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PubMed2006

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model.

BACKGROUND: Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. RESULTS: By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. CONCLUSION: IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at http://hanlab.genetics.ac.cn/IntNetDB.htm. Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer.

Algorithms↗

PubMed2005

Evolutionary cores of domain co-occurrence networks.

BACKGROUND: The modeling of complex systems, as disparate as the World Wide Web and the cellular metabolism, as networks has recently uncovered a set of generic organizing principles: Most of these systems are scale-free while at the same time modular, resulting in a hierarchical architecture. The structure of the protein domain network, where individual domains correspond to nodes and their co-occurrences in a protein are interpreted as links, also falls into this category, suggesting that domains involved in the maintenance of increasingly developed, multicellular organisms accumulate links. Here, we take the next step by studying link based properties of the protein domain co-occurrence networks of the eukaryotes S. cerevisiae, C. elegans, D. melanogaster, M. musculus and H. sapiens. RESULTS: We construct the protein domain co-occurrence networks from the PFAM database and analyze them by applying a k-core decomposition method that isolates the globally central (highly connected domains in the central cores) from the locally central (highly connected domains in the peripheral cores) protein domains through an iterative peeling process. Furthermore, we compare the subnetworks thus obtained to the physical domain interaction network of S. cerevisiae. We find that the innermost cores of the domain co-occurrence networks gradually grow with increasing degree of evolutionary development in going from single cellular to multicellular eukaryotes. The comparison of the cores across all the organisms under consideration uncovers patterns of domain combinations that are predominately involved in protein functions such as cell-cell contacts and signal transduction. Analyzing a weighted interaction network of PFAM domains of yeast, we find that domains having only a few partners frequently interact with these, while the converse is true for domains with a multitude of partners. Combining domain co-occurrence and interaction information, we observe that the co-occurrence of domains in the innermost cores (globally central domains) strongly coincides with physical interaction. The comparison of the multicellular eukaryotic domain co-occurrence networks with the single celled of S. cerevisiae (the overlap network) uncovers small, connected network patterns. CONCLUSION: We hypothesize that these patterns, consisting of the domains and links preserved through evolution, may constitute nucleation kernels for the evolutionary increase in proteome complexity. Combining co-occurrence and physical interaction data we argue that the driving force behind domain fusions is a collective effect caused by the number of interactions and not the individual interaction frequency.

Animals↗

PubMed2005

An evolutionary and functional assessment of regulatory network motifs.

BACKGROUND: Cellular functions are regulated by complex webs of interactions that might be schematically represented as networks. Two major examples are transcriptional regulatory networks, describing the interactions among transcription factors and their targets, and protein-protein interaction networks. Some patterns, dubbed motifs, have been found to be statistically over-represented when biological networks are compared to randomized versions thereof. Their function in vitro has been analyzed both experimentally and theoretically, but their functional role in vivo, that is, within the full network, and the resulting evolutionary pressures remain largely to be examined. RESULTS: We investigated an integrated network of the yeast Saccharomyces cerevisiae comprising transcriptional and protein-protein interaction data. A comparative analysis was performed with respect to Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii and Yarrowia lipolytica, which belong to the same class of hemiascomycetes as S. cerevisiae but span a broad evolutionary range. Phylogenetic profiles of genes within different forms of the motifs show that they are not subject to any particular evolutionary pressure to preserve the corresponding interaction patterns. The functional role in vivo of the motifs was examined for those instances where enough biological information is available. In each case, the regulatory processes for the biological function under consideration were found to hinge on post-transcriptional regulatory mechanisms, rather than on the transcriptional regulation by network motifs. CONCLUSION: The overabundance of the network motifs does not have any immediate functional or evolutionary counterpart. A likely reason is that motifs within the networks are not isolated, that is, they strongly aggregate and have important edge and/or node sharing with the rest of the network.

Drug Resistance, Fungal↗

PubMed2006

Influence of metabolic network structure and function on enzyme evolution.

