During development or in response to environmental stimuli, eukaryotic genes change both their expression and position in 3D nuclear space. Then, is a gene transcribed because of its position, or is position determined by transcription? Are genes stochastically or deterministically engaged in transcription cycles? Recent results confirm that RNA polymerases and their transcription factors play central roles in genome organization, and that stochastic events can give rise to apparently deterministic expression. As is so often the case in biology, structure both determines function and is influenced by it. We now know that genomes are not folded randomly , but whaOne might familiarize oneself with a city (an interphase nucleus) by walking down its roads (chromatin fibres), or wandering around its neighbourhoods (from fibre to fibre).A walk down the fibre would take you through alternating genic and non-genic regions a. A metaHox promoters can restart rapidly and serve as transcriptional insulators in concert with DSIF and NELF [We would often see RNA polymerase II along the way, and both genome-wide ‘run-ons’ and chromatin immunoprecipitation (ChIP) coupled to deep-sequencing now allow accurate localization of where the enzyme is ••,6••. Pand NELF •. RNAi-mand NELF .CTCF and its frequent partner, the cohesin subunit Rad21, mark boundaries a,b. Theyalu elements throughout the genome as low-affinity ‘parking lots’. On virus infection, it translocates to the interferon-β enhancer to induce new inter-/intra-chromosomal loops and stimulate transcription of target genes [Previously, transcriptional activators were thought to mark transcribed regions, but NFκB seems to use repeated et genes .Lap2b/emerin fusions [Heterochromatin is often peripheral. The striking exception of the rod nuclei of nocturnal mammals proves this rule: here, heterochromatin is central—an adaptation that channels more light to peripheral light receptors . Heteroc fusions ; tetheri fusions .To what extent does location determine expression? ChIP-chip has revealed the tissue-specific pattern of binding of transcription factors to human chromosome 21, and – when this whole chromosome is transplanted into a mouse nucleus – this pattern remains essentially unchanged; clearly, DNA sequence is a major determinant of expression •. AnalogTowns tend to have distinct financial, shopping, and residential zones. 3C coupled with deep-sequencing affirms that nuclei are also zoned. In yeast, contacts are non-randomly distributed, and – surprisingly – many are with mitochondrial and 2-micron plasmid DNA . In man,cis [trans [Results from 3C and FISH substantiate the long-held view that enhancers contact target promoters both in cis  and trans [trans   active transcription units ; a typicIn a city, different locales have their own temporal rhythms.de novo [A developmentally controlled gene in Dictyostelium is transcribed in discrete pulses separated by irregular intervals, and this stochastic pulsing was more likely to recur than to initiate de novo  , and there may be method in such madness; transcribing a long gene can convert space into time. For example, tiling microarrays reveal that a pioneering polymerase takes more than an hour to transcribe a 220-kbp human gene switched on by TNFα. Polymerases that initiate subsequently on it soon abort if the pioneer is still transcribing; as a result, mature message is produced in one pulse after ∼1 h . Introdu kb, and Different city neighbourhoods may be filled with different sights, sounds and smells, but they usually have the same street features and general layout. Whereas ten years ago nuclei were only charted imprecisely, we are well on our way to mapping them at ever-increasing resolution. More and more of these maps are positioning RNA polymerases and their associated factors at important nodes in the genomic network b–d, so tDuring the review process of this paper a new report was published describing the association of an inducible human gene (urokinase-type plasminogen activator) with specific transcription factories, before its activation by an external stimulus; RNA polymerases in these ‘poised’ factories lacked the characteristic phosphorylation of Ser2 of their CTD, indicative of efficient elongation activity . Thus, y• of special interest•• of outstanding interestPapers of particular interest, published within the annual period of review, have been highlighted as:


We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments:  Taking the doubled-stranded DNA molecule as a reference, cis elements are all those DNA regions – encoded in a plasmid or in a chromosome – in the vicinity of a gene. In complement, all the diffusible cellular molecules that are able to bind to the DNA are the trans factors. The coactivity of these molecular entities composes the minimal transcriptional regulatory system in all living organisms. In bacterial chromosomes, a transcription unit (TU) is the ordered assembly of the following genetic entities: a regulatory region, a transcription start site, one or more ORFs and a transcription termination site. When a TU comprises more than one ORF, the transcribed mRNA is called polycistronic; otherwise, it is called monocistronic. It is not uncommon for genes to be transcribed by several promoters; thus, TUs overlap. The collection of overlapping TUs constitutes an operon. Historically defined as a polycistronic TU, it has been observed that operons always contain a promoter that transcribes the whole set of genes conforming its TUs. The regulatory region contains cis elements such as the promoter – where the RNA polymerase initially binds – and transcription factor-binding sites (TFBS) – where transcription factors (TFs) bind to modulate the binding of the RNA polymerase  activated in response to environmental stress. Usually, every bacterium has one protein member from the σ54 family. RNA polymerase associated with a member of this family recognizes promoters that are different from those exclusively recognized when associated with a member of σ70. However, there are exceptions where two different σ factors bind to the same promoter (Transcription initiation in bacteria requires proteins known as sigma factors (σ). These factors – with even dozens of different types per genome – are essential for proper promoter recognition by RNA polymerase  has a dual function over some TUs using the previous strategy  to c. 9 mbp (free-living bacteria) (Chromosome size in bacteria ranges from acteria) . The stracquired characteristics in a nongenetic manner: biochemical modifications in DNA or DNA-binding proteins can act as epigenetic markers. Bacterial DNA can be methylated in several ways, resulting in N4-methyl-cytosine (m4C), N6-methyl-adenine (m6A) and N5-methyl-cytosine (m5C). Among these three chemical markers, m4C has been clearly related to epigenetic transcriptional regulation besides its relation to other cellular processes -binding sites. In the repressed state, Lrp binds the proximal site interfering with transcription and Dam methylates the distal site blocking Lrp binding. The operon is derepressed when PapI dimerizes with Lrp. The PapI–Lrp complex has a higher affinity for the distal site, thus freeing the proximal site from Lrp. Dam methylates the proximal site and transcription begins  do not derepress the lac operon. However, once high IPTG concentrations have induced the transcription of the operon, it is possible to lower the IPTG concentration to noninducing levels and maintain induced a colony previously induced with high IPTG concentrations. This is because daughters of preinduced mothers have a high level of β-galactoside permease in their membranes. This allows them to import, even at low concentrations, IPTG and maintain the lac operon derepressed  is customarily drawn in a network of causal relationships known as a transcriptional regulatory network (TRN)  affect the transcription rate of TGs in drastically distinct ways. Evidence shows that nucleoid-associated TFs and DNA-supercoiling induce continuous changes in the transcription rate, whereas local TFs induce discrete changes (i.e. On/Off transcription states). These two aspects have been compared with the analog and digital components of electronic devices  and Mur  regulate iron homeostasis and manganese uptake, respectively , must be studied to understand TRN flexibility. For example, loss and duplication of TFs and TFBS may result in regulon expansion, shrinkage, fusions, fissions and even creation and destruction. It is possible to see the contribution of gene duplication at all levels of TRNs  by the compilation of different facts reported in research articles whose main interest could have not been to obtain a network, (ii) by ChIP-chip or ChIP-Seq and (iii) by computational methods with DNA sequences, microarrays or scientific articles as input data. We provide a short description of each one of these methods.strong and weak. Evidence is strong if, for example, it comes from footprinting or EMSA plus change in expression or binding site mutation plus change in expression. An example of weak evidence is: expression change detected in a microarray plus existence of a binding site – for a certain TF – detected ‘by eye’ by the researcher. Because of its nature, ‘weak’ interactions may become ‘strong’ interactions or may disappear depending on new evidence.Interactions derived from the literature are the standard to validate any computational or high-throughput experimental inference  related to transcriptional regulation is compiled. These nouns are concatenated with verbs – to regulate, to inhibit, to promote, etc. – to, depending on certain grammatical rules, discover regulatory interactions in a collection of related scientific articles. Using this method, from 200 000 E. coli can be perturbed globally, rewiring it to a great extent; this might be a consequence of the inherent plasticity of TRNs. For example, The regulatory network of In transcription, all the time TFs are binding to or unbinding from different sequences in the DNA. The greater the affinity, the greater the time they remain bound. If the sequence is regulatory, there is a likelihood that the rest of the transcription machinery assemblies begin transcription before the TF tears off from DNA by thermal fluctuations. In this picture, there is no natural threshold in affinity above which TFs undoubtedly induce transcription. In general, there are a variety of binding sites and for every one of them a TF will have a different affinity, inducing, with some probability, transcription. When promoters are strong and TFs abound, transcription is certain and has a well-defined rate  (Supposing complete characterized libraries of different promoters exist, the main challenge in synthetic circuits still remains: to integrate these small networks into the cell environment without killing the cell, for example without overproducing toxic intermediates or causing metabolic bottlenecks that would inhibit growth. The problem is to find the exact promoter strengths with the correct regulatory region to balance and coordinate the expression of multiple genes. One promising solution is to generate a library of networks and then select the best-performing one under a given criterion. This is the same strategy followed in the directed evolution of proteins, where a library of mutant protein sequences is created and then screened for the best variation of the protein. The difference in the library of networks lies in the fact that the mutations are in the noncoding regions that regulate transcription and translation. One example of this approach is the combinatorial synthesis of intergenic regions in operons to tune the translation of polycistronic transcripts (Even though recent progress to unravel the underlying mechanisms of transcriptional regulation has been spectacular, the community lacks an integrative framework to direct new advances. In this respect, systems biology in bacteria has the challenge to show its promised capabilities of new levels of integration and understanding combining modeling and experiments of the whole network and cell behavior. To achieve this, there are two complementary procedures: bottom-up and top-down schemes . Bottom-This review has focused on regulation by TFs. However, there are other layers of cellular regulation that ultimately influence regulation by TFs. This situation creates feedback loops that transmit information from almost any regulatory layer to any other one in order to maintain cellular homeostasis. This is in clear contrast with the isolated picture of TRN, where a cellular hierarchical decision-making structure is emphasized. Thus, a major conceptual challenge is to change our way of thinking about causality in a complex system with an important connectivity and an important amount of circularity, i.e. feedback loops, in the ‘decision’ network of gene regulation at the whole-cell level.


