Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation

Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro

Here, we introduce the use of ANI-ML potentials as a rescoring function in the host-guest interaction in molecular docking. Our results show that the "docking power" of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (Mpro). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.

Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis

Introduction

Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities.

Methods

Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications.

Results

Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation.

Discussion

These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).

Platelet-Rich Plasma Lysate-Incorporating Gelatin Hydrogel as a Scaffold for Bone Reconstruction

In implant dentistry, large vertical and horizontal alveolar ridge deficiencies in mandibular and maxillary bone are challenges that clinicians continue to face. One of the limitations of porous blocks for reconstruction of bone in large defects in the oral cavity, and in the musculoskeletal system, is that fibrin clot does not adequately fill the interior pores and does not persist long enough to accommodate cell migration into the center of the block. The objective of our work was to develop a gelatin-based gel incorporating platelet-rich plasma (PRP) lysate, to mimic the role that a blood clot would normally play to attract and accommodate the migration of host osteoprogenitor and endothelial cells into the scaffold, thereby facilitating bone reconstruction. A conjugate of gelatin (Gtn) and hydroxyphenyl propionic acid (HPA), an amino-acid-like molecule, was commended for this application because of its ability to undergo enzyme-mediated covalent cross-linking to form a hydrogel in vivo, after being injected as a liquid. The initiation and propagation of cross-linking were under the control of horseradish peroxidase and hydrogen peroxide, respectively. The objectives of this in vitro study were directed toward evaluating: (1) the migration of rat mesenchymal stem cells (MSCs) into Gtn–HPA gel under the influence of rat PRP lysate or recombinant platelet-derived growth factor (PDGF)-BB incorporated into the gel; (2) the differentiation of MSCs, incorporated into the gel, into osteogenic cells under the influence of PRP lysate and PDGF-BB; and (3) the release kinetics of PDGF-BB from gels incorporating two formulations of PRP lysate and recombinant PDGF-BB. Results: The number of MSCs migrating into the hydrogel was significantly (3-fold) higher in the hydrogel group incorporating PRP lysate compared to the PDGF-BB and the blank gel control groups. For the differentiation/osteogenesis assay, the osteocalcin-positive cell area percentage was significantly higher in both the gel/PRP and gel/PDGF-BB groups, compared to the two control groups: cells in the blank gels grown in cell expansion medium and in osteogenic medium. Results of the ELISA release assay indicated that Gtn–HPA acted as an effective delivery vehicle for the sustained release of PDGF-BB from two different PRP lysate batches, with about 60% of the original PDGF-BB amount in the two groups remaining in the gel at 28 days. Conclusions: Gtn–HPA accommodates MSC migration. PRP-lysate-incorporating hydrogels chemoattract increased MSC migration into the Gtn–HPA compared to the blank gel. PRP-lysate- and the PDGF-BB-incorporating gels stimulate osteogenic differentiation of the MSCs. The release of the growth factors from Gtn–HPA containing PRP lysate can extend over the period of time (weeks) necessary for bone reconstruction. The findings demonstrate that Gtn–HPA can serve as both a scaffold for cell migration and a delivery vehicle that allows sustained and controlled release of the incorporated therapeutic agent over extended periods of time. These findings commend Gtn–HPA incorporating PRP lysate for infusion into porous calcium phosphate blocks for vertical and horizontal ridge reconstruction, and for other musculoskeletal applications.

In silico optimization of heparin microislands in microporous annealed particle (MAP) hydrogel for endothelial cell migration

