Simulation Of Biological Networks Research Articles

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.

Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.

How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery

The pharmaceutical industry suffered a significant decline of innovation in the last few decades, whose simple reason is complex biology. Artificial intelligence (AI) promises to make the entire drug discovery and development process more efficient. Here I consider the potential benefits of using AI to deepen our mechanistic understanding of disease by leveraging data and knowledge for modeling and simulation of genome-scale biological networks. I outline recent developments that are moving the field forward and I identify several overarching challenges for advancing the state of the art towards the successful integration of AI with modeling and simulation in drug discovery.

SystemC Implementation of Stochastic Petri Nets for Simulation and Parameterization of Biological Networks

Model development and simulation of biological networks is recognized as a key task in Systems Biology. Integrated with in vitro and in vivo experimental data, network simulation allows for the discovery of the dynamics that regulate biological systems. Stochastic Petri Nets (SPNs) have become a widespread and reference formalism to model metabolic networks thanks to their natural expressiveness to represent metabolites, reactions, molecule interactions, and simulation randomness due to system fluctuations and environmental noise. In the literature, starting from the network model and the complete set of system parameters, there exist frameworks that allow for dynamic system simulation. Nevertheless, they do not allow for automatic model parameterization, which is a crucial task to identify, in silico, the network configurations that lead the model to satisfy specific temporal properties. To cover such a gap, this work first presents a framework to implement SPN models into SystemC code. Then, it shows how the framework allows for automatic parameterization of the networks. The user formally defines the network properties to be observed and the framework automatically extrapolates, through Assertion-based Verification (ABV), the parameter configurations that satisfy such properties. We present the results obtained by applying the proposed framework to model the complex metabolic network of the purine metabolism. We show how the automatic extrapolation of the system parameters allowed us to simulate the model under different conditions, which led to the understanding of behavioral differences in the regulation of the entire purine network. We also show the scalability of the approach through the modeling and simulation of four biological networks, each one with different structural characteristics.

Systems Analysis of Insulin and IGF1 Receptors Networks in Breast Cancer Cells Identifies Commonalities and Divergences in Expression Patterns.

Insulin and insulin-like growth factor-1 (IGF1), acting respectively via the insulin (INSR) and IGF1 (IGF1R) receptors, play key developmental and metabolic roles throughout life. In addition, both signaling pathways fulfill important roles in cancer initiation and progression. The present study was aimed at identifying mechanistic differences between INSR and IGF1R using a recently developed bioinformatics tool, the Biological Network Simulator (BioNSi). This application allows to import and merge multiple pathways and interaction information from the KEGG database into a single network representation. The BioNsi network simulation tool allowed us to exploit the availability of gene expression data derived from breast cancer cell lines with specific disruptions of the INSR or IGF1R genes in order to investigate potential differences in protein expression that might be linked to biological attributes of the specific receptor networks. Modeling-generated information was corroborated by experimental and biological assays. BioNSi analyses revealed that the expression of 75 and 71 genes changed during simulation of IGF1R-KD and INSR-KD, compared to control cells, respectively. Out of 16 proteins that BioNSi analysis was based on, validated by Western blotting, nine were shown to be involved in DNA repair, eight in cell cycle checkpoints, six in proliferation, eight in apoptosis, seven in oxidative stress, six in cell migration, two in energy homeostasis, and three in senescence. Taken together, analyses identified a number of commonalities and, most importantly, dissimilarities between the IGF1R and INSR pathways that might help explain the basis for the biological differences between these networks.