Duf must be expressed, and function, in founder cells (FCs). A fixed number of FCs are chosen from a pool of equivalent myoblasts and serve to attract fusion-competent myoblasts (FCMs) to fuse with them to form a multinucleate muscle-fibre. The spatial and temporal regulation of duf expression and function are important and play a deciding role in choice of fibre number, location and perhaps size. We have used a combination of bioinformatics and functional enhancer deletion approaches to understand the regulation of duf. By transgenic enhancer-reporter deletion analysis of the duf regulatory region, we found that several distinct enhancer modules regulate duf expression in specific muscle founders of the embryo and the adult. In addition to existing bioinformatics tools, we used a new program for analysis of regulatory sequence, PhyloGibbs-MP, whose development was largely motivated by the requirements of this work. The results complement our deletion analysis by identifying transcription factors whose predicted binding regions match with our deletion constructs. Experimental evidence for the relevance of some of these TF binding sites comes from available ChIP-on-chip from the literature, and from our analysis of localization of myogenic transcription factors with duf enhancer reporter gene expression. Our results demonstrate the complex regulation in each founder cell of a gene that is expressed in all founder cells. They provide evidence for transcriptional control—both activation and repression—as an important player in the regulation of myoblast fusion. The set of enhancer constructs generated will be valuable in identifying novel trans-acting factor-binding sites and chromatin regulation during myoblast fusion in Drosophila. Our results and the bioinformatics tools developed provide a basis for the study of the transcriptional regulation of other complex genes.A key early player in the regulation of myoblast fusion is the gene  Drosophila melanogaster (reviewed in Notch (N) mediated lateral inhibition and other signaling pathways in situ hybridization or reporter-gene expression, sns and duf are expressed transiently in FCs and FCMs respectively and are turned off soon after the fusion process is complete duf and sns are subject to strong transcriptional regulation.Multinucleate muscle fibres form by the regulated fusion of myoblasts. Muscles of different shapes and sizes are made as a result of coordinated myoblast fusion and morphogenesis. This process is perhaps best studied in the embryonic muscles of the fruitfly Drosophila adult and invertebrates, muscles consist of many myotubes bundled together to form a contractile element. Drosophila adult muscle precursor cells segregate as sister cells from embryonic founders and give rise to all adult muscles in the pupa twi expression, proliferate during larval life and remain associated with imaginal discs in the thorax and neurons in the abdomen In the duf is therefore important to understand the transcriptional machinery that recognizes a FC. This will also allow us to understand the role of different factors responsible for transcriptional control of duf in different FCs and thereby development of muscle pattern.The size of the muscle fibre is probably dependent upon the number of fusion events ab initio by looking for short inexact repeated patterns or “motifs”. Enhancers and cis-regulatory modules can be predicted by clustering predicted binding sites, an approach taken by programs such as Stubb Drosophila genomes Bioinformatics tools can predict possible transcription factor (TF) binding sites, either by comparing with previously identified sites for known TFs, or duf. In addition to existing bioinformatics tools, we used a new program for analysis of regulatory sequence, PhyloGibbs-MP, described in a recent paper duf regulatory region using reporter constructs reveals specific aspects of duf regulation during Drosophila myogenesis. We find that several distinct enhancer modules regulate duf expression in specific muscle founders of the embryo and the adult. While embryonic enhancers are proximal, adult-specific enhancers are located more distal to the duf start site. These results merited a further, detailed study of the 10 kb region upstream of duf. We made a list of 44 position weight matrices for transcription factors relevant to mesoderm development, of which 38 were constructed using either the FlyReg database for DNAse I footprints cis-regulatory modules as well as binding sites for individual transcription factors. The results complement our deletion analysis by identifying transcription factors whose predicted binding regions match with our deletion constructs. Most predicted sites are conserved in other Drosophila species, suggesting functional importance. Experimental evidence for the relevance of some of these TF binding sites comes from both specific and global ChIP-on-chip analysis from the literature, using key mesodermal regulators We have used a combination of bioinformatics and functional enhancer deletion approaches to understand the regulation of duf and of the first intron (29 kb) were made using Matinspector Professional® cis-regulatory elements regulating duf expression in FCs. The consensus sequences (and source) for these factors are described in Preliminary studies of the 40 kb region upstream of duf. The arrangement of these putative binding sites in duf upstream region is shown in Analysis of this sequence revealed presence of many strong binding sites for nuclear effectors of different intercellular signaling pathways. Some clustering of binding sites was seen in the 10 kb region immediately upstream of Drosophila melanogaster and Drosophila pseudoobscura genomes. A significant number of the putative binding sites for signaling pathway effectors and transcription factors with mesodermal role and early patterning genes map within or in the vicinity of conserved sequence stretches  from the duf 5′ region. Fragments representing progressive deletions from both distal and proximal ends were amplified and cloned as EcoRI - BamHI fragments into pCaSpeR AUG βGal duf enhancer constructs were analyzed for reporter expression during embryonic and adult myogenesis. Additional duf enhancer-deletion reporter constructs for further analysis (results not discussed) are in Supplementary data To characterize the regulatory potential of putative duf expression during embryonic muscle development has been characterized using mRNA in-situ hybridizations rP298 lacZ, duf locus, which reproduces duf -like reporter- expression in all founder cells during embryonic duf upstream reporter constructs were compared with rP298 lacZ  also show strong expression in somatic muscle FCs very similar to duf −5.1 kb lacZ . From this expression analysis it is clear that elements in the 10 kb region 5′ of duf are capable of driving reporter expression in different subsets of muscle FCs. This also indicates that there are independent modules for duf expression in different muscles. These modules are not noticeably overlapping, eliciting expression in a different subset of muscles with every addition of a few hundred base-pairs of the enhancer. Each additional fragment in the 5′ represses the expression seen in a smaller proximal fragment and directs expression in different domains of duf expression. All the necessary elements for this complex spatio-temporal regulation of duf expression in all embryonic muscle FCs appears to be located in the 10 kb region 5′ of duf coding region.Deletions from the proximal end i.e. enhancer constructs without sequences close to the start site, show a different pattern of reporter expression.  −0.6 kb , duf −7. −3.8 kb  fragment −3.8 kb , . The eer cells . Prelimiduf upstream-lacZ reporters appears to be in specific subsets of FCs in different constructs. We double labeled enhancer-reporter constructs with mesodermal and founder- cell markers to verify this. Expression patterns of the duf enhancer lines were also confirmed by co-localization with duf Gal4 driven UAS-GFP , Vg and even-skipped (Eve). These transcription factors mark different somatic muscles as follows: Kr is expressed in DA1, DO1, LL1, LT2, LT4, VL3, VA2, VO2 and VO4 somatic FCs  and duf −5.1 kb lacZ are expressed weakly in LT2, not detectable in VA2 but strongly in all other Kr positive somatic FCs. duf −8.2−0.6 kb Gal4 is expressed weakly in VO4 but in none of the other Kr positive FCs. rP298 lacZ was used as positive control for comparison. Colocalization of all duf enhancer constructs with Eve also revealed that none of the enhancer reporters tested expressed ectopically in pericardial cells  but not tin. Wildtype duf is not expressed in the developing cardioblasts and loss of duf function does not affect the formation of the embryonic heart duf −2.4 kb lacZ is completely repressed in a slightly larger duf enhancer fragment: duf −3.0 kb lacZ. Similarly, the reporter expression in duf −3.0 kb lacZ is also seen in the embryonic central nervous system in a large subset of neuroblasts as compared to wildtype duf at stage 16. All the larger duf enhancer constructs do not show ectopic reporter expression in the ventral thoracic segments, cardioblasts or embryonic central nervous system. This suggests the presence of repressor elements that would restrict duf expression specifically to different founder cells.Interestingly, in two cases, rP298 lacZ or duf Gal4-UAS lacZ was used for wildtype comparison  and during pupal myogenesis for all the different muscle subtypes in the thorax, and Dorsal, Ventral and Lateral muscles in the abdomen. Expression was compared by colocalization with mouse monoclonal antibody 22C10, which marks neurons and the abdominal founder myoblasts very clearly mparison .duf enhancer deletion constructs during different stages of adult myogenesis reveals several interesting aspects of duf regulation in the adult. All smaller duf enhancer constructs up to 3.8 