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PubMed2002

A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain.

STUDY OBJECTIVE: Chest pain is the second most common chief complaint presented to the emergency department. Although the causes of chest pain span the clinical spectrum from the trivial to the life threatening, it is often difficult to identify which patients have the most common life-threatening cause, cardiac ischemia. Because of the potential for poor outcome if this diagnosis is missed, physicians have had a low threshold for admitting patients with chest pain to the hospital, the vast majority of whom are found not to have cardiac ischemia. In an earlier study with a large chest pain patient registry, an artificial neural network was shown to be able to identify the subset of patients who present to the ED with chest pain who have sustained acute myocardial infarction. The objective of this study was to use the same registry to determine whether a network could be trained accurately to identify the larger subset of patients who have cardiac ischemia. METHODS: Two thousand two hundred four adult patients presenting to the ED with chest pain who received an ECG were used to train and test an artificial neural network to recognize the presence of cardiac ischemia. Only the data available at the time of initial patient contact were used to replicate the conditions of real-time evaluation. Forty variables from patient history, physical examination, ECG, and the first set of chemical cardiac marker determinations were used to train and subsequently test the network. The network was trained and tested by using the jackknife variance technique to allow for the network to be trained on as many of the features of the small subset of ischemic patients as possible. Network accuracy was compared with 2 existing aids to the diagnosis of cardiac ischemia, as well as a derived regression model. RESULTS: The network had a sensitivity of 88.1% (95% confidence interval [CI] 84.8% to 91.4%) and a specificity of 86.2% (95% CI 84.6% to 87.7%) for cardiac ischemia despite the fact that a mean of 5% of all required network input data and 41% of cardiac chemical marker data were missing. The network also performed more accurately than the 3 other tested approaches. CONCLUSION: These data suggest that an artificial neural network might be able to identify which patients who present to the ED with chest pain have cardiac ischemia with useful sensitivities and specificities.

Chest Pain↗
PubMed2005

Network structure and attitudes toward collaboration in a community partnership for diabetes control on the US-Mexican border.

PURPOSE: This study seeks to provide an examination of a health policy network operating in a single, small community along the US-Mexican border. The purpose of the paper is to discuss why and how this network evolved, and then to present findings on how the network was structured. Analysis will focus especially on agency involvement, or "embeddedness" in the network, and its relationship to attitudes held by network members regarding trust, reputation, and perceived benefit. DESIGN/METHODOLOGY/APPROACH: Data were collected from 15 public and nonprofit agencies trying to work collaboratively to influence local policy and services regarding the prevention of obesity-related chronic disease, especially diabetes. Embeddedness was measured in three different ways and both confirmed and unconfirmed networks were assessed. Network analysis methods were utilized as well as nonparametric correlation statistics. FINDINGS: The network was found to be densely connected through unconfirmed linkages, but much less so when these links were confirmed. Strongest findings were found for shared information. Measures of agency embeddedness in the network were strong predictors of agency reputation, but findings for trust and perceived benefit were generally weak. ORIGINALITY/VALUE: From a practice perspective, the study points to the problems in building and sustaining community-based chronic disease health networks, especially in a small community with substantial health needs. The research also contributes to theory on embeddedness and to methodology for collecting and analyzing data on community health networks.

Adult↗
PubMed2005

An improved genetic algorithm based fuzzy-tuned neural network.

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.

Algorithms↗
PubMed2005

Effects of random external background stimulation on network synaptic stability after tetanization: a modeling study.

We constructed a simulated spiking neural network model to investigate the effects of random background stimulation on the dynamics of network activity patterns and tetanus induced network plasticity. The simulated model was a "leaky integrate-and-fire" (LIF) neural model with spike-timing-dependent plasticity (STDP) and frequency-dependent synaptic depression. Spontaneous and evoked activity patterns were compared with those of living neuronal networks cultured on multi-electrode arrays. To help visualize activity patterns and plasticity in our simulated model, we introduced new population measures called Center of Activity (CA) and Center of Weights (CW) to describe the spatio-temporal dynamics of network-wide firing activity and network-wide synaptic strength, respectively. Without random background stimulation, the network synaptic weights were unstable and often drifted after tetanization. In contrast, with random background stimulation, the network synaptic weights remained close to their values immediately after tetanization. The simulation suggests that the effects of tetanization on network synaptic weights were difficult to control because of ongoing synchronized spontaneous bursts of action potentials, or "barrages." Random background stimulation helped maintain network synaptic stability after tetanization by reducing the number and thus the influence of spontaneous barrages. We used our simulated network to model the interaction between ongoing neural activity, external stimulation and plasticity, and to guide our choice of sensory-motor mappings for adaptive behavior in hybrid neural-robotic systems or "hybrots."

