In silico identification of specialized secretory-organelle proteins in apicomplexan parasites and in vivo validation in Toxoplasma gondii

Apicomplexan parasites, including the human pathogens Toxoplasma gondii and Plasmodium falciparum, employ specialized secretory organelles (micronemes, rhoptries, dense granules) to invade and survive within host cells. Because molecules secreted from these organelles function at the host/parasite interface, their identification is important for understanding invasion mechanisms, and central to the development of therapeutic strategies. Using a computational approach based on predicted functional domains, we have identified more than 600 candidate secretory organelle proteins in twelve apicomplexan parasites. Expression in transgenic T. gondii of eight proteins identified in silico confirms that all enter into the secretory pathway, and seven target to apical organelles associated with invasion. An in silico approach intended to identify possible host interacting proteins yields a dataset enriched in secretory/transmembrane proteins, including most of the antigens known to be engaged by apicomplexan parasites during infection. These domain pattern and projected interactome approaches significantly expand the repertoire of proteins that may be involved in host parasite interactions.     

The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems

Multiscale computational modeling of drug delivery systems (DDS) is poised to provide predictive capabilities for the rational design of targeted drug delivery systems, including multi-functional nanoparticles. Realistic, mechanistic models can provide a framework for understanding the fundamental physico-chemical interactions between drug, delivery system, and patient. Multiscale computational modeling, however, is in its infancy even for conventional drug delivery. The wide range of emerging nanotechnology systems for targeted delivery further increases the need for reliable in silico predictions. This review will present existing computational approaches at different scales in the design of traditional oral drug delivery systems. Subsequently, a multiscale framework for integrating continuum, stochastic, and computational chemistry models will be proposed and a case study will be presented for conventional DDS. The extension of this framework to emerging nanotechnology delivery systems will be discussed along with future directions. While oral delivery is the focus of the review, the outlined computational approaches can be applied to other drug delivery systems as well.     

Understanding structural/functional properties of immunoconjugates for cancer therapy by computational approaches

Monoclonal antibodies coupled to highly toxic molecules (immunoconjugates) are currently being developed for cancer therapy. We have used an in silico procedure for evaluating some physicochemical properties of two tumor-targeting anti-HER2 immunoconjugates: (a) the single-chain antibody scFv(FRP5) linked to a bacterial toxin, that has been recently progressed to phase I clinical trial in human cancer; (b) the putative molecule formed by the intrinsically stable scFv(800E6), which has been proposed as toxin carrier to cancer cells in human therapy, joined to the same toxin of (a). Structural models of the immunoconjugates have been built by homology modeling and assessed by molecular dynamics simulations. The trajectories have been analyzed to extract some biochemical properties and to assess the potential effects of the toxin on the structure and dynamics of the anti-HER2 antibodies. The results of the computational approach indicate that the antibodies maintain their correct folding even in presence of the toxin, whereas a certain stiffness in correspondence of some structural regions is observed. Furthermore, the toxin does not seem to affect the antibody solubility, whereas it enhances the structural stability. The proposed computational approach represent a promising tool for analyzing some physicochemical properties of immunoconjugates and for predicting the effects of the linked toxin on structure, dynamics, and functionality of the antibodies.     

ECHO,the executable CHOndrocyte: A computational model to study articular chondrocytes in health and disease

Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues.We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1β (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1β induced inflammation in human primary chondrocytes.     

Multiscale agent-based cancer modeling

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.     

On an infrastructure to support sharing and aggregating pre- and post-publication systems biology research data

The move towards in silico experimentation has resulted in the use of computational models, in addition to traditional experimental models, to generate the raw data that is analysed and published as research findings. This change requires new methods to be introduced to facilitate independent validation of the underlying models and the reported results. The promotion of co-operative research has the potential to help to both validate results and explore wider problem areas. In this paper we leverage and extend two existing software frameworks to develop an infrastructure that has the potential to both promote the sharing of data between researchers pre-publication and enable access to the data for interested parties post-publication. The pre-publication sharing of data would enable larger problem spaces to be explored by distributed research groups; enabling access to the data post-publication would allow reviewers and the wider community to independently verify the published results which would, in the longer term, help to increase confidence in published results. The framework is used to perform reproducible and numerically validated individual-based computational experiments into the onset of colorectal cancer. Existing results are verified and new insights into the top-down versus bottom-up hypothesis of colorectal crypt invasion are given.     