kb show no, or comparable to background, reporter expression in both thoracic as well as abdominal muscle founder cells of the adult. duf −3.8 kb lacZ shows weak expression in lateral abdominal muscles  which indicates that region around −3.0 −5.1 kb upstream of duf has a module for expression in lateral abdominal muscles. None of the constructs up to those with −5.5 kb of 5′ sequence are expressed in dorsal abdominal muscles or in any of the thoracic muscles. duf −8.2−0.6 Gal4 is expressed strongly in a subset of dorsal as well as lateral abdominal muscles . First, we made a list of high-quality PWMs for factors of known importance in mesoderm development, using sequences from PWMs, using the Flyreg database of DNAse I footprints et al.The complex and non cumulative expression pattern observed in duf. Finally, we used the same PWMs and PhyloGibbs-MP to confirm the prediction of the enhancers as well as to predict individual binding sites for transcription factors.Next, we used the module prediction program Stubb We used the 31 PWMs described above with Stubb duf, and also on its 29 kb intron, using a window size of 1000 bp and a shift of 100 bp. The free-energy profile shows significant enhancer structure: the first 10 kb upstream have significant binding free energies for these factors, but there also occur peaks at about 12 kb −15 kb upstream and 25 kb upstream  and the state of present experimental knowledge, the significance estimates are probably very accurate in experimental systems too.PhyloGibbs is a motif-finder with the ability to incorporate orthologous sequence from closely-related species that may have significant non-functional conservation. It reports predictions with significance estimates that are posterior probabilities, obtained from extended sampling, that the predictions are binding sites given the prior assumptions. We showed ab initio or with the help of “informative priors” such as our 31 mesoderm-relevant position weight matrices. While Stubb uses PWMs directly to predict sites, PhyloGibbs-MP seeks sites that are similar to one another, and only uses the PWMs to bias the search (in the form of a Bayesian prior). Thus, predictions differ between the two programs: PhyloGibbs-MP will fare poorly when only very few copies of a site are present, but will pick up more numerous sites even if they correspond relatively poorly to the known PWMs, or do not correspond to the supplied PWMs at all. It will, however, report a match to a known PWM only if the match is significant, that is, the PWM generated by the PhyloGibbs-MP alignment corresponds sufficiently closely with the prior PWM.PhyloGibbs-MP is an update to PhyloGibbs, which, among other new features, has the ability to localise predictions to small regions with a specified size and average spacing, meant to represent CRMs. Its predictions of CRMs here mainly agree with Stubb′s. However, its main task is to predict binding sites, either duf, and also on the intron, and found a similar modular structure as with Stubb . We find predicted binding sites for several key factors .  While some factors have sites scattered throughout the enhancer, others appear to be localised to certain regions of the enhancer. Most significant predictions by Stubb and Phylogibbs  on the 30 kb upstream of th Stubb . The fig factors . Predict factors . Differeylogibbs  are in gDrosophila embryo. This includes dorsal and ventral group of somatic founders, ectopic expression in cardioblasts, longitudinal visceral founders and large subset of somatic founders. No significant expression is detected in any of the adult muscles in constructs up to duf −3.8 kb lacZ. In this region, among the signaling pathway effectors, strong Pnt binding sites are predicted by both PhyloGibbs and Stubb Weak Su(H) and Pan (dTCF) sites are predicted by Stubb. Phylogibbs predicts strong Mad binding sites and few Brk binding sites in the proximal constructs. Both programs predict couple of Ci binding sites in the −3.0 kb construct  that gets restricted more specifically to somatic muscles FCs in duf −3.8 kb lacZ.Several clusters of binding sites are also predicted for TFs that are important for mesoderm development . Twi, wh3.8 kb lacZ  drive expression in adult specific muscle FC cells. Stubb predicts two clusters of Ecdysone-induced protein 74EF (Eip74EF) binding sites in duf enhancer. One cluster is in constructs −5.5 kb and −6.4 kb and a second cluster is constructs −8.6 kb and −9.8 kb. Stubb also predicts another Pnt, Pan, Mad and Ci cluster between −5.5 kb to −8.6 kb region. Stubb predicts ladybird group binding sites in 5.5 kb lacZ. dMef2 cluster is predicted by both programs in the 5.5 kb construct and another cluster in the distal region between −7.2 kb to −9.5 kb (by PhyloGibbs). This region shows distinct enhancer activity during adult myogenesis. STAT binding sites are found in two clusters, one cluster in proximal enhancers, which show embryonic expression and second cluster in distal enhancers, which show adult expression  enhancer that drove expression in dorsal FCs- indicating that the FC gene regulation was not exclusively regulated by these five TFs. But with a smaller and more general subset of TFs i.e. Pnt, Twi, and Tin, four new enhancers that drive FC like expression were identified duf regulation during embryonic and adult myogenesis also reflects this complexity. They demonstrate the complex regulation of a gene that is expressed in all founder cells. Spatio-temporally regulated activation/repression of specific duf enhancer modules in distinct muscle types suggests an elegant mechanism of generating muscle diversity by transcriptional control of a key player in myoblast fusion.A gene which is expressed in all cells of one category—duf −1.0 kb, close to the transcriptional start site has basic elements that accurately identify mesodermal lineage , the reporter expression is very well restricted to specific muscle types. Several ChIP-on-chip results indicate Twi binds between −2.4 kb to −3.8 kb of the duf enhancer  sites in the −1.5 to −2.4 kb using Stubb. When this fragment (duf −2.4 to −1.5 kb) was analyzed independently, it was found to be insufficient to drive any reporter expression. Interestingly, duf −2.4 kb lacZ is expressed strongly in cardioblasts and very weakly in all somatic muscles, while the smaller duf −1.5 kb construct is expressed in large subset of somatic muscle FCs.The smallest enhancer fragment, enhancer . In thisenhancer  indicateduf −3.8 kb lacZ and duf −5.1 kb lacZ are expressed strongly in majority of the somatic muscle FCs but not in any of the gut muscles. duf −5.1 kb lacZ is also expressed clearly in adult lateral abdominal muscles which is not seen in duf −5.3 kb lacZ and duf −5.5 kb lacZ.RTK signalling and Twi are critical for the somatic muscle fate duf enhancer fragments  express strongly in visceral muscles of the embryo and also in the persisitent larval muscles, which are FCs of the DLMs, at the onset of adult development.Body wall (somatic) muscles provide the force for the peristaltic locomotion of the larva while the gut  muscles provide the peristaltic force for movement of food during digestion. Longitudinal and circular muscles of the midgut as well as the visceral muscles of the foregut and hindgut arise from different primordia and follow diverse developmental pathways shn mutants: shn mediates action of Dpp on mesodermal cells by inducing bapduf enhancer in mesodermal cells from stage 6–8 of embryonic development , has putative binding sites in the region >5 kb from the start site. PhyloGibbs predictes dMef2 sites between −7.2 to −9.5 kb . Severalduf start site  characteristic of TREs (Trithorax Response Elements) are found in −4.0 kb from the art site . Sites fart site . This reart site .duf, but only very little binding of PHO, in Sg4 tissue culture cells. However, data from similar experiments using Drosophila embryos suggest that PHOL binds upstream of the duf promoter. PcG protein binding at duf has not been detected, but a binding peak for ASH1 is usually associated with PcG target genes when they are derepressed, has been found .Similarly, putative binding sites for PHO (pleiohomeotic) and PHO-like polycomb group proteins (PcG) that bind to PREs (Polycomb group Response Elements) are found between −8.0 kb to −9.3 kb region . ChIP-onduf enhancer region appears restricted to specific regions. TRE/PRE mediated silencing may be responsible for switching between embryonic and adult specific enhancers by restricting access of TF to chromatin in appropriate tissue and time points. Experiments designed to test the presence or absence of each of these factors during embryonic or adult myogenesis would help answer this question more accurately.PREs and TREs in duf during Drosophila myogenesis. A set of enhancer constructs generated for this study will be valuable reagents in identifying important trans-acting factor-binding sites and chromatin regulation during myoblast fusion. We describe several contexts where the bioinformatics predictive tools and ChIP-on-chip approaches have great value and others where, clearly, more information is needed before predictive tools can be applied.Taken together, this study has uncovered a complex regulatory mechanism operating to control important myoblast fusion gene such as duf with BamHI or EcoRI overhangs to assist cloning. Details of the primers used and genomic location they cover are tabulated in Supplementary data Specific primers were designed to PCR upstream fragments of UAS lacZ and UAS GFP stocks were from Bloomington Stock centre. rP298 (duf) lacZduf Gal4duf locus faithfully reproduces the wildtype duf expression pattern in muscle FCs. All transgenic duf enhancer-reporter deletion lines described were generated for this study.The following fly strains were used: Anti-β-galactosidase antibodies raised in Rabbit (Molecular Probes) or in mouse  were used at a dilution of 1∶5000 and 1∶50 