Intelligent Design, the best explanation of Origins

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Modularity in biological systems

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1 Modularity in biological systems on Thu Jan 04, 2018 5:45 am


Modularity in biological systems

From molecular to modular cell biology 2
Nature magazine, 2 DECEMBER 1999
Cellular functions, such as signal transmission, are carried out by ‘modules’ made up of many species of interacting molecules. Although living systems obey the laws of physics and chemistry, the notion of function or purpose differentiates biology from other natural sciences. Function has produced the living cell, with a unique set of properties that distinguish it from inanimate systems of interacting molecules.

Comment: Function is not a creative mechanism. Function is the end result of unique set of properties that distinguish it from inanimate systems of interacting molecules.

Cells exist far from thermal equilibrium by harvesting energy from their environment. They are composed of thousands of different types of molecule. They contain information for their survival and reproduction, in the form of their DNA. Their interactions with the environment depend in a byzantine fashion on this information, and the information and the machinery that interprets it are replicated by reproducing the cell. How do these properties emerge from the interactions between the molecules that make up cells. Most biological functions arise from interactions among many components. For example, in the signal transduction system in yeast that converts the detection of a pheromone into the act of mating, there is no single protein responsible for amplifying the input signal provided by the pheromone molecule.

We argue here for the recognition of functional ‘modules’ as a critical level of biological organization. Modules are composed of many types of molecule. They have discrete functions that arise from interactions among their components (proteins, DNA, RNA and small molecules), but these functions cannot easily be predicted by studying the properties of the isolated components.

Have you read this carefully? Let it sink in for a moment. This sentence advocates basically for Behe's concept of irreducible complexity. The only difference is the different wording.  " Discrete functions ", can be translated to irreducible functions, or irreducible structures, or structures that are irreducibly complex. A certain biological function cannot be achieved by less than certain interactions and a certain number of components, while these isolated components have no functions by their own. Bingo !! 

We believe that general ‘design principles’ — profoundly shaped by the constraints of evolution— govern the structure and function of modules.

Comment: They said right. They believe !! Why not?  We believe design principles emanate from an intelligent designer? 

Is cell biology modular?
A functional module is, by definition, a discrete entity whose function is separable from those of other modules. This separation depends on chemical isolation, which can originate from spatial localization or from chemical specificity. A ribosome, the module that synthesizes proteins, concentrates the reactions involved in making a polypeptide into a single particle, thus spatially isolating its function. A signal transduction system, on the other hand, such as those that govern chemotaxis in bacteria or mating in yeast, is an extended module that achieves its isolation through the specificity of the initial binding of the chemical signal (for example, chemoattractant or pheromone) to receptor proteins, and of the interactions between signalling proteins within the cell. Modules can be insulated from or connected to each other. Insulation allows the cell to carry out many diverse reactions without cross-talk that would harm the cell, whereas connectivity allows one function to influence another.

Are modules real?
Several lines of evidence suggest that they are. Some modules, such as those for protein synthesis, DNA replication, glycolysis, and even parts of the mitotic spindle (the cellular machinery that ensures the correct distribution
of chromosomes at cell division), have been successfully reconstituted in vitro.

Most functional properties of a module are collective properties, arising from the properties of the underlying components and their interactions.

There is no function for most individual proteins. But joined in an interdependent manner, underlying components and their interactions confer function collectively to a highly ordered, complex, machine-like module. In other words: There is only function for an individual protein as discrete part of a biological module. 

Cells contain from millions to a few copies of each of thousands of different components, each with very specific interactions. In biology each of the components is often a microscopic device in itself, able to transduce energy and
work far from equilibrium.

In our opinion, the most effective language to describe functional modules and their interactions will be derived from the synthetic sciences, such as computer science or engineering, in which function appears naturally.

Comment: No. Specific functions are the goal, and engineering and computer science are the methods to reach the goal.