Action Potentials↗
PubMed2006

Versatility and connectivity efficiency of bipartite transcription networks.

The modulation of promoter activity by DNA-binding transcription regulators forms a bipartite network between the regulators and genes, in which a smaller number of regulators control a much lager number of genes. To facilitate representation of gene expression data with the simplest possible network structure, we have characterized the ability of bipartite networks to describe data. This has led to the classification of two types of bipartite networks, versatile and nonversatile. Versatile networks can describe any data of the same rank, and are indistinguishable from one another. Nonversatile networks require constraints to be present in data they describe, which may be used to distinguish between different network topologies. By quantifying the ability of bipartite networks to represent data we were able to define connectivity efficiency, which is a measure of how economic the use of connections is within a network with respect to data representation and generation. We postulated that it may be desirable for an organism to maximize its gene expression range per network edge, since development of a regulatory connection may have some evolutionary cost. We found that the transcriptional regulatory networks of both Saccharomyces cerevisiae and Escherichia coli lie close to their respective connectivity efficiency maxima, suggesting that connectivity efficiency may have some evolutionary influence.

Escherichia coli Proteins↗
PubMedDate not supplied

Technical and economic evaluation method for use in improving infectious animal disease surveillance networks.

With hope of improving the increasing number of epidemiological surveillance networks for animal diseases set up in recent years, a qualitative and quantitative technical and economic evaluation tool was developed and then applied to three epidemiological surveillance networks: RENESA (a French surveillance network for salmonella and mycoplasma contamination in poultry production units subject to official sanitary controls), the French Foot and Mouth Disease Epidemiovigilance Network and REPIMAT (the epidemiological surveillance network in Chad for major cattle diseases). We identified critical points in epidemiological surveillance networks using a modified version of the hazard analysis: critical control point (HACCP) method. An evaluation grid was then developed and validated by experts who were consulted in accordance with the Delphi method. A questionnaire to collect the information required for the evaluation and a scoring guide were then designed. Our evaluation procedure also included a calculation of the annual operating costs for two of the three networks studied. On the basis of the detailed results of the technical and economic evaluation, we formulated specific suggestions for improving the networks. The cost of implementing these proposals was calculated. We then simulated the effects of implementing each of the proposed improvements and a new global evaluation score was determined for each network. The 'cost per point' of each improvement was then calculated and discussed. This tool for the technical and economic evaluation of epidemiological surveillance networks for animal diseases is proposed so that it may be tested on a far wider scale and eventually be used in improving the functioning of such networks and for risk analysis in international trade.

Animals↗
PubMed1991

Medical network security and viruses.

Medical network as connecting Hospital Information Systems are needed in order to exchange, compare and make accessible data. The use of OSI standard communication protocols (open-network environment) will allow to interconnect multiple vendor systems and to accommodate a wide range of underlaying of communication technologies. The security of information on a given host may become dependent of the security measures employed by the network and by other hosts. Computer viruses modifies the executable code and thrive in network environment filled with personal computers and third-party software. Most networks and computers, permit users to share files; this, let the viruses to bypass the security mechanisms of almost every commercial operating system. However, computer viruses axes not the only threat to the information in a network environment. Other as deliberate (passive attacks -wire-tapping-) and accidental threat (unauthorized access to the information) are potential risks to the security information. Cryptographic techniques that now are widely used can resolve the external security problems of the network and improve the internal security ones. This paper begins describing the threats to security that arise in an open-network environment, and goes to establish the security requirements of medical communication networks. This is followed by a description of security services as: confidentiality, integrity, authentication, access control, etc., that will be provided to include security mechanisms in such network. The integration of these security mechanisms into the communication protocols allows to implement secure communication systems that not only must provide the adequate security, but also must minimize the impact of security on other features as for example the efficiency. The remainder of the paper describes how the security mechanisms are formed using current cryptographic facilities as algorithms, one-way functions, cryptographic systems (symmetric and asymmetric), etc. Emphasis is placed on the method to obtain these mechanisms. We will obtain several mechanisms of varying strength for the provision of each security service. Finally, the security mechanisms are structured into several mutually related areas of network security and are presented in a formal form.