respectively. Anti-Twist antibody , was used pre-adsorbed at a dilution of 1∶500 to late stage (15–16) embryos and then used. Anti-Tinman antibody (Manfred Frasch) was used at a dilution at 1∶200, Anti-Evenskipped (Nipam Patel) at 1∶10, Anti Kr at 1∶500, Mouse 22C10  at 1∶75 and Anti GFP (Molecular Probes) at 1∶500. Secondary antibodies conjugated to Alexa Fluor dyes- Alexa 488 and Alexa 568  was used. Fluorescent preparations were scanned using the confocal microscopes  or Ziess LSM 510Meta (Carl Zeiss GmbH). Raw images were analyzed using Confocal Assistant (version 4.02) or Ziess Image Viewer  and processed in Adobe® Photoshop® CS3 version 10.0.duf genomic region for potential TF binding sites, Matinspector Professional® GCCGNCGC] TGGGWGGTC] TGGCGYY] TYAAGTG] TCAAGTGG] YTAWWWWTAR] RTATATRTA] RTTTWAYGTAAY] cis-regulatory elements regulating duf expression in FCs.For initial screen of D. melanogaster in the Flyreg database D. simulans, D. yakuba, D. erecta, D. ananassae, D. pseudoobscura, using PhyloGibbs-MP as described in 44 weight matrices for transcription factors, or groups of related homeodomain factors, were chosen based on their known relevance to mesoderm development (as given by their flybase annotations or literature references) and availability of position weight matrices or binding site data. For the factors bin, brk, byn, dl, en, E(spl), Mad, pan, sna, Su(H), twi, br-Z1, br-Z2, br-Z3, br-Z4, Cf2-II, Eip74EF, exd, ey, gsb, gsb-n, Med, prd, srp, toy, sd, Trl, matrices were constructed from DNAse-I footprints available for duf, and in the approximately 29 kb intron, using these 31 input weight matrices. In release 4 coordinates, the upstream region selected was 2926233–2956233 and the intron was 2956475–2985408, on chromosome X. Orthologous sequence from D. pseudoobscura was selected , and Stubb was run on these sequences in multi-sequence mode.  The raw input and output files and exact command lines are in Supplementary Stubb D. pseudoobscura, D. yakuba, D. simulans , and re-aligned with Sigma.  In this run, PhyloGibbs-MP searched for motifs in 500bp contiguous regions, separated on average by 500 bp , and within each such region, on each sequence, the probability of a given site being a binding site (for any factor) is 0.005. Upto 55 motifs are sought (of which up to 31 may be associated with the prior PWMs), and each 500 bp module may contain up to 40 motifs. The raw input and output files and exact command lines are in Supplementary PhyloGibbs-MP was run, with these prior weight matrices and in “module-finding” mode, on the same upstream and intronic regions. The upstream region used was 2926000–2956000, and the intron region 2956475–2985408, on chromosome X. Orthologous sequence was taken from duf) was examined in detail. Sequence preparation was as described above, but PhyloGibbs-MP was run treating all of the sequence as potentially regulatory (the specified “module dimension” being the entire length of the sequence). Also, after preliminary runs revealed poor or non-specific predictions for many factors, the list of input factors was trimmed to the following: bin, byn, E(spl), toy, kni, shn1, shn2, STAT, Ci, dmef, brk, dl, Mad, pan, Su(H), twi, Cf2-II, Eip74EF, exd, Kr, Med, prd, sd, Trl, Pnt, Abd-B group, Antp group, Ap group, Bcd group, Ladybird group, NK2 group. These were separated into two groups, one with consensus motifs 8 bp long or shorter, and the other with longer consensus motifs, because PhyloGibbs-MP cannot handle differing lengths of motifs and, while robust to small variations, will show inferior performance with highly varying motif sizes. The raw input and output files and exact command lines are in Supplementary The region 2945000–2956000 . The arrangement of these putative binding sites found in duf upstream region along with primer binding sites used for cloning different duf enhancer constructs are shown. Putative GAGA binding sites characteristic of TREs and PHO and PHO-like binding sites PREs are also highlighted.MatInspector results for potential TF binding sites in 10 kb (0.07 MB XLS)Click here for additional data file.Datasheet S2duf upstream sequence between Drosophila melanogaster and Drosophila pseudoobscura. This table lists putative binding sites in the 10 kb upstream of duf from Drosophila melanogaster compared with that of Drosophila pseudoobscura. The + or − sign in the parenthesis indicating the location of the binding site on the coding or non-coding strand in this sequence respectively. Primer binding sites used for cloning are shown in bold green. Light blue and pale green numbers indicate the stretch of sequence identical between the two genomes. Sites highlighted in red  and green (transcription factors) are well conserved. Those in orange are weakly conserved; those in light yellow are conserved in position/location and those shown in white are unique to D. melanogaster.Conserved TF binding sites in the −10 kb (0.11 MB XLS)Click here for additional data file.Datasheet S3duf enhancer region. Matching predictions are highlighted along with the sequence and distance from duf start site and primers used for cloning the enhancer fragments.Predictions from Stubb and PhyloGibbs-MP that match several published consensus sites. This table shows comparison of different predictions of putative TF binding sites using Stubb, PhyloGibbs-MP, and MatInspector Professional® and published consensus sequences in −10 kb (0.05 MB XLS)Click here for additional data file.Figure S1duf enhancer reporter deletion constructs. Additional set of enhancer deletion constructs available for studying transcriptional regulation of duf. Preliminary analysis has been carried out on some of the constructs. duf −0.6 to 6.0 kb and duf 6.0 to 8.5 kb (magenta bars) covering part of the first intron sequence were tested and they are not expressed in any muscles. duf −14 to −15 kb Gal4 showed no expression in mesoderm/muscles. This small fragment has ectopic expression in epidermal cells that appears to be apodemes (muscle attachment sites within the epidermis). Others constructs are available as sequence verified plasmids for further detailed analysis of duf enhancer region.Additional (0.20 MB TIF)Click here for additional data file.Figure S2duf enhancer reporter deletion constructs. Lateral view confocal images of embryos from different duf enhancer reporter lines showing reporter expression during important embryonic myogenesis. Reporter expression was assayed by using antibodies against βGal. Gal4 transgenic lines were crossed with UAS-lacZ. A. Stage 16 duf Gal4 embryo showing expression in all the somatic  muscles. In comparison, duf −3.8 kb Gal4 reporter expression (B) is seen not in all, but a large subset of somatic muscles. duf −3.8 kb Gal4 shows clear expression in easily identifiable subset of somatic muscle FCs at stage 13 (shown in C). In comparison, in lacZ version of the same construct (duf −3.8 kb lacZ shown in D), reporter expression appears diffuse in some somatic FCs. Whereas, in a slightly larger construct duf −4.6 kb lacZ (shown in C), the reporter expression is more stronger in majority of the somatic muscle FCs. duf −7.9−3.8 kb Gal4 reporter expression is specifically seen in circular visceral muscle FCs (arrow in F and G) and few somatic FCs (white arrow heads G and H). Ectopic expression is also seen in some other cells at later stages (red arrow heads in G and H). duf −7.2 kb lacZ is expressed in all the somatic muscle FCs (arrow heads in I and J) and also in circular visceral muscles (arrows in J) revealed by optical slices closer to the centre of the embryo. Reporter expression of duf −7.2 kb lacZ is weak compared to other reporter constructs. duf −6.4 kb lacZ (K-M) shows ectopic expression in the epidermis and trachea (red arrow heads in L and M) and very weak expression in the somatic muscles. duf −4.6−3.0 kb Gal4 (N and O) shows ectopic reporter expression the epidermis. Scale bar  = 50 microns.Expression pattern of additional (1.90 MB TIF)Click here for additional data file.Figure S3duf enhancer lacZ reporter expression colocalizes with duf Gal4; UAS GFP in stage 14 embryos. Confocal projections of different duf enhancer lacZ reporter construct with duf Gal4; UAS GFP in stage 14 embryos. The embryos are double labeled with antibodies against β galactosidase (in red) to show reporter expression and GFP (in green) to report wildtype duf expression in muscle FCs driven by duf Gal4. The size of the construct is indicated on panel showing colocalization (in yellow). Expression of different duf enhancer lacZ constructs is seen in specific founders that give rise to different muscles of the embryo. Expression in the visceral muscles is obscured by the overlying somatic muscles. Complete colocalization with duf Gal4 driven GFP is seen in somatic muscles of duf −5.3 kb lacZ embryos. Lateral view embryos with anterior is to the left and dorsal to the top. Scale Bar = 100 microns.