Gene regulatory network controlling embryonic specification in the sea urchin

The current state of the gene regulatory network for endomesoderm specification in sea urchin embryos is reviewed. The network was experimentally defined, and is presented as a predictive map of cis-regulatory inputs and functional regulatory gene interconnections (updated versions of the network and most of the underlying data are at ). The network illuminates the 'whys' of many aspects of zygotic control in early sea urchin development, both spatial and temporal. The network includes almost 50 genes, and these are organized in subcircuits, each of which executes a particular regulatory function.     

Gene regulatory networks in the early ascidian embryo

Ascidians, or sea squirts, are tunicates that diverged from the vertebrate lineage early in the chordate evolution. The compact and simple organization of the ascidian genome makes this organism an ideal model system for analyzing gene regulatory networks in embryonic development. Embryos contain relatively few cells and gene activities by individual cells have been determined. Here we review and discuss advances in our understanding of the ascidian embryogenesis emerging from genomic expression studies and analyses at the single cell level.     

A short consensus repeat-containing complement regulatory protein of lamprey that participates in cleavage of lamprey complement 3

The prototype of the short consensus repeat (SCR)-containing C regulatory protein is of interest in view of its evolutionary significance with regard to the origin of the C regulatory system. Lamprey is an agnathan fish that belongs to the lowest class of vertebrates. Because it does not possess lymphocytes, it lacks Ig and consequently the classical C pathway. We identified an SCR-containing C regulatory protein from the lamprey. The primary structure predicted from the cDNA sequence showed that this is a secretary protein consisting of eight SCRs. This framework is similar to the alpha-chain of C4b-binding protein (C4bp). SCR2 and -3 of human C4bp are essential for C4b inactivation, and this region is fairly well conserved in the lamprey protein. However, the other SCRs of this protein are similar to those of other human C regulatory proteins. The lamprey protein binds to the previously reported lamprey C3b/C3bi deposited on yeast and cleaves lamprey C3b-like C3 together with a putative serum protease. The scheme resembles the C regulatory system of mammals, where factor I and its cofactor inactivate C3b. Unlike human cofactors, the lamprey protein requires divalent cations for C3b-like C3 cleavage. Its artificial membrane-anchored form protects host cells from lamprey C attack via the lectin pathway. Thus, the target of this protein appears to be C3b and/or its family. We named this protein Lacrep, the lamprey C regulatory protein. Lacrep is a member of SCR-containing C regulators, the first of its kind identified in the lowest vertebrates.     

Flexibility in the patterning and control of axial locomotor networks in lamprey

In lower vertebrates, locomotor burst generators for axial muscles generally produce unitary bursts that alternate between the two sides of the body. In lamprey, a lower vertebrate, locomotor activity in the axial ventral roots of the isolated spinal cord can exhibit flexibility in the timings of bursts to dorsally-located myotomal muscle fibers versus ventrally-located myotomal muscle fibers. These episodes of decreased synchrony can occur spontaneously, especially in the rostral spinal cord where the propagating body waves of swimming originate. Application of serotonin, an endogenous spinal neurotransmitter known to presynaptically inhibit excitatory synapses in lamprey, can promote decreased synchrony of dorsal-ventral bursting. These observations suggest the possible existence of dorsal and ventral locomotor networks with modifiable coupling strength between them. Intracellular recordings of motoneurons during locomotor activity provide some support for this model. Pairs of motoneurons innervating myotomal muscle fibers of similar ipsilateral dorsoventral location tend to have higher correlations of fast synaptic activity during fictive locomotion than do pairs of motoneurons innervating myotomes of different ipsilateral dorsoventral locations, suggesting their control by different populations of premotor interneurons. Further, these different motoneuron pools receive different patterns of excitatory and inhibitory inputs from individual reticulospinal neurons, conveyed in part by different sets of premotor interneurons. Perhaps, then, the locomotor network of the lamprey is not simply a unitary burst generator on each side of the spinal cord that activates all ipsilateral body muscles simultaneously. Instead, the burst generator on each side may comprise at least two coupled burst generators, one controlling motoneurons innervating dorsal body muscles and one controlling motoneurons innervating ventral body muscles. The coupling strength between these two ipsilateral burst generators may be modifiable and weakening when greater swimming maneuverability is required. Variable coupling of intrasegmental burst generators in the lamprey may be a precursor to the variable coupling of burst generators observed in the control of locomotion in the joints of limbed vertebrates.     