Computer Communication Networks↗
PubMed2004

Estimation of bullet striation similarity using neural networks.

A new method that searches for similar striation patterns using neural networks is described. Neural networks have been developed based on the human brain, which is good at pattern recognition. Therefore, neural networks would be expected to be effective in identifying striated toolmarks on bullets. The neural networks used in this study deal with binary signals derived from striation images. This signal plays a significant role in identification, because this signal is the key to the individually of the striations. The neural network searches a database for similar striations by means of these binary signals. The neural network used here is a multilayer network consisting of 96 neurons in the input layer, 15 neurons in the middle, and one neuron in the output layer. Two signals are inputted into the network and a score is estimated based on the similarity of these signals. For this purpose, the network is assigned to a previous learning. To initially test the validity of the procedure, the network identifies artificial patterns that are randomly produced on a personal computer. The results were acceptable and showed robustness for the deformation of patterns. Moreover, with ten unidentified bullets and ten database bullets, the network consistently was able to select the correct pair.

Databases as Topic↗
PubMed1999

Endothelial cell apoptosis in capillary network remodeling.

We hypothesized that the regulation of apoptosis is an important determinant of capillary network structure. Using human umbilical vein endothelial cells (HUVEC) in in vitro model systems of capillary tube formation, we initially documented that apoptosis is a prominent feature of network formation. Perturbations of integrin-matrix signaling by the administration of either colchicine or an anti-alpha(v)beta3 antibody resulted in the dissolution of the tubular network in association with increased apoptosis. The activation of the alpha(v)beta3 integrin induced increased expression of the anti-apoptotic gene bcl-2 and conferred resistance to the proapoptotic effect of TGF-beta1. In contrast to the stable networks formed by HUVEC, bovine aortic endothelial cells (BAEC) exhibited a more dynamic process of network formation and spontaneous involution. The inhibition of BAEC apoptosis by stable transfection of bcl-2 prevented the involution of the network. We hypothesized that TGF-beta1 present within the model system mediated network involution by inducing BAEC death. Indeed, blockade of TGF-beta1 with neutralizing antibodies reduced BAEC apoptosis and preserved the network structure. As observed with HUVEC networks, stable BAEC networks formed during blockade of TGF-beta1 were also dependent on the survival-promoting effects of matrixintegrin interactions. This study suggests that capillary network structure is determined by the balance of proapoptotic vs. anti-apoptotic signals mediated by the engagement of cytokine and integrin receptors within the milieu.

Animals↗
PubMed2005

Automated analysis of meta-analysis networks.

The high information content in large data sets from voxel-based meta-analyses is complex, making it hard to readily resolve details. Using the meta-analysis network as a standardized data structure, network analysis algorithms can examine complex interrelationships and resolve hidden details. Two new network analysis algorithms have been adapted for use with meta-analysis networks. The first, called replicator dynamics network analysis (RDNA), analyzes co-occurrence of activations, whereas the second, called fractional similarity network analysis (FSNA), uses binary pattern matching to form similarity subnets. These two network analysis methods were evaluated using data from activation likelihood estimation (ALE)-based meta-analysis of the Stroop paradigm. Two versions of these data were evaluated, one using a more strict ALE threshold (P < 0.01) with a 13-node meta-analysis network, and the other a more lax threshold (P < 0.05) with a 22-node network. Java-based applications were developed for both RDNA and FSNA. The RDNA algorithm was modified to provide multiple subnets or maximal cliques for meta-analysis networks. Three different similarity measures were evaluated with FSNA to form subsets of nodes and experiments. RDNA provides a means to gauge importance of metanalysis subnets and complements FSNA, which provides a more comprehensive assessment of node similarity subsets, experiment similarity subsets, and overall node-to-factors similarity. The need to use both presence and absence of activations was an important finding in similarity analyses. FSNA revealed details from the pooled Stroop meta-analysis that would otherwise require separate highly filtered meta-analyses. These new analysis tools demonstrate how network analysis strategies can simplify greatly and enhance voxel-based meta-analyses.

