Lamarckian evolution of the giant Mimivirus in allopatric laboratory culture on amoebae
published: 05 July 2012

Philippe Colson
Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes
Institut de Recherche pour le Développement (IRD) 3R198
Facultés de Médecine et de Pharmacie
UMR) 7278
Centre National de la Recherche Scientifique Unité Mixte de Recherche
INSERM U1095
IHU Méditerranée Infection
Aix-Marseille University
MarseilleFrance

Fédération de Bactériologie-Hygiène-Virologie
National Center for Biotechnology Information
Pôle des Maladies Infectieuses et Tropicales Clinique et Biologique
Centre Hospitalo-Universitaire Timone
MarseilleFrance, USA

Alabama School of Medicine
University of South
USA

Faculté de Médecine
Didier Raoult, Unité des Rickettsies, Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes
Hua Xie
Meharry Medical College
Aix-Marseille Université, 27 boulevard Jean Moulin13385, CEDEX 05MarseilleUSA, France

Didier Raoult email@example.com
Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes
Institut de Recherche pour le Développement (IRD) 3R198
Facultés de Médecine et de Pharmacie
UMR) 7278
Centre National de la Recherche Scientifique Unité Mixte de Recherche
INSERM U1095
IHU Méditerranée Infection
Aix-Marseille University
MarseilleFrance

Fédération de Bactériologie-Hygiène-Virologie
National Center for Biotechnology Information
Pôle des Maladies Infectieuses et Tropicales Clinique et Biologique
Centre Hospitalo-Universitaire Timone
MarseilleFrance, USA

Alabama School of Medicine
University of South
USA

Faculté de Médecine
Didier Raoult, Unité des Rickettsies, Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes
Hua Xie
Meharry Medical College
Aix-Marseille Université, 27 boulevard Jean Moulin13385, CEDEX 05MarseilleUSA, France

Eugene V Koonin
Jon Audia
Lamarckian evolution of the giant Mimivirus in allopatric laboratory culture on amoebae
published: 05 July 201210.3389/fcimb.2012.00091ORIGINAL RESEARCH ARTICLE Edited by: Reviewed by: *Correspondence:MimivirusLamarckian evolutionDarwinian evolutiongene expression profiletranscription profilegenome reductionallopatry
Acanthamoeba polyphaga Mimivirus has been subcultured 150 times on germ-free amoebae. This allopatric niche is very different from that found in the natural environment, where the virus is in competition with many other organisms. In this experiment, substantial gene variability and loss occurred concurrently with the emergence of phenotypically different viruses. We sought to quantify the respective roles of Lamarckian and Darwinian evolution during this experiment. We postulated that the Mimivirus genes that were down-regulated at the beginning of the allopatric laboratory culture and inactivated after 150 passages experienced Lamarckian evolution because phenotypic modifications preceded genotypic modifications, whereas we considered that genes that were highly transcribed in the new niche but were later inactivated obeyed Darwinian rules. We used the total transcript abundances and sequences described for the genes of Mimivirus at the beginning of its laboratory life and after 150 passages in allopatric culture on Acanthamoeba spp. We found a statistically significant positive correlation between the level of gene expression at the beginning of the culture and gene inactivation during the 150 passages. In particular, the mean transcript abundance at baseline was significantly lower for inactivated genes than for unchanged genes (165 ± 589 vs. 470 ± 1,625; p < 1e-3), and the mean transcript levels during the replication cycle of Mimivirus M1 were up to 8.5-fold lower for inactivated genes than for unchanged genes. In addition, proteins tended to be less frequently identified from purified virions in their early life in allopatric laboratory culture if they were encoded by variable genes than if they were encoded by conserved genes (9 vs. 15%; p = 0.062). Finally, Lamarckian evolution represented the evolutionary process encountered by 63% of the inactivated genes. Such observations may be explained by the lower level of DNA repair of useless genes.Lamarckian evolution of the giant MimivirusFIGURE 1 | A schematic diagram of the major steps and causes and effects in Darwinian and Lamarckian evolution.In Darwinian evolution (left), genotypic change precedes phenotypic change, whereas these changes occur in the opposite order in Lamarckian evolution (right). FIGURE 2 | A schematic diagram of the collection, isolation, and experiments conducted for Mimivirus and Mamavirus. Frontiers in Cellular and Infection Microbiology www.frontiersin.org July 2012 | Volume 2 | Article 91 | 2 Citation: Colson P and Raoult D (2012) Lamarckian evolution of the giant Mimivirus in allopatric laboratory culture on amoebae. Front. Cell. Inf. Microbio. 2:91.