BACKGROUND: Most studies of molecular evolution are focused on individual genes and proteins. However, understanding the design principles and evolutionary properties of molecular networks requires a system-wide perspective. In the present work we connect molecular evolution on the gene level with system properties of a cellular metabolic network. In contrast to protein interaction networks, where several previous studies investigated the molecular evolution of proteins, metabolic networks have a relatively well-defined global function. The ability to consider fluxes in a metabolic network allows us to relate the functional role of each enzyme in a network to its rate of evolution. RESULTS: Our results, based on the yeast metabolic network, demonstrate that important evolutionary processes, such as the fixation of single nucleotide mutations, gene duplications, and gene deletions, are influenced by the structure and function of the network. Specifically, central and highly connected enzymes evolve more slowly than less connected enzymes. Also, enzymes carrying high metabolic fluxes under natural biological conditions experience higher evolutionary constraints. Genes encoding enzymes with high connectivity and high metabolic flux have higher chances to retain duplicates in evolution. In contrast to protein interaction networks, highly connected enzymes are no more likely to be essential compared to less connected enzymes. CONCLUSION: The presented analysis of evolutionary constraints, gene duplication, and essentiality demonstrates that the structure and function of a metabolic network shapes the evolution of its enzymes. Our results underscore the need for systems-based approaches in studies of molecular evolution.

Amino Acid Substitution↗

PubMed2006

Changes in the use of postnatal steroids for bronchopulmonary dysplasia in 3 large neonatal networks.

BACKGROUND: Postnatal corticosteroids were widely used in the 1990s in an attempt to reduce the incidence of bronchopulmonary dysplasia. However, high rates of short-term adverse effects and impaired neurodevelopmental outcomes were seen. In early 2002, a joint statement of the American Academy of Pediatrics and Canadian Paediatric Society called for limitation in the use of postnatal corticosteroids. The impact of this statement is not known. OBJECTIVES: The purpose of this work was to determine the frequency of postnatal corticosteroid use and mortality and morbidities over time, particularly before and after the joint statement. DESIGN/METHODS: We conducted a retrospective analysis of cohort data within 3 large network registries (the National Institute of Child Health and Development Neonatal Research Network [18 centers], the Vermont Oxford Network [444 centers], and the Canadian Neonatal Network [10 centers]) for the following 3 periods: prestatement (2001), statement (2002), and poststatement (2003) of very low birth-weight infants (501-1500 g). The National Institute of Child Health and Development Neonatal Research Network and the Vermont Oxford Network were also analyzed for longer-term trends from 1990 to 2003. Postnatal corticosteroid use, mortality at discharge, and neonatal morbidities (bronchopulmonary dysplasia at 36 weeks, late-onset infection >72 hours of age, necrotizing enterocolitis treated with surgery, and length of stay) between periods were compared. RESULTS: Mean birth weight (range: 1022-1060 g), postmenstrual age (28 weeks), and gender (51% male) were similar between the networks. Race differed with more black infants in the National Institute of Child Health and Development Neonatal Research Network than the Vermont Oxford Network (38% vs 24%). Antenatal steroid use was similar (range: 61%-75%). Postnatal corticosteroid use rose from 1990 (8%-16%), peaked in 1996-1998 (24%-28%), and began to decline in 1999. Use in 2003 was significantly less than in 2001. Mortality and major morbidities were similar. CONCLUSIONS: Postnatal corticosteroid use had decreased significantly in 3 large neonatal networks before the joint statement with further decreases after the statement with no apparent impact on mortality and short-term morbidity. Despite substantial decreases, approximately 8% of very low birth-weight infants continue to be treated with postnatal corticosteroid.

Adrenal Cortex Hormones↗

PubMed2006

Network analysis of the protein chain tertiary structures of heterocomplexes.

In this paper, the tertiary structures of protein chains of heterocomplexes were mapped to 2D networks; based on the mapping approach, statistical properties of these networks were systematically studied. Firstly, our experimental results confirmed that the networks derived from protein structures possess small-world properties. Secondly, an interesting relationship between network average degree and the network size was discovered, which was quantified as an empirical function enabling us to estimate the number of residue contacts of the protein chains accurately. Thirdly, by analyzing the average clustering coefficient for nodes having the same degree in the network, it was found that the architectures of the networks and protein structures analyzed are hierarchically organized. Finally, network motifs were detected in the networks which are believed to determine the family or superfamily the networks belong to. The study of protein structures with the new perspective might shed some light on understanding the underlying laws of evolution, function and structures of proteins, and therefore would be complementary to other currently existing methods.

Models, Molecular↗

PubMed1994

The network of hematopoietic cytokines.

