Thomas Mueller
email: thomas.mueller@iaf.cnrs-gif.fr

Philippe Vernier
email: philippe.vernier@iaf.cnrs-gif.fr

Mario F Wullimann
email: mario.wullimann@iaf.cnrs-gif.fr

A phylotypic stage in vertebrate brain development: GABA cell patterns in zebrafish compared to mouse

Keywords: cortex, eminentia thalami, Dlx, Mash, PCNA, pallium, proneural genes, subpallium, ventral thalamus, Zash, zona limitans intrathalamica

A recent comparison of early forebrain gene expression in mouse and zebrafish revealed highly comparable expression patterns of developmentally relevant genes, for example of proneural (Neurogenin1, NeuroD, Mash1/Zash1a) genes involved in neurogenesis at a particular time window (mouse: embryonic day 12.5/13.5; zebrafish: 3 days). Here, we extend this analysis to the description of GABA cell patterns in the early postembryonic zebrafish brain (i.e., during early secondary neurogenesis). We find again an astonishing degree of correspondences of GABA cell patterns between zebrafish and mouse during this previously established critical time window, for example regarding absence of GABA cells in certain forebrain regions (pallium, dorsal thalamus, eminentia thalami) or with respect to spatiotemporal occurrence of GABA cells (e.g., late cerebellar GABA cells). Furthermore, there is perfect correlation with previously established proneural gene expression patterns (i.e., absence of Mash1/Zash1a gene expression in GABA cell free forebrain regions) between mouse and zebrafish. The available information in additional vertebrate species, especially in Xenopus, is also highly consistent with our analysis here and suggests that a "phylotypic stage" of neurogenesis during vertebrate brain development may be present.

Introduction

The anatomical regionalization of the vertebrate forebrain, that is, the demarcation of spatially defined areas in the anterior neural tube (discrete progenitor domains), reflects the action of external signals and intrinsic genetic pathways, promoting subsequent morphogenetic events. This represents a crucial step of neural development when neuroblasts acquire a specific identity that will determine further differentiation and the phenotype of the derived neural cells. A recent comparison of early forebrain gene expression in mouse and zebrafish revealed many detailed topological correspondences despite serious divergences in forebrain morphogenesis and resulting adult neuroanatomy (Wullimann and Mueller, 2004b). These divergences are primarily due to different modes of telencephalic development (evagination in mammals/eversion in teleosts). However, highly comparable subpallial versus pallial (i.e., cortical) expression patterns of neurogenic (Notch, Delta) and proneural genes (Neurogenin, NeuroD, Mash or Zash), as well as of other genes involved in the regional specification and differentiation of the neural tube (Pax6, Dlx1/2, Emx1/2, Tbr1) are evident when one takes into account the topological transformations of the telencephalon in the zebrafish as compared to the mouse, especially of the pallial sheet.

The determination of the neural phenotype depends on an ongoing succession of cell-fate restriction in which the expression of neurogenic and proneural genes in spatially defined regions of the neuroepithelium is a key-event (Bertand et al., 2002). The functional consequences of these gene expressions are considerably better understood in the mouse than in the zebrafish. In the murine brain, γaminobutyric acid (GABA) -ergic cells are born and determined in the embryonic ventral subpallium (i.e. the medial ganglionic eminence), the determination of which depends on a genetic pathway that includes the regionalized expression of Dlx1/2, Nkx2.1 and Lhx6 and of the proneural Mash1 gene. Then, a large fraction of these ventrally born GABA neurons migrate tangentially into the pallium, i.e., the future cortex (see Wullimann and Mueller, 2004b for a discussion of recent literature) where they become interneurons. Additional subpallial GABA-positive cells which invade predominantly the posterior cortex, including the hippocampus, arise from the (largely Nkx2.1 expression-free) caudal ganglionic eminence [START_REF] Nery | The caudal ganglionic eminece is a source of distinc cortical and subcorticla cell populations[END_REF][START_REF] Xu | Origins of cortical interneuron subtypes[END_REF]. Possibly, also the lateral ganglionic eminence may contribute some GABAergic cells to the pallium in a late phase of tangential migration of subpallial cells into pallium (Marìn and Rubenstein, 2001). In contrast, the determination and differentiation of glutamatergic cells of the pallium (cortex) depends on the concerted activity of Neurogenin1 and NeuroD in areas where Pax6, Emx1/2, Tbr1 and Lhx9 are expressed in a regionalized manner (see Wullimann and Mueller, 2004b for a discussion of recent literature).

In the mammalian diencephalon, a similar situation regarding differential gene activity and neurochemical phenotype does exist. For example, GABA cells are seen early in the ventral thalamus (prethalamus) and pretectum, while GABA cells are initially absent in the embryonic dorsal thalamus and habenula, where such cells are observed only later [START_REF] Katarova | Domain-restricted expression of two glutamic acid decarboxylase genes in midgestation mouse embryos[END_REF][START_REF] Hayes | Two epochs in the development of γ-aminobutyric acidergic neurons in the ferret thalamus[END_REF][START_REF] Jones | Expression of regulatory genes during differentiation of thalamic nuclei in mouse and monkey[END_REF]. Interestingly, the mouse ventral thalamus (and pretectum, for that matter) is at the same time characterized by Dlx1/2 (Bulfone et al., 1993a;b;[START_REF] Puelles | Comparison of the mammalian and avian telencephalon from the perspective of gene expression data[END_REF]2000) and Mash1 expression [START_REF] Lo | Mammalian achaete-scute homolog 1 is transiently expressed by spatially restricted subsets of early neuroepithelial and neural crest cells[END_REF][START_REF] Ma | Mash1 and neurogenin1 expression patterns define complementary domains of neuroepithelium in the developing CNS and are correlated with regions expressing Notch ligands[END_REF][START_REF] Horton | Correct coordination of neuronal differentiation events in ventral forebrain requires the bHLH factor Mash1[END_REF][START_REF] Torii | Transcription factors Mash-1 and Prox-1 delineate early steps in differentation of neural stem cells in the developing central nervous system[END_REF][START_REF] Fode | A role for neural determination genes in specifying the dorsoventral identity of telencephalic neurons[END_REF]. Thus, in the ventral thalamus, as in the subpallium, the production of GABA cells may depend on the combinatorial activity of Dlx1/2 and Mash1, although this hypothesis has not been directly investigated. Taken together, these observations suggest that there are strictly defined temporal and spatial requirements for a given neuronal phenotype to be differentiated (e.g., for GABAergic neurons), and this may represent a strong developmental constraint on spatiotemporal gene expression patterns. If so, this must be reflected in the conservation of gene expression patterns in a large taxon, such as vertebrates.

While there is good correspondence between the zebrafish and the mouse on the level of brain gene expression patterns, for example regarding transcripts of Dlx1/2 and Zash1a (or Mash1, respectively) in subpallium and ventral thalamus, the distribution of GABA cells is not documented at the relevant stages of zebrafish brain development. In the zebrafish, either only very early, embryonic stages have been looked at [START_REF] Martin | Sequence and expression of glutamic acid decarboxylase isoforms in the developing zebrafish[END_REF][START_REF] Doldan | Distribution of GABA-immunolabeling in the early zebrafish (Danio rerio) brain[END_REF] or only the larval hindbrain has been given attention [START_REF] Higashijima | Distribution of prospective glutamatergic, glycinergic, and GABAergic neurons in embryonic and larval zebrafish[END_REF]. To establish the zebrafish as a neurogenetic model which allows direct comparisons with the mouse, it is necessary to define fundamental patterns of neurochemical phenotype formation. In the present paper, we document the dynamics of GABA cell patterns in various postembryonic zebrafish brain stages (i.e., during secondary neurogenesis) and relate these neurochemical patterns to the distribution of proliferative (PCNA) cells and to previously reported expression domains of Zash1a and other proneural gene expression. Additional histochemical stainings for Nissl-substance and/or cell nuclei are used here to further illuminate the relationship of GABA-positive cells versus postmitotic and proliferative GABA-negative cells. Extending our recent analysis (Wullimann and Mueller, 2004b), we find an astonishing degree of correspondences of GABA cell patterns between zebrafish and mouse during a previously established critical time window (zebrafish: 2 to 3 days postfertilization, mouse: embryonic day 12.5/13.5). We also noticed similar correspondence regarding neurogenic and proneural gene expression in yet another vertebrate model animal, the African clawed-frog Xenopus laevis at a defined developmental stage (i.e. stage 48; Wullimann et al., in press). We thus propose that there is sort of a "phylotypic stage" of brain development in vertebrates, as has been suggested for general development [START_REF] Slack | The zootype and the phylotypic stage[END_REF]. This may reflect a strong developmental constraint on neural differentiation, which depends on the strict spatiotemporal patterning of specific gene expressions.

Material and Methods

GABA-Immunohistochemistry

Zebrafish were kept and bred according to [START_REF] Westerfield | The zebrafish book[END_REF] in the local zebrafish facility of the Centre Nationale de la Recherche Scientifique (CNRS) of Gif-sur-Yvette. For -aminobutyric acid (GABA)immunohistochemistry, a total of 106 embryonic and postembryonic zebrafish have been used (34 hpf to 4 dpf, staged after [START_REF] Kimmel | Stages of embryonic development of the zebrafish[END_REF]. The animals were anesthetized with tricaine methanesulfonate (MS 222,Sigma), fixed in 4% paraformaldehyde plus 0.5% glutaraldehyde in Sörensen phosphate buffer (PB,pH 7.4,4°C,overnight). After rinsing two times in PB, specimens were dehydrated in a graded series of ethanol and transferred into paraffin. Paraffin sections are 6 to 12 µm in thickness. The section plane employed was chosen as to deliver horizontal sections in the forebrain and transverse sections in the midand hindbrain, which is possible because of the ventral bending of the forebrain relative to the anterioposterior axis. This plane is meanwhile used routinely in our studies as it has proven to be of great value in cross-species comparisons. Sections were deparaffined and then rinsed three times for 15 min in phosphate buffered saline (PBS, pH 7.4), preblocked in bovine serum albumine (BSA)-PBS (3% BSA) for 30 min, and then blocked with Normal Goat Serum (NGS, Vectastain, Vector Labs., Burlingame, CA) in BSA-PBS (3 drops NGS/10 ml BSA-PBS) for 30 min. Incubation with the primary antibody (polyclonal rabbit anti-GABA antibody from Sigma-Aldrich, Inc., product number A 2052; the antibody is raised against a GABA-BSA conjugate) diluted betweem 1/2000 and 1/5000 in BSA-PBS containing 0.5% Triton X-100 has been carried out overnight. The detection step with the secondary fluorescence-coupled antibody (Oregon Green, goat anti-rabbit IgG from Molecular Probes, Leiden, Netherlands, catalog number A-11055) for 1 hour followed after three rinses of 15 minutes in PBS. To better distinguish GABA-positive cells from GABAnegative proliferative and postmitotic cells, preparations were counterstained with 4',6-diamidino-2phenylindole, dihydrochloride (DAPI, Molecular Probes, catalog number D21490) and part of these sections subsequently also with NeuroTrace® 530/615 red fluorescent Nissl stain (Molecular Probes, catalog number N21482). Each incubation step (5 min) included three additional 15 minutes PBS washing steps. Finally, slides were coverslipped with DAKO fluorescence mounting medium after 3 x 15 minutes washes in PBS. Negative controls have been performed by ommiting the primary antiserum.

