In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, rates of live birth following clinical ART remain low worldwide. Grading is based on the embryo shape at a limited number of stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Here, we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from the nuclear structure in live-cell fluorescence images of mouse embryos from zygote to across a wide range of stages. The input is morphological features of cell nuclei, which were extracted as multivariate time-series data by using the segmentation algorithm for mouse embryos. The classification accuracy of our method (83.87%) greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. Our method also has a new attention mechanism that allows us to determine which values of multivariate time-series data, used to describe nuclear morphology, were the basis for the prediction. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.

MOTIVATION: The Systems Biology Markup Language (SBML) is currently supported by >230 software tools, among which 160 support numerical integration of ordinary differential equation (ODE) models. Although SBML is a widely accepted standard within this field, there is still no language-neutral library that supports all features of SBML for simulating ODE models. Therefore, a demand exists for a simple portable implementation of a numerical integrator that supports SBML to enhance the development of a computational platform for systems biology. RESULTS: We implemented a library called libSBMLSim, which supports all the features of SBML and confirmed that the library passes all tests in the SBML test suite including those for SBML Events, AlgebraicRules, 'fast' attribute on Reactions and Delay. LibSBMLSim is implemented in the C programming language and does not depend on any third-party library except libSBML, which is a library to handle SBML documents. For the numerical integrator, both explicit and implicit methods are written from scratch to support all the functionality of SBML features in a straightforward implementation. We succeeded in implementing libSBMLSim as a platform-independent library that can run on most common operating systems (Windows, MacOSX and Linux) and also provides several language bindings (Java, C#, Python and Ruby). AVAILABILITY: The source code of libSBMLSim is available from http://fun.bio.keio.ac.jp/software/libsbmlsim/. LibSBMLSim is distributed under the terms of LGPL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. AVAILABILITY AND IMPLEMENTATION: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. CONTACT: [email protected].

MOTIVATION: BioPAX is a standard language for representing and exchanging models of biological processes at the molecular and cellular levels. It is widely used by different pathway databases and genomics data analysis software. Currently, the primary source of BioPAX data is direct exports from the curated pathway databases. It is still uncommon for wet-lab biologists to share and exchange pathway knowledge using BioPAX. Instead, pathways are usually represented as informal diagrams in the literature. In order to encourage formal representation of pathways, we describe a software package that allows users to create pathway diagrams using CellDesigner, a user-friendly graphical pathway-editing tool and save the pathway data in BioPAX Level 3 format. AVAILABILITY: The plug-in is freely available and can be downloaded at ftp://ftp.pantherdb.org/CellDesigner/plugins/BioPAX/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Resource reduction design techniques play an important role to implement large-scale FPGA-based accelerator systems in floating point applications since available resources on FPGAs are limited. This paper proposes a dataflow graph classification method which makes groups of graphs based on their similarity in order to bring out efficient graph combining. Aiming at finding effective parameters for the k-means algorithm, various parameter combinations are evaluated and compared in terms of resource reduction effects and performance. The experimental results using an FPGA-based biochemical simulator reveal that the graph clustering that uses information on the maximum common subgraphs achieve 73.3% of resource reduction rate while alleviating the performance degradation.

One of the obvious advantages of FPGA-based reconfigurable computing is customizability of a tradeoff point between performance and hardware costs. However, this tradeoff has rarely been discussed in a whole application level, which is the most important view for application users. This paper presents empirical evaluation of a hardware module sharing technique which can shift a tradeoff point of area and performance on an FPGA-based biochemical simulator. The biochemical simulation results are discussed in terms of hardware costs, simulation throughput, parallelism extracted in simulation hardware, and data transfer overheads.

Stochastic simulation of biochemical reaction networks are widely focused by life scientists to represent stochastic behaviors in cellular processes. Stochastic algorithm has loop-and thread-level parallelism, and it is suitable for running on application specific hardware to achieve high performance with low cost. We have implemented and evaluated the FPGA-based stochastic simulator according to theoretical research of the algorithm. This paper introduces an improved architecture for accelerating a stochastic simulation algorithm called the Next Reaction Method. This new architecture has scalability to various size of FPGA. As the result with a middle-range FPGA, 5.38 times higher throughput was obtained compared to software running on a Core 2 Quad Q6600 2.40GHz.

Understanding of the logic and dynamics of gene-regulatory and biochemical networks is a major challenge of systems biology. To facilitate this research topic, we have developed a modeling/simulating tool called CellDesigner. CellDesigner primarily has capabilities to visualize, model, and simulate gene-regulatory and biochemical networks. Two major characteristics embedded in CellDesigner boost its usability to create/import/export models: 1) solidly defined and comprehensive graphical representation (systems biology graphical notation) of network models and 2) systems biology markup language (SBML) as a model-describing basis, which function as intertool media to import/export SBML-based models. In addition, since its initial release in 2004, we have extended various capabilities of CellDesigner. For example, we integrated other systems biology workbench enabled simulation/analysis software packages. CellDesigner also supports simulation and parameter search, supported by integration with SBML ODE Solver, enabling users to simulate through our sophisticated graphical user interface. Users can also browse and modify existing models by referring to existing databases directly through CellDesigner. Those extended functions empower CellDesigner as not only a modeling/simulating tool but also an integrated analysis suite. CellDesigner is implemented in Java and thus supports various platforms (i.e., Windows, Linux, and MacOS X). CellDesigner is freely available via our Web site.