An integrated computational workflow for efficient and quantitative modeling of renin inhibitors

A new integrated computational workflow that couples the strength of the molecular overlay methods to achieve rapid and automated alignments along with 3D-QSAR techniques like CoMFA and CoMSIA for quantitative binding affinity prediction is presented. The results obtained from such techniques are compared with rule-based Topomer CoMFA method, where possible. The developed 3D-QSAR models were prospectively used to predict the affinities of new compounds designed through R-group deconvolution starting from the core chemical scaffold and subsequent virtual combinatorial library enumeration. The general applicability of the seamless in silico modeling workflow is demonstrated using several datasets reported for small molecule inhibitors of renin.     

Establishment and validation of computational model for MT1-MMP dependent ECM degradation and intervention strategies

MT1-MMP is a potent invasion-promoting membrane protease employed by aggressive cancer cells. MT1-MMP localizes preferentially at membrane protrusions called invadopodia where it plays a central role in degradation of the surrounding extracellular matrix (ECM). Previous reports suggested a role for a continuous supply of MT1-MMP in ECM degradation. However, the turnover rate of MT1-MMP and the extent to which the turnover contributes to the ECM degradation at invadopodia have not been clarified. To approach this problem, we first performed FRAP (Fluorescence Recovery after Photobleaching) experiments with fluorescence-tagged MT1-MMP focusing on a single invadopodium and found very rapid recovery in FRAP signals, approximated by double-exponential plots with time constants of 26 s and 259 s. The recovery depended primarily on vesicle transport, but negligibly on lateral diffusion. Next we constructed a computational model employing the observed kinetics of the FRAP experiments. The simulations successfully reproduced our FRAP experiments. Next we inhibited the vesicle transport both experimentally, and in simulation. Addition of drugs inhibiting vesicle transport blocked ECM degradation experimentally, and the simulation showed no appreciable ECM degradation under conditions inhibiting vesicle transport. In addition, the degree of the reduction in ECM degradation depended on the degree of the reduction in the MT1-MMP turnover. Thus, our experiments and simulations have established the role of the rapid turnover of MT1-MMP in ECM degradation at invadopodia. Furthermore, our simulations suggested synergetic contributions of proteolytic activity and the MT1-MMP turnover to ECM degradation because there was a nonlinear and marked reduction in ECM degradation if both factors were reduced simultaneously. Thus our computational model provides a new in silico tool to design and evaluate intervention strategies in cancer cell invasion.     

An overview of the diverse roles of G-protein coupled receptors (GPCRs) in the pathophysiology of various human diseases

G-protein coupled receptors (GPCRs) modulate diverse cellular responses to the majority of neurotransmitters and hormones within the human body. They exhibit much structural and functional diversity, and are responsive to a plethora of endogenous (biogenic amines, cations, lipids, peptides, and glycoproteins) and exogenous (therapeutic drugs, photons, tastants, and odorants) ligands and stimuli. Due to the key roles of GPCRs in tissue/cell physiology and homeostasis, signaling pathways associated with GPCRs are implicated in the pathophysiology of various diseases, ranging from metabolic, immunological, and neurodegenerative disorders, to cancer and infectious diseases. Approximately 40% of clinically approved drugs mediate their effects by modulating GPCR signaling pathways, which makes them attractive targets for drug screening and discovery. The pace of discovery of new GPCR-based drugs has recently accelerated due to rapid advancements in high-resolution structure determination, high-throughput screening technology and in silico computational modeling of GPCR binding interaction with potential drug molecules. This review aims to provide an overview of the diverse roles of GPCRs in the pathophysiology of various diseases that are the major focus of biopharmaceutical research as potential drug targets.     