Designing experiments that aid in the identification of regulatory networks.

Predictive mathematical models of the interactions of a genetic network can provide insight into the mechanisms of gene regulation, the role of various genes within a network and how multiple genes interact leading to complex traits. However, identification of the parameters and interactions is currently a limiting step in the development of such models. This work reviews the state of the art for design of experiments in biological systems and demonstrates the need for improved design of experiments through the use of a model system. Appropriate design of experiments has a profound impact on the ability to identify a model and on the quality of resulting identified model. Key issues include the selection of appropriate input sequences (e.g. random, independent multivariate inputs) and the selection of the sampling frequencies. This work demonstrates that these issues are especially important in the identification of biochemical networks and that the traditional biochemical approach is incapable of truly identifying the behavior present in such networks.     

Mapping regulatory networks in microbial cells.

Genome sequences are the blueprints of diverse life forms but they reveal little information about how cells make coherent responses to environmental changes. The combined use of gene fusions, gene chips, 2-D polyacrylamide gel electrophoresis, mass spectrometry and 'old-fashioned' microbial physiology will provide the means to reveal a cell's regulatory networks and how those networks are integrated.     

Modeling postural control in the lamprey

 A phenomenological model of the mechanism of stabilization of the body orientation during locomotion (dorsal side up) in the lamprey is presented. The mathematical modeling is based on experimental results obtained during investigations of postural control in lampreys using a combined in vivo and robotics approach. The dynamics of the model agree qualitatively with the experimental data. It is shown by computer simulations that postural correction commands from reticulospinal neurons provide information sufficient to stabilize body orientation in the lamprey. The model is based on differences between the effects exerted by the vestibular apparatus on the left and the right side. Received: 16 February 2000 / Accepted in revised form: 29 September 2000     

A Markovian approach to the control of genetic regulatory networks

This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while state transitions are considered to be probabilistic. An algorithm is proposed to determine a sequence of control actions that drives (without state feedback) the state of a given network to within a desired state set with a prescribed minimum or maximum probability. A heuristic is proposed and shown to improve the efficiency of the algorithm for a class of genetic networks.     

Gene regulatory networks generating the phenomena of additivity, dominance and epistasis

We show how the phenomena of genetic dominance, overdominance, additivity, and epistasis are generic features of simple diploid gene regulatory networks. These regulatory network models are together sufficiently complex to catch most of the suggested molecular mechanisms responsible for generating dominant mutations. These include reduced gene dosage, expression or protein activity (haploinsufficiency), increased gene dosage, ectopic or temporarily altered mRNA expression, increased or constitutive protein activity, and dominant negative effects. As classical genetics regards the phenomenon of dominance to be generated by intralocus interactions, we have studied two one-locus models, one with a negative autoregulatory feedback loop, and one with a positive autoregulatory feedback loop. To include the phenomena of epistasis and downstream regulatory effects, a model of a three-locus signal transduction network is also analyzed. It is found that genetic dominance as well as overdominance may be an intra- as well as interlocus interaction phenomenon. In the latter case the dominance phenomenon is intimately connected to either feedback-mediated epistasis or downstream-mediated epistasis. It appears that in the intra- as well as the interlocus case there is considerable room for additive gene action, which may explain to some degree the predictive power of quantitative genetic theory, with its emphasis on this type of gene action. Furthermore, the results illuminate and reconcile the prevailing explanations of heterosis, and they support the old conjecture that the phenomenon of dominance may have an evolutionary explanation related to life history strategy.     