All procedures in this work were executed on fixed specimens and did not involve animal experiments. Anesthetization and subsequent fixation of embryos and hatchlings followed official French regulations of animal treatment.

Specifity of the anti-GABA antibody

The primary polyclonal Sigma anti-GABA antibody used here has been previously shown to visualize specifically GABAergic cells at larval zebrafish hindbrain and spinal cord levels by [START_REF] Higashijima | Distribution of prospective glutamatergic, glycinergic, and GABAergic neurons in embryonic and larval zebrafish[END_REF] and our results are identical at these levels. In addition, we looked at embryonic stages of zebrafish brain and spinal cord development (data not shown) to ensure that GABA cell patterns as detected with the Sigma-antibody are identical to already well established embryonic GABA cell patterns based on the usage of a different antibody (Chemicon, [START_REF] Martin | Sequence and expression of glutamic acid decarboxylase isoforms in the developing zebrafish[END_REF]. Moreover, our results concerning GABA cell patterns in the larval zebrafish brain resemble those documented for another teleost, the three-spined stickleback Gasterosteus aculeatus [START_REF] Ekström | Ontogeny of GABA-immunoreactive neurons in the central nervous system in a teleost, Gasterosteus aculeatus L[END_REF], although our anatomical interpretation differs considerably (see discussion). Since embryonic and larval brain cells may contain high levels of GABA only transiently and do not necessarily represent precursors of later, permanent GABAergic phenotypes, we explicitely refer in this study to GABA-positive cells (or GABA cells).

PCNA-Immunohistochemistry

Photomicrographs of material stained for the proliferating cell nuclear antigen (PCNA) have been taken from data generated during previous studies. Briefly, for detection of the proliferating cell nuclear antigen (PCNA), Bouin's-fixed (24 h fixation time) and paraffin-embedded material was used. The section plane is as explained above. The thickness of transverse sections amounted to 7-10µm. The standard biotin-avidin system (Vectastain ABC-kit, peroxidase mouse IgG, PK-4002, Vector Labs., Burlingame, CA) was used as detection system. To unmask PCNA in fixed material, slides were put in the microwave oven for 10-20 min in citric acid-citrate buffer (pH 6,0, after Romeis 1989) at 700 W before incubation with the primary antibody. Incubation with blocking serum for 30 minutes was followed by incubation with the primary monoclonal antibody against a linear epitope of PCNA (PC10, DAKO, Glostrup, Denmark; 1:500) diluted in phosphate buffered saline (pH 7.2) containing 3 % bovine serum albumine (BSA) overnight. Diaminobenzidine (DAB) was used as chromogen and the horseradish peroxidase reaction product was intensified according to Adams (1981).

The specificity of the monoclonal PCNA-antibody used here has been described by Waseem and Lane (1990). It is commercially available meanwhile and has been applied to detect consistently PCNA in various vertebrate species, including the zebrafish, Danio rerio [START_REF] Wullimann | Postembryonic neural proliferation in the zebrafish forebrain and its relationship to prosomeric domains[END_REF]Wullimann and Knipp, 2000). The detection of PCNA with the antibody from DAKO has been already described extensively in the zebrafish brain and the specificity of the antibody and its degree of reliability of PCNA as a marker for proliferation has been discussed there [START_REF] Wullimann | The central nervous system[END_REF]Puelles, 1999, Mueller and[START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF].

Terminology

Neuroanatomical designations for the zebrafish brain are taken from the atlas of early zebrafish brain development [START_REF] Mueller | Atlas of early zebrafish brain development: a tool for molecular neurogenetics[END_REF], which is based on publications by [START_REF] Wullimann | Postembryonic neural proliferation in the zebrafish forebrain and its relationship to prosomeric domains[END_REF], Mueller andWullimann (2002, 2003), Wullimann and Mueller (2004a,b). Designations for the mouse brain (only Fig. 5) have been adopted from [START_REF] Katarova | Domain-restricted expression of two glutamic acid decarboxylase genes in midgestation mouse embryos[END_REF].

Photomicrograph production

Fluorescence detection of GABA, DAPI and NeuroTrace® Nissl staining has been undertaken with a NIKON eclipse E800 fluorescence microscope by using the appropriate filter sets. Photomicrographs have been shot with the NIKON Digital Camera DXM 1200. Digital photoprocessing -including sharpening, color and brightness adjustments as well as merging -has been carried out with Photoshop Elements. Picture arrangements and designation labeling have been done with Corel Draw

Results

Various postembryonic zebrafish stages were studied between 2 to 5 days postfertilization (dpf), but we will focus on the documentation of γ-aminobutyric acid (GABA) cell distribution at the onset of secondary neurogenesis (i.e., at 2 dpf when relatively strong proliferation and weak differentiation is present; Figs. 1; 2A-C,J), and at 3 dpf (with reduced proliferation and stronger differentiation; Figs. 2D-I; 3:4A,C,E,G). The GABA-immunostained sections were also prepared for DAPI nuclear stain (2 dpf), as well as additionally with the fluorescent NeuroTrace® Nissl stain (3 dpf). These double and triple stains allow to distinguish GABA-negative proliferative cells from GABA-negative postmitotic cells because mitotic cells appear brighter blue than postmitotic cells. Furthermore, if fluorescent-stained additionally for the Nissl substance, the postmitotic GABA-negative cells stand out against the mitotic ones with a brighter red stain (see below). In selected cases at 3 dpf, we will additionally relate the distribution of GABA cells to proliferative cells shown with PCNA.

GABA cell patterns at 2 days

Telencephalon. At the level of the olfactory bulb (Fig. 1A), GABA-positive cells surround the glomeruli while the ventral proliferation zone remains free of label. The olfactory epithelium is GABA-negative at all levels. More caudally, the subpallium emerges (Fig. 1B-C), where again the medial proliferation zones are GABA-free, whereas there are many GABA-positive cells lateral to the mitotic subpallial cells. It appears that an early telencephalic migrated area (i.e., M4, not shown) is GABA-free. The pallium remains free of GABA-positive cells at least at rostral levels (Fig. 1B-C). At caudal levels, GABA cells might be present in postmitotic pallial cells already. Approaching precommissural subpallial levels, strongly stained fibers assemble bilaterally in the subpallium. At the level of the anterior commissure, these fibers continue into the commissure and cross brain sides. However, a central part of the commissure remains free of label (not shown). The subpallial GABA-positive cells continue to be present at supra-and postcommissural levels, but there is a distinct gap formed of GABA-immunonegative cells before the again GABA-positive cells of the preoptic region emerge (see below).

Eminentia thalami and preoptic region. At the caudal telencephalic pole, eminentia thalami and preoptic region emerge. Many cells of the preoptic region are GABA-positive (Fig. 1D-F). Some of these cells lie directly caudally to the anterior commissure and are contiguous with the dorsally lying postcommissural GABA cells of the subpallium. Slightly more caudally, however, the subpallial GABA cells fade out, and GABA-negative cells occur before the main population of dorsal preoptic GABA cells comes in more ventrally at this level. The proliferative anterior part and all of the central, periventricular cells of the preoptic region are GABA-immunonegative. In contrast to the preoptic region, the entire eminentia thalami remains free of GABA-positive cells (Fig. 1D-E), including its most laterally migrated part (M3) dorsal (i.e., caudal) to the lateral forebrain bundle. The lateral forebrain bundle serves as a diagnostic landmark at this level, and many of its fibers are GABA-positive.

Diencephalon. The diencephalon presents a particularly complex pattern of GABA-positivity. At anterior levels, both dorsal and ventral thalami are GABA-free, consistent with the presence of large proliferative zones there (see [START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]; the habenula is proliferative at its base, but shows some GABA-positive cells towards the pial periphery (Fig. 1C). Also the epiphysis contains some GABA-positive cells. More posteriorly in the thalamus, GABA-positive cells start to occur at the boundary of dorsal and ventral thalami, these lie either in the most dorsal (i.e., caudal) ventral thalamus towards or within the zona limitans intrathalamica. In contrast, the ventral remainder of the ventral thalamus is highly proliferative at this level and its cells are GABA-free (not shown). However, slightly more posteriorly, many GABA-positive cells suddenly appear in the entire ventral thalamus (prethalamus) with the exception of a conspicuous GABAnegative cell stripe towards the dorsal thalamus (Fig. 1D). This GABA-negative stripe is adjacent to an again GABA-positive stripe of cells extending from ventricle to pia which has been noted above already. GABA cells of the ventral thalamus and those of the above mentioned positive stripe are continuous at the pial periphery. The conspicuous GABA cell stripe is close to or lies within the zona limitans intrathalamica (see discussion). The dorsal thalamus remains free of GABA cells also at this level (Fig. 1D). Towards most posterior thalamic levels, GABA cells are seen in the periphery of the dorsal thalamus which seem to grade into those of the pretectum, which contains many strongly stained GABA-positive cells (Fig. 1E-F). Distinct GABA-positive fiber staining is seen in the posterior commissure and in the lateral fibers tracts of the diencephalon which continue into the postoptic commissure.

GABA cells are seen in the most lateral gray matter, remote from the respective proliferation zones of the basal plate portions of the diencephalon, i.e., posterior tuberculum, especially at anterior, but much less at posterior levels (Fig. 1F-G), and in the region of the nucleus of the medial longitudinal fascicle (basal part of P1; Fig. 1G). Dorsal and ventral parts of the posterior tuberculum (i.e., basal plate of P2/P3) are hard to discriminate from each other based on GABA cell patterns anteriorly. More posteriorly, a band of GABA-positive cells appears to extend into the boundary zone between dorsal and ventral posterior tubercular portions. Posterior to the postoptic commissure, the rostral and intermediate hypothalamus display GABA-positive cells at the utmost periphery (Fig. 1G-H), with GABA-negative proliferative cells being extensive around the hypothalamic ventricle. The posterior tubercular commissure contains many GABA-immunostained fibers. Posterior to it, also the caudal hypothalamus contains GABA cells at the periphery (Fig. 2A-B). There is also GABA-immunoreactivity in cell bodies of the pituitary. The retina as well as the optic nerve and tract remain entirely free of GABA-immunoreactivity at 2 dpf.

Mesencephalon. There is a considerable population of GABA-positive cells in the optic tectum in its central part towards the periphery, but not in the highly proliferative most anterior and most posterior parts, and also not in the basal, medial and lateral proliferation zones along the entire tectum (Fig. 1E-H; 2A-B). In the torus semicircularis, there appear to be some medially located GABA-positive cells (Fig. 2A). The basal plate tegmentum contains many GABA-positive cells lateral to the medial proliferation zone. In contrast, in the isthmus (midbrain-hindbrain-boundary region; T/MO), a distinct gap of GABA-positivity is seen (Fig. 2A), before the more caudally located rhombencephalic and valvular GABA cells come in (see next paragraph). An increasing mass of GABA-positive fibers accumulates bilaterally in the ventral mesencephalic tegmentum.