INTRODUCTION

Two primary mechanisms of evolution, Lamarckian and Darwinian, have been generally recognized (Koonin, 2009;Koonin and Wolf, 2009). Among the elements that differentiate the theory of evolution of Lamarck (1809) from that of Darwin (1859) is the central Lamarckian concept that phenotypic changes result from adaptation to the environment and can be transmitted vertically. According to this view, phenotypic changes precede genotypic changes (Figure 1). In contrast, in the current vision of evolutionary biology and in accordance with the post-Darwinian modern synthesis, genetic modifications produce phenotypic changes and precede selection of the fittest in a given environment (Koonin, 2009). In this scenario, genotypic changes precede phenotypic changes.

Our strain of Acanthamoeba polyphaga Mimivirus was recently subcultured 150 times on germ-free amoebae. This allopatric niche is very different from that found in the natural environment of Mimivirus, where the virus is in competition with many other organisms (Raoult and Boyer, 2010;Boyer et al., 2011;Figure 2). An interesting feature of this process of experimental evolution was the occurrence of Mimivirus gene variability and loss concurrently with the emergence of phenotypically different viruses (Boyer et al., 2011). The observed phenotypic changes included a lack of surface fibers and morphologically different viral factories compared with the first generation of wild-type Mimivirus at the beginning of the allopatric laboratory culture. In addition, another team had analyzed the transcriptome of this first generation of Mimivirus over its entire replication cycle (Legendre et al., 2010). These studies provided an opportunity to assess the respective roles of Lamarckian and Darwinian evolution in Mimivirus (Figures 1-3). Thus, we analyzed the nucleotide and amino acid variability of Mimivirus genes during the maintenance of the allopatric laboratory culture on amoebae by comparing their sequences in Mimivirus M4, recovered after 150 passages, with those in M1, recovered at the beginning of the in vitro culture (Figures 2 and 3). To quantify the apparent Lamarckian and Darwinian evolutionary processes in this experiment, we postulated that the inactivated genes of Mimivirus that had been down-regulated at an early phase in the new niche of the virus experienced Lamarckian evolution, as phenotypic modifications preceded genotypic modifications (Figure 1). Indeed, the regulation of gene transcription has been increasingly described in association with phenotypic changes that occur in life forms when they are introduced to a new biological and ecological niche (Revel et al., 2002;La et al., 2008;Smith and Kruglyak, 2008). An example of the dramatic alteration of gene expression was recently reported in a plant bacterium following host switching (Oshima et al., 2011). In contrast, inactivated Mimivirus genes that were normally transcribed during the early phase of life in the new niche were considered to obey Darwinian rules (Figure 1).

MATERIALS AND METHODS

MIMIVIRUS M1 AND M4 GENES STUDIED

The sets of genes for Mimivirus at the beginning of its laboratory life (Mimivirus M1) and after 150 passages in allopatric culture in Acanthamoeba polyphaga (Mimivirus M4) corresponded to those reported previously (Raoult et al., 2004;Boyer et al., 2011;Figure 2

GROUPS OF GENES DEFINED BASED ON THEIR VARIABILITY DURING ALLOPATRIC LABORATORY CULTURE

The genes present in Mimivirus M1 were classified into two major groups: group A is composed of genes that remained unchanged (i.e., showed 100% nucleotide identity) in the genome of Mimivirus M4, whereas group B includes Mimivirus genes showing variability at the nucleotide level during the allopatric laboratory culture (nucleotide identity <100%). The variable genes with a frameshift associated with a >30% size reduction of their product were considered to be inactivated. Group C is composed of Mimivirus M1 genes lost during the transition to the Mimivirus M4 genome, as indicated by the absence of significant BLASTn hits; these genes are located within two large fragments of the genome of Mimivirus M1 that have been deleted in Mimivirus M4. These two large deletions described by Boyer et al. have been considered to be "catastrophic" events that are neither Lamarckian nor Darwinian evolutionary processes, and they were, therefore, not included in our analysis (Boyer et al., 2011).