The essence of computational science is the capacity to engineer circuits that transform information from one form into another on the basis of a set of rules. How might the lessons learned here apply to biology? Evolution selects those members of a genetically diverse population whose descendants proliferate rapidly and survive over many generations. One way of ensuring long-term survival is to use information about the current environment to predict
possible future environments and generate responses that maximize the chance of survival and reproduction. This process is a computation, in which the inputs are environmental measurements, the outputs are signals that modulate behaviour, and the rules generate the outputs from the environmental inputs.

Comment: The author is moving goalposts, ignoring entirely the fact that transforming information from one form into another on the basis of a set of rules is essentially a mental process, which has never been observed to emerge spontaneously without conscient intelligent guidance. Evolution is a totally inadequate explanation. And the authors do not address what they should but rather move goalposts to population genetics.

The properties of a module’s components and molecular connections between them are analogous to the circuit diagram of an electrical device.

Comment: In most cases, modules also have no function, unless interconnected with other modules. So a goal or advantage of survival is only achieved when following things are right:

- selection of the right building blocks
- order  and assemble them into functional molecules
- production of protein subunits
- assembly of all subunits to get functional holoenzymes and proteins
- mechanisms to direct these proteins to the right place
- assembly into functional modules
- instructions to create metabolic networks, where the modules are connected to a functional whole
- creation of different cells
- assembly of these differentiated cells into tissues
- assembly of tissues into organs
- assembly of organs into organisms

Biological systems are built and organized in a modular manner, and in various hierarchies, starting from genetics to protein structure, and biological networks of modular partitioning of the geometry of biological space. The question is how these structures could have emerged. Naturalism proposes spontaneous organization to a structured form. But are evolutionary mechanisms, and in case of the structures for life to begin, random events of chemical reactions giving rise to highly organized and complex structures sufficient?  Science-based on secular foundation has a huge task to explain how biology nucleated from among the many possibilities in chemistry.

A number of the design principles of biological systems are familiar to engineers. Positive feedback loops can drive rapid transitions between two different stable states of a system, and negative feedback loops can maintain an output parameter within a narrow range, despite widely fluctuating input. Coincidence detection systems require two or more events to occur simultaneously. In order to activate an output. Amplifiers are built to minimize noise relative to signal, for instance by choosing appropriate time constants for the circuits. Parallel circuits (fail-safe systems) allow an electronic device to survive failures in one of the circuits.

Designs such as these are common in biology.
For example, one set of positive feedback loops drives cells rapidly into mitosis, and another makes the exit from mitosis a rapid and irreversible event16. Negative feedback in bacterial chemotaxis allows the sensory system to detect subtle variations in an input signal whose absolute size can vary by several orders of magnitude.

Coincidence detection lies at the heart of much of the control of gene transcription in eukaryotes, in which the promoters that regulate gene transcription must commonly be occupied by several different protein transcriptions
factors before a messenger RNA can be produced. Signal transduction systems would be expected to have their characteristic rate constants set so as to reject chance fluctuations, or noise, in the input signal. DNA replication involves a fail-safe system of error correction, with proofreading by the DNA polymerase backed up by a mismatch repair process that removes incorrect bases after the polymerase has moved on. A failure in either process still allows cells to make viable progeny, but simultaneous failure of both is lethal.

In both biological and man-made systems, reducing the frequency of failure often requires an enormous increase in the complexity of circuits. Reducing the frequency at which individual cells give rise to cancer to about 10–15 has required human cells to evolve multiple systems for preventing mutations that could generate cancer cells, and for killing cells that have an increased tendency to proliferate.

Biological systems can both resist and exploit random fluctuations, or noise. Thus, evolutionary adaptation depends on DNA being mutable, but because most mutations are neutral or deleterious, the rate of mutation is under rigorous genetic control.

The emergence of modular organization of biological structure is described as a symmetry-breaking phase transition, with modularity as the order parameter.  1

In the natural world, one often finds modular, hierarchical structures. Metabolic Networks, Gene Networks, and Protein-Protein Interaction Networks are often modularly built. We can find modularity, and it has been observed in all parts of biology on scales from proteins and genes, to cells, to organs, to ecosystems.   The advantage of modularity is commonly employed in engineering. The claim of secular science is that biological systems are not designed, but supposedly shaped by evolution. Explaining the evolutionary emergence of modularity has however been a challenge, and so far no consensus has been reached. Modular subsystems arrange and connect into networks. Proteins are often made up of almost independent modules. Topological analysis of networks of genes or proteins has revealed modularity as well. Motifs and modules have been found in transcriptional regulation networks, and modules have been found across all scales in metabolic networks. Animal body plans can also be decomposed into clear structural or functional units. Food webs also show compartmentalization. Thus, a hierarchy of modules can be observed that spans many scales of biology.