Analytic performance modeling and analysis of detailed neuron simulations

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state-of-the-art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering, which is based on “white-box,” that is, first-principles performance models, to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive codesign efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually codesign of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

An Algorithm Based on Heap of Binary Search Tree to Solve Graph Coloring Problem

Graph coloring problem is one of most frequent studied problem in the graph theory due to its uses in different area of applications like simulation of biological networks, communication networks, register allocation and many more. This problem involves the coloring of the vertices of the given a graph G (V, E) with number of available colors in such a manner that adjacent vertices must assign colors different with each other. In this paper we present a hybrid approach to assign the colors to vertices of the given graph that is based on adjacency matrix and search tree data structure. The coloring process for a particular vertex in the graph will done by getting the feasible colors available in the color list. The feasible colors that may be assigned to a vertex, retrieved from the vertex-color binary search tree generated initially for available colors. The proposed solution for the graph coloring problem is efficient in terms of its running time complexity and it will work without affecting its complexity for any kind of graph.

Petri-net-Framework: modeling and simulation of biological networks based on Petri-nets

Petri-net-Framework: modeling and simulation of biological networks based on Petri-nets

BioNSi: A Discrete Biological Network Simulator Tool.

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise

Due to the complexity of biological systems, simulation of biological networks is necessary but sometimes complicated. The classic stochastic simulation algorithm (SSA) by Gillespie and its modified versions are widely used to simulate the stochastic dynamics of biochemical reaction systems. However, it has remained a challenge to implement accurate and efficient simulation algorithms for general reaction schemes in growing cells. Here, we present a modeling and simulation tool, called ‘GeneCircuits’, which is specifically developed to simulate gene-regulation in exponentially growing bacterial cells (such as E. coli) with overlapping cell cycles. Our tool integrates three specific features of these cells that are not generally included in SSA tools: 1) the time delay between the regulation and synthesis of proteins that is due to transcription and translation processes; 2) cell cycle-dependent periodic changes of gene dosage; and 3) variations in the propensities of chemical reactions that have time-dependent reaction rates as a consequence of volume expansion and cell division. We give three biologically relevant examples to illustrate the use of our simulation tool in quantitative studies of systems biology and synthetic biology.

The Relevance of Topology in Parallel Simulation of Biological Networks

Important achievements in traditional biology has deepened the knowledge about living systems leading to an extensive identification of parts-list of the cell as well as of the interactions among biochemical species responsible for cell's regulation. Such an expanding knowledge also introduces new issues. For example the increasing comprehension of the inter- dependencies between pathways (pathways cross-talk) has resulted, on one hand, in the growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge remains unchanged: to be able to assemble the knowledge of every 'piece' of a system in order to figure out the behavior of the whole (integrative approach). In light of these considerations high performance computing plays a fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high dimensional systems. Here we introduce and discuss a methodology aimed at alleviating the burden of simulating complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting the computational space of a simulation trace and delegating the computation of fragments to a number of parallel processes.

Reconstruction of biological networks based on life science data integration.

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

KID - an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes

BackgroundThe amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed.DescriptionHere we present a text mining algorithm for the extraction of kinetic information such as KM, Ki, kcat etc. as well as associated information such as enzyme names, EC numbers, ligands, organisms, localisations, pH and temperatures. Using this rule- and dictionary-based approach, it was possible to extract 514,394 kinetic parameters of 13 categories (KM, Ki, kcat, kcat/KM, Vmax, IC50, S0.5, Kd, Ka, t1/2, pI, nH, specific activity, Vmax/KM) from about 17 million PubMed abstracts and combine them with other data in the abstract.A manual verification of approx. 1,000 randomly chosen results yielded a recall between 51% and 84% and a precision ranging from 55% to 96%, depending of the category searched.The results were stored in a database and are available as "KID the KInetic Database" via the internet.ConclusionsThe presented algorithm delivers a considerable amount of information and therefore may aid to accelerate the research and the automated analysis required for today's systems biology approaches. The database obtained by analysing PubMed abstracts may be a valuable help in the field of chemical and biological kinetics. It is completely based upon text mining and therefore complements manually curated databases.The database is available at http://kid.tu-bs.de. The source code of the algorithm is provided under the GNU General Public Licence and available on request from the author.

