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A note on layer-cake communication systems and protocols

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There is a live exchange on the molecular nanotech communication systems in the cell that is trying to reduce them to Chemistry; where a chemical reaction is a physical process. Accordingly, I beg to remind one and all regarding layered communication systems and protocols:

This is an elaboration of the general communication system:

A communication system

Here is how Yockey summarised it:

Yockey’s analysis of protein synthesis as a code-based communication process

Where, the standard genetic code [one of about two dozen dialects] reads like:

The Genetic code uses three-letter codons to specify the sequence of AA’s in proteins and specifying start/stop, and using six bits per AA

The double helix:

As was noted by Crick, right from the outset:

Crick’s letter

In context:

Now, we can see for ourselves just how desperate objectors to the design inference must be in the face of the point that D/RNA expresses a string data structure carrying a prong-height-based alphanumeric, 4 state per character code that uses chemical interactions and geometry at physical level. It uses the code to address algorithms for protein synthesis. Thus, strings, codes, algorithms [which are goal directed], so too, language.

All of this is an exceedingly strong sign of intelligent design. END

14 Replies to “A note on layer-cake communication systems and protocols

  1. 1
    kairosfocus says:

    A note on layer-cake communication systems and protocols

  2. 2
    jawa says:

    KF,

    One could also argue that all computer processes could be explained away as pure physical phenomena. 😉

    Both hardware and software depend on physics to operate.

    The hydroelectric power is based on physical phenomena.

    The cars, trains, boats, submarines, airplanes depend on physics to operate.

    In all those cases the designers have taken physics into account very seriously.

    The Newtonian math so elegantly presented in Principia Matemática – a classic jewel in the history of science- describes physical phenomena.

    The biochemical processes in the biological systems can be explained away with chemistry which can be explained away with physics.

    But that would be one category of explanation: the physical side of the story.

    As Professor John Lennox has said, to the question “why is the water in the kettle boiling?” a valid answer could be “the electric current going through a resistance generates enough energy in the form of heat that is passed to the water molecules which then increasingly move in all directions until they reach a point where the water is transformed from liquid to vapor – boiling point (certainly a very primitive explanation at elementary school level).

    But another valid explanation could be that the water in the kettle is boiling now because I want a cup of tea.

    Both explanations don’t conflict with each other.

    We could add another valid explanation: because the kettle was designed purposely to perform that specific function: boil water in a convenient and practical way.
    No conflict whatsoever between the three explanations.
    Just different categories of explanations.

    In all the above cases, even the simple kettle, we see functional information that is observed through physical phenomena.

    The kettle has a switch to turn it on in reaction to a manual human action or a timer mechanism connected to the kettle. Somebody had to make the decision to turn it on manually or to setup the timer according to some schedule. Here we’re talking information. The kettle parts are put together according to a functional configuration. As far as I’m aware of, that is intelligent design.

    I have to stop here but will try to come back to this later.

  3. 3
    kairosfocus says:

    Jawa, likewise, your comment. KF

  4. 4
  5. 5
    kairosfocus says:

    Jawa, yes, there is much more. However, every school child knows about protein coding segments and they are algorithmic code bearing strings, with huge implications. KF

  6. 6
    jawa says:

    KF,

    Yes, agree.

    The DNA sequences in the TF-binding sites (aka “landing pads”) conform to a regulatory code associated with the different cell types. This is a hot fascinating research topic these days: an unbelievable multilevel control system. The best engineers and scientists can barely start to scratch the surface of this mystery that seems to point to ID on steroids. Control systems engineers drool uncontrollably in complete unbelief when discovering these fascinating things, like a hungry dog that discovers a juicy beefsteak.

    5-month old paper:

    “Deciphering eukaryotic gene-regulatory logic with 100 million random promoters”
    Nature Biotechnology

    “How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear”
    These are not epigenetic markers or histone codes. These are DNA nucleotide sequences.

    But of course, as we all should know very well, all those marvelous things magically appeared through undirected processes. Yeah, right.
    If you know of somebody that believes that fairytale, tell them that they can buy -at a heavily discounted price- an oceanfront penthouse with private beach access right in the center of Kansas. 😉

  7. 7
    jawa says:

    KF,

    Yes, agree that the amazing semiotics seen in the correlation of the GCAT-based codons with the aminoacids that form the proteins leaves no doubts about the designed origin of the biological systems, as you clearly present in your OP here.

  8. 8
    OLV says:

    More ID:

    Designing Eukaryotic Gene Expression Regulation Using Machine Learning

    Machine learning (ML) models can predict gene expression levels from DNA sequences, given sufficiently large datasets. Such datasets are now rapidly becoming available for regions that regulate eukaryotic gene expression, namely promoters and untranslated regions (UTRs).

    These predictive models are increasingly used in algorithms for designing novel regulatory regions to achieve a desired fine-tuned expression level.
    ML models of gene expression will enable synthetic biologists to rationally engineer complex pathways and circuits.
    With increasing attention to interpretability of ML models, they may also help to gain deeper understanding of eukaryotic gene regulation.