Biomaterials capable of generating growth factor gradients have shown success in guiding tissue regeneration, as growth factor gradients are a physiologic driver of cell migration. Of particular importance, a focus on promoting endothelial cell migration is vital to angiogenesis and new tissue formation. Microporous Annealed Particle (MAP) scaffolds represent a unique niche in the field of regenerative biomaterials research as an injectable biomaterial with an open porosity that allows cells to freely migrate independent of material degradation. Recently, we have used the MAP platform to heterogeneously include spatially isolated heparin-modified microgels (heparin microislands) which can sequester growth factors and guide cell migration. In in vitro sprouting angiogenesis assays, we observed a parabolic relationship between the percentage of heparin microislands and cell migration, where 10% heparin microislands had more endothelial cell migration compared to 1% and 100%. Due to the low number of heparin microisland ratios tested, we hypothesize the spacing between microgels can be further optimized. Rather than use purely empirical methods, which are both expensive and time intensive, we believe this challenge represents an opportunity to use computational modeling. Here we present the first agent-based model of a MAP scaffold to optimize the ratio of heparin microislands. Specifically, we develop a two-dimensional model in Hybrid Automata Library (HAL) of endothelial cell migration within the unique MAP scaffold geometry. Finally, we present how our model can accurately predict cell migration trends in vitro, and these studies provide insight on how computational modeling can be used to design particle-based biomaterials. STATEMENT OF SIGNIFICANCE: : While the combination of experimental and computational approaches is increasingly being used to gain a better understanding of cellular processes, their combination in biomaterials development has been relatively limited. Heparin microislands are spatially isolated heparin microgels; when located within a microporous annealed particle (MAP) scaffold, they can sequester and release growth factors. Importantly, we present the first agent-based model of MAP scaffolds to optimize the ratio of heparin microislands within the scaffold to promote endothelial cell migration. We demonstrate this model can accurately predict trends in vitro, thus opening a new avenue of research to aid in the design of MAP scaffolds.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Data Mining Meets Machine Learning: A Novel ANN-based Multi-Body Interaction Docking Scoring Function (MBI-Score) based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-Ligand Complexes

Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. It is a simple machine-learning-based statistical function that employs frequent geometric and chemical patterns of interacting atoms at protein-ligand interfaces. The patterns are derived by mining interfaces of "native" protein-ligand complexes. Each interface is represented by a graph where nodes are atoms and edges connect protein-ligand interfacial atoms located within certain cutoff distance of each other. Applying frequent subgraph mining to these interfaces provides "native" frequent patterns of interacting atoms. Subsequently, given a pose for a protein-ligand complex of interest, the pose-scoring function (the information-processing unit or neuron) calculates the degree of matching between the interaction patterns present at the pose's interface and the native frequent patterns. The pose-scoring function takes into account the frequency of occurrence of the matching native patterns, the size of the match, and the degree of geometrical similarity between pose-specific and matching native frequent patterns. This novel "multi-body interaction" pose-scoring function (MBI-Score) was validated using two databases, PDBbind and Astex-85, and it outperformed seven commonly used commercial scoring functions. MBI-Score is available at www.khashanlab.org/mbi-score.

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model

MOTIVATION

The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway.

RESULTS

We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway.

AVAILABILITY AND IMPLEMENTATION

We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CGIDLA:Developing the Web Server for CpG Island Related Density and LAUPs (Lineage-Associated Underrepresented Permutations) Study

It is well known that CpG island plays an important role in gene methylation. Since CpG island is closely related to human genetic characteristics such as TATA-box, tissue expression specificity, and LAUPs (Lineage-associated Underrepresented Permutations), it is important to investigate the sequence specificity of CpG island as well as the potential genetic characteristics related to CpG island to further understand the methylation related regulation mechanism. Therefore, this study develops such an online service website for CpG island related density and LAUPs analysis (CGIDLA, www.combio-lezhang.online/cgidla/index.html), that not only can investigate the relationship among the CpG island density, TATA-box feature, and expression breadth of human genes, but also deposit LAUPs of 32 representative species to help molecular biologists investigate the relationship between CpG island and LUAPs. Moreover, CGIDLA provides the source code download service and the related LAUPs counting functions.

Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis.

RESULTS

Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma.

CONCLUSIONS

We summarize the whole process of the experiment and discuss how to expand our experiment in the future.

Review of Artificial Intelligence Applications and Algorithms for Brain Organoid Research

The human brain organoid is a miniature three-dimensional tissue culture that can simulate the structure and function of the brain in an in vitro culture environment. Although we consider that human brain organoids could be used to understand brain development and diseases, experimental models of human brain organoids are so highly variable that we apply artificial intelligence (AI) techniques to investigate the development mechanism of the human brain. Therefore, this study briefly reviewed commonly used AI applications for human brain organoid-magnetic resonance imaging, electroencephalography, and gene editing techniques, as well as related AI algorithms. Finally, we discussed the limitations, challenges, and future study direction of AI-based technology for human brain organoids.