In order to simulate large scale biological models with a reconfigurable FPGA-based biochemical simulator system, reduction of required resources are essential. This paper proposes a method which combines common terms in rate law functions appeared in biochemical models and generates a shared hardware module used for numerical integration. In this approach, two functions are combined in a tree structure level, followed by pipeline scheduling and arithmetic module binding. The evaluation result reveals that this approach reduces hardware resources by 31.4% on average at the cost of 14.4% throughput degradation.

This paper introduces a scalable FPGA implementation of a stochastic simulation algorithm (SSA) called the Next Reaction Method. There are some hardware approaches of SSAs that obtained high-throughput on reconfigurable devices such as FPGAs, but these works lacked in scalability. The design of this work can accommodate to the increasing size of target biochemical models, or to make use of increasing capacity of FPGAs. Interconnection network between arithmetic circuits and multiple simulation circuits aims to perform a data-driven multi-threading simulation. Approximately 8 times speedup was obtained compared to an excution on Xeon 2.80GHz.

This paper studies several designs of network-based FPGA implementation of a stochastic simulation algorithm called the next reaction method, known for its large number of calculation involved. The procedure is divided into several subdivisions which will be implemented as independent modules, and they are connected with configurable interconnection networks so as to provide high throughput. By performing a multi-threading simulation, 3.6 times speedup was obtained compared with an execution on general purpose processors.

ReCSiP is an FPGA-based biochemical simulator to accelerate kinetic simulations of biochemical pathways. Biochemical models are described as a set of ordinary differential equations (ODEs). Each equation in the model is called "rate law function", which represents the velocity of corresponding biochemical reaction mechanism. ReCSiP achieves high-throughput simulation with statically pipelined rate law function modules and numerical integration modules on an FPGA. This paper shows the basic structure of ReCSiP, and results of evaluation in 2 aspects: area and throughput. As the summary of evaluation, 1) about 64% of the total circuit area is occupied by floating-point arithmetic units, and 2) with an XC2VP70, ReCSiP at 90MHz can achieve 20times or more speedup compared to Intel's Pentium4 microprocessor at 3.2GHz

Stochastic simulation of biochemical systems has become one of major approaches to study life processes as system, yet is a computational challenge to run the simulation due to its vast calculation cost. This paper shows the implementation and evaluation of a stochastic simulation algorithm (SSA) called "first reaction method" on an FPGA-based biochemical simulator. It achieves high throughput by (1) consecutively throwing data into deeply-pipelined floating point arithmetic units, and (2) by distributing multiple simulators for parallel execution. As the result of evaluation on an FPGA-based simulation platform called ReC-SiP2, the simulator outperforms execution on Xeon 2.80 GHz by approximately 80 times, even with large-scale biochemical systems

A reconfigurable biochemical simulator by solving ordinary differential equations has received attention as a personal high speed environment for biochemical researchers. For efficient use of the reconfigurable hardware, static scheduling of high-throughput arithmetic pipeline structures is essential. This paper shows and compares some scheduling alternatives, and analyzes the tradeoffs between performance and hardware amount. Through the evaluation, it is shown that the sharing first scheduling reduces the hardware cost by 33.8% in average, with the up to 11.5% throughput degradation. Effects of sharing of rate law functions are also analyzed.

Today, mathematical modeling and simulation of biochemical pathways take a major role in biological researches. However, modern microprocessors cannot provide enough throughputs to explore the large parameter space of target pathways. To address this problem, ReCSiP (a reconfigurable cell simulation platform), an FPGA-based biochemical simulator is proposed. It's an ODE-based simulator, which solves the rate-law functions. The framework proposed in this paper, enables to simulate pathways consisting many different types of chemical reactions by connecting the rate-law modules (solvers) on an FPGA. It provides the solver-to-solver communication mechanism on an FPGA and automatic configuration software to generate the circuit.

Network-based parallel processing using commodity personal computers has been widely developed. Since such systems require high degree of flexibility and scalability of wiring, a high-speed network with an irregular topology is often needed. In traditional routing algorithms for irregular networks, available paths are considerably restricted in order to avoid deadlocks. In this paper we propose a novel routing algorithm called left-up-first turn routing (L-turn routing), which makes a better traffic balancing in irregular networks by building a specific spanning tree. Result of simulations shows that L-turn routing achieves better performance than traditional ones with each topology.

Recursive Diagonal Torus (RDT), a class of interconnection network is proposed for massively parallel computers with up to 2/sup 16/ nodes. By making the best use of a recursively structured diagonal mesh (torus) connection, the RDT has a smaller diameter (e.g., it is 11 for 2/sup 10/ nodes) with a smaller number of links per node (i.e., 8 links per node) than those of the hypercube. A simple routing algorithm, called vector routing, which is near-optimal and easy to implement is also proposed. Although the congestion on upper rank tori sometimes degrades the performance under the random traffic, the RDT provides much better performance than that of a 2D/3D torus in most cases and, under hot spot traffic, the RDT provides much better performance than that of a 2D/3D/4D torus. The RDT router chip which provides a message multicast for maintaining cache consistency is available. Using the 0.5 /spl mu/m BICMOS SOG technology, versatile functions, including hierarchical multicasting, combining acknowledge packets, shooting down/restart mechanism, and time-out/setup mechanisms, work at a 60 MHz clock rate.