Brain Mapping↗
PubMed2003

Sound recognition and localization in man: specialized cortical networks and effects of acute circumscribed lesions.

Functional imaging studies have shown that information relevant to sound recognition and sound localization are processed in anatomically distinct cortical networks. We have investigated the functional organization of these specialized networks by evaluating acute effects of circumscribed hemispheric lesions. Thirty patients with a primary unilateral hemispheric lesion, 15 with right-hemispheric damage (RHD) and 15 with left-hemispheric damage (LHD), were evaluated for their capacity to recognise environmental sounds, to localize sounds in space and to perceive sound motion. One patient with RHD and 2 with LHD had a selective deficit in sound recognition; 3 with RHD a selective deficit in sound localization; 2 with LHD a selective deficit in sound motion perception; 4 with RHD and 3 with LHD a combined deficit of sound localization and motion perception; 2 with RHD and 1 with LHD a combined deficit of sound recognition and motion perception; and 1 with LHD a combined deficit of sound recognition, localization and motion perception. Five patients with RHD and 6 with LHD had normal performance in all three domains. Deficient performance in sound recognition, sound localization and/or sound motion perception was always associated with a lesion that involved the shared auditory structures and the specialized What and/or Where networks, while normal performance was associated with lesions within or outside these territories. Thus, damage to regions known to be involved in auditory processing in normal subjects is necessary, but not sufficient for a deficit to occur. Lesions of a specialized network was not always associated with the corresponding deficit. Conversely, specific deficits tended not be associated predominantly with lesions of the corresponding network; e.g. deficits in auditory spatial tasks were observed in patients whose lesions involved to a larger extent the shared auditory structures and the specialized What network than the specialized Where network, and deficits in sound recognition in patients whose lesions involved mostly the shared auditory structures and to a varying degree the specialized What network. The human auditory cortex consists of functionally defined auditory areas, whose intrinsic organization is currently not understood. In particular, areas involved in the What and Where pathways can be conceived as: (1) specialized regions, in which lesions cause dysfunction limited to the damaged part; observed deficits should be then related to the specialization of the damaged region and their magnitude to the extent of the damage; or (2) specialized networks, in which lesions cause dysfunction that may spread over the two specialized networks; observed deficits may then not be related to the damaged region and their magnitude not proportional to the extent of the damage. Our results support strongly the network hypothesis.

Acute Disease↗
PubMed2005

Patterns of spontaneous activity in unstructured and minimally structured spinal networks in culture.

The rhythmic activity observed in locomotion is generated by local neuronal networks in the spinal cord. The alternating patterns are produced by reciprocal connections between these networks. Synchronous rhythmic activity, but not alternation, can be reproduced in disinhibited networks of dissociated spinal neurons of rats. This suggests that a specific network architecture is required for pattern generation but not for rhythm generation. Here we were interested in the recruitment of neurons to produce population bursts in unstructured and minimally structured cultures of rat spinal cord grown on multielectrode arrays. We tested whether two networks, connected by a small number of axons, could be functionally separated into two units and generate more complex patterns such as alternation. In the unstructured cultures, we found that the recruitment of the neurons into bursting populations is divided into two steps: the fast recruitment of a "trigger network", consisting of intrinsically firing cells connected in networks with short delays, and slow recruitment of the rest of the network. One or several trigger networks were observed in a single culture and could account for variable patterns of propagation. In the minimally structured cultures, a functional separation between loosely connected networks was achieved. Such separation led either to an independent bursting between the networks or to synchronized bursting with long and variable delays. However, no qualitatively novel pattern such as alternation could be generated. In addition, we found that the strength of reciprocal inhibitory connections was modulated by spontaneous activity.

Action Potentials↗
PubMed1995

Chiari's network: normal anatomic variant or risk factor for arterial embolic events?