TRANSCRIPTION AND EXPRESSION PROFILES OF MIMIVIRUS M1 GENES

The transcription profile of the first generation of Mimivirus corresponded to the transcript abundances determined by Legendre et al. at T = 0,1.5,3,6,9,and 12 h of the viral replication cycle (Legendre et al., 2010). These results were those for Mimivirus M1' and were considered to correspond to an early stage of the laboratory culture after Mimivirus moved from a sympatric niche to an allopatric niche (Figure 2). For each gene, the total number of normalized reads counts encompassing the different time points of the replication cycle was used (Legendre et al., 2010). The mean [±standard deviation (SD)] of gene total transcript abundance was 372 ± 1,342. The median value for all of the genes was 69. Gene expression was considered to be high at the beginning of the laboratory culture if the transcript abundance was equal to or greater than the median transcript abundance for all Mimivirus genes. Conversely, genes were considered to be weakly expressed at the beginning of the laboratory culture if the transcript abundance was lower than the median value for all genes. Centiles were calculated for the values of transcript abundance for all genes, and the proportions of unchanged and inactivated genes were calculated per groups of 10 centiles. The proportion of Lamarckian evolution was inferred from the proportion of Mimivirus genes weakly expressed at the beginning of the culture relative to the proportion inactivated after 150 passages in allopatric laboratory culture. Finally, the proteins cited as showing an association with Mimivirus M1 are those previously identified from purified virions by capillary LC-MS/MS, 2D gel electrophoresis, and MALDI-TOF mass spectrometry (Renesto et al., 2006).

COMPARISON OF THE TRANSCRIPTION PROFILES, EXPRESSION, AND VARIABILITY OF MIMIVIRUS M1 GENES

We tested whether the transcription levels obtained for Mimivirus M1' genes at the baseline state of the laboratory culture predict the inactivation of these genes after 150 passages on germfree amoebae (Figures 2 and 3). The proportions of highly and weakly transcribed genes, of the genes among the 25 most highly expressed or with transcript abundances above the 70th centile were compared between unchanged and variable or inactivated genes. In addition, the mean transcript abundance was compared among groups of genes defined on the basis of their variability during the allopatric laboratory culture. The correlations between the initial transcript abundance for each gene and the nucleotide variability or the number of nucleotide and amino acid positions that varied during the 150 passages were also studied. Moreover, both the transcript abundance and the number of differences between corresponding genes in Mimivirus M1 and M4 were plotted according to the location of the genes within the genome. For improved clarity, the mean values calculated for a sliding window of 10 genes and a step of 1 gene were presented with Microsoft Excel software. The number of A-or T-homopolymers with a stretch of ≥4 was determined for each Mimivirus gene. The occurrence of nucleotide differences between Mimivirus M4 and M1 flanking such a homopolymer was also assessed. In the statistical analysis of the data, proportions were compared with a corrected chi-square test or a Fisher exact test, and comparisons of means were performed with OpenEpi Epidemiologic Calculators v. 2.3.1 (www.OpenEpi.com). Linear regression was performed with MedCalc v. 184.108.40.206 (http://www.medcalc.org). P values <0.05 were considered to be statistically significant.

RESULTS

Of the 960 genes present in Mimivirus M1, excluding those lost during the allopatric laboratory culture and representing large deletions, 606 (77%) were unchanged in the Mimivirus M4 genome (group A), and 185 (23%) were variable at the nucleotide level (group B) in the genome of Mimivirus M4. A total of 83 genes (10%) were considered to have been inactivated during the allopatric laboratory culture. The number of variable nucleotide positions between the same genes in Mimivirus M1 and M4 ranged between 0 and 7 (mean ± SD, 1.8 ± 1.3; Figure 4). The mean transcript abundance at baseline was significantly lower for inactivated genes than unchanged genes (165 ± 589 vs. 470 ± 1,625, respectively; p < 1e-3) and for variable genes than unchanged genes (141 ± 415 vs. 470 ± 1,625, respectively; p < 1e-3; Table 1; Figures A1 and 5). In addition, the mean transcript levels at different time points of the replication cycle of Mimivirus M1 were up to 8.5-fold lower for inactivated genes than unchanged genes (Figure 6). Moreover, the proportion of inactivated genes was significantly lower among the genes that were highly expressed at baseline than among those that were weakly expressed [7.7% (31/405) vs. 13.5% (52/386); p = 0.0077; relative risk (RR), 0.57 (95% confidence limits for RR (CI95), 0.37-0.87)] (Figure 3), and the proportion of variable genes was significantly lower among the genes that were highly expressed at baseline than among those that were weakly latter gene was weakly expressed at baseline in the laboratory culture. The proportion of inactivated genes was the lowest [2.4% (2/82)] for those with a transcript abundance between the 80th and 90th centiles (313-691). This proportion was < 5% above the 80th centile and >10% below the 80th centile (Figure 3). Moreover, the proportion of variable genes ranged from 6.3% (5/79) for those with a transcript abundance above the 90th centile to 38.0% (30/79) for those with a transcript abundance between the 10th and 20th centiles (Figure 3). Among the 79 Mimivirus M1 genes with a transcript abundance above the 90th centile, 5 (6%) were variable and 3 (4%) were inactivated. The three genes that were inactivated were two hypothetical proteins (R401, R750b) and an S/T protein kinase (R400). Conversely, among the 82 Mimivirus M1 genes with a transcript abundance below the 10th centile (<10), 17 (21%) were variable and 8 (10%) were inactivated. The distribution of genes according to their levels of transcript abundance showed very low proportions above the 80th centile for inactivated and variable genes, whereas the distribution of unchanged genes was homogeneous (Figure 7). Finally, the proportion of Lamarckian evolution, as defined by the proportion of genes weakly expressed at the beginning of the allopatric laboratory culture among those inactivated, was 63%.