Cerebellum and rhombencephalon. The posterior main part of the cerebellum, the cerebellar corpus, is free of GABA cells at 2 dpf; but in the periphery of the anterior cerebellar valvula, some GABA cells are present towards the periphery (Fig. 2B). This is consistent with the delayed cerebellar maturation (i.e., strong proliferation, neuronal determination, but no differentiation at 2 dpf) observed previously [START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]. In contrast, the medulla oblongata contains many GABA-positive cells lateral to the Vshaped proliferative zone (Fig. 2B-C). Two observations can be made in this part of the brain. Obviously, in the anterior medulla oblongata (ventral to the cerebellum), the GABA cells appear more homogeneously clustered than in the posterior part. There, conspicuous radially oriented stripes of GABA-positive cells are seen to alternate with GABA-negative stripes (see inset in lower right panel, Fig. 2J). The second observation is that there are no GABA cells at all in the lateral medulla oblongata where the proliferative rhombic lip is located (see inset in lower right panel, Fig. 2J) from which many cells appear to stream ventrolaterally away from it [START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]. Again strong GABA-immunoreactivity is seen in the ventrolaterally located white matter of the medulla oblongata which contains longitudinally running fibers tracts. All cranial nerve ganglia/nerves are free of GABA-immunoreactivity.

GABA cell patterns at 3 days

There is an obvious overall increase in the degree of differentiation in the entire zebrafish brain at 3dpf. As a consequence, proliferation zones and postmitotic GABA-positive and GABA-negative cell populations may be discriminated even better from each other than at 2 days. This is demonstrated in certain photographs (Figs. 2D-I; 3), where triple-labelled zebrafish brain sections showing immunostained GABA-positive structures (green), DAPI stained cell nuclei (blue) and NeuroTrace® stained Nissl substance (i.e., rough endoplasmic reticulum; red), reveal those three cell populations at various places quite clearly. The bright blue signal characterizes the enlarged mitotic nuclei of cells in ventricularly located proliferation zones. The extent of proliferation zones can be verified by PCNA stained sections at comparable brain levels paralleling the triple stained sections. The green (cytoplasmic) GABA signal is seen to surround more weakly blue stained nuclei of postmitotic cells that are in positions migrated away from the ventricle. The remaining postmitotic cells, that are GABA-negative, exhibit the red signal strongly within the extended cytoplasm surrounding the small postmitotic nuclei.

Telencephalon. The overall distribution of GABA cells in the zebrafish telencephalon is similar to the situation at 2 dpf. Clearly, the pallium appears much larger relative to the subpallium at 3 days. There are many GABA-positive cells in the olfactory bulb (now better identifiable as periglomerular cells; Fig. 3A) as well as in the subpallium (Fig. 3B), with the exclusion of the respective proliferation zones. Some GABApositive cells are distinctly set apart dorsally from the subpallium and appear to have invaded the pallium, probably coming from the subpallium (arrowheads in Fig. 3B; single arrowhead in Fig. 4A). But the majority of postmitotic pallial cells are GABA-negative, recognizable by the characteristic combination of red Nissl and blue nuclear DAPI stain, which is very distinct from the bright blue stain of proliferative cells at the pallial ventricle. The early migrated telencephalic region M4 stands out very clearly at 3 dpf and remains GABAnegative. Although many GABA-positive fibers are present in the lateral telencephalon which continue into the anterior commissure, their relative proportion within the latter seems smaller than at 2 dpf, indicating that fibers containing different neurotransmitters are increasingly invading the anterior commissure at 3 days. Many supra-and postcommissural GABA-positive subpallial cells are present and this population fades out before the bulk of preoptic and ventral thalamic GABA-positive cells emerge more posteriorly. The olfactory epithelium remains GABA-negative.

Eminentia thalami and preoptic region. The preoptic region exhibits a large proliferation zone especially at its rostral pole, with postmitotic cells accumulating in the posterior part. Accordingly, many GABA-positive cells are present in this posterior preoptic area, whereas the anterior part remains largely free of GABA cells (Fig. 3B-E). The eminentia thalami, including its migrated region M3, remains free of a GABA signal (Fig. 3D) as is the case at 2 dpf.

Diencephalon. The distribution of GABA cells in the diencephalon is highly comparable to 2 days, although a greater degree of differentiation is visible. The dorsal thalamus remains free of GABA cells. Also the anterior (proliferative) parts of epiphysis, habenula, and ventral thalamus (prethalamus) remain free of GABA cells, but these regions all contain many GABA cells more posteriorly, as does the pretectum (Fig. 3C-H). The diencephalic basal plate, i.e., dorsal and ventral parts of the posterior tuberculum, contain many GABA cells at anterior, but almost none at posterior levels. Interestingly, in the migrated area of the posteior tuberculum M2, no GABA cells are observed (Fig. 3H). Towards the caudal end of the dorsal thalamus, some GABA cells appear and grade into those of the pretectum. In all parts of the hypothalamus, GABA cells are seen to accumulate in the pial periphery, as at 2 days.

At 3 days, a new distinct population of GABA-positive cells is seen in the inner nuclear layer of the retina, likely representing differentiating amacrine cells (e.g., arrowheads in Fig. 3C; three arrowheads in Fig. 4A).

Mesencephalon. The situation in the optic tectum is very similar to 2 days: GABA-positive cells are present in its central part, but they are absent in the (highly proliferative) most anterior and most posterior tectum, and also in the basal, medial and lateral proliferation zones along the entire tectum (Fig. 2D-G; 3G-H). In the torus semicircularis, there appear to be GABA-positive cells (Fig. 2G). Basal plate tegmentum contains many GABA-positive cells lateral to the medial proliferation zone which are set apart from the rhombencephalic ones by a GABA-free zone in the isthmus (midbrain-hindbrain-boundary region; T/MO). An even larger mass of GABA-positive fibers accumulates bilaterally in the ventral mesencephalic tegmentum.

Cerebellum and rhombencephalon. GABA cells at 3 dpf are now not only seen in the valvula, but additionally in the corpus cerebelli (Fig. 2H). The medulla oblongata still contains many GABA-positive cells lateral to the V-shaped proliferative zone. As at 2 days, the GABA cells in the anterior medulla oblongata (ventral to the cerebellum), appear more homogeneously clustered than in the posterior part. There, conspicuous radially oriented stripes of GABA-positive cells are seen to alternate with GABAnegative stripes (Fig. 2I). There are still no GABA cells at all in the lateral medulla oblongata where the proliferative rhombic lip is located (Fig. 2I) from which many cells appear to stream ventrolaterally away from it [START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]. Strong GABA fiber immunoreactivity is seen in the ventrolaterally located medullary white matter containing longitudinally running fibers tracts. All cranial nerve ganglia/nerves remain free of GABA-immunoreactivity.

Discussion

In the adult vertebrate brain, glutamatergic and GABAergic neurons represent the most abundant excitatory and inhibitory neurochemical subtypes, respectively. During mammalian telencephalic development, two neurogenetic pathways, which involve different proneural genes (neurogenin/NeuroD versus Mash1), act locally and separately in the generation of glutamatergic and GABAergic cells in pallium (cortex) and subpallium, respectively. Thus, cortical glutamatergic cells are born at the pallial ventricle, migrate along the radial glia into the periphery and differentiate directly within the developing pallium. In contrast, the large numbers of adult cortical GABAergic cells are produced in the ventral subpallium (mostly medial ganglionic eminence; MGE, but also caudal ganglionic eminence, see Introduction) during development, and these cells invade the cortex by tangential migration (Anderson et al., 1997a(Anderson et al., , 1997b;;[START_REF] Casarosa | Mash1 regulates neurogenesis in the ventral telencephalon[END_REF][START_REF] Eisenstat | DLX-1, DLX-2, and DLX-5 expression define distinct stages of basal forebrain differentiation[END_REF][START_REF] Sussel | Loss of Nkx2.1 homeobox gene function results in ventral to dorsal molecular respecification within the basal telencephalon: evidence for a transformation of the pallidum into the striatum[END_REF][START_REF] Marín | Origin and molecular specification of striatal interneurons[END_REF]Marìn and Rubenstein, 2001;[START_REF] Nery | The caudal ganglionic eminece is a source of distinc cortical and subcorticla cell populations[END_REF][START_REF] Schuurmans | Molecular mechanisms underlying cell fate specification in the developing telencephalon[END_REF][START_REF] Yun | Modulation of the notch signaling by Mash1 and Dlx1/2 regulates sequential specification and differentiation of progenitor cell types in the subcortical telencephalon[END_REF][START_REF] Zhao | The LIM-homeobox gene Lhx8 is required for the development of many cholinergic neurons in the mouse forebrain[END_REF][START_REF] Xu | Origins of cortical interneuron subtypes[END_REF].

Recently, a highly comparable situation regarding differential (complementary) proneural gene expression (that is, the neurogenin/NeuroD versus the Mash1 pathways, known to be related to the alternative development of glutamatergic and GABAergic neurons in mammals, respectively) has been revealed in the zebrafish forebrain [START_REF] Wullimann | Expression of Zash-1a in the postembryonic zebrafish brain allows comparison to mouse Mash1 domains[END_REF]2004a;b;[START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF][START_REF] Mueller | Atlas of early zebrafish brain development: a tool for molecular neurogenetics[END_REF]. However, the occurence of GABA-positive cells remains largely unexplored in the postembryonic zebrafish brain. In the present contribution, we establish in the model animal zebrafish with immunohistochemical means the detailed distribution of early postembryonic GABA-positive cells. Their relationship to proliferating and GABA-negative postmitotic cells, as well as to neurogenic and proneural gene expression domains, shall be discussed first. In a second part of the discussion, a comparison with similar data in other vertebrate groups is offered.

Zebrafish brain GABA cell populations in the functional developmental context.

Several reports indicate that the earliest differentiating zebrafish brain neurons building the early axonal scaffold contain GABA around 24 h [START_REF] Martin | Sequence and expression of glutamic acid decarboxylase isoforms in the developing zebrafish[END_REF][START_REF] Doldan | Distribution of GABA-immunolabeling in the early zebrafish (Danio rerio) brain[END_REF] or at least that GABApositive cells are closely associated with these first neurons. In any case, a more massive production of GABA cells is observed somewhat later in most parts of the early zebrafish brain approaching 2 days postfertilization (dpf), as shown in the present study. This developmental pattern is different from the emergence of an initially low and slowly increasing number of locally restricted cells exhibiting either the tyrosine hydroxylase positive neurochemical phenotype, representing dopaminergic and noradrenergic cells [START_REF] Guo | Mutations in the zebrafish unmask shared regulatory pathways controlling the development of catecholaminergic neurons[END_REF][START_REF] Holzschuh | Dopamine transporter expression distinguishes dopaminergic neurons from other catecholaminergic neurons in the developing zebrafish embryo[END_REF][START_REF] Holzschuh | Genetic analysis of the roles of Hh, FGF8, and Nodal signaling during catecholaminergic system development in the zebrafish brain[END_REF][START_REF] Rink | Development of the catecholaminergic system in the early zebrafish brain: an immunohistochemical study[END_REF]McLean and Fetcho, 2004a;b) or the cholinergic phenotype (T. Mueller, P. Vernier and M.F. Wullimann, unpublished observations). A similar slow developmental pattern is seen for serotoninergic cells (McLean and Fetcho, 2004a;b;[START_REF] Rink | The too few mutnat selectively affects subgroups of monoaminergic neurons in the zebrafish forebrain[END_REF]. The numeric dominance of early GABA-positive cells may be related to non-classical functions of GABA outside of neurotransmission during development, for example in the early axonal scaffold. Acetylcholinesterase (AChE) has been widely used to characterize first differentiating neurons, for example those of the axonal scaffold [START_REF] Hanneman | Early expression of acetylcholinesterase activity in functionally distinct neurons of the zebrafish[END_REF][START_REF] Wilson | The development of a simple scaffold of axon tracts in the brain of the embryonic zebrafish, Brachydanio rerio[END_REF][START_REF] Chitnis | Axonogenesis in the brain of zebrafish embryos[END_REF][START_REF] Ross | Axonogenesis and morphogenesis in the embryonic zebrafish brain[END_REF] and it is assumed that AChE also has non-classical functions (outside of enzymatic acetylcholine breakdown), for example in axon guidance and/or in establishing stability of synapses, at least at the peripheral nervous neuromuscular junction [START_REF] Downes | Acetylcholinesterase function is dispensable for sensory neurite growth but is critical for neuromuscular synapse stability[END_REF]. However, in postembryonic stages beginning with 2 dpf, the high number of GABA cells more likely directly represents the relative proportion of this functional neurochemical phenotype versus others.