OBJECTIVES: This study was performed to assess the prevalence of Chiari's network in patients undergoing transesophageal echocardiography and to determine whether this anomaly is associated with other cardiac lesions or is characterized by typical clinical findings. BACKGROUND: Chiari's network is a congenital remnant of the right valve of the sinus venosus. It has been found in 1.3% to 4% of autopsy studies and is believed to be of little clinical consequence. METHODS: Video recordings of 1,436 consecutive adult patients evaluated by transesophageal echocardiography over a 30-month period were reviewed for the presence of Chiari's network. Echocardiographic contrast studies had been performed in all patients with Chiari's network and were compared with those of 160 consecutive patients without a Chiari net, serving as a control group. RESULTS: Chiari's network was present in 29 of 1,436 patients (prevalence 2%). A frequently associated finding was a patent foramen ovale in 24 (83%) of the 29 patients with Chiari's network versus 44 (28%) of 160 control patients (p < 0.001). Intense right-to-left shunting occurred significantly more often in patients with Chiari's network than in control patients (16 [55%] of 29 patients vs. 19 [12%] of 160 control patients, p < 0.001). Another frequent association was an atrial septal aneurysm in 7 (24%) of 29 patients. The indication for transesophageal echocardiography was a suspected cardiac source of arterial embolism in 24 (83%) of 29 patients with a Chiari net, 13 of whom (54%) had recurrent embolic events. Chiari's network was significantly more common in patients with unexplained arterial embolism than in patients evaluated for other indications (24 [4.6%] of 522 patients vs. 5 [0.5%] of 914 patients, p < 0.001). Potential causes for arterial embolism were present in 9 of the 24 patients with a Chiari net and embolic events (atrial septal aneurysm in 7, cerebrovascular lesion in 2). In 15 (62%) of 24 patients only a patent foramen ovale could be identified. Three patients had deep venous thrombosis and pulmonary embolism at the time of arterial embolism; none had a thrombus detected within the network. CONCLUSIONS: In patients undergoing transesophageal echocardiography, the prevalence of Chiari's network was 2%, which is consistent with autopsy studies. By maintaining an embryonic right atrial flow pattern into adult life and directing the blood from the inferior vena cava preferentially toward the interatrial septum, Chiari's network may favor persistence of a patent foramen ovale and formation of an atrial septal aneurysm and facilitate paradoxic embolism.

Abnormalities, Multiple↗
PubMed2006

The association between change in social network characteristics and non-fatal overdose: results from the SHIELD study in Baltimore, MD, USA.

BACKGROUND: Social network factors have been reported to be associated with non-fatal overdose. Yet, few studies have examined how changes in social network characteristics may influence overdose risk. The purpose of this study was to examine the relationship between changes in social network and non-fatal overdose. METHODS: Data for this study came from 659 participants enrolled in the Self-Help in Eliminating Life-Threatening Diseases (SHIELD) study, who reported details about their non-fatal overdose experience between enrollment and a follow-up visit. Social network characteristics were described at both time points and net change in network composition was calculated. RESULTS: The sample was predominately male (56%), African-American (96%) and unemployed (78%). Experience of non-fatal overdose between time points was reported by 15%. Older age was associated with non-fatal overdose. Interaction between incarceration status and drug use was statistically significant. Protective factors were having a denser network at baseline and a network that became denser after adjusting for gender, homelessness, incarceration, drug use and total network size. CONCLUSIONS: Drug users' social networks are an important target for overdose prevention interventions. Further research on overdose risk and movement of specific network members in and out of networks is warranted.

Adult↗
PubMed2005

Friends in the 'hood: Should peer-based health promotion programs target nonschool friendship networks?

PURPOSE: To examine the characteristics of inner city African-American adolescents nonschool-based and school-based friendship networks and to explore the influence of these networks on health risk behavior. METHODS: We assessed close friendships networks in a probability sample of inner city African-American youth living in a single neighborhood and describe the networks and health risk behavior of network members. The initial probability sample was obtained via telephone (Random Digit Dialing [RDD] sampling) and followed up with in-person interviews with telephone respondents (seeds). Subsequently, seeds' friends were recruited and completed an in-person interview. RESULTS: A majority of friendship networks included some nonschool friends (57%) and 24% of networks were composed exclusively of nonschool friends. As expected, youth were more likely to spend time with school-based friends on weekdays. On weekends, youth were equally likely to spend time with both school and nonschool networks. Youth in the same friendship group tended to engage in similar behaviors. Health risk behaviors were high regardless of whether networks were nonschool based, mixed, or school based. CONCLUSIONS: The high proportion of nonschool friendships suggests that out-of-school networks may be an important influence in this population. Youth spend time with their friends, regardless of network type, on weekends, and weekends are a high-risk period for health-damaging behaviors. Levels of experience with health risk behaviors suggest that both school and nonschool environments require intervention. Future social influence prevention efforts that are broad-based are likely to have maximal impact.