Oligonucleotide fingerprint identification for microarray-based pathogen diagnostic assays

MOTIVATION: Advances in DNA microarray technology and computational methods have unlocked new opportunities to identify 'DNA fingerprints', i.e. oligonucleotide sequences that uniquely identify a specific genome. We present an integrated approach for the computational identification of DNA fingerprints for design of microarray-based pathogen diagnostic assays. We provide a quantifiable definition of a DNA fingerprint stated both from a computational as well as an experimental point of view, and the analytical proof that all in silico fingerprints satisfying the stated definition are found using our approach. RESULTS: The presented computational approach is implemented in an integrated high-performance computing (HPC) software tool for oligonucleotide fingerprint identification termed TOFI. We employed TOFI to identify in silico DNA fingerprints for several bacteria and plasmid sequences, which were then experimentally evaluated as potential probes for microarray-based diagnostic assays. Results and analysis of approximately 150 in silico DNA fingerprints for Yersinia pestis and 250 fingerprints for Francisella tularensis are presented. AVAILABILITY: The implemented algorithm is available upon request.     

Mathematical and computational modeling in biology at multiple scales

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

Exploring c-Met kinase flexibility by sampling and clustering its conformational space

It is now widely recognized that the flexibility of both partners has to be considered in molecular docking studies. However, the question how to handle the best the huge computational complexity of exploring the protein binding site landscape is still a matter of debate. Here we investigate the flexibility of c-Met kinase as a test case for comparing several simulation methods. The c-Met kinase catalytic site is an interesting target for anticancer drug design. In particular, it harbors an unusual plasticity compared with other kinases ATP binding sites. Exploiting this feature may eventually lead to the discovery of new anticancer agents with exquisite specificity. We present in this article an extensive investigation of c-Met kinase conformational space using large-scale computational simulations in order to extend the knowledge already gathered from available X-ray structures. In the process, we compare the relevance of different strategies for modeling and injecting receptor flexibility information into early stage in silico structure-based drug discovery pipeline. The results presented here are currently being exploited in on-going virtual screening investigations on c-Met.     

A metabolic network approach for the identification and prioritization of antimicrobial drug targets

For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.     

LncRNA TUSC8 inhibits the invasion and migration of cervical cancer cells via miR‐641/PTEN axis

Cervical cancer (CC) is the fourth leading cause of cancer‐related death in women worldwide. There is an urgent need to find novel targets for the treatment of CC. Recently, microRNA have emerged as critical factors in tumorigenesis. In this study, we aimed to investigate the mechanism of miR‐641 on the migration and invasion of CC cells. In silico analysis revealed putative interaction between miR‐641 and phosphatase and tensin homolog (PTEN) RNA/lncRNA tumor suppressor candidate 8 (TUSC8). Hence we evaluated the expression of TUSC8, miR‐641, and PTEN. We found that the expressions of TUSC8 and PTEN were decreased in CC tissues, whereas miR‐641 expression was increased. Inhibition of miR‐641 suppressed the migration and invasion of Hela cells. In addition, TUSC8 and PTEN were upstream and downstream target molecule of miR‐641, respectively. Overexpression of TUSC8 promoted PTEN expression, and suppressed the invasion and migration of Hela cells, whereas miR‐641 mimic treatment changed the effects. These results demonstrated that overexpression of TUSC8 could inhibit the invasion and migration of CC cells by upregulating PTEN via miR‐641.     

Cancer markers: Integratively annotated classification

Translational cancer genomics research aims to ensure that experimental knowledge is subject to computational analysis, and integrated with a variety of records from omics and clinical sources. The data retrieval from such sources is not trivial, due to their redundancy and heterogeneity, and the presence of false evidence. In silico marker identification, therefore, remains a complex task that is mainly motivated by the impact that target identification from the elucidation of gene co-expression dynamics and regulation mechanisms, combined with the discovery of genotype–phenotype associations, may have for clinical validation. Based on the reuse of publicly available gene expression data, our aim is to propose cancer marker classification by integrating the prediction power of multiple annotation sources. In particular, with reference to the functional annotation for colorectal markers, we indicate a classification of markers into diagnostic and prognostic classes combined with susceptibility and risk factors.     