Modeling osteoinduction in titanium bone scaffold with a representative channel structure

Optimizing scaffold architecture for perfect osteointegration depends on good understanding of bone ingrowth in the porous space of implants. This study developed an immunoregulatory agent-based model to discover the osteoinduction mechanism in porous scaffolds. Immunoreaction, macrophage polarization, and the corresponding growth factors were combined in the model, and all played critical roles in recruiting osteogenic cells that migrated into the scaffolds. Angiogenesis was also considered in this model. The bone ingrowth predicted by the model coincides with results from published in vivo experiments. Simulation results suggested that the pore architecture affected the diffusion process of chemotactic factors in the scaffolds, subsequently influencing the complex reactions of diverse cells and the osteoinduction location. In flexural pore spaces, bone formation spread from the periphery into the center of scaffolds due to larger M2 phenotype macrophage populations colonizing boundary regions and the distribution of corresponding growth factors concentration. In straight channels, osteogenic cells migrated further inward and osteoinduction initiated in deeper position as a result of the deeper distribution of osteogenic cytokines concentration field.

Exploring the dynamics and interplay of human papillomavirus and cervical tumorigenesis by integrating biological data into a mathematical model

Background

Cervical cancer is the fourth most common tumor in women worldwide, mostly resulting from high-risk human papillomavirus (HR-HPV) with persistent infection.

Results

The present discoveries are comprised of the following: (i) A total of 16.64% of the individuals were positive for HR-HPV infection, with 13.04% having a single HR-HPV type and 3.60% having multiple HR-HPV types. (ii) Cluster analysis showed that the infection rate trends of HPV31 and HPV33 in all infections as well as HPV33 and HPV35 in single infections in precancerous stages were very similar. (iii) The single/multiple infection proportions of HR-HPV demonstrated a trend that the multiple infections rates of HR-HPV increased as the disease developed.

Conclusions

The HR-HPV prevalence in outpatients was 16.64%, and the predominant HR-HPV types in the study were HPV52, HPV58 and HPV16. HR-HPV subtypes with common biological properties had similar infection rate trends in precancerous stages. Especially, as the disease development of precancer evolved, defense against HPV infection broke, meanwhile, the potential of more HPV infection increased, which resulted in increase of multiple infections of HPV.

In Situ Biological Transmutation of Catalytic Lactic Acid Waste into Calcium Lactate in a Readily Processable Three-Dimensional Fibrillar Structure for Bone Tissue Engineering

A bioinspired three-dimensional (3D) fibrous structure possesses biomimicry, valuable functionality, and performance to scaffolding in tissue engineering. In particular, an electrospun fibrous mesh has been studied as a scaffold material in various tissue regeneration applications. We produced a low-density 3D polycaprolactone/lactic acid (LA) fibrous mesh (3D-PCLS) via the novel lactic-assisted 3D electrospinning technique exploiting the catalytic properties of LA as we reported previously. In the study, we demonstrated a strategy of recycling the LA component to synthesize the osteoinductive biomolecules in situ, calcium lactate (CaL), thereby forming a 3D bioactive PCL/CaL fibrous scaffold (3D-SCaL) for bone tissue engineering. The fiber morphology of 3D-PCLS and its packing degree could have been tailored by modifying the spinning solution and the collector design. 3D-SCaL demonstrated successful conversion of CaL from LA and exhibited the significantly enhanced biomineralization capacity, cell infiltration and proliferation rate, and osteoblastic differentiation in vitro with two different cell lines, MC3T3-e1 and bone marrow stem cells. In conclusion, 3D-SCaL proves to be a highly practical and accessible strategy using a variety of polymers to produce 3D fibers as a potential candidate for future regenerative medicine and tissue engineering applications.

Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

CpG-island-based annotation and analysis of human housekeeping genes

By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CpG islands (CGIs), their methylation states, CpG spacing and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be explained at individual gene and gene-family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI) and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA box on HK genes, remains to be elucidated. First, this study introduces such a problem-solving protocol for human-genome annotation, which is based on a combination of GTEx, JBLA and Gene Ontology (GO) analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; the HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic based on GO enrichment analysis. Finally, we demonstrate that Hadoop MapReduce-based MR-JBLA algorithm is more efficient than the original JBLA in k-mer counting and CGI-associated gene analysis.