(2.53 MB TIF)Click here for additional data file.Figure S4duf −2.4 kb lacZ and duf −3.0 kb lacZ. Confocal projections of duf enhancer lacZ constructs showing ectopic reporter expression in cardioblasts and neuroblasts. Top panels are stage 16 dorsal view of duf Gal4; UAS lacZ and duf −2.4 kb lacZ embryos double labeled with antibodies against β galactosidase (green) and Tin (magenta) to mark the cardioblasts that form the heart. Bottom panels are stage 16 ventral view of duf −3.0 kb lacZ and rP298 (duf) lacZ embryos double labeled with antibodies against β galactosidase (green) and Eve (red) to mark large subset of neuroblasts. Box region is ∼125 microns covering A2-A5 abdominal segments in the top panel, and A2-A6 in the bottom panel, magnified to show details in individual channels. duf −2.4 kb lacZ is strongly expressed in two cells that do not express Tinman and weakly in other in cardioblasts (arrows in D – F). Wildtype duf expression in duf Gal4; UAS lacZ is not detected in developing cardioblasts (arrow in B). Wildtype duf is expressed in CNS midline by stage 16 . duf −3.0 kb lacZ is also expressed in the CNS but in a larger subset of neuroblasts (K) when compared to wildtype duf (H). (All images- anterior to the left).Descripton of ectopic expression observed in (3.69 MB TIF)Click here for additional data file.Figure S5Sequence logos corresponding to position weight matrices generated by PhyloGibbs-MP. Sequence logos corresponding to position weight matrices generated by PhyloGibbs-MP from FlyReg DNAse I footprints database (0.18 MB PDF)Click here for additional data file.Figure S6duf Upstream. Predictions of individual binding sites for various factors from Stubb and PhyloGibbs-MP in the duf 11 kb region. For both programs, weight matrices for 31 mesoderm-relevant factors, as discussed in the text, were used as priors. The first set of high priority factors are shown and discussed in Additional PhyloGibbs-MP and Stubb (0.82 MB TIF)Click here for additional data file.Table S1duf upstream genomic fragments. Specific primers were designed to PCR different upstream fragments of duf. Most of the forward primers (F) have EcoRI (E) and reverse primers (R) have either BamHI (B) or EcoRI as the ectopic restriction site, which were included in the 5′ end of the primers as overhangs to assist in cloning. An additional five to eight bases were added after the restriction site to the primer for improved digestion by the restriction enzymes. The primer matches the target sequence exactly 3′ to the restriction site (shown as a slash). Distance (in base pairs) from duf transcription start site is indicated as start and end.Details of the primers designed to amplify different (0.04 MB DOC)Click here for additional data file.Dataset S1duf and approximately 29 kb intron, using 45 input weight matrices. In release 4 coordinates, the upstream region selected was 2926233–2956233 and the intron was 2956475–2985408, on chromosome X. Orthologous sequence from D. pseudoobscura was selected. As part of the process of running Stubb, these sequences were re-aligned with LAGAN and Stubb was run on these sequences in multi-sequence mode. The raw input and output files are in Supplementary Data S11.The raw input and output files of enhancer predictions using Stubb. Stubb version 2.1 was run on the 30 kb region upstream of (4.59 MB ZIP)Click here for additional data file.Dataset S2D. pseudoobscura, D. yakuba, D. simulans and re-aligned with Sigma. The raw output files are in Supplementary Data S12.The raw input and output files of enhancer predictions using PhyloGibbs-MP. PhyloGibbs-MP was run, with these prior weight matrices and in “module-finding” mode, on the same upstream and intronic regions. The upstream region used was 2926000–2956000, and the intron region 2956475–2985408, on chromosome X. Orthologous sequence was taken from (0.12 MB ZIP)Click here for additional data file.Dataset S3duf) was examined in detail. Sequence preparation was as described above, but PhyloGibbs-MP was run treating 2/3 of the sequence, rather than half the sequence, as potentially regulatory (the specified “module dimension” being the entire length of the sequence). The raw output is in Supplementary data 13.The raw output of the analysis of 11 kb upstream region using PhyloGibbs-MP. The region 2945000–2956000 (about 11 kb upstream of (0.06 MB ZIP)Click here for additional data file.


Escherichia coli regulate the transcription rate of their own genes. This suggests that auto-regulation has one or more important functions. Here, one possible function is studied. Often the transcription rate of an auto-regulator is also controlled by additional transcription factors. In these cases, the way the expression of the auto-regulator responds to changes in the concentrations of the “input” regulators (the response function) is obviously affected by the auto-regulation. We suggest that, conversely, auto-regulation may be used to optimize this response function. To test this hypothesis, we use an evolutionary algorithm and a chemical–physical model of transcription regulation to design model cis-regulatory constructs with predefined response functions. In these simulations, auto-regulation can evolve if this provides a functional benefit. When selecting for a series of elementary response functions—Boolean logic gates and linear responses—the cis-regulatory regions resulting from the simulations indeed often exploit auto-regulation. Surprisingly, the resulting constructs use auto-activation rather than auto-repression. Several design principles show up repeatedly in the simulation results. They demonstrate how auto-activation can be used to generate sharp, switch-like activation and repression circuits and how linearly decreasing response functions can be obtained. Auto-repression, on the other hand, resulted only when a high response speed or a suppression of intrinsic noise was also selected for. The results suggest that, while auto-repression may primarily be valuable to improve the dynamical properties of regulatory circuits, auto-activation is likely to evolve even when selection acts on the shape of response function only.As many as 59% of the transcription factors in  Bacteria adjust which proteins they make, and how many copies of each kind, depending on their environment. The production rate of each regulated protein is controlled by a special class of proteins called transcription factors. The rate at which a certain protein is produced usually depends on the cellular concentrations of a few such transcription factors. When circumstances change, the concentrations of these transcription factors alter too and consequently the production rates of all proteins regulated by them are adjusted. Interestingly, many transcription factors also regulate their own synthesis rate. This suggests that this self-regulation must have one or more important functions. In this article we study one possible function. In order for cells to function properly each protein concentration has to respond in a particular way to changes in transcription factor concentrations. We have studied how bacteria can optimize and fine-tune these responses. To this end, we formulated a physical model of the regulation by transcription factors and performed computer simulations. These simulations show that self-regulation—and in particular self-activation—is often a useful tool to achieve the prescribed response. Therefore we conclude that natural selection on the regulation of protein levels could naturally lead to self-regulation. Escherichia coli regulate the transcription rate of their own gene. In fact, 59% of the TFs are known to auto-regulate and the list is growing Many transcription factors (TFs) in E. coli are known to respond to at least two additional regulators Auto-regulating transcription factors are typically regulated by other TFs too. In fact, 23 auto-regulating TFs in In the past years, several other functions of auto-regulation have been proposed. Negative auto-regulation has been shown to decrease the sensitivity of expression levels to intrinsic fluctuations in the transcription rate under certain conditions in silico cis-regulatory regions with predefined response functions. The evolutionary algorithm subjects a population of model cis-regulatory regions to rounds of mutation and selection. The mutations are introduced at the level of base-pair sequences while the selection step is based on the emerging network properties calculated using the model of transcription regulation. In the course of these simulations complex promoter designs develop that perform the desired function; these designs often reveal new design principles. In earlier work, auto-regulation was not included in this method. In contrast, we now use an extended version of the method to design cis-regulatory constructs that can exploit feedback.To study the benefits of auto-regulation we use a computational approach that we developed recently cis-regulatory regions in real cells essentially implement logical decisions Gates are computational devices that produce an output signal depending on one or more input signals; logic gates are gates that implement a binary (Boolean) decision rule. For example, a transcriptional AND gate would be a gene whose expression (the “output”) is regulated by two TFs  such that it is transcribed only if both TF1 and TF2 have a sufficient expression level Many cis-regulatory sequences resulting from the simulations by calculating DNA footprints for the resulting transcription factors and promoter sequences. These footprints show that auto-regulation—in particular auto-activation—is often used in these cis-regulatory regions; indeed, further analysis shows that auto-activation can be used to construct “better” transcriptional logic gates by allowing for more switch-like, “steep” response functions. However, the use of auto-regulation in shaping response functions is not limited to creating switch-like functions. To demonstrate this, we also applied our method to the design of cis-regulatory constructs that respond in a linear fashion to input concentrations. Again we find that auto-activation emerges spontaneously in the results.We analyze the Finally, we also performed simulations in which we selected for designs with desirable dynamical qualities. First, we adjusted the method to select for gates with a short response time. Second, we performed selection against intrinsic fluctuations. In agreement with earlier results Before describing the results we first provide a detailed description of the model and the algorithm used.in silico cis-regulatory regions with a predefined function. The model of transcription regulation is constructed such that all properties of a model regulatory network follow entirely from the sequences of TFs and cis-regulatory regions. Binding sites of TFs are therefore not specified beforehand but appear gradually in the course of the simulations. The model is an extension to the one described in detail in our earlier publication We combine a model of transcription regulation and an evolutionary algorithm to design tf3, and at most two “input” transcription factors, TF1 and TF2. The gene tf3 codes for another transcription factor called TF3. All three TFs can regulate the transcription rate of tf3 by binding to its cis-regulatory region.  