As shown in the present study, there is a clear mutual exclusive relationship of GABA and PCNA positive cells at postembryonic zebrafish stages (2/3 dpf), demonstrating that GABA cells are never proliferative (compare left and right sections of Fig. 4). Furthermore, additional distinct populations of GABAnegative -but postmitotic -cells can be discriminated among differentiating neuronal cells in the forebrain. This is most evident in the pallium, dorsal thalamus or eminentia thalami. These histogenetic domains are governed by neurogenin/neurod gene expression [START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]Wullimann and Mueller, 2004b) and lack GABA cells at this time point. In sharp contrast, other particular histogenetic forebrain units, such as the subpallium, preoptic region or ventral thalamus, are dominated by GABA-positive postmitotic cells. Thus, there is a clear spatial correlation in particular forebrain areas, such as subpallium, preoptic region or ventral thalamus, between the occurence of GABA cells and cells expressing certain proneural genes (Zash1a; [START_REF] Wullimann | Expression of Zash-1a in the postembryonic zebrafish brain allows comparison to mouse Mash1 domains[END_REF], but not others (such as neurogenin, neuroD). In contrast, the GABA-negative pallium, dorsal thalamus and eminentia thalami are at the same time free of Zash1a expression, but they do express neurogenin and neuroD instead. The hypothalamus falls into the Zash1a positive category, but the ongoing elevated level of periventricular proliferation allows for GABA cells only to be present at the pial periphery there. Later, in the adult teleostean brain -as in all vertebrates -, GABA cells are more ubiquituously distributed, (goldfish: [START_REF] Martinoli | Distribution of GABA-ir neurons in the forebrain of the goldfish, Carassius auratus[END_REF]eel: Médina et al., 1994;trout: Anglade et al., 1999;zebrafish: Kim et al., 2004), including pallium (or cortex) and dorsal thalamus. The mechanisms of how these cells reach these initially GABA-free locations are being elucidated at the moment in other model systems (see above, tangential migration into cortex). The data presented here for the zebrafish allow now for comparable studies of neurogenesis in this model animal.

In the light of these data, the interpretation of the distribution of GABA cells according to a prosomeric pattern with cell groups in each diencephalic neuromere (also in the dorsal thalamus) in the developing brain of another teleost, the stickleback Gasterosteus aculeatus, seems doubtful [START_REF] Ekström | Ontogeny of GABA-immunoreactive neurons in the central nervous system in a teleost, Gasterosteus aculeatus L[END_REF]. Almost certainly, the dorsal thalamus is also GABA-negative in the stickleback, at lest during early development. This species shows after around 100 h of development GABA-positive cell stripes in lateral view that have been interpreted as belonging to P1 (pretectum), P2 (dorsal thalamus) and P3 (ventral thalamus). However, our data in the zebrafish indicate in comparison that the stickleback GABA stripes in fact represent tegmental, pretectal, and zona limitans intrathalamica populations, respectively, and that the population assigned to P4 in the stickleback is the ventral thalamic population.

In contrast to the above discussed case of alternative activity of two neurogenetic pathways in adjacent regions (e.g., pallium and subpallium), there are also zebrafish brain areas where both pathways act during postembryonic zebrafish brain development, but in different cells. These regions include habenula and epiphysis, the region of the nucleus of the medial longitudinal fascicle and the pretectum, the posterior tuberculum, optic tectum and torus semicircularis, as well as the mesencephalic tegmentum, the medulla oblongata, and the cerebellum [START_REF] Wullimann | Expression of Zash-1a in the postembryonic zebrafish brain allows comparison to mouse Mash1 domains[END_REF][START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]. How is the distribution of GABA cells there? While it is not easy to interpret the patterns in each single region, two cases may be distinguished. A first case is evident in the posterior tuberculum (and possibly also in the pretectum). In the posterior tuberculum, GABA cells are increasingly more located towards the periphery of the gray matter towards 3 dpf, whereas the postmitotic cells close to the proliferation zones are GABAnegative at 3 dpf. It is known that these later postmitotic cells are neurod positive at this stage and that the Zash1a expression is more peripheral [START_REF] Wullimann | Expression of Zash-1a in the postembryonic zebrafish brain allows comparison to mouse Mash1 domains[END_REF][START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]. This indicates that the GABA cells have been generated earlier and are displaced peripherally already. Nevertheless, the question whether identical or different ventricular sectors within the posterior tuberculum sequentially give rise to GABA cells first, and subsequently to other cells, needs further investigation. An interesting fact in this context is that the early migrated posterior tubercular region M2 is also GABA cell free (Fig. 3H). The M2 has been shown to maintain an unusual peripheral proliferation and neurod expression [START_REF] Mueller | BrdU-, neuroD-(nrd) and Hu-studies show unusual non-ventricular neurogenesis in the postembryonic zebrafish forebrain[END_REF], indicating that this region mostly consists of -and likely continues to produce -glutamatergic cells. This is in line with the finding that the likely adult derivative of the M2, the preglomerular region, is largely GABA cell free in other teleosts species (goldfish: [START_REF] Martinoli | Distribution of GABA-ir neurons in the forebrain of the goldfish, Carassius auratus[END_REF]eel: Médina et al., 1994;trout: Anglade et al., 1999). This also fits with the ascending sensory relay function of the adult preglomerular complex [START_REF] Wullimann | The central nervous system[END_REF]Wullimann and Mueller, 2004b).

A second case of co-occurence of the two neurogenetic pathways is present in the medulla oblongata and the cerebellum. In the cerebellar plate, there is a repetitive, alternating expression of neurod and Zash1a within the ventral proliferation zone and the Zash1a expression has been hypothesized to be involved in GABAergic cell production, such as the Purkinje cells [START_REF] Wullimann | Expression of Zash-1a in the postembryonic zebrafish brain allows comparison to mouse Mash1 domains[END_REF][START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF]. Indeed, we observe in the present paper some first GABA cells in the zebrafish cerebellar plate at 3 dpf. Similarly, in the medulla oblongata, radially oriented stripes of neurod expression have been observed previously [START_REF] Mueller | Anatomy of neurogenesis in the zebrafish brain[END_REF] and Zash1a expression is also present there, although its exact pattern needs re-examination. In the present contribution, we describe similar stripes of GABA-positive cells in the medulla oblongata. Moreover, [START_REF] Higashijima | Distribution of prospective glutamatergic, glycinergic, and GABAergic neurons in embryonic and larval zebrafish[END_REF] have recently nicely demonstrated alternative stripes of GABAergic and glutamatergic cells in the postembryonic zebrafish hindbrain. Altogether, this indicates strongly that the neurod stripes conform to the glutamate cells and that the zones of Zash1a expression are correlated with the stripes of GABA cells in the zebrafish medulla oblongata. Thus, both in the cerebellum and in the medulla oblongata, alternative domains of GABAergic and glutamatergic cell production appear to be present at the same time in a repetitive, alternating manner.

The genetic and cellular mechanisms that regulate and maintain these three different patterns of GABAergic versus glutamatergic cell production during this period of zebrafish brain neurogenesis must now be elucidated and the data presented here will facilitate such attempts greatly. An interesting issue in this context is to identify those genes that have roles in the posterior zebrafish brain comparable to those of Dlx1/2 in the forebrain.

Zebrafish brain GABA cell populations in comparative perspective.

The developmental emergence of GABA-containing cells has been studied in various vertebrates, such as the lamprey [START_REF] Pombal | Prosomeric map of the lamprey forebrain based on calretinin immunocytochemistry, Nissl stain and ancillary markers[END_REF]Puelles, 1999, Meléndez-Ferro et al., 2002;2003), the clawed-frog Xenopus laevis [START_REF] Roberts | The early development of neurons with GABA immunoreactivity in the CNS of Xenopus laevis embryos[END_REF][START_REF] Barale | Immunohistochemical investigation of gammaaminobutyric acid ontogeny and transient expression in the central nervous system of Xenopus laevis tadpoles[END_REF] or the mouse [START_REF] Katarova | Domain-restricted expression of two glutamic acid decarboxylase genes in midgestation mouse embryos[END_REF][START_REF] Miyoshi | Identfication of a novel basic helix-loop-helix gene, Heslike, and its role in GABaergic neurogenesis[END_REF] and ferret [START_REF] Hayes | Two epochs in the development of γ-aminobutyric acidergic neurons in the ferret thalamus[END_REF]. An interspecific comparison reveals many correspondences to our report in the zebrafish, especially regarding the early GABA cell free forebrain regions (see below), and, thus, makes the zebrafish more suitable as a neurogenetic model, especially for looking at molecular mechanisms guiding post-primary (secondary) neurogenesis.

Mouse/Ferret:

There is almost perfect correspondence between the cellular distribution of glutamic acid decarboxylase (GAD) in the mouse (E 12.5; [START_REF] Katarova | Domain-restricted expression of two glutamic acid decarboxylase genes in midgestation mouse embryos[END_REF] and our zebrafish GABA cell patterns around 3 dpf, as summarized in a schematic comparison (Fig. 5). Notably, the same GABA cell free forebrain areas, i.e., pallium (cortex), dorsal thalamus and thalamic eminence clearly show up in both species, as discussed above. Moreover, correspondences also apply to the temporal sequence of GAD/GABA-positivity between the two species. At embryonic day 10.5, many brain regions are still negative for GAD (for example: cortex, epiphysis, dorsal thalamus, thalamic eminence, superior and inferior colliculi, septum, cerebellum, posterior preoptic region, amygdala). While some will remain GAD-negative until much later (cortex, dorsal thalamus, thalamic eminence), most other areas will show GAD-positivity during the next two embryonic days (compare with dark gray areas in Fig. 5). However, some regions (indicated in light gray in Fig. 5) only appear at the earliest at embryonic day 12.5 or even later (e.g. the cerebellum or the olfactory bulb). Similarly, we see many more GABA cells in the zebrafish olfactory bulb at 3 dpf than at 2 dpf. Also, the first cerebellar GABA-positive cells are only seen at 3 dpf in the zebrafish. However, such developmental sequences of GABA cell occurrence are much less obvious in the zebrafish than in the mouse because of the much shorter developmental time in the former. Also in another mammal, the ferret, GABA cells are seen much earlier (14 days) in the ventral thalamus, the pretectum, and in the habenula, but appear much later in the dorsal thalamus [START_REF] Hayes | Two epochs in the development of γ-aminobutyric acidergic neurons in the ferret thalamus[END_REF], likely by invasion from an extrinsic source. Similarly, absence of Dlx expression and later emergence of GABA cells in the dorsal thalamus (as opposed to ventral thalamus) has been noted in mouse and monkeys [START_REF] Jones | Expression of regulatory genes during differentiation of thalamic nuclei in mouse and monkey[END_REF].