Comprehensively benchmarking applications for detecting copy number variation

Motivation: Recently, copy number variation (CNV) has gained considerable interest as a type of genomic variation that plays an important role in complex phenotypes and disease susceptibility. Since a number of CNV detection methods have recently been developed, it is necessary to help investigators choose suitable methods for CNV detection depending on their objectives. For this reason, this study compared ten commonly used CNV detection applications, including CNVnator, ReadDepth, RDXplorer, LUMPY and Control-FREEC, benchmarking the applications by sensitivity, specificity and computational demands. Taking the DGV gold standard variants as a standard dataset, we evaluated the ten applications with real sequencing data at sequencing depths from 5X to 50X. Among the ten methods benchmarked, LUMPY performs the best for both high sensitivity and specificity at each sequencing depth. For the purpose of high specificity, Canvas is also a good choice. If high sensitivity is preferred, CNVnator and RDXplorer are better choices. Additionally, CNVnator and GROM-RD perform well for low-depth sequencing data. Our results provide a comprehensive performance evaluation for these selected CNV detection methods and facilitate future development and improvement in CNV prediction methods.

As an important type of genomic structural variation, CNVs are associated with complex phenotypes because they change the number of copies of genes in cells, affecting coding sequences and playing an important role in the susceptibility or resistance to human diseases. To identify CNVs, several experimental methods have been developed, but their resolution is very low, and the detection of short CNVs presents a bottleneck. In recent years, the advancement of high-throughput sequencing techniques has made it possible to precisely detect CNVs, especially short ones. Many CNV detection applications were developed based on the availability of high-throughput sequencing data. Due to different CNV detection algorithms, the CNVs identified by different applications vary greatly. Therefore, it is necessary to help investigators choose suitable applications for CNV detection depending upon their objectives. For this reason, we not only compared ten commonly used CNV detection applications but also benchmarked the applications by sensitivity, specificity and computational demands. Our results show that the sequencing depth can strongly affect CNV detection. Among the ten applications benchmarked, LUMPY performs best for both high sensitivity and specificity for each sequencing depth. We also give recommended applications for specific purposes, for example, CNVnator and RDXplorer for high sensitivity and CNVnator and GROM-RD for low-depth sequencing data.

Computed tomography angiography-based analysis of high-risk intracerebral haemorrhage patients by employing a mathematical model

Background

Haemorrhagic stroke accounts for approximately 31.52% of all stroke cases, and the most common origin is hypertension. However, little is known about the method to identify high-risk populations of hypertensive intracerebral haemorrhage.

Results

The results showed that the angle between the middle cerebral artery and the internal carotid artery (AMIC), the distance between the beginning of the median artery and superior trunk (DMS), and the density (CT value) of the lenticulostriate artery (CTL) were statistically significant enough to cause intracerebral haemorrhage. In addition, we chose these three potential features for the ensemble learning classification model. Our developed ensemble-learning method outperforms not only previous work but also three other classic classification methods based on accuracy measurements.

Conclusions

The developed mathematical model in the present study is efficient in predicting the probability of intracerebral haemorrhage.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2741-5) contains supplementary material, which is available to authorized users.

An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking

Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.

Developing an Agent-Based Drug Model to Investigate the Synergistic Effects of Drug Combinations

The growth and survival of cancer cells are greatly related to their surrounding microenvironment. To understand the regulation under the impact of anti-cancer drugs and their synergistic effects, we have developed a multiscale agent-based model that can investigate the synergistic effects of drug combinations with three innovations. First, it explores the synergistic effects of drug combinations in a huge dose combinational space at the cell line level. Second, it can simulate the interaction between cells and their microenvironment. Third, it employs both local and global optimization algorithms to train the key parameters and validate the predictive power of the model by using experimental data. The research results indicate that our multicellular system can not only describe the interactions between the microenvironment and cells in detail, but also predict the synergistic effects of drug combinations.