We sought to visually assess the relationship between gene transcription and nucleotide variability between the same genes in Mimivirus M1 and M4. For this purpose, we plotted the mean values for these two parameters along the genome according to a sliding window of 10 genes (step = 1) because representing the values for all genes did not allow sufficient legibility. As shown in Figure A2, this representation clearly indicates a strong inverse correlation between the initial transcript abundance and further gene variability. Thus, the regions of the genome composed of genes that were initially weakly expressed are those in which the genes showed the greatest variability. It might be hypothesized that mutations observed in the genome of Mimivirus M4 relative to Mimivirus M1 are the result of sequencing errors. Thus, the mean ± SD number of A-or T-homopolymers with a stretch of ≥4 per gene was 11.2 ± 9.1 (range, 0-79), and the mean ± SD number of nucleotide differences that flanked such homopolymers was 1.2 ± 1.0 (range, 0-6). Nevertheless, the mean number of homopolymers per 100 nucleotides was similar for the unchanged and variable genes, 1.0 ± 0.5 (range, 0-3) and 1.2 ± 0.3 (0-2), respectively. This work is based on data obtained for Mimivirus at different stages of its early life under laboratory conditions (Figure 2). However, it is very unlikely that the genome sequences have been significantly affected by the initial number of subcultures on Acanthamoeba spp. Indeed, the genomes of Mimivirus M1, Mimivirus M1' and Acanthamoeba castellanii Mamavirus, another strain of Mimivirus, are highly similar although the viruses experienced different numbers of passages (from less than 10 to approximately 50) on Acanthamoeba spp. The comparison of the Mimivirus M1 genome with that recently recovered by ultra-deep sequencing of genomic DNA and total RNA on a SOLiD platform (Legendre et al., 2011), which we called Mimivirus M1' , showed that the two sequences differ by only 196 substitutions, 29 deletions and 174 insertions. The comparison of the Mamavirus genome with the Mimivirus genome showed ≈99% nucleotide identity in their alignable regions, which represent nearly the entire length of these genomes. Moreover, the Mamavirus and Mimivirus pairs of genes with bidirectional best hits show a mean nucleotide identity of 98.8%, and the majority of the pairs have identity levels greater than 99%. Interestingly, among the 19 genes present in the Mimivirus genome and absent in Mamavirus that were considered in the present work (not located within the large deletions), 16 (84%) have been classified as weakly expressed, and 4 (25%) of these genes are among the inactivated genes. Moreover, among the 22 genes in which frameshifts were identified in a comparison of the Mimivirus and Mamavirus genomes, 15 (68%) were weakly expressed at baseline, and three (20%) were inactivated.

DISCUSSION

Our work shows that the majority (63%) of the genes inactivated during Mimivirus evolution in the allopatric laboratory culture was initially weakly expressed and that low gene expression at the beginning of the culture is significantly positively correlated with gene inactivation and variability during the 150 passages. A possible bias exists and is related to the initial stages of culture. Thus, Mimivirus M1 might have been selected from the original pool of viruses recovered from their natural environment due to a growth advantage when it was first moved to the laboratory culture environment, as is the case for every artificial system of culture. This issue has not been studied in our laboratory or by other investigators. However, the comparison of the genomes of Mamavirus (considered to be another strain of Mimivirus), Mimivirus M1 and Mimivirus M1' revealed very few differences despite differences in the number of passages on amoebae. In addition, the aim of the present work was to assess the capacity to predict genes that will be inactivated under stable laboratory conditions based on their initial transcription profile.