The zona limitans intrathamica (ZLI) forms a boundary and a signaling center critical for establishing differences between dorsal and ventral thalamus [START_REF] Larsen | Boundary formation and compartition in the avian diencephalon[END_REF][START_REF] Zeltser | A new developmental compartment in the forebrain regulated by Lunatic fringe[END_REF][START_REF] Kiecker | Hedgehog signaling from the ZLI regulates diencephalic regional identity[END_REF]. The ZLI also plays a crucial role for the development of GABAergic cells in the mammalian diencephalon. The Pitx2 gene coding for a homeodomain transcription factor has been implicated in the differentiation of GABAergic cells [START_REF] Martin | Pitx2 distinguishes subtypes of terminally differentiated neurons in the developing mouse neuroepithelium[END_REF]. This gene is expressed in the ZLI, adjacent to GABAergic cells on both sides, as well as in GABA cells of the ventral thalamus (i.e., zona incerta), mesencephalon and rhombencephalon. GAD-positive cells have been reported in or close to the mouse ZLI ( [START_REF] Katarova | Domain-restricted expression of two glutamic acid decarboxylase genes in midgestation mouse embryos[END_REF], very similarly to our report of GABA cell distribution in ventral thalamus and ZLI. The Dlx1 [START_REF] Kitamura | Expression patterns of Brx1 (Rieg gene), Sonic hedgehog, Nkx2.2, Dlx1 and Arx during zona limitans intrathalamica and embryonic ventral lateral geniculate nuclear formation[END_REF] and Mash1 [START_REF] Miyoshi | Identfication of a novel basic helix-loop-helix gene, Heslike, and its role in GABaergic neurogenesis[END_REF] genes have been reported in or close to the mouse ZLI, indicating that the presence of those proneural genes may be correlated with GABA cell development as in other forebrain areas discussed above.

Frog:

There are reports on GABA-immunoreactive cells in the Xenopus embryo [START_REF] Roberts | The early development of neurons with GABA immunoreactivity in the CNS of Xenopus laevis embryos[END_REF]) and tadpole [START_REF] Barale | Immunohistochemical investigation of gammaaminobutyric acid ontogeny and transient expression in the central nervous system of Xenopus laevis tadpoles[END_REF]. While the embryonic GABA cell pattern resembles the early scaffold described elsewhere, the subpallial cluster (their rostral forebrain neurons) appears only in the late embryo. In the (postembryonic) tadpole, the GABA distribution indicates that preoptic and ventral thalamic populations leave a gap of unstained cells in between possibly representing the eminentia thalami (as described analogously by proneural gene expression, e.g., neuroD; Wullimann et al., in press). Also in the adult Xenopus brain [START_REF] Brox | Expression of the genes GAD67 and Distal-less-4 in the forebrain of Xenopus laevis confirms a common pattern in tetrapods[END_REF], the eminentia thalami remains free of GABA (but not the ventral thalamus, the preoptic region, or the subpallium).

Lamprey:

Studies on the development of GABA-positive cells in the lamprey brain [START_REF] Meléndez-Ferro | Ontogeny of γ-aminobtyric acid-immunoreactive neuronal populations in the forebrain and midbrain of the sea lamprey[END_REF]2003) also largely agree with patterns reported here in the zebrafish, e.g., regarding early presence versus absence of GABA cells in subpallium versus pallium. Furthermore, there are many more GABA cells towards the ventricle in the ventral (their rostral) thalamus compared to the dorsal (their caudal) thalamus. There, GABA cells are seen towards the periphery and some pictures (their figure 3 F) indicate a GABA cell free zone in the dorsal thalamus. Many GABA cells are seen early in the preoptic and hypothalamic region, as in the zebrafish. Also in agreement with our data is the report that the adult lamprey brain shows GABAnegativity in the eminentia thalami [START_REF] Pombal | Prosomeric map of the lamprey forebrain based on calretinin immunocytochemistry, Nissl stain and ancillary markers[END_REF].

In summary, we propose here, that these correspondences concerning GABA cell development in early vertebrate brain stages of different taxa -as exemplified in detail between zebrafish and mouse above -are part of a common basic vertebrate pattern of neuronal phenotype development. The early massive production of GABA-positive cell masses on which we focus in the present paper is only one characteristic of the whole pattern. Comparisons between zebrafish, mouse and Xenopus show that a particular stage may be identified in each model animal when this basic pattern of neurogenesis becomes most apparent. This pattern reflects the expression of a set of given patterning and proneural genes, coordinated by external signaling pathways (such as for example, Shh, BMPs, Wnts, FGFs), the combination of which is required to provide neuroblasts with specific differentiation programs [START_REF] Bertrand | Proneural genes and the specification of neural cell types[END_REF]. Thus, this pattern reflects the spatial (and temporal) constraint on gene expression, which in turn, is necessary to determine the fate of neuroblasts born at a defined place.

We propose that this pattern corresponds to some sort of phylotypic stage in vertebrate neurogenesis (as suggested for general body development; [START_REF] Slack | The zootype and the phylotypic stage[END_REF] useful to identify for crossspecies comparisons and for the improvement of each given model system in the investigation of neurogenesis. For example, our study here indicates that there is an early mass production of GABA cells, that may be accompanied by the production of glutamatergic cell masses, followed in subsequent steps by the emergence of less abundant neurochemical neuronal cell types, such as dopaminergic, serotoninergic, and cholinergic cells. After neural determination took place, the derived cells can migrate and express their phenotype at a distance from the place of birth and determination. The concept of a phylotypic stage of neuronal phenotype development as proposed here may help in the integration of such observations in a particular and also in between diverse vertebrate organisms.

Figure legends

Fig. 1 :

 1 Figure legendsFig.1: GABA-ir cell bodies and fibers in 2 dpf zebrafish brain from olfactory bulb (A) to mesencephalic (H) levels shown in transverse sections. Brain sections are counterstained with DAPI for cell nuclei. Schema in lower right corner shows levels of all transverse sections at 2 days shown in Figures1 and 2. Arrowhead in (H) denotes GABA-positive cells at the utmost periphery of the intermediate hypothalamus. Scale bar correponds to 0.1 mm.

This work has been supported by grants from CNRS, Institut Universitaire France (IUF) and Sanofi-Aventis-Pharma. T.M. is supported by a fellowship from Sanofi-Aventis-Pharma.

Abbreviations


Nadia Maïzi

Edi Assoumou

Exercices prospectifs via le modèle MARKAL : spécificités françaises ?

Résumé-Les stratèges qui se préoccupent des questions liées à l'Energie, doivent, dans un contexte international complexe, se doter d'outils normatifs performants pour faire face à des contraintes multiformes (quotas d'émissions, taxes, ouverture des marchés, raréfaction de la ressource).

Dans cet article, nous montrerons comment une optimisation technico-économique de la chaîne énergétique française, au moyen du modèle MARKAL-France, permet à l'horizon 2050, une approche prospective pertinente des émissions de CO 2 .

I. INTRODUCTION

La déréglementation des marchés bouleverse le monde de l'énergie. Les acteurs traditionnels doivent désormais s'adapter à la nouvelle donne concurrentielle dans un contexte mondial où les ressources peu coûteuses et les surcapacités sont vouées à disparaître. Par ailleurs, la pression environnementale s'accroît. La suppression des émissions de soufre, l'exigence d'un ratio minimum d'énergies renouvelables en Europe, la restriction des émissions de gaz à effet de serre ... contraignent l'offre des acteurs au travers de mécanismes nombreux, complexes et non encore définitifs.

Face à cette complexité, les conséquences et impacts des choix stratégiques sont difficilement appréciables « par dires d'experts » et l'exercice de prospective recourant à la modélisation, l'optimisation et l'aide à la décision, devient un outil normatif précieux. Car tant pour les stratégies d'entreprises que pour les décisions politiques, cet exercice permet d'évaluer pour reprendre la formule de Pierre Massé, « un souhaitable qui apparaisse plausible à l'esprit prospectif et qui devienne probable pour l'esprit attaché à sa réalisation ».

Comme en témoigne le recensement fourni par un rapport du Plan [START_REF] Giraud | Effet de serre : modélisation économique et décision publique[END_REF], il existe de nombreuses approches utilisées à des fins de prospective énergétique, réparties suivant trois grandes classes : les modèles IAM (Integrated Assessment Model) [START_REF] Rotmans Jan | Uncertainty Management in Integrated Assessment Modeling: Towards a Pluralistic Approach[END_REF], [START_REF] Parson | Integrated assesment models of global climate change[END_REF], les modèles économiques top-down (pour approche descendante) [START_REF] Percebois | L'apport de la théorie économique aux débats énergétiques[END_REF] et les modèles technologiques bottom-up (pour approche ascendante) [START_REF] Girod | Energie : fonctions de production et modèles de demande[END_REF].

Cependant, pour analyser les principaux déterminants du système énergétique à une échelle régionale donnée, pour anticiper les évolutions et les impacts des prix des énergies à moyen et long terme, pour estimer les émissions de polluants, toutes ces approches ne sont pas équivalentes. Car sans un modèle de la chaîne énergétique suffisamment agrégé pour intégrer les stratégies proposées et suffisamment détaillé pour prendre en compte les contraintes techniques et environnementales, on ne saurait répondre. Les modèles bottom-up de type MARKAL1 [START_REF] Fishbone | MARKAL, a linear programming model for energy systems analysis: technical description of the BNL version[END_REF] (MARKAL pour Market Allocation) -outils ouverts et utilisés par une large communauté internationale [START_REF]Energy Technology Systems Analysis Programme[END_REF] 2 -offrent, pour partie, une réponse à ce besoin.

Dans ce qui suit, nous proposons de motiver notre choix de MARKAL à des fins prospectives.

Tout d'abord, nous décrirons les principes sur lesquels repose cette modélisation en insistant sur les avantages qu'offre une approche technologique. Nous rappellerons ensuite le contexte français en matière de politique énergétique et ses spécificités. Enfin, nous illustrerons l'apport du modèle pour le traitement de quelques questions relatives aux émissions de CO 2 à l'horizon 2050.

II. LE RECOURS A LA MODELISATION PROSPECTIVE

A. Le modèle MARKAL

Les modèles MARKAL permettent l'optimisation d'un coût technique, économique ou environnemental associé à une représentation technico-économique du système énergétique. Cette optimisation peut être réalisée à moyen et long terme pour des échelles spatiales différentes (régionale, nationale, mondiale…).

