cells and the few small rigid platelets lead to high platelet concentrations near vessel walls where platelets contribute to clotting. Receptor-ligand pairs with diverse kinetic and mechanical characteristics work synergistically to arrest rapidly flowing cells on an injured vessel. Variations in hydrodynamic stresses switch on and off the function of key clotting polymers. Protein transport to, from, and within a developing clot determines whether and how fast it grows. We review ongoing experimental and modeling research to understand these and related phenomena.

Systems modeling of Ca(2+) homeostasis and mobilization in platelets mediated by IP3 and store-operated Ca(2+) entry

Resting platelets maintain a stable level of low cytoplasmic calcium ([Ca(2+)]cyt) and high dense tubular system calcium ([Ca(2+)]dts). During thrombosis, activators cause a transient rise in inositol trisphosphate (IP3) to trigger calcium mobilization from stores and elevation of [Ca(2+)]cyt. Another major source of [Ca(2+)]cyt elevation is store-operated calcium entry (SOCE) through plasmalemmal calcium channels that open in response to store depletion as [Ca(2+)]dts drops. A 34-species systems model employed kinetics describing IP3-receptor, DTS-plasmalemma puncta formation, SOCE via assembly of STIM1 and Orai1, and the plasmalemma and sarco/endoplasmic reticulum Ca(2+)-ATPases. Four constraints were imposed: calcium homeostasis before activation; stable in zero extracellular calcium; IP3-activatable; and functional SOCE. Using a Monte Carlo method to sample three unknown parameters and nine initial concentrations in a 12-dimensional space near measured or expected values, we found that model configurations that were responsive to stimuli and demonstrated significant SOCE required high inner membrane electric potential (>-70 mV) and low resting IP3 concentrations. The absence of puncta in resting cells was required to prevent spontaneous store depletion in calcium-free media. Ten-fold increases in IP3 caused saturated calcium mobilization. This systems model represents a critical step in being able to predict platelets' phenotypes during hemostasis or thrombosis.

Modeling the zebrafish segmentation clock's gene regulatory network constrained by expression data suggests evolutionary transitions between oscillating and nonoscillating transcription

During segmentation of vertebrate embryos, somites form in accordance with a periodic pattern established by the segmentation clock. In the zebrafish (Danio rerio), the segmentation clock includes six hairy/enhancer of split-related (her/hes) genes, five of which oscillate due to negative autofeedback. The nonoscillating gene hes6 forms the hub of a network of 10 Her/Hes protein dimers, which includes 7 DNA-binding dimers and 4 weak or non-DNA-binding dimers. The balance of dimer species is critical for segmentation clock function, and loss-of-function studies suggest that the her genes have both unique and redundant functions within the clock. However, the precise regulatory interactions underlying the negative feedback loop are unknown. Here, we combine quantitative experimental data, in silico modeling, and a global optimization algorithm to identify a gene regulatory network (GRN) designed to fit measured transcriptional responses to gene knockdown. Surprisingly, we find that hes6, the clock gene that does not oscillate, responds to negative feedback. Consistent with prior in silico analyses, we find that variation in transcription, translation, and degradation rates can mediate the gain and loss of oscillatory behavior for genes regulated by negative feedback. Extending our study, we found that transcription of the nonoscillating Fgf pathway gene sef responds to her/hes perturbation similarly to oscillating her genes. These observations suggest a more extensive underlying regulatory similarity between the zebrafish segmentation clock and the mouse and chick segmentation clocks, which exhibit oscillations of her/hes genes as well as numerous other Notch, Fgf, and Wnt pathway genes.

Pairwise agonist scanning-flow cytometry (PAS-FC) measures inside-out signaling and patient-specific response to combinatorial platelet agonists

Understanding the response of cells to multiple stimuli is vital for predicting donor specific responses and better understanding the signaling pathways involved. This is of particular importance in platelets because exposure of phosphatidylserine (PS) occurs upon costimulation but not with a single agonist. Here, we describe a multiplexed pairwise agonist scanning-flow cytometry (PAS-FC) method of measuring platelet inside-out responses to all pairs of six platelet agonists (convulxin, SFLLRN, AYPGKF, ADP, U46619, and PGE(2)) used at their EC(50) concentrations. These agonists allowed exploration of platelet signaling downstream of GPVI, PAR-1, PAR-4, P2Y(1), P2Y(12), TP, and IP receptors. The three-color flow cytometry method simultaneously measured integrin α(IIb)β(3) activation with PAC-1 antibody, P-selectin exposure (via α granule release) with anti-P-selectin, and PS exposure with annexin V. These responses were consistent across a healthy male donor pool. In duplicate measurements with each donor, 4 of the 10 donors had a sufficiently unique 45-parameter (15 pairs × 3 colors) phenotype to self-cluster (P < 0.001). This method has the potential for efficiently scanning for patient specific responses across a broad agonist-receptor space.