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

EZH2-, CHD4-, and IDH-linked epigenetic perturbation and its association with survival in glioma patients

Glioma is a complex disease with limited treatment options. Recent advances have identified isocitrate dehydrogenase (IDH) mutations in up to 80% lower grade gliomas (LGG) and in 76% secondary glioblastomas (GBM). IDH mutations are also seen in 10%-20% of acute myeloid leukemia (AML). In AML, it was determined that mutations of IDH and other genes involving epigenetic regulations are early events, emerging in the pre-leukemic stem cells (pre-LSCs) stage, whereas mutations in genes propagating oncogenic signal are late events in leukemia. IDH mutations are also early events in glioma, occurring before TP53 mutation, 1p/19q deletion, etc. Despite these advances in glioma research, studies into other molecular alterations have lagged considerably. In this study, we analyzed currently available databases. We identified EZH2, KMT2C, and CHD4 as important genes in glioma in addition to the known gene IDH1/2. We also showed that genomic alterations of PIK3CA, CDKN2A, CDK4, FIP1L1, or FUBP1 collaborate with IDH mutations to negatively affect patients' survival in LGG. In LGG patients with TP53 mutations or IDH1/2 mutations, additional genomic alterations of EZH2, KMC2C, and CHD4 individually or in combination were associated with a markedly decreased disease-free survival than patients without such alterations. Alterations of EZH2, KMT2C, and CHD4 at genetic level or protein level could perturb epigenetic program, leading to malignant transformation in glioma. By reviewing current literature on both AML and glioma and performing bioinformatics analysis on available datasets, we developed a hypothetical model on the tumorigenesis from premalignant stem cells to glioma.

Neural Vascular Mechanism for the Cerebral Blood Flow Autoregulation after Hemorrhagic Stroke

During the initial stages of hemorrhagic stroke, including intracerebral hemorrhage and subarachnoid hemorrhage, the reflex mechanisms are activated to protect cerebral perfusion, but secondary dysfunction of cerebral flow autoregulation will eventually reduce global cerebral blood flow and the delivery of metabolic substrates, leading to generalized cerebral ischemia, hypoxia, and ultimately, neuronal cell death. Cerebral blood flow is controlled by various regulatory mechanisms, including prevailing arterial pressure, intracranial pressure, arterial blood gases, neural activity, and metabolic demand. Evoked by the concept of vascular neural network, the unveiled neural vascular mechanism gains more and more attentions. Astrocyte, neuron, pericyte, endothelium, and so forth are formed as a communicate network to regulate with each other as well as the cerebral blood flow. However, the signaling molecules responsible for this communication between these new players and blood vessels are yet to be definitively confirmed. Recent evidence suggested the pivotal role of transcriptional mechanism, including but not limited to miRNA, lncRNA, exosome, and so forth, for the cerebral blood flow autoregulation. In the present review, we sought to summarize the hemodynamic changes and underline neural vascular mechanism for cerebral blood flow autoregulation in stroke-prone state and after hemorrhagic stroke and hopefully provide more systematic and innovative research interests for the pathophysiology and therapeutic strategies of hemorrhagic stroke.

A systems biology approach to studying the molecular mechanisms of osteoblastic differentiation under cytokine combination treatment

Recent studies revealed that sequential release of bone morphogenetic protein 2 and insulin-like growth factor 1 plays an important role in osteogenic process, suggesting that cytokines bone morphogenetic protein 2 and insulin-like growth factor 1 function in a time-dependent manner. However, the specific molecular mechanisms underlying these observations remained elusive, impeding the elaborate manipulation of cytokine sequential delivery in tissue repair. The aim of this study was to identify the key relevant pathways and processes regulating bone morphogenetic protein 2/insulin-like growth factor 1-mediated osteoblastic differentiation. Based on the microarray and proteomics data, and differentiation/growth status of mouse bone marrow stromal cells, we constructed a multiscale systems model to simulate the bone marrow stromal cells lineage commitment and bone morphogenetic protein 2 and insulin-like growth factor 1-regulated signaling dynamics. The accuracy of our model was validated using a set of independent experimental data. Our study reveals that, treatment of bone marrow stromal cells with bone morphogenetic protein 2 prior to insulin-like growth factor 1 led to the activation of transcription factor Runx2 through TAK1-p38 MAPK and SMAD1/5 signaling pathways and initiated the lineage commitment of bone marrow stromal cells. Delivery of insulin-like growth factor 1 four days after bone morphogenetic protein 2 treatment optimally activated transcription factors osterix and β-catenin through ERK and AKT pathways, which are critical to preosteoblast maturity. Our systems biology approach is expected to provide technical and scientific support in optimizing therapeutic scheme to improve osteogenesis/bone regeneration and other essential biological processes.