Dans MARKAL, les secteurs que l'on veut analyser sont considérés comme des chaînes de transformations subies par des ressources primaires énergétiques dans le but de satisfaire différentes demandes finales de services. La chaîne énergétique est décrite de l'amont (production et offre énergétique) à l'aval (secteurs économiques utilisant l'énergie finale) en passant par tous les secteurs intermédiaires consommateurs ou producteurs d'énergie comme l'illustre la figure Fig. 1. Dans cette représentation, une technologie est définie comme un convertisseur de « vecteurs énergétiques » et est associée à chaque étape de transformation. Enfin, il est possible à partir de données technicoéconomiques et d'hypothèses exogènes sur les tendances (demande, prix des ressources, taux d'actualisation, etc.) de fournir les évolutions -et leurs variantes -(typiquement tous les 5 ans jusqu'en 2050) des principaux déterminants du système énergétique, à savoir :

• l'impact des prix des énergies à moyen et long terme • l'estimation des émissions de polluants • la simulation des différentes compétitions technologiques et économiques • la prise en compte de certaines mesures incitatives de réduction des émissions de gaz à effet de serre • l'effet de ruptures technologiques • le rôle des mesures liées à la maîtrise de la demande d'énergie • l'impact du secteur des transports • l'impact de différents projets de recherche et développement (performances énergétiques, projets d'énergies renouvelables, maîtrise de la demande d'électricité…) • les conséquences des politiques énergétiques (par exemple les objectifs du protocole de Kyoto ont été envisagés via MARKAL pour la planification nationale de l'Inde, du Canada, de la Suisse, … [START_REF] Loulou | GHG Abatement in Central Canada with Inter-provincial Cooperation[END_REF]). Décliné au niveau français, nous allons voir les enjeux que le modèle MARKAL permet d'aborder.

III. LES SPECIFICITES FRANÇAISES

Comme le rappelle le livre blanc sur les énergies [START_REF]Livre blanc sur les énergies[END_REF], la politique énergétique française de l'après guerre a connu trois grandes périodes :

« Les années 50 ont été marquées par le souci d'une énergie nationale et abondante : charbon et hydroélectricité, les années 60 par la recherche d'une énergie à meilleur marché : le pétrole. Les crises pétrolières des années 70 ont conduit à privilégier davantage l'indépendance stratégique et le rééquilibrage de notre balance commerciale ».

Il s'en est suivi le lancement, il y a 30 ans, d'un programme nucléaire de grande envergure (50 GW ont été mis en service entre 1980 et 1990 [START_REF] Bataille | La durée de vie des centrales nucléaires et les nouveaux types de réacteurs[END_REF]) faisant du paysage énergétique français une exception au niveau mondial.

A. Le secteur de la production d'électricité

La figure Fig. Cette répartition est fondamentalement différente de la répartition mondiale de la production d'électricité [START_REF] Rogner | Nuclear Power And Climate Change[END_REF] comme le montre la figure Fig. 3.

B. Les émissions de GES (Gaz à Effet de Serre)

Pour l'année 2000, la totalité des émissions en équivalent CO 2 , pour les usages liés à la consommation d'énergie de l'ensemble des secteurs de l'offre et de la demande, s'est chiffrée à 440 Mt [START_REF]Centre Interprofessionnel Technique d'Etude de la Pollution Atmosphérique[END_REF]. Comme pour la production d'électricité, la France se singularise par une contribution des secteurs les plus émissifs inversée par rapport à la moyenne mondiale représentée figure Fig. 5. [START_REF] Rogner | Nuclear Power And Climate Change[END_REF]. En effet, le secteur des transports est le premier contributeur à ces émissions (32,6%), suivi par l'industrie (26,7%) et les secteurs résidentiel et tertiaire (21,7%). La part de la production d'électricité et de chaleur ne compte que pour 10,1%, ce qui souligne le caractère faiblement émissif de ce secteur, conséquence du choix d'une option nucléaire prépondérante pour la France.

C. Les questions de prospective

Ces singularités françaises conditionnent les orientations de la stratégie énergétique de la France à l'horizon 2050 et en particulier de l'évolution du mix énergétique dans un contexte où les engagements nationaux au niveau environnemental sont importants :

• Les principaux résultats sont :

• une baisse à moyen terme (2020-2030) de la part du nucléaire avec le déclassement des centrales existantes, suivi dans le plus long terme d'une mise en service progressive de centrales EPR ; • une compensation de cette baisse à moyen terme par 3 Ces hypothèses de prix sont celles retenues dans le cadre du projet européen NEEDS où est réalisée la modélisation de l'Europe des 25. une réduction des exportations ; • une augmentation de la part du charbon avec l'évolution des prix du pétrole ; • une augmentation de la part du gaz naturel.

Pour ces résultats, MARKAL évalue les émissions de CO 2 associées aux différents secteurs émissifs sur l'horizon envisagé figure Fig. 7. Les spécificités du système énergétique français se trouvent ainsi prolongées dans le scénario de base avec une augmentation des émissions de CO 2 dans les transports et une contribution toujours faible du secteur de la production d'énergie. Nous avons positionné le scénario tendanciel DGEMP [START_REF]Direction Générale de l'Energie et des Matières Premières[END_REF] représenté par la courbe rouge par rapport à ces résultats. A l'évidence, l'écart en tendance constaté impose de confronter dans le détail les hypothèses retenues pour chaque scénario. Mais il a l'avantage de mettre en exergue la différence de philosophies entre une « approche tendancielle » et une « approche de type MARKAL » :

Emissions de CO2

• un tendanciel évalue les performances d'un système énergétique proche de celui de l'année de base et dans le prolongement des évolutions historiques. Son horizon de pertinence est plutôt la simulation de scénarios de faible rupture sur le court et le moyen terme (où les tendances historiques peuvent être réputées dominantes) ; • une approche MARKAL-France optimise librement des choix technologiques en fonction de leurs performances coût/efficacité et en prenant en compte les meilleures technologies disponibles. L'intérêt économique de changements de technologies sur le moyen et le long terme est donc évalué. Le scénario de base MARKAL n'est donc pas un tendanciel au sens du scénario DGEMP.

C. Zoom sur le secteur des transport

L'évolution du mix des transports dans le scénario de base est illustrée Fig. 8. Dans ce scénario, la consommation d'énergie, après avoir stagné pendant les 15 premières années de l'horizon prospectif, croit de manière très significative après 2020. Cela traduit le fait que l'amélioration de l'efficacité énergétique est compensée par un accroissement de la demande de mobilité, notamment pour les transports maritimes et aériens pour lesquels l'usage du kérosène et du fioul lourd domine. Du fait du niveau des prix du pétrole, le gaz prend également une part significative pour les carburants utilisés pour le transport de marchandise à partir de 2030.

Malgré la substitution vertueuse (en terme d'émissions de CO 2 ) du pétrole par du gaz naturel, ces résultats montrent que l'utilisation des technologies les plus compétitives économiquement, n'inverse pas réellement la tendance en terme d'émissions de CO 2 du secteur des transports.

C'est pourquoi, il devient pertinent d'évaluer les technologies les plus rentables sous contraintes d'émissions de CO 2 . Il existe plusieurs possibilités de traduction de la contrainte CO 2 dans MARKAL. Une première option consiste à imposer, pour un scénario alternatif, une contrainte maximale d'émissions et d'analyser sa pertinence et sa faisabilité en terme de chemin technologique.

La répartition de cette contrainte dans le temps influe fortement sur les investissements réalisés. Par exemple, l'étude [START_REF] Assoumou | Reducing greenhouse gas emissions by a factor of 4 by 2050: a bottom-up analysis of post Kyoto emissions targets for France[END_REF] propose des chemins alternatifs, R1, R2, de la figure Fig. 9., pour atteindre l'objectif de réduction par 4 des émissions de CO2 (par rapport à leur niveau de 1990) à l'horizon 2050. On constate pour le scénario R2, l'objectif facteur 4 est atteint grâce à une transition vers l'hydrogène et les biocarburants, accompagnée d'une disparition quasi totale des carburants pétroliers pour le transport terrestre à partir de 2045.

D. Raffinements pour le secteur de la production d'électricité

La seconde spécificité de la France est le poids du parc électronucléaire dans la production électrique et son corollaire en terme de faibles émissions de CO 2 du secteur électrique. Le scénario de base, représenté figure Fig. 6., maintient à l'horizon 2050 cette caractéristique avec un déploiement important de centrales de type EPR.

MARKAL offre la possibilité d'évaluer des évolutions alternatives. Les études [START_REF] Assoumou | Modélisation MARKAL pour la planification énergétique long terme dans le contexte français[END_REF]- [START_REF] Maïzi | Energy mix planning for the French electricity production sector[END_REF] comparent deux variantes de politique de déploiement du nucléaire en France à l'horizon 2050 pour le secteur électrique : l'une sans limite de part de marché, l'autre avec une limitation à 50% de la part de marché du nucléaire à l'horizon 2050. Ces études portent cependant sur un modèle exclusivement électrique et reposent sur des hypothèses différentes de celles retenues pour le scénario de base 4 .

Les figures Fig. 12. et Fig. 13. montrent l'évolution du mix énergétique dans les deux cas. On constate que les technologies de substitution mises en place pour pallier la contrainte de déploiement sur le nucléaire s'appuient sur une ressource charbon (centrales à lit fluidisé), la plus rentable pour une production en base cf figure Fig. 14.. Il est à noter que pour réaliser cette étude, nous avons utilisé une version améliorée de MARKAL, dite MARKAL-flexible [START_REF]Direction Générale de l'Energie et des Matières Premières[END_REF], développée dans le but de pouvoir tenir compte de l'usage différencié de moyens de production d'électricité en fonction de la dynamique des appels.

Cette approche flexible permet d'analyser l'effet d'une contrainte CO 2 sur un parc électrique français dont la part de moyens de production thermique est renforcée. Cette contrainte CO 2 est traduite ici à travers une taxe de 20 €/tonne de CO 2 émise. On constate que l'application de cette taxe modifie fortement la structure de la production thermique avec un arbitrage en faveur du gaz (figure Fig. 11). Les lits fluidisés sortent du parc de production et les cycles combinés assurent l'essentiel de la fourniture d'électricité. Ce scénario fait aussi apparaître dès 2015 une nouvelle technologie gaz avec des niveaux d'émissions faibles pour la production d'électricité en base : des centrales hybrides pile à combustible SOFC avec reformage du gaz /turbine à gaz [START_REF]Direction générale de l'énergie et des matières premières[END_REF]. Ces technologies jouent un rôle important lorsque la politique nucléaire est limitée : en effet, il faut répondre aux besoins de production en base et lorsque la taxe CO 2 est prise en compte les cycles combinés sont pénalisés (les émissions sont de 353gCO 2 /kWh pour les cycles combinés contre 300gCO 2 /kWh pour les centrales hybrides). On voit donc émerger une nouvelle technologie qui devient compétitive.

Enfin, on peut observer l'évolution des émissions de CO 2 sur tout l'horizon en fonction de la politique nucléaire retenue et de l'application ou non de la taxe environnementale. Les résultats représentés figure Un déploiement modéré de l'EPR conduit à un appel plus important des centrales thermiques. Les émissions croissent alors continuellement sur toute la période pour atteindre 219,1 Mt en 2050 soit une multiplication par 6,9 du niveau de 2000. La taxe réduit fortement l'appel au charbon et donc les quantités de CO 2 émises. Les émissions sont néanmoins multipliées par 3,2 par rapport à 2000.

