Evolutionary Informatics and Evolutionary Design

In evolutionary informatics, at least two paramaters are needed for evolutionary algorithms.
Heritability and Selection.

Heritability implies the following:

  1. “Parents” give rise to “offspring”.
  2. Traits from “parents” are passed on to “offspring”.
  3. Each “offspring” from a “parent” signifies a new generation.
  4. Variation between generations may or may not occur.

Selection implies that certain traits that are not on a fitness landscape will not be selected.

Let’s look at Autodock as an example and how it relates to evolutionary informatics. Autodock employs a genetic evolutionary algorithm in order to try and predict the orientation of a ligand within a protein.

The ligand is the heritable structure. (A ligand is any structure that binds to a protein, e.g. a therapeutic molecule)
The protein is the fitness landscape.
The genetic evolutionary algorithm provides the variation and selection parameters.
Consider the following diagram:


Figure 1: A) Basic lay out of memetic algorithms. A population of individuals is randomly seeded with regard to fitness (initialized). The individuals are randomly mutated and their fitness is measured. Individuals with optimal fitness are further mutated until convergence of a local optima is reached. The process is carried out for the entire initialized population. The global optima is selected from the various local optima. B) Fitness landscape with local optima (A, B and D) and a global optima (C). In a memetic algorithm, the initial population of individual are randomly seeded and can be viewed as any of the arrows indicated in the figure.

A few important aspects from the figure:

  1. Fitness depends on the phenotype.
  2. Fitness (in the case of Autodock) is the capability of the ligand phenotype to bind and stay bound to the protein.
  3. The parameters for succesful binding are many. For Autodock, the following are included:

[]Van der Waals interactions
]Electrostatic interactions
]Hydrogen bond interactions
[]Torsional free energy
]Conformational interactions

If certain parameters (above) are not on a fitness landscape for a certain ligand phenotype such as the absence of hydrogen bonds at a particular area of the protein, such a trait will not aid in ligand binding for a particular ligand with hydrogen bonds. Therefore,hydrogen bonding (as a trait) will not be on the fitness landscpe and is thus not a selectable trait.

Autodock uses a Solis & Wets search algorithm to probe the fitness landscape of a particular protein. (See figure below)


Autodock Solis & Wets algorithm.

The surface of a protein is where the binding of the ligand will occur, thus 3-dimensionally, the fitness landscape would look something like this:


Rapamycin ligand bound to the mTOR protein.

So how does the algorithm find the local optima within proteins?

With autodock, a population of individuals (ligands) are randomly placed within the receptor. The conformation ligand-protein interactions are measured for each individual and is then followed by a conformational “mutation” (See image below).


Ligand “mutation”.

The binding energy for each conformation “mutation” is measured until a local optima for a specific population of individuals is reached. The binding energy of the local optima of each population is measured, and the global optima is the population of individuals that have the best binding energy (See below).

If the evolutionary algorithm is well designed, the conformation of the global optima will correspond to the experimentally determined crystallographic pose. The Root Means Squared Deviation (RMSD) of a docked ligand compared the to the crystallographic pose is generally used as a good indicator. A RMSD value less than 2 is considered a success. In the case of the Autodock software, the global optima is supposed to correlate with the crystallographic pose (RMSD <2).

As an example, a ligand was docked into a protein with the following results.


Docked ligand positions and binding energies

As seen here, the global optima corresponded reasonably well to the crystallographic pose (RMSD<1.8 ), meaning the software sucessfully probed the fitness landscape of the protein to find the optimal solution.

Autodock is thus a nice example of how evolutionary informatics and evolutionary design principles can be applied to design optimal structures such as therapeutically relevant compounds/ligands.

Now let’s consider another example in nature and how heritability and selection is applied.

As an example, consider the following diagram.


A fitness landscape (From here)

Again, a few important aspects from the figure:

  1. Fitness depends on the phenotype
  2. Fitness in this case is the capability of the phenotype to reproduce (self-replicate)
  3. The parameters for succesful self-replication are many. A few examples:
    A) Fast replicators (e.g. bacteria)
    B) Intelligent replicators (e.g. monkeys)
    C) Cooperative replicators (e.g. ants)
    D) A combination of the above (e.g. humans)
    E) Population dynamics
    F) And others…etc.

Therefore, if certain parameters are not on a fitness landscape for a certain phenotype (such as the capacity to construct a car, such a trait will not be selected in the next generation if the population of phenotypes consist of bacteria.)

The aim of this thread is to:

  1. Discuss evolutionary dynamics and fitness landscapes and how it is related to nature and other evolutionary algorithms.
  2. See if there are any parallels between the two examples of how evolutionary informatics are applied in molecular biology.
  3. How evolutionary dynamics and evolutionary design principles can be applied to real world problems.

How does this prove your god?

1) Discuss evolutionary dynamics and fitness landscapes and how it is related to nature and other evolutionary algorithms.

One thing that is interesting about the docking software is that because it seeds the ligands randomly within the protein and the position of the protein is “mutated” randomly, you will get different results every time. See figure below when 4 docking runs are run with the same ligand and the same protein.