Many theories have been proposed to explain how and under which conditions modularity emerges.

The question is if natural selection, genetic drift, or gene flow, can account for modularity. Genes have only purpose when they can encode sufficient instructions and the necessary amount of useful information required to make all protein subunits required for a holoenzyme or protein, with all cofactors and co-enzymes, which fulfill specific functions in a biological system. A fully operational and functional protein or holoenzyme confers only function when inserted in a higher biological system. 

Theories based on the naturalistic framework try to explain the emergence of modularity through direct or indirect fitness benefits such as enhanced evolvability, facilitated horizontal gene transfer, or improved robustness, or hypothesize that a changing environment selects for adaptable frameworks, and that competition among different evolutionary frameworks leads to selection of structures with the most efficient dynamics, which are the modular ones. 
Most neutral theories for the emergence of modularity have focused on the idea of duplication. If parts of a system are duplicated, the result will be more modular than the original system.

This sounds all very "sciency", but cannot hide the fact that these explanations do not address the core of the problem. Modules have only function and purpose in an integrated network, where various submodules contribute to a system that confers only function when all modules are in place, and correctly interconnected. Irreducible and interdependent structures extend in all biology and challenge commonly proposed evolutionary explanations. They seem profoundly inadequate.  A certain threshold of complexity and system completeness is required to change from a state of affairs of functionless to a functional system, and pre-vision of the functional goal is essential.   Another challenge is the fact that often certain functions are reached by either a) convergent evolution, or b) different mechanisms and routes which is another challenge for evolutionary hypotheses.

Modularity and Dynamics of Cellular Networks 3
December 29, 2006
Many phenotypes and behaviors cannot be attributed to isolated components. Rather, they arise from characteristics of cellular networks, which represent connections between molecules in cells. Unlike random networks, cellular networks contain characteristic topological patterns that enable their functionality. The components of cellular networks, including proteins, DNA, and other molecules, act in concert to carry out biological processes. These functionally related components often interact with one another, forming modules in cellular networks. Modules are bigger building units that exhibit a certain functional autonomy. Modules may contain motifs as their structural components. Modules may maintain certain properties such as robustness to environmental perturbations and evolutionary conservations. Modularity exists in a variety of biological contexts, including protein complexes, metabolic pathways, signaling pathways, and transcriptional programs. For transcriptional programs, modules are defined as sets of genes controlled by the same set of TFs under certain conditions . Motifs and modules are also found in protein–protein interaction (PPI) networks and metabolic networks, which may be indicative of multi-subunit protein complexes or members of metabolic pathways. For these networks, modules can be defined as subnetworks whose components' entities (e.g., proteins or metabolites) are more likely to be connected to each other than to entities outside the subnetworks.

Is Modularity a Pre-Requisite for Evolvability? 4
January 22, 2014
Information precedes evolution rather than arising out of it. If modularity enables evolvability, what happens before the modularity? Where did the modularity originally come from? This is the core of intelligent design – that information and its similar entities are requirements of evolution, not products of it. 

Yeast Transcriptional Regulatory Modules
Nodes represent modules, and boxes around the modules represent module groups. Directed edges represent regulatory relationship. The functional categories of the modules are color-coded. 

Parallels between computational and biological systems.
(A) Biological and computational systems often coordinate and maintain functionality without relying on a central controller.
(B) Networks are often the medium through which processes spread, either on the Web or in the cell.
(C) Modularity is a common design principle in programming languages and also serves as the basis for increasing complexity and flexibility in evolving systems.
(D) Stochastic processes in computational and biological settings help to remain robust in constrained, noisy environments.


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