algorithm presented in The transcription rate of The model described above contains the generic mechanisms that are responsible for many of the known transcription regulatory interactions. Yet, several mechanisms that play a role in specific systems are not included; for instance, the model does not include DNA looping, extended We model the dynamics of the concentration of TF3, The concentrations We choose the constants In order to design networks with a prescribed function an evolutionary algorithm was used. A population of 200 transcriptional gates was subjected to 1000 cycles of mutation, selection and replication. Initially, all gates had random sequences. Auto-regulation was not imposed, but the system was free to exploit it by evolving binding sites for TF3. The details of the evolutionary algorithm were chosen to combine an effective optimization of the gates with computational efficiency; we emphasize that we do not intend to faithfully mimic biological evolution.cis-regulatory sequences (with probability cis-regulatory region). If this happened, a base pair was selected at random from the cis-regulatory sequence and substituted by a randomly chosen nucleotide. Second, insertions or deletions of a random base pair occurred in cis-regulatory regions  to select those sites that are likely to be important. For each TF We define Below, we describe the results of the simulations. We regularly compare the results to our previous work in which auto-regulation was excluded Ref. . In someconditional auto-activation. This mechanism occurred in AND and ACT (activation) gates  gates. As it turns out, whenever steep repression is required, we also find strong auto-activation; this occurred in every simulation run for our NAND, NOR and IN gates (20 repeats each), with cis-regulatory region of a typical NAND gate using auto-activation. The corresponding response function plotted in activation sites for both input TFs. This counter-intuitive feature turned out to enhance the sharpness of the response. At low TF concentrations, the activation sites counter-act the repression module, so that the expression stays high. At higher TF concentrations, however, the repression module dominates and represses transcription. Under the parameters used designs of this type reached a modest fold-change of The design that resulted when auto-activation was excluded is composed of a hetero-cooperative repression module (a tandem series of repressor sites to which both input TFs bind cooperatively). The function of this module is to repress transcription only when both TF1 and TF2 are present in sufficiently high concentrations, as required of a NAND gate. In Ref. tf3 is highly expressed, aided by auto-activation. As the concentrations of TF1 and TF2 are increased, the repression module starts to compete with the auto-activation module. Quite suddenly, the repression module wins this competition and displaces RNAP from the promoter. The strong, cooperative repression module now leads to a rather complete inhibition. The new design can lead to fold-changes of In the new results  the actitf3. In the second scenario we use the same configuration, but add an activator site for TF3. Thus, auto-activation competes with cooperative repression. The fitness of each design is optimized using the fitness function for the IN gate. As can clearly be seen in To study the mechanism responsible for the steepness of the response function we again analyzed a minimal model. In Does the sensitivity of the response function 9 have an upper bound, as is the case for Hill functions? To answer this question we again study the minimal model mathematically.negative . Finally, a high sensitivity in one point does not guarantee that the response function switches from high to low in a narrow interval as is required by the fitness function; this explains why in the plots in We note that the sensitivity is optimal for linear goal functions.In both previous cases, auto-regulation was used to obtain the steep or switch-like behavior required to approximate the binary responses of logic gates. Indeed, sharp responses are observed and probably required in many real examples; nevertheless many genes respond in a more gradual manner to their input signals  always use auto-regulation, with Indeed, simulation results for linear repression  auto-regulation is never used. To test if these results are an artifact of the algorithm, we studied a series of models , and In fact, the initial condition of the simulations, in which the gate is completely dysfunctional, is a local optimum of this fitness score. This is because initially the steady-state expression level is negligible so that the response time for initial condition i.e., fluctuations in the concentration of Second, we selected against intrinsic noise, In order to treat intrinsic fluctuations in a tractable manner we now replaced the ordinary differential equation 4 by the following stochastic differential equation:d by see :(18)The In We performed simulations with several values for cis-regulatory regions became completely dysfunctional; this can be understood from the fact that both the response time and the noise reduction can be optimized by abolishing expression altogether. Figure 7 in In activating gates  auto-repression resulted in all simulation runs with i.e., if As we explained, the response functions of the NAND, NOR and IN gates benefit from auto-activation; in those gates auto-activation occurred unless the selection pressure on the dynamical properties dominated  versus 30 out of 62 auto-repressing TFs (48%); this indeed suggests that auto-activators are more likely to have additional inputs  while the 62 auto-repressors have 50 inputs in total (0.81 on average). Evidently, auto-activators have significantly more inputs than auto-repressors  no input TF is known.E. coli.Our results shed new light on the use of auto-regulation. We described three situations in which auto-activation can be used to improve the response function of promoters. First, if auto-activation is conditional on the presence of other TFs, it can give rise to sensitive responses that otherwise require multiple cooperative binding sites of the input TF. Presumably, not all input TFs can bind cooperatively to multiple binding sites; in those cases conditional auto-activation can serve as an alternative. Secondly, auto-activation can strongly contribute to the sensitivity of the response of repression systems. Whenever sharp repression is required, auto-activation can have a selective advantage. Thirdly, we showed that auto-activation is also useful if a linearly decreasing response function is desired. Together, such mechanisms may help explain the large number of auto-activators present in Auto-repression never appeared in the simulation results if selection was based on the response function of the gates only. Most likely, the limited use of auto-repression in shaping response functions is due to its general tendency to decrease the fold-change and sensitivity of the response. A low fold-change or sensitivity can typically also be achieved without auto-repression by tuning both the promoter strength and the affinities of the TF binding sites. Nevertheless, we cannot exclude the possibility that auto-repression would show up in simulations selecting for response functions different from the ones presented here.If the fitness function was altered to favor a fast response or suppression of intrinsic transcriptional noise, auto-repression did emerge. It has been suggested before that the function of negative auto-regulation is to regulate such dynamic properties cis-regulatory regions can be perceived as adaptive curve fitting. Allowing for auto-regulation gives gene-regulatory systems additional degrees of freedom to optimize their performance, and it would perhaps be more surprising if this freedom were not exploited. We therefore expect that the conclusions based on the idealized gates studied in this work are also relevant for real biological systems requiring more complex response functions.In retrospect, the emergence of auto-regulation is hardly surprising. The evolution of e.g. when sensitive repression is required) whereas in other cases there is only a small difference between the quality of the response function for designs with or without auto-regulation. This leads one to wonder whether in the latter case natural selection on the shape of the response would be large enough to evolve and maintain auto-regulation, in particular in the presence of noise. This is largely an open question; yet, the fact that some E. coli promoters contain a large number of TF binding sites many of which contribute only marginally to the expression . Auto-regulation is also widespread in eukaryotes Even though in eukaryotes the mechanisms of gene regulation are generally different and various additional layers of regulation exist, recent work has shown that many basic principles of prokaryotic gene regulation—in particular the interplay between cooperative binding and competitive inhibition—are equally important in eukaryotes . Fluctuations may in some cases be beneficial or even necessary. For instance, when cells respond to a fluctuating environment via the strategy of stochastic switching, fluctuations are essential Text S1Supplementary information. All supplementary information is contained in this document. It contains supplementary figures, detailed mathematical derivations, additional analyses, and numerical tests of approximations used in the main text.(0.81 MB PDF)Click here for additional data file.