V. CONCLUSION

Grâce à la possibilité de définir des contraintes additionnelles de manière souple ou d'imposer des valeurs alternatives aux paramètres descriptifs du système énergétique, voire d'améliorer la représentation de certains secteurs spécifiques, MARKAL permet la simulation d'un large éventail de scénarios et l'analyse des choix technologiques correspondants. A travers quelques exemples nous avons illustré ces possibilités, notamment en combinant deux scénarios technologiques de déploiement futur des centrales nucléaires de type EPR à l'application ou non d'une taxe sur les émissions de CO 2 .

L'approche MARKAL qui sous tend cette étude a permis de proposer différentes alternatives de l'avenir énergétique; son usage peut être dérivé afin de permettre « l'initiation » de l'industrie française, pour qu'elle

• prépare ses futurs marchés en intégrant les enjeux environnementaux dans ses choix stratégiques • intègre en amont des objectifs d'éco-conception.

Il faudra pour cela améliorer le réalisme de l'exercice prospectif et notamment pouvoir intégrer les modes de transport et de distribution de l'énergie, éléments d'arbitrage décisifs sur lesquels peuvent s'opérer des transferts de pollution. En ceci on dépasserait les méthodologies prospectives classiques, où les capacités de transport sont ajustées a posteriori aux scénarios d'offre et de demande.

Fig. 1 .

 1 Fig. 1. Le système énergétique de référence B. Potentialités du modèle MARKAL La représentation technologique désagrégée décrite cidessus, constitue l'atout principal de l'approche MARKAL. En premier lieu, cette approche permet de créer une économie virtuelle où les différentes technologies sont mises en concurrence. Concrètement, une formulation explicite des relations inputs outputs pour chaque technologie, permet sur l'horizon choisi (30 à 50 ans) et pour une demande finale donnée, de minimiser le coût global actualisé et d'obtenir les niveaux d'activités et d'investissements correspondants. En second lieu, elle permet d'évaluer les émissions de certains polluants de la famille des gaz à effet de serre et, audelà, de faire l' « Analyse du Cycle de Vie » des scénarios envisagés. C'est une potentialité importante notamment si l'on veut obtenir : • une description complète des modes de consommation d'énergie (incluant distribution, qualité et fiabilité de la fourniture), • une analyse précise des substitutions entre les formes et vecteurs d'énergie, • une interprétation de la notion de besoin énergétique en terme de service et d'équipement, • une meilleure évaluation des énergies renouvelables.

  2. [11]-[12], illustre la répartition de la production électrique française pour l'année 2005 : 78,27% est d'origine nucléaire, 10.23% est d'origine hydraulique et environ 10% d'origine thermique. Cette part de production thermique joue un rôle essentiel dans la gestion du réseau (pointe, besoins d'ajustement, services…).

Fig. 2 .Fig. 3 .GWFig. 4 .

 234 Fig. 2. Production électrique française 2005

Fig. 5 .

 5 Fig. 5. Décomposition sectorielle des émissions mondiales de GES 2004

Fig. 6 .

 6 Fig. 6. Consommation en énergie primaire pour la France (usages énergétiques)

Fig. 7 .

 7 Fig. 7. Evolution des émissions de CO2

Fig. 8 .

 8 Fig. 8. Consommation d'énergie dans le secteur des transports

Fig. 9 .Fig. 10 .

 910 Fig. 9. Evolution des émissions de CO 2 pour les consommations d'énergie Le chemin R0 (rouge) correspond au scénario de base (« laisser faire ») tel qu'évalué figure Fig. 7. Le chemin R1 (bleu) a été obtenu en imposant une contrainte d'émission uniquement sur la dernière période de l'horizon. Enfin, le chemin R2 (vert) résulte d'un étalement dans le temps des contraintes d'émission.A partir de ces profils d'émissions maximales, MARKAL-

Fig. 11 .

 11 Fig. 11. Mix pour la production thermique: Effet d'une taxe CO 2 de 20€/t sur le scénario EPR modéré

Fig. 12 .Fig. 12 .

 1212 Fig. 12. Effet d'une taxe sur les émissions de CO 2 du secteur de la production d'électricité Avec 51,12 Mt de CO 2 , le scénario de déploiement de l'EPR conduit en 2050 à une augmentation de 60% des émissions par rapport à 2000 (31,66 Mt) avec un pic à 108,9 Mt en 2020. Lorsque la taxe environnementale est appliquée, les émissions baissent de 39% en 2050 (19,5 Mt) par rapport à 2000. Le pic d'émission de 2020-2025 n'est plus que de 61,3 Mt.Un déploiement modéré de l'EPR conduit à un appel plus important des centrales thermiques. Les émissions croissent alors continuellement sur toute la période pour atteindre 219,1 Mt en 2050 soit une multiplication par 6,9 du niveau de 2000. La taxe réduit fortement l'appel au charbon et donc les quantités de CO 2 émises. Les émissions sont néanmoins multipliées par 3,2 par rapport à 2000.

Fig. 12 .Fig. 13 .Fig. 14 .

 121314 Fig. 12. Mix pour la production d'électricité : scénario de base

  dans le cadre du protocole de Kyoto, stabiliser les

	pour tous les services avec globalement : une
	croissance rapide de la demande de mobilité dans		14
	Hydro les transports, une croissance soutenue pour		12
	16% l'industrie, une croissance plus lente pour le		10	F-gas
	résidentiel avec une stagnation de la population et Coal 39% du nombre de logements	Gt Ceq	6 8	N2O CH4
	Nuclear 5. une évolution des prix des ressources fossiles 16% données Tableau 1 3		2 4	CO2
	2000 2005 2020 2050 27,89 54 48 60 2,78 5,24 6,6 8 Oil 7% Gas 20% charbon ($/tonnes) 34,93 61,3 Renewables pétrole ($/baril) 2% gaz ($/MBTU) 67 71	0 Source: EDGAR (3.2/FT2000) / IEA (2006) Energy supply Transport	Residential & commercial buildings	Industry	Agriculture and Forestry
	TABLEAU 1. HYPOTHESE DE PRIX D'IMPORTATION DES RESSOURCES FOSSILES
	6. investissements dans les nouveaux réacteurs
	nucléaires au rythme : d'un prototype (1.5 GW)
	en 2012, d'une période d'observation entre 2012
	et 2020, d'un maximum de nouvelles capacités
	installées de 3 GW/an après 2020.
	On peut remarquer que
	• l'hypothèse 4 a pour conséquence la diminution de la demande en énergie utile pour le chauffage individuel, à cause de la rénovation du bâtiment ancien, et d'une meilleure efficacité dans le neuf ; • l'hypothèse 6 sur le développement du nucléaire prolonge le rythme d'intégration de nouvelles capacités de production tel qu'établi dans le rapport prévisionnel pour le remplacement des réacteurs existants [18] sur la période 2020-2030.	émissions de GES en 2010 par rapport à leur niveau en 1990 • au niveau européen : -assurer à hauteur de 21% la contribution des renouvelables dans la consommation domestique d'électricité en 2010 -diminuer de 20% les émissions globales (sans déclinaison nationale) de GES en Europe à l'horizon 2020 par rapport à leur
							niveau de 1990
	B. Consommation globale d'énergie primaire et émissions	• au niveau national réduire par 4 les émissions de
	de CO2 dans le scénario de base				GES par habitant à l'horizon 2050 par rapport à leur
	MARKAL-France permet d'évaluer l'évolution du mix		niveau de 1990.
	énergétique	fournissant	l'ensemble	des	secteurs	C'est notamment pour évaluer les conséquences de ces
	consommateurs d'énergie jusqu'en 2050. Cette évolution de	engagements internationaux que nous avons développé le
	la consommation totale d'énergie primaire est donnée figure	modèle MARKAL-France.
	Fig.6.					IV. APPLICATIONS DU MODELE MARKAL-FRANCE
						Afin d'illustrer les possibilités de l'approche MARKAL
						nous la déclinerons pour l'ensemble des secteurs contributifs
						aux émissions. Puis, nous nous focaliserons sur l'étude
						spécifique de deux secteurs particuliers :
						1. le secteur des transports, plus grand contributeur aux
							émissions, et qui présente donc un intérêt certain pour
							le déploiement d'une politique de maîtrise des
							émissions ;
						2. le secteur électrique, singulier par son orientation
							nucléaire, et remarquable par le poids de la part
							thermique dans le bilan global de ses émissions.
						A. Le scénario de base pour la France
						Afin d'établir une référence pour la comparaison entre
						scénarios, nous avons bâti un scénario, dit scénario de base.
						Il repose sur les hypothèses suivantes :
							1. absence de contraintes sur les émissions de GES
							2. pas de contraintes environnementales
							3. un taux d'actualisation de 8%
							4. une projection de la croissance de la demande

Derrière l'appellation MARKAL on comprendra MARKAL/TIMES dernière version du modèle.

Ce modèle développé sous l'égide de l'AIE (Agence Internationale de l'Energie), est aujourd'hui utilisé par quelques 77 équipes de modélisation dans plus de

pays ce qui permet les échanges au sein d'une large communauté internationale d'utilisateurs.

Il s'agit principalement : • de la demande d'électricité à 2030 qui est issue du volet électricité du scénario tendanciel 2004 de la DGEMP [21] et qui est ensuite prolongée à 2050 par le scénario H1 du rapport Charpin-Dessus-Pellat [22] ; • des hypothèses de prix constants pour les ressources qui sont celles du scénario DGEMP 2004 (pétrole à 30$/baril, gaz naturel à 4 $/Mbtu, charbon à 45$/t). Ces hypothèses, quoique simples permettent, dans un cadre normatif, d'isoler plus clairement (avant toute combinaison à d'autres effets) l'amplitude potentielle des changements induits.

95

REMERCIEMENTS

Nous remercions Marc Bordier (CMA/EMP), Gilles Guerassimoff (CMA/EMP) membres du projet MARKAL ainsi que Vincent Mazauric (Schneider Electric/EMP) pour les nombreuses discussions enrichissantes que nous avons partagées autour de ces sujets.


Antonia Bertolino
email: antonia.bertolino@isti.cnr.it

Antonello Calabrò
email: calabro@isti.cnr.it

Felicita Di Giandomenico
email: digiandomenico@isti.cnr.it

Marco Martinucci
email: martinucci@isti.cnr.it

Paolo Masci
email: masci@isti.cnr.it

Automated Refinement of Dependability Analysis through Monitoring in Dynamically Connected Systems

Keywords: Model-based analysis, Monitoring, On-line refinement

Model-based analysis is a well-established method to assess the dependability of a system before deployment. It is well known that, in highly dynamic contexts, the accuracy of the analysis results can be limited because unpredictable phenomena may affect the system during its operation. In such contexts, the analysis typically needs to be refined with data obtained from real system executions. In this paper we tackle the issue of refining model-based dependability analysis in automated systems through monitoring. Specifically, we report on our preliminary results on the development of a system that exploits the synergic use of an automated approach for model-based dependability analysis and a flexible monitoring architecture.