Cell viability, multiplication, and differentiation to the various hematopoietic cell lineages are induced by a multigene cytokine family, and hematopoiesis is controlled by a network of interactions between these cytokines. This network includes positive regulators such as colony-stimulating factors and interleukins, and negative regulators such as transforming growth factor-beta and tumor necrosis factor. The functioning of the network requires an appropriate balance between positive and negative regulators, and the selective regulation of programmed cell death (apoptosis) by interaction of cytokines with their receptors. The cytokine network, which has arisen during evolution, allows considerable flexibility, depending on which part of the network is activated, and the ready amplification of response to a particular stimulus. This amplification occurs by autoregulation and transregulation of genes for the hematopoietic cytokines. There is also a transregulation by these cytokines of cytokine receptors. In addition to the flexibility of this network, both for response to present day infections and to infections that may develop in the future, a network may also be necessary to stabilize the whole system. The existence of a network and the cytokine-receptor regulation of apoptosis has to be taken into account in the clinical use of cytokines for therapy. Cytokines that regulate hematopoiesis induce the expression of genes for transcription factors. Cytokine signaling through transcription factors can thus ensure the autoregulation and transregulation of cytokine and receptor genes that occur in the network. Interactions between the cytokine network and transcription factors can also ensure production of specific cell types and stability of the differentiated state.

Biological Evolution↗

PubMed1999

[Do general practitioners want to manage chronic hepatitis C and take part in hepatitis C health networks? A national survey].

OBJECTIVES: To assess information that general practitioners had on hepatitis C and on the hepatitis C network in hospitals and private practice. METHODOLOGY: A national telephone survey of 604 general practitioners was conducted between March 18 and 23, 1998. RESULTS: Screening and management of hepatitis C was important for 89% and 97% of general practitioners. Screening was performed in relation to the relative risk (IV drug users 89%, blood transfusion before 1991 88%). General practitioners wanted more information on treatment (54%), patient counselling (42%) and the potential risks of the disease (42%). Of 604 general practitioners, 6% were involved in a hepatitis C network, while 21% were involved in another network (drug users 9%, AIDS 8%). Of the 94% general practitioners who were not part of the network, 33% were willing to join a hepatitis C network. Only 56% were aware of a hepatitis C network (press article 30%, mailing 17% or local meeting 12%). The difficulties for the involvement of general practitioners were: lack of time, topics not adapted to daily practice and geographic constraints (74%), too few patients in their practice (52%), no need (38%), the idea itself of a network and lack of information (28%). CONCLUSION: General practitioners screen patients at risk of hepatitis C. They want to be better informed about treatment, patient counselling, and the potential risks of hepatitis C. They are less involved in hepatitis C networks than in other networks (drug, AIDS). However, one third of general practitioners would like to be involved in a hepatitis C network. These results could be useful for implementing post-graduate courses and general practitioner training.

Adult↗

PubMed2001

First steps in developing a managed clinical network for vascular services in Lanarkshire.

AIM: To survey key personnel involved in setting up a Managed Clinical Network (MCN) for vascular services in Lanarkshire to assess their views, knowledge and understanding about networking, with a view to facilitating the implementation of the MCN. DESIGN: A questionnaire was designed covering current networking practice, use of protocols, audit and training. It was piloted with the MCN core group and extended to the wider steering group. Semi-structured interviews were completed with core group members focusing on the structure and management of the network. SETTING: Lanarkshire Health Board, Lanarkshire Acute Hospital Trust and Lanarkshire Primary Care Trust. SUBJECTS: The core group and steering group for the development of a Managed Clinical Network for Vascular Surgical Services. RESULTS: Clinicians tend to have more contacts with clinicians and managers with managers. There was close and frequent networking among clinicians of equal status. Respondents shared increased expectations for participation in audit, working to protocols, training and development and improved means of obtaining patients' views and providing information. All respondents recognised the necessity for network leadership although, in this study, it was not possible to define the style of leadership. CONCLUSION: This study identified a degree of existing, informal networking among members of the core group and steering group and it will be important to build on that foundation as the MCN develops. Further research is required to determine the most appropriate form of leadership for the network, and to identify, more clearly, what is understood by a managed network and what accountability there should be.

Attitude of Health Personnel↗

PubMed2005

Topology of mammalian transcription networks.