Drosophila embryo, a spatio-temporal gene/protein interaction network model is proposed to mimic dynamic behaviors of protein synthesis, protein decay, mRNA decay, protein diffusion, transcription regulations and autoregulation to analyze the interplay of genes and proteins at different compartments in early embryogenesis. In this study, we use the maximum likelihood (ML) method to identify the stochastic 3-D Embryo Space-Time (3-DEST) dynamic model for gene/protein interaction network via 3-D mRNA and protein expression data and then use the Akaike Information Criterion (AIC) to prune the gene/protein interaction network. The identified gene/protein interaction network allows us not only to analyze the dynamic interplay of genes and proteins on the border of eve stripes but also to infer that eve stripes are established and maintained by network motifs built by the cooperation between transcription regulations and diffusion mechanisms in early embryogenesis. Literature reference with the wet experiments of gene mutations provides a clue for validating the identified network. The proposed spatio-temporal dynamic model can be extended to gene/protein network construction of different biological phenotypes, which depend on compartments, e.g. postnatal stem/progenitor cell differentiation.In order to investigate the possible mechanisms for eve stripe formation of  Drosophila embryogenesis, i.e. the syncytial blastoderm stage, is completed two hours after the onset of fertilization and periodic segments are then characterized. Before the determination of periodic segments, the embryo is not yet separated by membranes, and macromolecules such as transcription factors (TFs) can diffuse freely and regulate downstream target genes in neighboring nucleus. Hence, at the syncytial blastoderm stage diffusion mechanism is fast enough to vary the concentrations of TFs in transcription regulations. Through a series of high/low affinity bindings of transcription regulations, downstream genes are dictated to express in their corresponding space of an embryo. Therefore, we assume that the transcription regulation and diffusion mechanism may play a cooperative role in characterizing embryonic segments.An early embryonic stage in Drosophila embryo, a spatio-temporal model is needed. In recent studies, early embryogenesis in Drosophila includes at least 31 genes in subdividing the embryonic patterns into 14 segmental primordia along the anterior-posterior (A-P) axis .bicoid (bcd), caudal (cad), hunchback (hb), giant (gt), knirps (kni), Krüppel (Kr), tailless (tll), even-skipped (eve), fushitarazu (ftz), hairy, odd-skipped (odd), paired (prd), runt and sloppy-paired (slp)) have been provided and studied during the early developmental stages of Drosophila melanogaster. The 14 early development-related genes can be roughly divided into three classes, i.e. maternal genes, gap genes and pair-rule genes, which have been regarded as hierarchical transcription regulations with positive auto-regulations to generate and refine the constitutions of segments.9Although some topics about protein diffusion have been well studied,bcd, cad and hb, diffuse and regulate gap genes with different expression levels in each spatial region along the A-P axis of the Drosophila embryo. The gap genes, i.e. gt, hb, kni, Kr and tll, define roughly the differences between two neighboring stripes by protein diffusion. The pair-rule genes, i.e. eve, ftz, hairy, odd, prd, runt and slp, define periodic patterns of the embryo by transcription regulation and protein diffusion. Two of these pair-rule genes, i.e. eve and ftz, are involved in defining even and odd segments of the 14 segmental primordia along the A-P axis.Three classified genes  into which the 14 early development-related genes can be divided are described in detail in the following. The maternal genes, i.e. Drosophila, the three-dimensional (3-D) spatio-temporal expression data of 14 early development-related proteins (http://flyex.ams.sunysb.edu/flyex/),http://flyex.ams.sunysb.edu/lab/gaps.html)Drosophila development. Interestingly, by comparing the normalized protein spatio-temporal expression data with mRNA spatio-temporal expression data, the trends of gene expressions along the A-P axis are found.At the early developmental stages of ensional -D spatioDrosophila development, nonlinear 2-D dynamic models have been employed to analyze the transcription regulation properties and effect of gap genes on eve stripe formation.Drosophila development since not only protein but also mRNA 3-D spatio-temporal data are both available for dynamic interplay of genes and proteins at different compartments of Drosophila embryo in early embryogenesis. The mechanisms of early Drosophila development in the whole embryo can be unraveled clearly if the dynamic interactions of genes and proteins are considered at different compartments in early embryogenesis. Therefore, in this study, we propose a stochastic 3-D dynamic model for constructing the gene/protein interaction network of early Drosophila development.In recent years, since the development of experimental techniques has increased the quality and amount of available mRNA and protein expressions, many approaches, e.g. fuzzy logic,Drosophila embryo. In this biological development approach, it is assumed that transcription regulations consist of cis-effect and trans-effect. Since edges, i.e. transcription regulations, in a gene regulatory network must be constantly selected in order to survive randomization forces, trans-effects, which are the binding affinities of specific transcription factors to cis-regulatory regions in the promoter of the target gene, would be varied rapidly while cis-effects, which are regulated directly by physical attachment of TF’s binding cis-regulatory regions, are relatively fixed.Drosophila development. From the identified stochastic 3-DEST model, we can not only find the significant transcription regulations of the corresponding TFs, which control the anterior/posterior border formation of eve stripes, but also validate these results with wet experiments. In order to validate the identified effect of transcription regulation and diffusion on early Drosophila development, the wet experiments, i.e. gene mutations,Drosophila development. From the perspective of the network motifs of the identified gene/protein interaction network in the embryo, we find that transcription regulations and protein diffusion mechanisms may play a cooperative role in the formation of eve stripes in early Drosophila development.In this study, we focus on the topic of investigating the possible mechanisms for the eve stripe formation of Drosophila development.To identify the dynamic behavior of the early development-related genes, the procedure of system identification in early embryogenesis is divided into four steps. First, utilizing fully the well-published spatio-temporal data and the prior knowledge of early embryogenesis, we construct a stochastic 3-DEST model to identify the molecular dynamics of gene/protein interaction network in early embryogenesis. Second, for system modeling, we use Eve’s spatial expression at the cleavage cycle 14A temporal class 8 (c14A8) of the nuclear cleavage to settle stripe boundaries and region boundaries of each stripe for dividing the embryo into seven eve stripes along the A-P axis and into three spatial regions  along the D-V axis, respectively. Third, for the early development-related genes, since a part of the mRNA spatio-temporal data are unavailable, we incorporate the available mRNA and protein spatio-temporal expression data with the back-propagating NN training method to train and simulate the mimic data for the unavailable mRNA spatio-temporal expression data see . Fourth,Remark: If the information of cooperation bindings is richer in future, the transcriptional regulations due to cooperation binding can be easily extended to the regulation candidates of the 3-DEST model, which can improve the proposed model of gene/protein network but with increased computation burden when using the AIC method in early embryogenesis.Xi and Yi to represent the 3-D spatio-temporal mRNA profiles of the ith target gene and its corresponding TFs, respectively. According to the transcription regulation model proposed in previous studies,ith target gene and their upstream regulatory TFs in the gene/protein interaction network of Drosophila development is proposed as follows:Xi represents the mRNA expression of the ith target gene, Yj denotes the expression of the jth TF of the target gene, and f) defined as f(Y) = Yn/(Pn + Yn) is a sigmoid function to denote the regulatory bindings of TFs on the promoters of targets.P is defined as the means of protein expressions, which imply cis-effects of transcription regulations. The term ith target gene from its TFs. αi stands for mRNA decay rate for the ith gene and is equal to the synthesis rate of the ith protein, and λj stands for protein decay rate. κi and jϖ are basal level of mRNA and protein generation, respectively, and they satisfy κi, ϖj ≥ 0. The diffusion operator ▿2 = ∂2/∂x2 + ∂2/∂y2 is the Laplacian operator in 2-D to denote the diffusion of protein at the location . In αi Xi in the downstream translation process. In the second equation of jth TF, Yj, is assumed to be produced in the translation process by the corresponding mRNA αi Xi from the upstream transcription process and decayed by degradation λj Yj and diffusion γj▿2Yj.jth TF are represented by γj. βij denotes the regulatory ability of the jth TF (or regulatory protein), Yj, on the promoter region of the target gene Xi. βij > 0 stands for the ith target gene activated by the jth TF (prospective activator) or not repressed by the jth TF (prospective repressor) while βij < 0 stands for the ith target gene not activated by the jth TF (prospective activator) or repressed by the jth TF (prospective repressor).Drosophila development is constructed by linking up all target genes through the regulations of their upstream TFs, Yj in Xj and diffused from Yj in the neighborhood. Model uncertainty, fluctuations of the basal levels and measurement noises in the mRNA (transcription) dynamics and protein (translation) dynamics are denoted by stochastic noise υi and ζj, respectively. x and y denote the location of the embryo in the 2-D space, i.e. the coordination in the x-axis and y-axis.In previous studies, dynamic models with protein synthesis, protein diffusion and protein decay have been utilized in the description of the mechanism of embryonic development.Remark: The dynamic model in ith gene; and the mRNA productive rate is mainly due to the transcription regulations of 14 proteins (i.e. TFs), the influence of basal level and degradation of mRNA. The noise υi denotes the fluctuation of basal level, measurement noise and modeling residue. Since the expression levels of TFs can be altered with different spatial regions of the whole embryo by diffusion mechanism, the relationship of transcription regulation between one TF and its target gene is also different in different spatial regions. The second equation of x, y). The protein productive rate is mainly influenced by the translation of mRNA, diffusion from the neighboring space, and degradation rate of the protein. The noise ζj is due to the fluctuation of the basal level of protein, measurement noise and modeling error. The model in x, y). The parameters of the stochastic spatio-temporal