Run 1


Run 2


Run 3


Run 4

However, all four runs still converged on a the same global optimum after the evolutionary algorithms were completed. And the global optimum corresponded reasonably well to the crystallographic pose (the optimal design).

Looking at the evolution of life, you will quickly notice that it is filled with examples of convergence. For example:

  1. The spectacular convergence of abiogenesis into a universal highly optimized genetic code that governs just about all life forms on earth.

  2. Beautiful structural convergence on several levels. e.g. Convergent Evolution

  3. Molecular convergence
    Carbonic anhydrases
    And many more.

Mmm, looks like cut and paste is too easy these days. Let me try:
Please guys, this is not productive at all. Irreverend, you are quadruple posting and Mechanist you are mass cross posting without making any useful arguments. Please don’t reply to this post - start a new thread or PM/email privately.

Another ID dogturd being dropped around the forums.

It’s one of the newer ID “wedges” - compare Bill Dumbski at:

Evolutionary Informatics as Intelligent Design and not as Theistic Evolution



Wily critters these bacteria with their genetic circuits, evolutionary algorithms and highly optimal code.

Bacteria ‘Invest’ Wisely to Survive Uncertain Times, Scientists Report

ScienceDaily (Dec. 1, 2009) — Like savvy Wall Street money managers, bacteria hedge their bets to increase their chances of survival in uncertain times, strategically investing their biological resources to weather unpredictable environments.
In a new study available online and featured on the cover of Cell, UT Southwestern Medical Center researchers describe how bacteria play the market so well. [b]Inside each bacterial cell are so-called genetic circuits that provide specific survival skills. Within the bacteria population, these genetic circuits generate so much diversity that the population as a whole is more tolerant of -- and is more likely to survive -- a wide range of variability in the environment.[/b]

“We have found that a particular genetic circuit is responsible for generating diversity within the bacteria population,” said senior author Dr. Gürol Süel, assistant professor of pharmacology and in the Cecil H. and Ida Green Comprehensive Center for Molecular, Computational and Systems Biology at UT Southwestern.

This diversity, like a diversified investment portfolio, means that each bacterium has characteristics that allow it to survive under certain conditions, said Dr. Süel. “When conditions are highly variable, some individual bacteria are equipped to thrive in the highs or lows, while others tank,” he said. "It’s like the stock market. If you invest all your money in just one stock, and conditions change to lessen or completely eliminate its value, you won’t survive financially. Similarly, in the case of these bacteria, if all the cells were adapted to only a small, rigid set of environmental factors, the population would be wiped out if conditions unexpectedly changed.

“There seems to be an optimization going on in these organisms,” he added.

By generating diversity, genetic circuits ensure enough cells will survive to carry over the population, especially in times of variable conditions, Dr. Süel explained. Essentially, variability of bacterial cells appears to match the variability in the environment, thereby increasing the chances of bacterial survival, he said.

Genetic circuits are distinct sets of genes and proteins within cells that interact in a specific pattern, resulting in some biological process. In this study, the researchers focused on a genetic circuit within a bacterium that controls the transformation of bacteria cells in and out of a state called competence. Differences in the duration of the competence state have particular survival advantages, depending on the environmental conditions.

Biological “noise” in the genetic circuit, which comes from random fluctuations in the chemical reactions involved in the pattern of interactions, is similar to the undesirable noise – like static heard on AM radio – found in electrical circuits. In biological systems, however, biochemical “noise” is beneficial. In fact, it is the root mechanism that drives diversity within the bacteria population. Dr. Süel previously found that when noise reaches a certain level in some genetic circuits, it can prompt cells to transform from one cellular state to another.

For the current study, the researchers went beyond studying the native genetic circuit. Just as electronic maps can find alternate routes between two points, the UT Southwestern researchers also developed an alternative, synthetic genetic circuit that used a different architecture – or route – to accomplish the same function as the native circuit.

Dr. Süel believes his group is the first to insert such a synthetic genetic circuit into living bacterium and show that it can replace the biological function of the native version. He said his team was surprised to find that the behavior of the synthetic circuit was most precise, essentially generating less noise. The result was a population less diverse than the natural one. They were even more surprised to find that the lack of precision – or greater noisiness – in the native circuit ultimately allows bacteria to survive in a wider range of environments.

“It turns out that sometimes being sloppy can be good,” Dr. Süel said. “For these bacteria, the more variable they are, the better they will be able to perform because they can adapt to a wider range of environments.”

Dr. Süel said this approach of engineering alternative genetic circuits can in principle be applied even to human cells and possibly help explain why diseased cells have different survival capabilities than healthy ones.

Other UT Southwestern researchers involved were lead author Dr. Tolga Cagatay, instructor of pharmacology, and Dr. Marc Turcotte, assistant professor of pharmacology. Researchers from the California Institute of Technology and the Universitat Politecnica de Catalunya in Spain also participated.

The study was funded by the Welch Foundation, the James S. McDonnell Foundation, the European Commission and the Ministerio de Ciencia e Innovacion in Spain.