Models of signalling networks - what cell biologists can gain from them and give to them

Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

Drug synergy screen and network modeling in dedifferentiated liposarcoma identifies CDK4 and IGF1R as synergistic drug targets

Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depends on the activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies.

Systems biology of platelet-vessel wall interactions

Blood systems biology seeks to quantify outside-in signaling as platelets respond to numerous external stimuli, typically under flow conditions. Platelets can activate via GPVI collagen receptor and numerous G-protein coupled receptors (GPCRs) responsive to ADP, thromboxane, thrombin, and prostacyclin. A bottom-up ODE approach allowed prediction of platelet calcium and phosphoinositides following P2Y₁ activation with ADP, either for a population average or single cell stochastic behavior. The homeostasis assumption (i.e., a resting platelet stays resting until activated) was particularly useful in finding global steady states for these large metabolic networks. Alternatively, a top-down approach involving Pairwise Agonist Scanning (PAS) allowed large data sets of measured calcium mobilization to predict an individual's platelet responses. The data was used to train neural network (NN) models of signaling to predict patient-specific responses to combinatorial stimulation. A kinetic description of platelet signaling then allows prediction of inside-out activation of platelets as they experience the complex biochemical milieu at the site of thrombosis. Multiscale lattice kinetic Monte Carlo (LKMC) utilizes these detailed descriptions of platelet signaling under flow conditions where released soluble species are solved by finite element method and the flow field around the growing thrombus is updated using computational fluid dynamics or lattice Boltzmann method. Since hemodynamic effects are included in a multiscale approach, thrombosis can then be predicted under arterial and venous thrombotic conditions for various anatomical geometries. Such systems biology approaches accommodate the effect of anti-platelet pharmacological intervention where COX1 pathways or ADP signaling are modulated in a patient-specific manner.

Microfluidic assay of platelet deposition on collagen by perfusion of whole blood from healthy individuals taking aspirin

BACKGROUND

Microfluidic devices can create hemodynamic conditions for platelet assays. We validated an 8-channel device in a study of interdonor response to acetylsalicylic acid (ASA, aspirin) with whole blood from 28 healthy individuals.

METHODS

Platelet deposition was assessed before treatment or 24 h after ingestion of 325 mg ASA. Whole blood (plus 100 μmol/L H-d-Phe-Pro-Arg-chloromethylketone to inhibit thrombin) was further treated ex vivo with ASA (0-500 μmol/L) and perfused over fibrillar collagen for 300 s at a venous wall shear rate (200 s(-1)).

RESULTS

Ex vivo ASA addition to blood drawn before aspirin ingestion caused a reduction in platelet deposition [half-maximal inhibitory concentration (IC50) approximately 10-20 μmol/L], especially between 150 and 300 s of perfusion, when secondary aggregation mediated by thromboxane was expected. Twenty-seven of 28 individuals displayed smaller deposits (45% mean reduction; range 10%-90%; P < 0.001) from blood obtained 24 h after ASA ingestion (no ASA added ex vivo). In replicate tests, an R value to score secondary aggregation [deposition rate from 150 to 300 s normalized by rate from 60 to 150 s] showed R < 1 in only 2 of 28 individuals without ASA ingestion, with R > 1 in only 3 of 28 individuals after 500 μmol/L ASA addition ex vivo. At 24 h after ASA ingestion, 21 of 28 individuals displayed poor secondary aggregation (R < 1) without ex vivo ASA addition, whereas the 7 individuals with residual secondary aggregation (R > 1) displayed insensitivity to ex vivo ASA addition. Platelet deposition was not correlated with platelet count. Ex vivo ASA addition caused similar inhibition at venous and arterial wall shear rates.

CONCLUSIONS

Microfluidic devices quantified platelet deposition after ingestion or ex vivo addition of aspirin.