In this context, we observed that a high level of expression for a gene strongly predicted its absence of inactivation. This finding suggests that the adaptation of Mimivirus to the modification of its environment after infection of germ-free amoebae in vitro is associated with a down regulation of certain genes that tended to be degraded and not repaired because they had become useless. A mechanism of this type, in which adaptation to a new ecosystem determines a new phenotype and this new phenotype promotes genotype changes transmitted to future generations, is the form of evolution described by Lamarck. One could consider that genotypic changes in the Mimivirus M1 genome were actually transmitted to new generations, as gene losses are usually considered irreversible (Krylov et al., 2003). It was previously emphasized that the evolutionary rate of a gene sequence was negatively correlated with its level of expression or the abundance of its product (Pal et al., 2001;Koonin, 2011). This relationship was previously assessed by estimating the number of substitutions per nucleotide site between orthologous sequences in several lineages or organisms, but it has not been assessed for the same organism during experimental evolution, as is the case in the present work. In the same context, a positive correlation was recently described between the propensity for gene loss and a sequence's evolutionary rate and gene dispensability (Krylov et al., 2003). Moreover, it was found that highly expressed proteins evolve slowly (Drummond et al., 2005). In addition, it is known that DNA repair and damage processing particularly targets actively transcribed genes, as in the case of transcriptioncoupled repair (Hanawalt and Spivak, 2008). A similar process may explain the finding that down-regulated Mimivirus genes are more variable; they are less likely to be repaired than highly transcribed genes.

Lamarckian evolution may be involved in bacterial speciation events associated with a reduction of the genome size (Merhej et al., 2009), a finding opposed to the dominant model that considers that speciation and fitness gain are associated with an increase in gene repertoires. Thus, the major route of speciation (through adaptation to a given ecological niche) is typically through allopatry (Georgiades and Raoult, 2010) and is associated with genome size reduction through the loss of useless genes according to the mode described by Moran: "use it or lose it" (Moran, 2002). It is probable that such modifications of the gene repertoire are associated with the radical impossibility of returning to a previous ecosystem. Thus, in a noncompetitive environment during the 150 passages in allopatric culture, Mimivirus experienced a rapid and dramatic modification of its gene content that may substantially compromise its biological fitness in more complex environments. This observation is consistent with Ernst Mayr's vision of cause and effect in biology, in which the effects of changes differ in the short term and in the long-term (Mayr, 1961). In this case, we found that Mimivirus can be selected for rapid growth in an environment without competition. However, these changes prevent the virus from attaining fitness in competition with other intraamoebal organisms (Boyer et al., 2011). Therefore, the conservation of unused genes in allopatry is only important as a long-term strategy. Together with previous data, our results suggest that the transcriptome of Mimivirus may predict the evolution of its genome in a stable laboratory culture system and that Lamarckian evolution may contribute to the evolution of the Mimivirus genome in this environment. These findings offer an incentive to study the correlation between transcription profiles and the evolution of gene sequences and repertoires in particular organisms.

Frontiers in Cellular and Infection Microbiology www.frontiersin.org July 2012 | Volume 2 | Article 91 | 3 FIGURE 3 | A schematic diagram of the evolution of the gene content of Mimivirus during allopatric laboratory culture on amoebae. The diagram shows the transcript abundance for Mimivirus M1 genes and the proportions of inactivated genes after 150 passages.

FIGURE 5 |
5The distribution of transcript abundance in Mimivirus M1 for different groups/subgroups of genes defined based on their variability and evolution during 150 passages in allopatric laboratory culture on amoebae. The horizontal bars indicate the mean values for each group/subgroup.

FIGURE 6 | 6 FIGURE 7 |
667The ratio of the mean transcript abundance in Mimivirus M1 between unchanged and inactivated genes during allopatric laboratory culture on amoebae. Frontiers in Cellular and Infection Microbiology www.frontiersin.org July 2012 | Volume 2 | Article 91 | The distribution of unchanged, variable, and inactivated genes after 150 passages in allopatric laboratory culture on amoebae per each 10 centiles of transcript abundance, calculated for all Mimivirus M1 genes.

). BLASTn searches were performed with the Mimivirus M1 set of open reading frames (ORFs) against the Mimivirus M4 genome.

Table 1 | Comparative features for unchanged, variable, and inactivated Mimivirus genes after 150 passages in allopatric culture on amoebae.[18.8% (76/405) vs. 28.2% (109/386); p = 0.0017; RR,
0.66 (CI95, 0.51-0.86)]. Otherwise, the proportion of genes with
a transcript abundance greater than the 70th centile of the values
for all genes (178) was significantly lower among the inacti-
vated genes than among the unchanged genes [13.3 vs. 35.0%;

