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Scientific Papers
An invited paper by Drs. Khalil and Pitluk that establishes GNS as a thought leader in applying inference technologies to improve drug discovery. The paper examines crucial gaps in the knowledge formation process, consequences of the gaps and, how inference technologies can enable dramatic improvements in the drug discovery processes from specific projects to portfolio planning. ( PubMed citation )
Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models.
This paper points out the dire need for communicating between the disparate communities of scientists involved in systems biology in a manner that is both precise and jargon-free. It suggests guidelines for published papers in systems biology: experimental work with explicit uncertainties, theoretical work with clearly explained assumptions and approximations, and novel computationally generated hypotheses verified by experiment.
We study models of biochemical networks using Boolean networks with the number of inputs K to each element given by one of three distributions: delta function, Poisson, and power law (scale-free). We show that finite, scale-free networks are more ordered than the other two distributions, suggesting that the topology of scale-free biochemical networks may provide a source of order in living cells.
This paper introduces a concise, modular, and mathematically precise visual notation for the representation of biochemical networks. This language can be used to create an unambiguous diagram of a given network such that the diagram can be directly translated into a mathematical simulation of the system.
This paper establishes the concept of a minimal cell model, based on an earlier model of Escherichia coli, and describes its potential uses. Dimensionless rate parameters are used to generalize the rate parameters specific to the E. coli model, and experimental data from a variety of bacteria are used to justify this scaling.
Various patterns of oscillatory behavior of a mathematical model for calcium dynamics are analyzed, with emphasis on multirhythmic and chaotic bursting modes. The former appear through saddle-node-of-periodics bifurcations, the latter through two different routes to chaos: period doubling cascades and intermittency.
A new possible mechanism for complex calcium oscillations based on the interplay between three calcium stores in the cell is developed: the endoplasmic reticulum, mitochondria and cytosolic proteins. Depending on the permeability of the ER channels and on the kinetic properties of calcium binding to the cytosolic proteins, different patterns of complex calcium oscillations, such as multirhythmicity, bursting and chaos appear.
We developed a mathematical model of G1 progression using physiological expression and activity profiles from synchronized cells exposed to constant growth factors and included a metabolically responsive, activating modifier of cyclin E:Cdk2. Our mathematical model accurately simulates G1 progression, recapitulates observations from targeted gene deletion studies and serves as a foundation for development of therapeutics targeting G1 cell-cycle progression.
This review focuses on the concept of designing individualised therapeutics based on genomic and proteomic profile of malignant tissue. Genetic and epigenetic perturbations in signal pathways drive cancer growth, survival, invasion and metastatic spread. The burgeoning evidence which supports the concept that each patientís cancer has a unique complement of pathogenic genetic and molecular derangements is reviewed. Such evidence supports the strategy of individualised selection of a therapeutic complex from a menu of targeting options that best complements the specific oncomolecular profile of the 'tumourñhost' system.
Significant insight can be gained into complex biologic mechanisms of cancer via a combined computational and experimental systems biology approach. This review highlights some of the major systems biology efforts that were applied to cancer in the past year.
Using the Diagrammatic Cell Language™, Gene Network Sciences (GNS) has created a network model of interconnected signal transduction pathways and gene expression networks that control human cell proliferation and apoptosis. Using the cell simulation, GNS tested the efficacy of various drug targets and performed validation experiments to test computer simulation predictions.
Previous work has shown that dynamic heterogeneity and conduction block can occur in homogeneous heart fibres during prolonged pacing at rapid rates. Here we investigated the mechanism for conduction block following the delivery of one to four premature stimuli using a coupled maps computer model of a one-dimensional canine heart fibre.
A possible impact of the different gating kinetics of different inositol triphosphate receptor subtypes on the time course of cytosolic Ca(2+) concentration in rat tracheal smooth muscle cells upon agonist stimulation is studied. Response modes such as single spikes, several spikes with declining maxima, and sustained oscillations upon gradually increased stimulation with acetylcholine are explained.
Although alternans of action potential duration (APD) is a robust feature of the rapidly paced canine ventricle, currently available ionic models of cardiac myocytes do not recreate this phenomenon. To address this problem, we developed a new ionic model using formulations of currents based on previous models and recent experimental data.
Interruption of periodic wave propagation by the nucleation and subsequent disintegration of spiral waves is thought to mediate the transition from normal sinus rhythm to ventricular fibrillation. This sequence of events may be precipitated by a period doubling bifuration, manifest as a beat-to-beat alternation, or alternans, of cardiac action potential duration and conduction velocity. How alternans causes the local conduction block required for initiation of spiral wave reentry remains unclear, however. In the present study, a mechanism for conduction block was derived from experimental studies in linear strands of cardiac tissue and from computer simulations in ionic and coupled maps models of homogeneous one-dimensional fibers.
Theoretical studies have indicated that alternans of action potential duration is associated with a restitution relation with a slope >= 1. However, recent experimental findings suggest that the slope of the restitution relation is not necessarily predictive of alternans. Here, we compared a return map memory model to action potential data from an ionic model and found that the memory model reproduced dynamics that could not be explained by a unidimensional restitution relation.
We present a nonlinear dynamical systems analysis of the transition to conduction block in one-dimensional cardiac fibers. We study a simple model of wave propagation in heart tissue that depends only on the recovery of action potential duration and conduction velocity. |
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Copyright © 2007 Gene Network Sciences, Inc. All rights reserved. |
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