INTRODUCTION

Modern software applications are more and more conceived as dynamically adaptable and evolvable sets of components that must be able to modify their behaviour at run-time to tackle the continuous changes in the unpredictable openworld settings [START_REF] Baresi | Toward open-world software: Issue and challenges[END_REF]. In such partially unknown and evolving context, dependability analysis [START_REF] Avizienis | Basic concepts and taxonomy of dependable and secure computing[END_REF] calls for on-line support to enhance the accuracy of preliminary estimates performed at design-time.

Analysis at the early stage of a development process is of paramount importance to achieve the required functional and non-functional properties. Nevertheless, the incomplete a priori knowledge about the operating system and environment unavoidably undermines the accuracy of the considered elements and, hence, of the analysis results. * Contact author In this paper, we tackle the issue of refining model-based dependability analysis in automated systems through monitoring. We focus on the synergic use of an automated approach to perform model-based dependability analysis with a run-time monitoring of real system executions.

We present the work we are carrying out in the context of the European Project Connect [START_REF]Connect: Emergent connectors for Eternal Software Intensive Networked Systems[END_REF], which considers dynamic environments populated by heterogeneous networked systems willing to communicate.

In Connect, communication is seamlessly supported by Connect Enablers, which are automated components that make possible the interoperation between heterogeneous systems by synthesising and deploying, at run-time, mediating software bridges, called Connectors (see Figure 1). Specifically, the Connect architecture includes five main Enablers: Discovery, which discovers mutually interested Networked Systems (NSs), and retrieves information on the interfaces of NSs; Learning, which possibly completes the specifications of the NSs through a learning procedure; Synthesis, which performs the dynamic synthesis of mediating Connectors to enable interoperation among NSs willing to interact; Dependability, which uses a model-based analysis to support Synthesis in the definition of dependable Connectors; Monitoring, which continuously monitors the deployed Connector to update the Connector specification used by the other Enablers with run-time data. In this work, we report on the interplay between Dependability and Monitoring.

The rest of the paper is structured as follows. Section 2 and Section 3 briefly describe the functionalities of the Depend-

DEPENDABILITY ENABLER

The Dependability Enabler is in charge of performing modelbased dependability analysis to assess, before deployment, if the synthesised Connector is able to satisfy the dependability requirements requested by the NSs.

The Dependability Enabler is logically split into four main functional modules (see Figure 2): Builder, Analyser, Evaluator and Enhancer. The Builder module derives the dependability model of the Connected system from the specification provided by Synthesis. The Analyser module uses the generated dependability model to perform a quantitative assessment of the non-functional requirements reported by the Discovery Enabler. The Evaluator module checks the analysis results to determine if the non-functional requirements are met. If the requirements are not satisfied, the Evaluator activates the Enhancer module to determine possible solutions to improve the dependability level of the Connected System. If the requirements are satisfied, the Evaluator reports to Synthesis that the Connector can be successfully deployed, and reports to the Monitoring Enabler the aspects that must be observed for the Connector that is going to be deployed, e.g., transition durations and probability of transitions failure.

We realised a prototype implementation, denominated DEA1 , of the Dependability Enabler based on the Möbius [START_REF]Möbius tool[END_REF] analysis tool; further details on the architecture and the implementation of this Enabler can be found in [START_REF] Masci | Towards automated dependability analysis of dinamically connected systems[END_REF].

MONITORING ENABLER

The Monitoring Enabler is in charge of performing complex event recognition, as well as observing and notifying specific events occurrences.

The Monitoring Enabler used for this contribution, denominated Glimpse2 , has a highly flexible and lightweight ar- The Monitoring Enabler is driven by the Manager component (see Figure 3). The collection of raw data coming from the observed components is provided by Probes, which may be realised either by injecting code into an existing software, or by using proxies that intercept events to be analysed and send them to the Monitoring Bus. In this context, the Monitoring Enabler injects the Probes into the Connector to observe transitions. The communication backbone of the Monitoring Enabler is the Monitoring Bus, to which all the information (events, requests, responses) are sent on by: Probes, Connect Enablers, Complex Event Processor and by any other services joining Glimpse.

The Complex Event Processor, instructed with the information provided by the Dependability Enabler, is implemented using JBoss Drools Fusion [START_REF]Drools fusion: Complex event processor[END_REF], a rule engine based on Charles Forgy's Rete Algorithm [START_REF] Forgy | Rete: A fast algorithm for the many pattern/many object pattern match problem[END_REF], able to perform the analysis of the events streaming on the Monitoring Bus. The latter is implemented using ServiceMix4 [START_REF]Servicemix: an open source esb[END_REF] and ActiveMQ [START_REF]Activemq: A complete message broker[END_REF] technologies.

ENABLERS INTERACTIONS

The interactions between Dependability and Monitoring Enablers start when the Dependability Enabler determines that the synthesised Connector satisfies the required dependability level. Specifically, after the analysis phase, Dependability instructs Monitoring on the Connector and NSs aspects (among those used in the dependability analysis) that must be observed at run-time. The Monitoring Enabler, upon receiving the request, properly manages the probes embedded in the Connector. Once the Connector is deployed, data derived from real executions are sent to Dependability. The Dependability Enabler, in turn, performs a statistical analysis of the monitored observations and uses such information to check the accuracy of the model analysed before deployment. If the model parameters are found to be inaccurate, Dependability updates the model with the new values, and performs a new analysis. If the new analysis evidences that the deployed Connector needs adjustments, a new synthesis-analysis cycle starts.

With more details, Dependability and Monitoring interact by using a Publish/Subscribe protocol. The interaction pat- Each request message received by the Monitoring Enabler contains the following fields: (i) aspect, that specifies the aspect to be monitored; this field contains the identifier of a function, which is taken from an ontology shared between the Enablers (see Table 1 for some samples of functions), and the function parameters; (ii) an optional field timeFrame, that specifies the monitoring time frame; if this field is not included, the monitoring activity continues as long as the Dependability Enabler does not send a request to stop the observations.

Each response message sent by the Monitoring Enabler contains a instanceID field, which uniquely determines a Connector instance. The information on the Connector instance is provided to the Monitoring Enabler by the Probe whenever the Connector is activated, and is useful to perform instance-based statistical analysis.

APPLICATION EXAMPLE

In the following example, we focus on the Enablers interactions only, leaving out the actions taken by Dependability once obtained the values from Monitoring. To show a basic interaction between Dependability and Monitoring Enablers, we refer to the Distributed Market Scenario [START_REF] Di Giandomenico | Dependability analysis and verification for connected systems[END_REF]. The scenario considers a distributed market, where consumers execute a discovery protocol to gather information on the products sold by merchants. It is assumed that consumers and merchants use different communication protocols. With reference to recent works on synthesis of mediating Connectors [START_REF] Spalazzese | Mediating Connector Patterns for Components Interoperability[END_REF] and automata discovery/learning [START_REF] Raffelt | Learnlib: a library for automata learning and experimentation[END_REF], the specification of the Connected system is given with Labelled Transition Systems (LTSs) [START_REF] Keller | Formal verification of parallel programs[END_REF]. Specifically, consumers use the protocol represented by the LTS shown in Figure 5. We consider the case in which the parameters under monitoring are the duration of the transitions executed by the NS requesting the communication, because the Connector specification contains time-outs to limit the duration of the wait periods for the Connector. Hence, with reference to Figure 5, the parameters to be monitored are the consumer transitions tpListBrowse and tpBuyReq. The request messages sent by Dependability to Monitoring are shown in Listing 1. The predefined function transitionDuration(tr) is mapped into an XML aspect element with id attribute equal to transitionDuration. This operation generates a Drools rule, shown in Listing 2, that will be executed until the Dependability Enabler sends a stop request. An example of response message sent by the Monitoring Enabler is shown in Listing 3.

CONCLUSIONS

This paper has shown work-in-progress on the synergic use of stochastic model-based dependability analysis, conducted at system design-time, with run-time monitoring to improve the accuracy of estimates of dependability properties. The reference framework is that of the European project Connect, which investigates solutions to interoperability in heterogeneous, dynamic environment. The basic interplay between Dependability and Monitoring Enablers has been discussed, highlighting the benefits of the monitoring feed-backs on the model-based analysis. A simple application example has been also presented, which shows the operative steps of the Enablers interactions. Future work includes: (i) to finalise the definition of the statistical analysis on observed data, (ii) to set-up an appropriate data model to store the monitored data, (iii) to extend the monitoring approach in order to check dependability properties and to validate the correctness of the specifications of both Connector and NSs. All these points are in our current and future research agenda.

Figure 1 :

 1 Figure 1: Overview of the CONNECT architecture

Figure 2 :

 2 Figure 2: Dependability Enabler Architecture

Figure 3 :

 3 Figure 3: Monitoring Enabler Architecture

Figure 4 :

 4 Figure 4: Sequence diagram of the basic interaction pattern between Dependability and Monitoring

Table 1 :

 1 Examples of predefined functionstern is shown as a sequence diagram in Figure4, where we intentionally left out system start-up operations. Whenever Monitoring receives a request message on the service channel, a new channel dedicated to the requesting Enabler is set up to communicate monitored values. Monitoring sends response messages to Dependability as soon as the aspect of interest is available. The two Enablers exchange JMS messages whose payload is expressed in XML language. Each payload contains, among other fields, a connectorID field, which uniquely identifies the Connector specification. The value of connectorID is provided by the Synthesis Enabler along with the Connector specification.

Figure 5 : 5 < 8 < 11 <

 55811 Figure 5: LTS of the Consumer

1 2 s a l i e n c e 0 3 d 4 when 5 $ 6 $ 7 then 8 sListing 2 : 5 <

 234567825 rule " t r a n s i t i o n D u r a t i o n " i a l e c t " j a v a " aEvent : SimpleEvent ( t h i s . data == "s 0 " , t h i s . g e t S o u r c e I D = "xyz " ) ; bEvent : SimpleEvent ( t h i s a f t e r $aEvent , t h i s . g e t S o u r c e I D == $aEvent . g e t S o u r c e I D ) ; e n dR e s p o ns e ( EvaluateTimeStamps ( $aEvent . getTimestamp ( ) , $bEvent . getTimestamp ( ) ) ) ) ; 9 r e t r a c t ( $aEvent ) ; 10 r e t r a c t ( $bEvent ) ; 11 end Sample rule for checking a duration policy 1 <?xml v e r s i o n=" 1 . 0 " e n c o d i n g="UTF-8"?> 2 <r e s p o n s e c o n n e c t o r I D = "xyz " 3 i n s t a n c e I D = "abc " > 4 <a s p e c t i d = " t r a n s i t i o n D u r a t i o n " > t r a n s i t i o n s t a r t S t a t e = "s 0 " 6 l a b e l = " t p L i s t B r o w s e " 7 e n d S t a t e = "s 1 " /> 8 <double >1.0</ double> 9 </ t r a n s i t i o n > 10 </a s p e c t > 11 </r e s p o n s e > Listing 3: Sample response from Monitoring

DEpendability Analyser, http://dcl.isti.cnr.it/dea

Generic fLexIble Monitoring based on a Publish-Subscribe infrastructurE, http://labse.isti.cnr.it/tools/glimpse

ACKNOWLEDGEMENTS

This work is partially supported by the EU FP7 Project Connect (FP7-231167).