    Controlling the expression of genes is one of the key challenges of synthetic biology. Until recently fine-tuned control has been out of reach, particularly in eukaryotes owing to their complexity of gene regulation. With advances in machine learning (ML) and in particular with increasing dataset sizes, models predicting gene expression levels from regulatory sequences can now be successfully constructed.

    Such models form the cornerstone of algorithms that allow users to design regulatory regions to achieve a specific gene expression level. In this review we discuss strategies for data collection, data encoding, ML practices, design algorithm choices, and finally model interpretation. Ultimately, these developments will provide synthetic biologists with highly specific genetic building blocks to rationally engineer complex pathways and circuits.

  9. 9
    OLV says:

    More ID:

    Localization elements and zip codes in the intracellular transport and localization of messenger RNAs in Saccharomyces cerevisiae

    Intracellular trafficking and localization of mRNAs provide a mechanism of regulation of expression of genes with excellent spatial control. mRNA localization followed by localized translation appears to be a mechanism of targeted protein sorting to a specific cell?compartment, which is linked to the establishment of cell polarity, cell asymmetry, embryonic axis determination, and neuronal plasticity in metazoans. However, the complexity of the mechanism and the components of mRNA localization in higher organisms prompted the use of the unicellular organism Saccharomyces cerevisiae as a simplified model organism to study this vital process. Current knowledge indicates that a variety of mRNAs are asymmetrically and selectively localized to the tip of the bud of the daughter cells, to the vicinity of endoplasmic reticulum, mitochondria, and nucleus in this organism, which are connected to diverse cellular processes. Interestingly, specific cis?acting RNA localization elements (LEs) or RNA zip codes play a crucial role in the localization and trafficking of these localized mRNAs by providing critical binding sites for the specific RNA?binding proteins (RBPs). In this review, we present a comprehensive account of mRNA localization in S. cerevisiae, various types of localization elements influencing the mRNA localization, and the RBPs, which bind to these LEs to implement a number of vital physiological processes. Finally, we emphasize the significance of this process by highlighting their connection to several neuropathological disorders and cancers.

     

     

     

  10. 10
    OLV says:

    More ID:

    Soft Power of Nonconsensus Protein-DNA Binding
     

    if gene promoters are extended with DNA sequences containing repeating nucleotide patterns without specific protein-binding motifs, it is possible to predict the resulting changes in gene expression from so-called nonconsensus protein-DNA binding. The authors found that during embryonic stem cell (ESC) differentiation, transcription factor (TF) preferences for such simple nucleotide repeats undergo distinct changes. This suggests an intriguing possibility that nonconsensus binding may help direct TFs to different subclasses of binding sites in different cell types.

  11. 11
    OLV says:

    More ID:

    Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters
     

    Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of ‘epromoters’: dual-action CRMs with promoter and distal enhancer activity.

     

     

  12. 12
    OLV says:

    More ID:

    Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays
     

    The relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ?500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ? = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

     

    Changes in gene expression play a crucial role in a wide variety of cellular processes. Dissecting the precise mechanisms of gene regulation is therefore necessary to understand both the normal functioning of cells and the ways in which dysregulation of certain genes plays a role in disease states [1]. Gene expression in metazoans is regulated by several distinct classes of cis-regulatory elements (promoters, enhancers, insulators, and others), with the activity of multiple enhancers being integrated to determine the expression levels of the average mammalian gene [2]. The activity of each enhancer or promoter element itself is driven by the concerted action of multiple DNA binding proteins called transcription factors (TFs), which typically bind to combinatorial grammars of short sequence motifs embedded in regulatory DNA sequences.

     

    In conclusion, neural network models trained on a diverse collection of high-quality datasets coupled with powerful interpretation frameworks have the potential to finely decode cis-regulatory grammars and functional genetic variation in regulatory DNA sequences.

  13. 13
    OLV says:

    More ID:

    Uncovering tissue-specific binding features from differential deep learning

    Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expression programmes. Recently, deep learning models have become state-of-the-art in various pattern analysis tasks, including applications in the field of genomics. We therefore investigate the application of convolutional neural network (CNN) models to the discovery of sequence features determining cooperative and differential TF binding across tissues. We analyse ChIP-seq data from MEIS, TFs which are broadly expressed across mouse branchial arches, and HOXA2, which is expressed in the second and more posterior branchial arches. By developing models predictive of MEIS differential binding in all three tissues, we are able to accurately predict HOXA2 co-binding sites. We evaluate transfer-like and multitask approaches to regularizing the high-dimensional classification task with a larger regression dataset, allowing for the creation of deeper and more accurate models. We test the performance of perturbation and gradient-based attribution methods in identifying the HOXA2 sites from differential MEIS data. Our results show that deep regularized models significantly outperform shallow CNNs as well as k-mer methods in the discovery of tissue-specific sites bound in vivo.

    Our work shows that using deep learning, which increases non-linearity and provides a wider input context to a model, is beneficial in uncovering sequence features contributing to tissue-specific transcriptional regulation

     

     

  14. 14
    kairosfocus says:

    But, but, muh GOLD STANDARD . . .

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