Gene networks in glucocorticoid-evoked apoptosis of leukemic cells

To discover the genes responsible for the apoptosis evoked by glucocorticoids in leukemic lymphoid cells, we have begun gene array analysis on microchips. Three clones of CEM cells were compared: C7–14, C1–15 and C1–6. C7–14 and C1–15 are subclones from the original clones C7 (sensitive to apoptosis by glucocorticoids) and C1 (resistant). C1–6 is a spontaneous revertant to sensitivity from the C1 clone. Previously we presented data on the sets of genes whose expression is altered in these cell clones after 20 h exposure to dexamethasone (Dex). The two sensitive clones, which respond by undergoing apoptosis starting about 24 h after Dex is added, both showed >2.5-fold induction of 39 genes and 2-fold reduction of expressed levels from 21 genes. C1–15, the resistant clone, showed alterations in a separate set of genes.

In this paper, we present further analysis of the data on genes regulated in these cell clones after 20 h Dex and compare them with the genes regulated after 12 h Dex. Some, but not all the genes found altered at 20 h are altered at 12 h, consistent with our hypothesis that sequential gene regulation eventually provokes full apoptosis. We also compare the levels of basal gene expression in the three clones. At the basal level no single gene stands out, but small sets of genes differ >2-fold in basal expression between the two sensitive and the resistant clone. A number of the genes basally higher in the resistant clone are potentially anti-apoptotic. This is consistent with our hypothesis that the resistant cells have undergone a general shift in gene expression.     

External control in Markovian genetic regulatory networks: the imperfect information case

Probabilistic Boolean Networks, which form a subclass of Markovian Genetic Regulatory Networks, have been recently introduced as a rule-based paradigm for modeling gene regulatory networks. In an earlier paper, we introduced external control into Markovian Genetic Regulatory networks. More precisely, given a Markovian genetic regulatory network whose state transition probabilities depend on an external (control) variable, a Dynamic Programming-based procedure was developed by which one could choose the sequence of control actions that minimized a given performance index over a finite number of steps. The control algorithm of that paper, however, could be implemented only when one had perfect knowledge of the states of the Markov Chain. This paper presents a control strategy that can be implemented in the imperfect information case, and makes use of the available measurements which are assumed to be probabilistically related to the states of the underlying Markov Chain.     

On finite-horizon control of genetic regulatory networks with multiple hard-constraints

Background

Probabilistic Boolean Networks (PBNs) provide a convenient tool for studying genetic regulatory networks. There are three major approaches to develop intervention strategies: (1) resetting the state of the PBN to a desirable initial state and letting the network evolve from there, (2) changing the steady-state behavior of the genetic network by minimally altering the rule-based structure and (3) manipulating external control variables which alter the transition probabilities of the network and therefore desirably affects the dynamic evolution. Many literatures study various types of external control problems, with a common drawback of ignoring the number of times that external control(s) can be applied.

Results

This paper studies the intervention problem by manipulating multiple external controls in a finite time interval in a PBN. The maximum numbers of times that each control method can be applied are given. We treat the problem as an optimization problem with multi-constraints. Here we introduce an algorithm, the "Reserving Place Algorithm'', to find all optimal intervention strategies. Given a fixed number of times that a certain control method is applied, the algorithm can provide all the sub-optimal control policies. Theoretical analysis for the upper bound of the computational cost is also given. We also develop a heuristic algorithm based on Genetic Algorithm, to find the possible optimal intervention strategy for networks of large size.

Conclusions

Studying the finite-horizon control problem with multiple hard-constraints is meaningful. The problem proposed is NP-hard. The Reserving Place Algorithm can provide more than one optimal intervention strategies if there are. Moreover, the algorithm can find all the sub-optimal control strategies corresponding to the number of times that certain control method is conducted. To speed up the computational time, a heuristic algorithm based on Genetic Algorithm is proposed for genetic networks of large size.