dynamic model in Drosophila development. Our main purpose is to infer the possible mechanisms of eve stripe formation by investigating the estimated parameters κi, ϖj, αj, βij, λj and γj, i = 1, 2, ..., 14 of the system dynamic model in Eq. (1)x, y) on the continuous plane is transformed into the location  on the discrete plane. The discrete 3-DEST model is shown as follows:id,l,m ≥ 0, jw,l,m ≥ 0, k denotes the kth time point, l and m denote the location  on the discrete plane, and h is the distance between two locations along two axes, i.e. A-Paxis (hx) and D-V axis (hy). The parameters are defined as follows: di,l,m = ki· Δt, wj,l,m = ϖj ·Δt aj,l,m = αj·Δt, bij,l,m = βij·Δt, cj,l,m = 1 – λj· Δt, and ρj,l,m = γj·Δt where Δt ≈ 2.568 minutes. Then, by using the discrete 3-DEST model in ki, ϖj, αi, βij, λj and γj in Drosophila embryo in the following section.In the proposed stochastic dynamic model in ,m≥0 see , where kl, m) as the center of the spatial regions of the embryo by specifying the boundaries of the spatial regions. Along the A-P axis, two boundaries of the ith eve stripe are denoted by Bi and Bi + 1, respectively. The boundaries of seven eve stripes along the A-P axis are denoted by {B1, B2, ..., B8}  in the stripe-th eve stripe. The transformation from  in the whole embryo to  in the stripe-th eve stripe, i.e. Rstripe,lk, is given by m = k + 3*(stripe-1). For example,  =  in the second eve stripe, i.e. the spatial region R2,33 corresponds to  =  in the whole embryo, with l = 3 and m = 3 + 3*(2–1) = 6  = 6 . After tXi, Yj} for i,j ∈ {1, 2, ..., 14}, k ∈ {1,2, ..., N}, l = {1, 2, 3}, m = {1, ..., 21} are ready, the parameters of stochastic 3-DEST model can be estimated using Drosophila embryo. For the convenience of parameter estimation, When the data points {ɛi and δj are normally distributed, and the noise matrix El,m has an unknown covariance matrix ∑l,m to be estimated. Then we use the ML method to solve the parameter estimation problem with the optimum solution Θ̂l,m and ∑̂l,m. The likelihood function of Yl,m is defined as follows:Suppose the noise components M data points in Yl,m, i.e. M = 2·14(N – 1), can be defined asThe log-likelihood function for the given l,m and the covariance matrices of noise ∑l,m by maximizing the log-likelihood function Ll,m, i.e. We can estimate the unknown parameters Θdi,l,m ≥ 0, wj,l,m ≥ 0, |1 – aj,l,m| ≤ 1, lsqlin, is used in the estimation procedure of the parameter identification of the stochastic 3-DEST model  method, we will let http://flyex.ams.sunysb.edu/flyex/)http://flyex.ams.sunysb.edu/lab/gaps.html),Drosophila. The spatial regions are first defined as shown in Drosophila development is constructed. Then, we incorporate the AIC method into the identification process to prune the insignificant regulatory parameters and refine the model. This allows us to pick up the TFs, which are the most significant regulators for controlling the downstream genes in the early development of Drosophila.In this study, we incorporate two spatial-temporal data, protein data ) of runt at N = 39 and hairy at N = 41 (N represents number of interpolated data points), and there is no significant change in basal levels of protein ). In addition, the regulatory abilities ) of both runt and slp at N = 38 and 39 are pruned by the AIC method when compared with the others. Therefore, except a few variations in N = 38, 39 and 41 the other cases, i.e. N = 40, 42 and 44, are robust for system identification. Here, mRNA and protein data with 44 interpolation time points are chosen for parameter estimation of the stochastic 3-DEST model of the whole embryo. When spatio-temporal data are ready and the number of interpolated data points is decided (N = 44), system identification for parameter estimation in Eqs. (6)The real biological systems are always robust. Therefore the model of a biological system should be robust and the robustness is a validation of dynamic models for biological systems.Drosophila embryo are given in the following.i, j = 1, 2, ..., 14, l = 1, 2, 3, m = 1, 2, ..., 21. κ̂i ϖ̂j, α̂i, β̂ij, λ̂j, and γ̂j are estimated by ϕi and ϕj can be estimated in After the parameters in γ̂j and regulatory abilities ijβ̂ of the identified 3-DEST dynamic model in After system identification, the simulation results of the system model obtained using the ML estimation method and the AIC method are shown in Previous researchR2,22, R3,11, R3,33, R4,31, R6,11, R6,13, and R7,31 and Knirps in R1,12, R4,13, R5,23 and R7,13 positively and negatively regulate eve, respectively, and Eve in these regions simultaneously serves as a donor which diffuses through and affects the boundaries, i.e. stripe 1–2, stripe 3–4, stripe 4–5, stripe 5–6 and stripe 7-terminal  experiments. In this study, we provide a direction for other biologists at the wet experiments of transcription regulations especially in ChIP-chip experiments. For example, according to the robustness tests in R2,22 is positively regulated by Ftz and Knirps and is negatively self-regulated. The robust regulations are the most probable suggestion in transcription regulations of eve stripe formation.From the transcription regulations shown in R3,13, Runt->Ftz in R3,13 and Caudal->Runt in R4,11) and two diffusions . In addition, these three regulatory relationships exist respectively in at least four neighboring regions, i.e. Caudal->Ftz in R3,13, R3,12, R4,11 and R4,31, Runt->Ftz in R3,13, R3,12, R3,33 and R4,31 and Caudal->Runt in R4,11, R3,13, R4,12 and R4,31. Therefore, C15 is one of the FFLs , Slp in R2,12, R2,22 (C7 and I5), R3,31 (I12 and C18) and R3,12 (C19 and I4), Eve in R3,12, R3,13 (C14 and I9) and R4,11 (C13 and I9), and Ftz in R3,13 (C21 and I15) and R6,13 (C25 and I17). Among these modules, we find that Hunchback acts as a source of FFLs to activate Ftz as an output expressed in eve stripes 3, 4, 6 and 7. From the embryo with hb− mutants, eve stripes 2, 3, 4 and 7 are partially or completely deleted.R6,12 and R6,13 is activated respectively by I-FFL and combinative signaling module with Hunchback as an input source, Ftz in R6,12 and R6,13 is respectively negatively regulated and does not regulate eve. Therefore, we suggest that C-FFL, I-FFL and combinative signaling module are respectively important in activating speedy responses in R4,11, R4,12 and R7,11, activating a delayed response in R7,13 with the ability of noise filtering and activating a delayed prolonged response in R3,13.Moreover, in the large network, there exist a huge number of interaction patterns. Only a few types of interaction patterns called network motifs, which are embedded in the network and connected to each other, allow them to carry out their functions even in the presence of additional interactions. Mangan and Alonthe FFLs  found inthe FFLs  but alsoDrosophila. However, there are still three mechanisms of concern in Drosophila embryogenesis, i.e. protein-protein interactions, translation regulations and epigenetic regulations. In a recent study, protein-protein direct interactions are not found between the 14 early development-related TFs of Drosophila embryo,Drosophila embryogenesis the proteins of the trithorax (trxG) and Polycomb groups (PcG) modify chromatin via interacting with chromosomal elements, Cellular Memory Modules (CMMs). A nearby gene can be continuously transcribed through mitotic cell division and meiosis by a switched activated state of CMMs during Drosophila embryogenesis. Thus, CMMs could affect the patterning of cells by the transcriptional control of genes involved in embryonic patterning. In conclusion, trxG and PcG confer epigenetic regulations for different binding affinities of transcription regulation, i.e. trans-effect, that result in embryonic patterning throughout Drosophila embryogenesis.βij and basal levels of protein generation ϖj have implied the affection of epigenetic regulations on transcription regulations throughout eve stripe patterning of Drosophila embryogenesis. An example is shown in R2,22 in eve stripe formation.In this study, we are the first to combine mRNA dynamic equation with protein dynamic equation using spatio-temporal model to construct the gene/protein interaction network to investigate the gene/protein regulatory mechanisms of eve stripe formation in the early development of  and  are considered, biologists can examine gene Z’s expression in the corresponding region found in Y− embryos by filtered flu-orescence imaging after immunoperoxidase staining with polyclonal antibodies specific for Z. By comparing gene Z’s expression in wild-type with Y− embryos, the suggested FFLs in Drosophila development.In early embryogenesis, diffusion mechanism is needed not only for maternal genes but also for gap genes and pair-rule genes to regulate their target genes in the neighboring spatial regions, which can determine the roles of TFs in each region, i.e. donor/acceptor. Without the dynamic space-time model, the dynamics of TFs’ diffusions may not be easily observed from a system point of view, especially in 2-D space. The contributions of this study include the following. (1) Construction of a stochastic 3-DEST dynamic model for gene/protein interaction network which not only contains the concentration-dependent transcriptional abilities but also includes six stochastic processes to mimic the spatio-temporal dynamic interplay among the target genes and their regulatory TFs at the early embryonic stage (i.e. the following six processes (i) protein synthesis, (ii) protein decay, (iii) mRNA decay, (iv) protein diffusion, (v) transcription regulations, and (vi) autoregulation are involved in our dynamic model). (2) Utilization of the AIC to refine the stochastic 3-DEST dynamic model for gene/protein interaction network via pruning the insignificant transcription regulations in each spatial region. (3) Findings of transcription regulations in the seven eve stripes in the stochastic 3-DEST gene/protein interaction network. (4) Validating of the identified gene/protein interaction network by literature reference with the wet experiments of gene mutations. (5) Inference of transcription regulations and diffusion mechanisms for playing a cooperative role in the creation of FFLs to build eve stripes by speedy responses, delayed responses with the ability of noise filtering and delayed and prolonged responses. For the possible experimental validation of the feedforward loops (FFLs) in 3-DEST dynamic gene/protein interaction network, biologists can follow the similar experimental design inDrosophila embryo is probably not just built by the 14 early development-related genes, it is not a problem to estimate a more complicated dynamic regulatory network by the proposed method if much more mRNA and protein data are available in the future.However, one of the weaknesses in system identification is the increase in computation burden due to the use of the AIC method. Because one of our main purposes is to extract the significant transcription/translation regulations via pruning the insignificant transcription/translation regulations by using the AIC method, we use an explicit scheme with some stability constraints on the parameters to construct and then refine the gene/protein interaction network. Additionally, computation complexity will be increased, when the spatial regions are precisely specified. Moreover, a plenty of spatio-temporal data are needed in parameter estimation of the 3-DEST model. Although we know that eve stripes of the