p < 1e-3; RR, 0.38 (CI95, 0.22-0.66)]. This proportion was also
significantly lower among the variable genes than among the
unchanged genes [18.9 vs. 35.0%; p < 1e-3; RR, 0.29 (CI95,
0.19-0.46)]. The proportion of variable genes in the 25 genes
most expressed at baseline tended to be significantly lower than
the corresponding proportion of unchanged genes [0.5 vs. 3.5%;
p = 0.034; RR, 6.4 (CI95, 0.87-47.34)]. The negative predictive
value (NPV) of being inactivated was 92.3% for highly expressed
genes. In addition, this NPV was 95.5 and 96.9% for genes with
a transcript abundance greater than the 70th and 80th centile,
respectively, of the values calculated for all genes. Furthermore,
the proportion of genes encoding proteins identified from puri-
fied virions in their early life in allopatric laboratory culture
tended to be lower in the variable than in the unchanged genes
[9% (17/185) vs. 15% (88/606); p = 0.062; RR, 0.63 (CI95,
0.39-1.04)]; nine of the genes encoding proteins present in the
virions were inactivated. Also, among the 23 class I-III core
genes of the nucleocytoplasmic large DNA viruses (NCLDVs)
that were not located within the large deletions observed in the
Mimivirus M4 genome, 17 (74%) remained unchanged dur-
ing the 150 passages on germ-free amoebae. Eleven (65%) of
these 17 genes were highly expressed. In contrast, only one
(4.3%) NCLDV core gene of classes I-III was inactivated; this

Frontiers in Cellular and Infection Microbiology
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July 2012 | Volume 2 | Article 91 | 4
FIGURE 4 | The distribution of transcript abundance for Mimivirus M1
genes according to the number of differences between the nucleotide
sequences of the Mimivirus M4 and M1 genes. A difference corresponds

to the presence of different nucleotides at the same position within a
pairwise alignment provided by BLAST for the nucleotide sequences of
corresponding genes of Mimivirus M1 and Mimivirus M4.

Features of Mimivirus M1 genes
Group A: unchanged
Group B: variable genes

genes (n = 606)

All
Inactivated genes

(n = 185)
(n = 83)

Mean transcript abundance a
470 ± 1,625
141 ± 415  *
165 ± 589  *

Number of genes among the 25 most transcribed (%)
21 (3.5)
1 (0.5)  *
1 (1.2)  *

Number of genes with transcript abundance > the 70th centile for all genes (%)
212 (35)
35 (19)  *
11 (13)  *

Number of genes with transcript abundance ≤ the 10th percentile for all genes (%)
65 (10.7)
17 (9.2)
8 (9.6)

Proteins identified by proteomics (%)
88 (14.5)
17 (9.1)  †
9 (11)

 *  Proportion is significantly different from that of the genes of group A (p < 0.05; chi-square corrected test or Fisher's exact test).

 † Proportion tends to differ statistically from that of the genes of group A (0.05 < p < 0.1; chi-square corrected test).

a From Legendre et al. (2010).

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Conflict of Interest Statement:The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The variability was defined as the number of variable positions within genes. The yellow boxes toward the tips of the genome indicate large deletions encountered by the genome of Mimivirus during allopatric laboratory culture on amoebae, as previously reported(Boyer et al., 2011).APPENDIX
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SOFTWARE Open Access Condor-COPASI: high-throughput computing for biochemical networks along with full instructions on deployment and usage
2012

Edward Kent
Stefan Hoops
Pedro Mendes
SOFTWARE Open Access Condor-COPASI: high-throughput computing for biochemical networks along with full instructions on deployment and usage

BMC Systems Biology
6912012Systems biologyComputational modellingHigh-throughput computingDistributed computingSimulation
Background: Mathematical modelling has become a standard technique to improve our understanding of complex biological systems. As models become larger and more complex, simulations and analyses require increasing amounts of computational power. Clusters of computers in a high-throughput computing environment can help to provide the resources required for computationally expensive model analysis. However, exploiting such a system can be difficult for users without the necessary expertise. Results: We present Condor-COPASI, a server-based software tool that integrates COPASI, a biological pathway simulation tool, with Condor, a high-throughput computing environment. Condor-COPASI provides a web-based interface, which makes it extremely easy for a user to run a number of model simulation and analysis tasks in parallel. Tasks are transparently split into smaller parts, and submitted for execution on a Condor pool. Result output is presented to the user in a number of formats, including tables and interactive graphical displays. Conclusions: Condor-COPASI can effectively use a Condor high-throughput computing environment to provide significant gains in performance for a number of model simulation and analysis tasks. Condor-COPASI is free, open source software, released under the Artistic License 2.0, and is suitable for use by any institution with access to a Condor pool. Source code is freely available for download at

Background

Mathematical modelling is becoming increasingly recognized as an important tool in the study of biological systems [1]. Databases such as BioModels [2] provide access to peer-reviewed published models, which can be freely downloaded in the standards-compliant SBML format [3]. Model construction, however, is only the first in a number of steps that can be used to gain insight into the function of a complex biological system.