We present a first attempt to evaluate the generic topological principles underlying the mammalian transcriptional regulatory networks. Transcription networks, TN, studied here are represented as graphs where vertices are genes coding for transcription factors and edges are causal links between the genes, each edge combining both gene expression and trans-regulation events. Two transcription networks were retrieved from the TRANSPATH database: The first one, TN_RN, is a 'complete' transcription network referred to as a reference network. The second one, TN_p53, displays a particular transcriptional sub-network centered at p53 gene. We found these networks to be fundamentally non-random and inhomogeneous. Their topology follows a power-law degree distribution and is best described by the scale-free model. Shortest-path-length distribution and the average clustering coefficient indicate a small-world feature of these networks. The networks show the dependence of the clustering coefficient on the degree of a vertex, thereby indicating the presence of hierarchical modularity. Clear positive correlation between the values of betweenness and the degree of vertices has been observed in both networks. The top list of genes displaying high degree and high betweennes, such as p53, c-fos, c-jun and c-myc, is enriched with genes that are known as having tumor-suppressor or proto-oncogene properties, which supports the biological significance of the identified key topological elements.

Animals↗

PubMed2006

A study of network education application on nursing staff continuing education effectiveness and staff's satisfaction.

The rapid development of computer technology pushes Internet's popularity and makes daily services more timely and convenient. Meanwhile, it also becomes a trend for nursing practice to implement network education model to break the distance barriers and for nurses to obtain more knowledge. The purpose of this study was to investigate the relationship of nursing staff's information competency, satisfaction and outcomes of network education. After completing 4 weeks of network education, a total of 218 nurses answered the on-line questionnaires. The results revealed that nurses who joined the computer training course for less than 3 hours per week, without networking connection devices and with college degree, had the lower nursing informatics competency; while nurses who were older, at N4 position, with on-line course experience and participated for more than 4 hours each week, had higher nursing informatics competency. Those who participated in the network education course less than 4 hours per week were less satisfied. There were significant differences between nursing positions before and after having the network education. Nurses who had higher nursing information competency also had higher satisfaction toward the network education. Network education not only enhances learners' computer competency but also improves their learning satisfaction. By promoting the network education and improving nurses' hardware/software skills and knowledge, nurses can use networks to access learning resources. Healthcare institutions should also enhance computer infrastructure, and to establish the standards for certificate courses to increase the learning motivation and learning outcome.

Computer-Assisted Instruction↗

PubMed1988

[Social networks in schizophrenic disorders--a review].

In the fifties the "social network"-concept was developed in order to improve the analysis of the structure of different social groups; later on, its emphasis has shifted to the study of "personal networks", that deal with the structure of the social relations of a "focal individual". In the latter sense the term "social network" will be used in this paper. Social networks are discussed in terms of morphological characteristics such as the size or the number of clusters as well as in terms of interactional characteristics such as the number of social contacts of the focal person. Studies concerning the social networks of schizophrenics that have been carried out so far showed the following results: In comparison with the social networks of the mentally healthy those of schizophrenic patients are markedly smaller in size and contain a smaller number of clusters. The proportion of family members in the social networks of schizophrenics is higher than in those of the mentally healthy. Social relations of the latter generally show a more complex structure than those of the schizophrenics. The social networks of schizophrenics patients with multiple admissions are generally smaller than those of first admission schizophrenic patients who also have more extrafamilial contacts. -The last section of this paper discusses methodical problems of the existing studies that deal with the social networks of schizophrenic patients; some suggestions are made concerning the application of the network-concept to psychiatric therapy.

Family↗

PubMed2026

Schizophrenia and bipolar disorder: a comparative analysis of genetic and brain network connectivity.

BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision. METHODS: Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder. RESULTS: Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates. CONCLUSION: These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.

Humans↗

PubMed2025

Beyond Level-1: Identifiability of a Class of Galled Tree-Child Networks.

Inference of phylogenetic networks is of increasing interest in the genomic era. However, the extent to which phylogenetic networks are identifiable from various types of data remains poorly understood, despite its crucial role in justifying methods. This work obtains strong identifiability results for large sub-classes of galled tree-child semidirected networks. Some of the conditions our proofs require, such as the identifiability of a network's tree of blobs or the circular order of 4 taxa around a cycle in a level-1 network, are already known to hold for many data types. We show that all these conditions hold for quartet concordance factor data under various gene tree models, yielding the strongest results from 2 or more samples per taxon. Although the network classes we consider have topological restrictions, they include non-planar networks of any level and are substantially more general than level-1 networks - the only class previously known to enjoy identifiability from many data types. Our work establishes a route for proving future identifiability results for tree-child galled networks from data types other than quartet concordance factors, by checking that explicit conditions are met.

