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In this page the overview of the evolutionary process in SANTA is outlined.
The population in SANTA consists of individual organisms each of which contains a genome. The genome is a linear sequence of nucleotides but this can be specified as coding for amino acids and it can be partitioned with different modes and degrees of selection. Each individual stores its total fitness and other statistics that may be of interest such as the number of offspring. At present the program simulates only haploid populations and is ideal for simulating simple microorganisms such as viruses and bacteria and eukaryote organelles such as mitochondria.
The evolutionary process in SANTA is divided into a sequence of discrete components. Different processes can be selected for each of these components so that complex evolutionary interactions can be modeled. The simulation begins with an initial population of individuals with a specified or random genome sequence. Evolution then proceeds as follows:
The fitness of each genome is calculated using one or more fitness functions. These functions define the relative fitness of each possible state at each site in the genome. The simplest is a neutral model where all states have a fitness of 1.0. Other fitness functions are available: fitness functions here.
Different fitness functions can be defined for the nucleotide sequence and its amino acid translation. For example, a purifying selection function can model the constraints on amino acid replacement whilst nucleotide changes can be neutral. The fitness for a codon is then the product of the fitness for each of the three nucleotides and the fitness of the resulting amino acid.
Furthermore, different partitions of the genome can be given entirely different fitness functions (e.g., most sites under purifying selection but with a few sites under diversifying selection).
The next generation of individuals then selects their parents from the previous generation. This is done using a ‘roulette wheel’ selector where each individual is selected with replacement with a probability proportional to their genome’s fitness. The number of parents that are selected for each new individual depends on the mode of replication, below.
Together with the mutation component, below, the replication component is analogous to the actions of a polymerase complex and produces the genetic material for a new individual from one or more parents. The simplest replicator is clonal and the descendent inherits the genome of exactly 1 parent. We also provide a recombinant replicator that models a ‘template-switching’ polymerase. For this replicator, two parents are selected and a probability is defined of the polymerase switching between the parents’ templates as replication proceeds along the genome.
Although mutation also models the action of the polymerase, it is done as an independent process after replication. The user specifies a per-site, per-generation probability of mutation. The mutator component then applies mutations to the genome accordingly. For efficiency, the default mutator draws the number of mutations from a Poisson distribution with an expectation given by the number of nucleotides and the rate. These are then distributed uniformally across the sites. A bias towards transition-type mutations can be specified to reflect the action of some polymerases.
At predefined time-points or intervals, SANTA can be asked to report statistics about the current population, including average fitness, genetic diversity and number of unique genomes. A random sample of individuals of a specified size can also be taken and the genomic sequences recorded as a nucleotide or amino acid alignment for use in other software. Finally, it is possible for SANTA to keep track of the genealogy of the entire population and then provide the tree of the individuals sampled. This option has significant implications in terms of efficiency and memory requirements.
We have implemented a forwards-time discrete-generation gene sequence simulator designed to scale to large population sizes and microorganism-scale genome lengths. The simulator, SANTA, is written in Java and thus is cross-platform without recompilation. It is configured using control files written in XML for which an XML schema is provided so they can be easily created and edited in standard XML editors.
Given the large scale of a typical simulation, we have made every effort to use memory efficiently. For example, the genome sequences are stored in a central ‘gene-pool’ so that only unique genomes are stored with the individuals having only an index for the genome they currently carry. Individuals that replicate without any mutations thus just inherit this index. This also makes calculations of the population genetic diversity more efficient. We have also implemented an optional framework where genomes are stored as differences from a central ‘master’ sequence. This master sequence can be recalculated occasionally to release memory.
Samples of the population can be taken at specific times during the course of the simulation or at regular intervals. Sequences can be stored as FASTA or NEXUS format alignments.
SANTA is an open-source project hosted on a public source-code repository. It is written in an extremely modular way and we envisage a wide range of additional components being implemented as required by researchers.
- Empirical replicators and mutators. We aim to provide combinations of replicators and mutators based on empirically-estimated behaviours of particular polymerases. The first example of this will be the retrovirus polymerase, reverse transcriptase, that is used by HIV/AIDS pathogens.
- Dynamic fitness functions. In particular we are interested in providing fitness functions that model epistatic effects both at the amino acid and RNA secondary structure level, immunological fitness functions to model the action of cellular and antibody-mediated immune response for within-host pathogen research.
- Population subdivision/migration. This can be modeled by allowing new individuals to select parents non-uniformally from individuals in more than one deme.
- Population-size change. Non-constant demographic functions, such as logistic shapes or bottlenecks will be easily implemented our existing framework.