Many models consist of a set of ordinary differential equations (ODEs) describing how the concentrations of various chemical species change with time. The dynamic behaviour of the model can be examined through integration of the ODEs, a relatively computationally simple task. In many cases, it can be helpful to simulate the model behaviour using a stochastic or hybrid algorithm [4], which can yield more information than a deterministic solution can provide. Solving a model using a stochastic method requires more computational power than solving it deterministically [4], and must be repeated many times to yield information on the distribution of potential trajectories.

In addition to examining the dynamic behaviour of a model, we may also wish to analyse it using techniques such as sensitivity analysis -examining the influence of specific model parameters (such as kinetic rate constants) on a systems-level property (such as the flux through the main branch of a pathway). Other tasks commonly used to examine models include optimizations (finding the best set of parameters to maximize or minimize the value of a http://www.biomedcentral.com/1752-0509/6/91 particular objective function), or parameter fitting (finding the best set of parameters so that the model behaviour most represents that seen experimentally).

COPASI

A number of software tools are available for biological systems modelling. Some of these, such as MATLAB, are very flexible, but require users to have a knowledge of programming. Other tools, such as COPASI [5], are designed with a user-friendly interface, and do not require any programming expertise, while still providing sophisticated algorithms and analyses.

COPASI allows users to construct and analyse models using a graphical user interface (GUI). Simulations using a deterministic, stochastic or hybrid method can be performed at the click of a button. In addition to timecourse simulations, COPASI can perform a range of model analysis tasks, including steady-state analysis, sensitivity analysis, parameter estimation, and optimizations.

As models become more detailed and complex, simulation and analysis requires increasing amounts of computational power, potentially requiring more power than can be offered by even the most high-powered desktop machine.

Client-server simulation tools

The limitations of using personal computers to perform computationally intensive simulations have led to the development of a number of server-based systems biology tools. These allow a user to set up mathematical models and to display any results on their local machine, but to perform computationally-intensive simulations on one or more remote machines.

One such example is JWS Online [6], a modelling tool accessed through a web-based Java applet, which performs all simulations on a remote server. The computational capacity is limited by the performance of the server, and so it is not ideally suited for computationally intensive tasks. VCell [7] is another tool for model simulation and analysis. Models are prepared using a local Java interface, and simulations are performed on a distributed computing cluster operated by the VCell team, up to a maximum of 100 simulation repeats per submission. Parameter estimations can also be performed, but these are run locally.

Another way of running simulations remotely is to incorporate them into an automated distributed workflow, using a management tool such as Taverna [8]. COPASI Web Services [9] enables COPASI simulation and analysis tasks to be incorporated into a distributed workflow by setting up COPASI simulation servers to offer computing cycles. Using automated workflows allows for very flexible usage patterns, though creating workflows can be a complex process. In addition, users must have the computational resources available for running simulations.

Condor

Condor is a system for high-throughput computing, allowing computing jobs to be run on a pool of machines [10,11]. The pool can contain dedicated computers, though one of the main strengths of Condor is its ability to utilize non-dedicated machines during periods when they would otherwise be idle. For example, in academic institutions, large numbers of computers are in use during working hours on weekdays, but will sit unused overnight and at weekends. Condor is often configured to detect when a machine is not being used by its owner, and can then assign queued computing jobs to be run on it. Condor is compatible with most major operating systems, including Windows, Linux and OSX.

In order to submit a computing job to run on Condor, a job description file must first be prepared. This file contains information about the executable file to be run, any files that must be transferred to remote machines, and the software and hardware requirements of the job, such as the operating system and minimum amount of memory required. Once the job description has been prepared, it is submitted, and added to a queue for resources. A machine known as the Master decides when, and on which machine each job is to be run.

Condor pools can contain thousands of machines. The computing power of such pools can be most readily exploited when a computing task can be split into multiple, independent, jobs which can be run in parallel. However, the requirement to split large tasks into multiple smaller ones, along with a complex Condor job submission process involving command line tools, makes this difficult for many users.

Condor-COPASI

COPASI can be used, without any modification, with high-throughput computing environments such as Condor. However, in order to exploit the parallel nature of a Condor pool, any simulations or analyses must be manually split into multiple small, independent jobs a task that can be difficult. In addition, Condor requires the use of command-line tools to submit jobs and to monitor their status. This can be difficult, and may deter many users from making use of these facilities.

