Randy Heiland

Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model.

Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.

MutDB services: interactive structural analysis of mutation data

Non-synonymous single nucleotide polymorphisms (SNPs) and mutations have been associated with human phenotypes and disease. As more and more SNPs are mapped to phenotypes, understanding how these variations affect the function and expression of genes and gene products becomes an important endeavor. We have developed a set of tools to aid in the understanding of how amino acid substitutions affect protein structures. To do this, we have annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, if available. We then developed a novel web interface to this data that allows for visualization of the location of these substitutions. We have also developed a web service interface to the dataset and developed interactive plugins for UCSFs Chimera structural modeling tool and PyMOL that integrate our annotations with these sophisticated structural visualization and modeling tools. The web services portal and plugins can be downloaded from and the web interface is at .

Multicell Simulations of Development and Disease Using the CompuCell3D Simulation Environment

Mathematical modeling and computer simulation have become crucial to biological fields from genomics to ecology. However, multicell, tissue-level simulations of development and disease have lagged behind other areas because they are mathematically more complex and lack easy-to-use software tools that allow building and running in silico experiments without requiring in-depth knowledge of programming. This tutorial introduces Glazier-Graner-Hogeweg (GGH) multicell simulations and CompuCell3D, a simulation framework that allows users to build, test, and run GGH simulations.

Web Service Infrastructure for Chemoinformatics

The vast increase of pertinent information available to drug discovery scientists means that there is a strong demand for tools and techniques for organizing and intelligently mining this information for manageable human consumption. At Indiana University, we have developed an infrastructure of chemoinformatics Web services that simplifies the access to this information and the computational techniques that can be applied to it. In this paper, we describe this infrastructure, give some examples of its use, and then discuss our plans to use it as a platform for chemoinformatics application development in the future.

MutDB: update on development of tools for the biochemical analysis of genetic variation

Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB (http://www.mutdb.org), a tool aiming to aid bioinformatic studies by integrating publicly available databases of human genetic variation with molecular features and clinical phenotype data. MutDB, first developed in 2002, integrates annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, links to scores that predict functional disruption and other useful annotations. Though these functional annotations are mainly focused on nonsynonymous SNPs, some information on other SNP types included in dbSNP is also provided. Additionally, we have developed a new functionality that facilitates KEGG pathway visualization of genes containing SNPs and a SNP query tool for visualizing and exporting sets of SNPs that share selected features based on certain filters.

NLOPredict: Visualization and data analysis software for nonlinear optics

A data analysis and visualization program was developed to assist in the interpretation of second‐order nonlinear optical (NLO) processes, including vibrational sum‐frequency generation and electronically resonant second harmonic generation. A novel diagrammatic approach allows concise visual representations of the resonant NLO molecular response. By mapping the predicted NLO response as a function of molecular orientation, molecular modeling results can be combined with experimental measurements for orientational analysis. A method is developed and implemented to predict the nonlinear optical properties of the amide backbones in complete proteins with known structures. NLOPredict is available for most computer operating systems from http://sda.iu.edu/nlopredict/.

The cell behavior ontology: describing the intrinsic biological behaviors of real and model cells seen as active agents

Motivation: Currently, there are no ontologies capable of describing both the spatial organization of groups of cells and the behaviors of those cells. The lack of a formalized method for describing the spatiality and intrinsic biological behaviors of cells makes it difficult to adequately describe cells, tissues and organs as spatial objects in living tissues, in vitro assays and in computational models of tissues. Results: We have developed an OWL-2 ontology to describe the intrinsic physical and biological characteristics of cells and tissues. The Cell Behavior Ontology (CBO) provides a basis for describing the spatial and observable behaviors of cells and extracellular components suitable for describing in vivo, in vitro and in silico multicell systems. Using the CBO, a modeler can create a meta-model of a simulation of a biological model and link that meta-model to experiment or simulation results. Annotation of a multicell model and its computational representation, using the CBO, makes the statement of the underlying biology explicit. The formal representation of such biological abstraction facilitates the validation, falsification, discovery, sharing and reuse of both models and experimental data. Availability and implementation: The CBO, developed using Protégé 4, is available at http://cbo.biocomplexity.indiana.edu/cbo/ and at BioPortal (http://bioportal.bioontology.org/ontologies/CBO). Contact: jsluka@indiana.edu or Glazier@indiana.edu Supplementary information: Supplementary data are available at Bioinformatics online.

PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal “virtual laboratory” for such multicellular systems simulates both the biochemical microenvironment (the “stage”) and many mechanically and biochemically interacting cells (the “players” upon the stage). PhysiCell—physics-based multicellular simulator—is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility “out of the box.” The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a “cellular cargo delivery” system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.

Authentication and Authorization Considerations for a Multi-tenant Service

Distributed cyberinfrastructure requires users (and machines) to perform some sort of authentication and authorization (together simply known as \emph{auth}). In the early days of computing, authentication was performed with just a username and password combination, and this is still prevalent today. But during the past several years, we have seen an evolution of approaches and protocols for auth: Kerberos, SSH keys, X.509, OpenID, API keys, OAuth, and more. Not surprisingly, there are trade-offs, both technical and social, for each approach. The NSF Science Gateway communities have had to deal with a variety of auth issues. However, most of the early gateways were rather restrictive in their model of access and development. The practice of using community credentials (certificates), a well-intentioned idea to alleviate restrictive access, still posed a barrier to researchers and challenges for security and auditing. And while the web portal-based gateway clients offered users easy access from a browser, both the interface and the back-end functionality were constrained in the flexibility and extensibility they could provide. Designing a well-defined application programming interface (API) to fine-grained, generic gateway services (on secure, hosted cyberinfrastructure), together with an auth approach that has a lower barrier to entry, will hopefully present a more welcoming environment for both users and developers. This paper provides a review and some thoughts on these topics, with a focus on the role of auth between a Science Gateway and a service provider.

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling

Abstract Motivation Due to the complexity and heterogeneity of multicellular biological systems, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful. We present PhysiBoSS, an open source software which combines intracellular signalling using Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell). Results PhysiBoSS provides a flexible and computationally efficient framework to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from single-cell genotype to single-cell phenotype and emergent multicellular behaviour. PhysiBoSS thus becomes very useful when studying heterogeneous population response to treatment, mutation effects, different modes of invasion or isomorphic morphogenesis events. To concretely illustrate a potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment. We explored the effect of different treatments and the behaviour of several resistant mutants. We highlighted the importance of spatial information on the population dynamics by considering the effect of competition for resources like oxygen. Availability and implementation PhysiBoSS is freely available on GitHub (https://github.com/sysbio-curie/PhysiBoSS), with a Docker image (https://hub.docker.com/r/gletort/physiboss/). It is distributed as open source under the BSD 3-clause license. Supplementary information Supplementary data are available at Bioinformatics online.