Mathematical Concepts↗

PubMed2025

DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.

Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.

Leukemia↗

PubMed2006

The greater Denver Latino Cancer Prevention/Control Network. Prevention and research through a community-based approach.

The Latino/a Research & Policy Center (LRPC), at the University of Colorado (UC) at Denver and Health Sciences Center built the Greater Denver Latino Cancer Prevention Network, a successful cancer prevention network, in 6 Denver metro area counties. The Network consisted of 23 Latino community-based organizations, health clinics, social service agencies, faith-based groups, and employee-based organizations; 2 migrant health clinics; and 14 scientific partners including the UC Comprehensive Cancer Center, the Colorado Department of Public Health and Environment, and the American Cancer Society. The Network focused on 5 significant cancers: breast, cervical, lung, colorectal, and prostate cancer. The Steering Committee initiated a review process for junior researchers that resulted in 5 NCI-funded pilot projects. Pilot projects were conducted with various Latino populations. The Network developed community education and health promotion projects including the bilingual outreach play The Cancer Monologues. The Network's partnership also started and held 2 annual health fairs, Dia de la Mujer Latina/Day of the Latina Woman, and annual health prevention summits. The Special Population Network (SPN) adapted and revised a clinical trials education outreach module that reached Network community partners. SPN partners recruited Latino/a students to cancer research through a6-week NCI training program held yearly at the UCHSC campus. The Network methodology of bringing together the Latino community with the scientific community increased the level of awareness of cancer in the Latino community and increased cancer research and the level of engagement of the scientific partners with the Latino community. Cancer 2006. (c) 2006 American Cancer Society.

Biomedical Research↗

PubMed1996

Detection of seizure activity in EEG by an artificial neural network: a preliminary study.

Neural networks, inspired by the organizational principles of the human brain, have recently been used in various fields of application such as pattern recognition, identification, classification, speech, vision, signal processing, and control systems. In this study, a two-layered neural network has been trained for the recognition of temporal patterns of the electroencephalogram (EEG). This network is called a Learning Vector Quantization (LVQ) neural network since it learns the characteristics of the signal presented to it as a vector. The first layer is a competitive layer which learns to classify the input vectors. The second, linear, layer transforms the output of the competitive layer to target classes defined by the user. We have tested and evaluated the LVQ network. The network successfully detects epileptiform discharges (EDs) when trained using EEG records scored by a neurologist. Epochs of EEG containing EDs from one subject have been used for training the network, and EEGs of other subjects have been used for testing the network. The results demonstrate that the LVQ detector can generalize the learning to previously "unseen" records of subjects. This study shows that the LVQ network offers a practical solution for ED detection which is easily adjusted to an individual neurologist's style and is as sensitive and specific as an expert visual analysis.

Adolescent↗

PubMed2002

Dynamics of Boolean networks controlled by biologically meaningful functions.

The remarkably stable dynamics displayed by randomly constructed Boolean networks is one of the most striking examples of the spontaneous emergence of self-organization in model systems composed of many interacting elements (Kauffman, S., J. theor. Biol.22, 437-467, 1969; The Origins of Order, Oxford University Press, Oxford, 1993). The dynamics of such networks is most stable for a connectivity of two inputs per element, and decreases dramatically with increasing number of connections. Whereas the simplicity of this model system allows the tracing of the dynamical trajectories, it leaves out many features of real biological connections. For instance, the dynamics has been studied in detail only for networks constructed by allowing all theoretically possible Boolean rules, whereas only a subset of them make sense in the material world. This paper analyses the effect on the dynamics of using only Boolean functions which are meaningful in a biological sense. This analysis is particularly relevant for nets with more than two inputs per element because biological networks generally appear to be more extensively interconnected. Sets of the meaningful functions were assembled for up to four inputs per element. The use of these rules results in a smaller number of distinct attractors which have a shorter length, with relatively little sensitivity to the size of the network and to the number of inputs per element. Forcing away the activator/inhibitor ratio from the expected value of 50% further enhances the stability. This effect is more pronounced for networks consisting of a majority of activators than for networks with a corresponding majority of inhibitors, indicating that the former allow the evolution of larger genetic networks. The data further support the idea of the usefulness of logical networks as a conceptual framework for the understanding of real-world phenomena.

Algorithms↗