Reconstruction of biological networks based on life science data integration

SummaryFor the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

Petri net models for the semi-automatic construction of large scale biological networks

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. During the last 15 years, Petri nets have attracted more and more attention to help to solve this key problem. Regarding the published papers, it seems clear that hybrid functional Petri nets are the adequate method to model complex biological networks. Today, a Petri net model of biological networks is built manually by drawing places, transitions and arcs with mouse events. Therefore, based on relevant molecular database and information systems biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the application of Petri nets for modeling and simulation of biological networks. Furthermore, we will present a type of access to relevant metabolic databases such as KEGG, BRENDA, etc. Based on this integration process, the system supports semi-automatic generation of the correlated hybrid Petri net model. A case study of the cardio-disease related gene-regulated biological network is also presented. MoVisPP is available at http://agbi.techfak.uni-bielefeld.de/movispp/ .

Automatic Parameterisation of Stochastic Petri Net Models of Biological Networks

Stochastic simulations are able to capture the fine grain behaviour and randomness of outcome of biological networks not captured by deterministic techniques. As such they are becoming an increasingly important tool in the biological community. However, current efforts in the stochastic simulation of biological networks are hampered by two main problems: firstly the lack of complete knowledge of kinetic parameters; and secondly the computational cost of the simulations. In this paper we investigate these problems using the framework of stochastic Petri nets. We present a new stochastic Petri net simulation tool NASTY which allows large numbers of stochastic simulations to be carried out in parallel. We then begin to address the important problem of incomplete knowledge of kinetic parameters by developing a distributed genetic algorithm, based on NASTY's simulation engine, to parameterise stochastic networks. Our algorithm is able to successfully estimate kinetic parameters to replicate a system's behaviour and we illustrate this by presenting a case study in which the kinetic parameters are derived for a stochastic model of the stress response pathway in the bacterium E.coli.

Use of the circuit simulation program SPICE2 for analysis of the metabolism of anticancer drugs

Complex networks of biological processes are analogous to electrical circuits. For each step in a biological or electrical network, flow is dependent on the driving force and the conductivity of the step. The relationship between biological flows and their driving forces can therefore be expressed as relationships between analogous currents and voltages. The time dependence of approach to equilibrium or a steady state is determined by the rates of depletion of material in various compartments. Electrical capacitance is therefore analogous to compartment volume. Once these generalized concepts of flow, force and capacitance are recognized, it becomes clear that computer programs designed for analysis of electrical circuits may be used for simulation of biological networks. A set of simple mathematical descriptions of the individual steps and a diagram showing how the steps are arranged with respect to each other are all that is necessary to perform a simulation; there is no need for computer programming skills or differential equations. The use of SPICE2 for simulation of the cellular and plasma pharmacokinetics of cytosine arabinoside (araC) is described as an example. A network model is developed which considers cellular pharmacokinetics (membrane transport, intracellular phosphorylation and dephosphorylation), and plasma pharmacokinetics following infusions of araC.

Use of the circuit simulation program SPICE2 for analysis of the metabolism of anticancer drugs

Complex networks of biological processes are analogous to electrical circuits. For each step in a biological or electrical network, flow is dependent on the driving force and the conductivity of the step. The relationship between biological flows and their driving forces can therefore be expressed as relationships between analogous currents and voltages. The time dependence of approach to equilibrium or a steady state is determined by the rates of depletion of material in various compartments. Electrical capacitance is therefore analogous to compartment volume. Once these generalized concepts of flow, force and capacitance are recognized, it becomes clear that computer programs designed for analysis of electrical circuits may be used for simulation of biological networks. A set of simple mathematical descriptions of the individual steps and a diagram showing how the steps are arranged with respect to each other are all that is necessary to perform a simulation; there is no need for computer programming skills or differential equations. The use of SPICE2 for simulation of the cellular and plasma pharmacokinetics of cytosine arabinoside (araC) is described as an example. A network model is developed which considers cellular pharmacokinetics (membrane transport, intracellular phosphorylation and dephosphorylation), and plasma pharmacokinetics following infusions of araC.