Adolescent↗
PubMed2005

Graph theoretic modeling of large-scale semantic networks.

During the past several years, social network analysis methods have been used to model many complex real-world phenomena, including social networks, transportation networks, and the Internet. Graph theoretic methods, based on an elegant representation of entities and relationships, have been used in computational biology to study biological networks; however they have not yet been adopted widely by the greater informatics community. The graphs produced are generally large, sparse, and complex, and share common global topological properties. In this review of research (1998-2005) on large-scale semantic networks, we used a tailored search strategy to identify articles involving both a graph theoretic perspective and semantic information. Thirty-one relevant articles were retrieved. The majority (28, 90.3%) involved an investigation of a real-world network. These included corpora, thesauri, dictionaries, large computer programs, biological neuronal networks, word association networks, and files on the Internet. Twenty-two of the 28 (78.6%) involved a graph comprised of words or phrases. Fifteen of the 28 (53.6%) mentioned evidence of small-world characteristics in the network investigated. Eleven (39.3%) reported a scale-free topology, which tends to have a similar appearance when examined at varying scales. The results of this review indicate that networks generated from natural language have topological properties common to other natural phenomena. It has not yet been determined whether artificial human-curated terminology systems in biomedicine share these properties. Large network analysis methods have potential application in a variety of areas of informatics, such as in development of controlled vocabularies and for characterizing a given domain.

Algorithms↗
PubMed2006

Evolutionary dynamics of prokaryotic transcriptional regulatory networks.

The structure of complex transcriptional regulatory networks has been studied extensively in certain model organisms. However, the evolutionary dynamics of these networks across organisms, which would reveal important principles of adaptive regulatory changes, are poorly understood. We use the known transcriptional regulatory network of Escherichia coli to analyse the conservation patterns of this network across 175 prokaryotic genomes, and predict components of the regulatory networks for these organisms. We observe that transcription factors are typically less conserved than their target genes and evolve independently of them, with different organisms evolving distinct repertoires of transcription factors responding to specific signals. We show that prokaryotic transcriptional regulatory networks have evolved principally through widespread tinkering of transcriptional interactions at the local level by embedding orthologous genes in different types of regulatory motifs. Different transcription factors have emerged independently as dominant regulatory hubs in various organisms, suggesting that they have convergently acquired similar network structures approximating a scale-free topology. We note that organisms with similar lifestyles across a wide phylogenetic range tend to conserve equivalent interactions and network motifs. Thus, organism-specific optimal network designs appear to have evolved due to selection for specific transcription factors and transcriptional interactions, allowing responses to prevalent environmental stimuli. The methods for biological network analysis introduced here can be applied generally to study other networks, and these predictions can be used to guide specific experiments.

Amino Acid Motifs↗
PubMed2004

A proposal for using the ensemble approach to understand genetic regulatory networks.

Understanding the genetic regulatory network comprising genes, RNA, proteins and the network connections and dynamical control rules among them, is a major task of contemporary systems biology. I focus here on the use of the ensemble approach to find one or more well-defined ensembles of model networks whose statistical features match those of real cells and organisms. Such ensembles should help explain and predict features of real cells and organisms. More precisely, an ensemble of model networks is defined by constraints on the "wiring diagram" of regulatory interactions, and the "rules" governing the dynamical behavior of regulated components of the network. The ensemble consists of all networks consistent with those constraints. Here I discuss ensembles of random Boolean networks, scale free Boolean networks, "medusa" Boolean networks, continuous variable networks, and others. For each ensemble, M statistical features, such as the size distribution of avalanches in gene activity changes unleashed by transiently altering the activity of a single gene, the distribution in distances between gene activities on different cell types, and others, are measured. This creates an M-dimensional space, where each ensemble corresponds to a cluster of points or distributions. Using current and future experimental techniques, such as gene arrays, these M properties are to be measured for real cells and organisms, again yielding a cluster of points or distributions in the M-dimensional space. The procedure then finds ensembles close to those of real cells and organisms, and hill climbs to attempt to match the observed M features. Thus obtains one or more ensembles that should predict and explain many features of the regulatory networks in cells and organisms.

Cell Differentiation↗