Cellular mechanobiology and cancer metastasis

The primary cause of cancer treatment failure is invasion and metastasis, and invading tumor cells utilize many of the motility patterns that have been documented for normal morphogenesis. Recently, the role of mechanical forces in guiding various tissue and cell movements in embryonic development has been systematically analyzed with new experimental and computational methods. The tissue and cellular mechanobiology approach also holds promise for increasing the understanding of tumor invasion. In fact, the mechanical stiffness of tumors has correlated with invasiveness, and manipulation of extracellular matrix (ECM) stiffness in vitro has suppressed the cancer phenotype. Several important signaling molecules reside on the cytoskeleton, which is affected by external stress imparted by the ECM, and deformation of the nucleus can trigger the activation of certain genes. All these observations suggest that a synthesis of the biology of cancer cell invasion and cellular mechanobiology may offer new targets for the treatment of malignant disease. Accordingly, sensitive and relevant in vivo models and methods to study cancer mechanobiology are needed.     

Site-directed mutagenesis and PBAN activation of the Helicoverpa zea PBAN-receptor

Pheromone biosynthesis-activating neuropeptide (PBAN) and pyrokinins belong to a family of insect peptide hormones that have a common FXPRLamide C-terminal ending. The G-protein-coupled receptors (GPCRs) for this peptide family were first identified from a moth and Drosophila with sequence similarity to neuromedin U receptors from vertebrates. We have characterized the PBAN-receptor (PBAN-R or PR) active binding domains using chimeric GPCRs and proposed that extracellular loop 3 is critical for ligand selection. Here, we characterized the 3rd extracellular domain of PBAN-R through site-directed point mutations. Results are discussed in context of the structural features required for receptor activation using receptor activation experiments and in silico computational modeling. This research will help in characterizing these receptors towards a goal of finding agonists and/or antagonists for PBAN/pyrokinin receptors.     

The role of the microenvironment in tumor growth and invasion

Mathematical modeling and computational analysis are essential for understanding the dynamics of the complex gene networks that control normal development and homeostasis, and can help to understand how circumvention of that control leads to abnormal outcomes such as cancer. Our objectives here are to discuss the different mechanisms by which the local biochemical and mechanical microenvironment, which is comprised of various signaling molecules, cell types and the extracellular matrix (ECM), affects the progression of potentially-cancerous cells, and to present new results on two aspects of these effects. We first deal with the major processes involved in the progression from a normal cell to a cancerous cell at a level accessible to a general scientific readership, and we then outline a number of mathematical and computational issues that arise in cancer modeling. In Section 2 we present results from a model that deals with the effects of the mechanical properties of the environment on tumor growth, and in Section 3 we report results from a model of the signaling pathways and the tumor microenvironment (TME), and how their interactions affect the development of breast cancer. The results emphasize anew the complexities of the interactions within the TME and their effect on tumor growth, and show that tumor progression is not solely determined by the presence of a clone of mutated immortal cells, but rather that it can be ‘community-controlled’.     

Teaching cardiac electrophysiology modeling to undergraduate students: laboratory exercises and GPU programming for the study of arrhythmias and spiral wave dynamics

As part of a 3-wk intersession workshop funded by a National Science Foundation Expeditions in Computing award, 15 undergraduate students from the City University of New York(1) collaborated on a study aimed at characterizing the voltage dynamics and arrhythmogenic behavior of cardiac cells for a broad range of physiologically relevant conditions using an in silico model. The primary goal of the workshop was to cultivate student interest in computational modeling and analysis of complex systems by introducing them through lectures and laboratory activities to current research in cardiac modeling and by engaging them in a hands-on research experience. The success of the workshop lay in the exposure of the students to active researchers and experts in their fields, the use of hands-on activities to communicate important concepts, active engagement of the students in research, and explanations of the significance of results as the students generated them. The workshop content addressed how spiral waves of electrical activity are initiated in the heart and how different parameter values affect the dynamics of these reentrant waves. Spiral waves are clinically associated with tachycardia, when the waves remain stable, and with fibrillation, when the waves exhibit breakup. All in silico experiments were conducted by simulating a mathematical model of cardiac cells on graphics processing units instead of the standard central processing units of desktop computers. This approach decreased the run time for each simulation to almost real time, thereby allowing the students to quickly analyze and characterize the simulated arrhythmias. Results from these simulations, as well as some of the background and methodology taught during the workshop, is presented in this article along with the programming code and the explanations of simulation results in an effort to allow other teachers and students to perform their own demonstrations, simulations, and studies.