Systems biology of coagulation

Accurate computer simulation of blood function can inform drug target selection, patient-specific dosing, clinical trial design, biomedical device design, as well as the scoring of patient-specific disease risk and severity. These large-scale simulations rely on hundreds of independently measured physical parameters and kinetic rate constants. However, the models can be validated against large-scale, patient-specific laboratory measurements. By validation with high-dimensional data, modeling becomes a powerful tool to predict clinically complex scenarios. Currently, it is possible to accurately predict the clotting rate of plasma or blood in a tube as it is activated with a dose of tissue factor, even as numerous coagulation factors are altered by exogenous attenuation or potentiation. Similarly, the dynamics of platelet activation, as indicated by calcium mobilization or inside-out signaling, can now be numerically simulated with accuracy in cases where platelets are exposed to combinations of agonists. Multiscale models have emerged to combine platelet function and coagulation kinetics into complete physics-based descriptions of thrombosis under flow. Blood flow controls platelet fluxes, delivery and removal of coagulation factors, adhesive bonding, and von Willebrand factor conformation. The field of blood systems biology has now reached a stage that anticipates the inclusion of contact, complement, and fibrinolytic pathways along with models of neutrophil and endothelial activation. Along with '-omics' data sets, such advanced models seek to predict the multifactorial range of healthy responses and diverse bleeding and clotting scenarios, ultimately to understand and improve patient outcomes.

Microfluidics and coagulation biology

The study of blood ex vivo can occur in closed or open systems, with or without flow. Microfluidic devices, which constrain fluids to a small (typically submillimeter) scale, facilitate analysis of platelet function, coagulation biology, cellular biorheology, adhesion dynamics, and pharmacology and, as a result, can be an invaluable tool for clinical diagnostics. An experimental session can accommodate hundreds to thousands of unique clotting, or thrombotic, events. Using microfluidics, thrombotic events can be studied on defined surfaces of biopolymers, matrix proteins, and tissue factor, under constant flow rate or constant pressure drop conditions. Distinct shear rates can be generated on a device using a single perfusion pump. Microfluidics facilitated both the determination of intraluminal thrombus permeability and the discovery that platelet contractility can be activated by a sudden decrease in flow. Microfluidic devices are ideal for multicolor imaging of platelets, fibrin, and phosphatidylserine and provide a human blood analog to mouse injury models. Overall, microfluidic advances offer many opportunities for research, drug testing under relevant hemodynamic conditions, and clinical diagnostics.

Encoding and decoding cellular information through signaling dynamics

A growing number of studies are revealing that cells can send and receive information by controlling the temporal behavior (dynamics) of their signaling molecules. In this Review, we discuss what is known about the dynamics of various signaling networks and their role in controlling cellular responses. We identify general principles that are emerging in the field, focusing specifically on how the identity and quantity of a stimulus is encoded in temporal patterns, how signaling dynamics influence cellular outcomes, and how specific dynamical patterns are both shaped and interpreted by the structure of molecular networks. We conclude by discussing potential functional roles for transmitting cellular information through the dynamics of signaling molecules and possible applications for the treatment of disease.

Brodde M. State of the art in platelet function testing

Platelets perform many functions in hemostasis but also in other areas of physiology and pathology. Therefore, it is obvious that many different function tests have been developed, each one conceived and standardized for a special purpose. This review will summarize the different fields in which platelet function testing is currently in use; diagnostics of patients with bleeding disorders, monitoring patients' response to anti-platelet therapy, monitoring in transfusion medicine (blood donors, platelet concentrates, and after transfusion), and monitoring in perioperative medicine to predict bleeding tendency. The second part of the review outlines different methods for platelet function testing, spanning bleeding time, and platelet counting as well as determining platelet adhesion, platelet secretion, platelet aggregation, platelet morphology, platelet signal transduction, platelet procoagulant activity, platelet apoptosis, platelet proteomics, and molecular biology.

A review of macroscopic thrombus modeling methods

Hemodynamics applied to mechanobiology offers powerful means to predict thrombosis, and to understand the kinetics of thrombus formation on areas of vascular damage in blood flowing through the human circulatory system. Specifically, the advances in computational processing and the progress in modeling complex biological processes with spatio-temporal multi-scale methods have the potential to shift the way in which cardiovascular diseases are diagnosed and treated. This article systematically surveys the state of the art of macroscopic computational fluid dynamics (CFD) Computational fluid dynamics techniques for modeling thrombus formation, highlighting their strengths and weaknesses. In particular, a comprehensive and systematic revision of the hemodynamics models and methods is given, and the strengths and weaknesses of those employed for studying thrombus formation are highlighted.