Therefore, we developed Condor-COPASI, a tool which integrates COPASI with Condor, allowing users to perform a selection of model analysis and simulation tasks in parallel using a Condor pool. Condor-COPASI is designed to be very simple to use, with all user interaction taking place through a GUI. The process of splitting tasks into an optimal number of parallel jobs, and submitting them to the Condor pool is handled automatically, http://www.biomedcentral.com/1752-0509/6/91 without requiring user interaction. Once jobs have been submitted to the Condor pool, Condor-COPASI monitors their status, automatically emailing a notification to the user upon completion. Results can be displayed within the interface in a variety of formats, including tables and interactive graphical displays, or can be exported for further processing.

Implementation

Condor-COPASI is a server-based application, accessed by users through a web interface which is compatible with all modern web browsers. Access to the web interface is controlled on a per-user basis; the administrator must create an account for each user of the system. A database is used to store user account information, along with information about the computing jobs each user has submitted, such the submission and end times, the number of parallel jobs used and CPU hours used. Historical usage data is stored, and statistics can be displayed though the web interface to monitor how the system is being used. Other files, such as COPASI models, and outputted results are stored as flat files in a directory of the system.

We designed Condor-COPASI to run as a server-based application, since the alternative -a local application to be run on each users machine -would require Condor to be installed and configured on each individual local machine. Installing Condor and configuring it to be able to submit jobs is a complicated process, and in many cases impractical. In addition, Condor requires that the machine which is used to submit jobs must remain powered on while remote jobs are running; an onerous requirement for many users, particularly when jobs have a long run time and are submitted from a portable machine. Running as a server-based application removes this requirement for the individual users, since only the server must remain powered on while jobs are running.

Condor-COPASI is written in the Python programming language [12], using the Django web development framework [13]. It must be installed on a server which has access to a Condor pool and permission to submit jobs. Additionally a web server must also be installed (though this is somewhat standard). Finally, a Django-compatible database must be available -choices currently include MySQL, PostgreSQL, Oracle and SQLite.

Condor-COPASI allows users to submit a number of predefined tasks, each of which is amenable to running in parallel (see Use Cases for full details). Tasks are submitted by uploading a pre-prepared COPASI model using the web interface ( Figure 1). Condor-COPASI then automatically determines the best way to split the task into parallel, automatically creates the necessary files, and submits the parallel jobs to Condor.

While the web interface is written in Python, all simulation and analysis tasks are performed using the COPASI Simulation Engine. Condor-COPASI works by automatically modifying the XML-based COPASI file format, generating a custom COPASI model file for each parallel job. In addition to the model file, a Condor job specification file is automatically generated for each parallel job. These files are submitted to the Condor pool, along with a copy of the COPASI Simulation Engine (CopasiSE) binary for the appropriate machine architecture. The COPASI Simulation Engine carries out all computation on the remote machine, writing output to a text file. These text files are transferred back to the Condor-COPASI server, where they are processed and collated.

Various graphical plots can be produced. Static twodimensional charts such as depicting the dynamics of mean and standard deviation of particle numbers in stochastic simulations are generated using the Python Matplotlib library [14] ( Figure 2, Figure 3). An interactive bar chart, showing maximal and minimal sensitivity values of various model parameters for different potential parameter sets is also available. A scroll bar can be dragged to change the range of the parameter set, while the chart values update automatically. This feature is provided using the Google MotionChart API [15], the chart is displayed using a mixture of javascript and flash; all data is processed and rendered locally, and no data is sent to Google.

Results

Available use cases

Condor-COPASI currently provides access to seven use cases, each of which is likely to require significant amounts of computational power, and can be efficiently split into smaller independent parallel jobs.

Global sensitivity analysis

The global sensitivity analysis procedure, as described in [16], involves performing a number of optimizationsone maximization and one minimization for each parameter. Since each optimization can be run independently, the task can be trivially split, with one parallel job for each maximization or minimization. For example, a model with 30 parameters for which a sensitivity is to be calculated will generate 60 parallel jobs.

COPASI provides access to a number of optimization algorithms, including deterministic such as Truncated Newton [17] and stochastic such as Particle Swarm [18]. Any of these algorithms can be selected for use in this task. If a user wishes to try more than one optimization algorithm, multiple tasks should be prepared and submitted, each with a different algorithm selected.

Results are provided in table format, and through graphical summaries of the importance of each parameter to the target property of the model. Charts showing progress of the optimization tasks can also be generated, displaying http://www.biomedcentral.com/1752-0509/6/91 Figure 1 Screenshot of job submission process. A screenshot of the submission process for the global sensitivity analysis task. Other than preparing a COPASI model file, very little user interaction is required -all aspects of the Condor submission process are handled automatically.

the best optimization value against the number of steps taken by the algorithm.

Stochastic simulation repeat

This task allows for multiple repeats of a stochastic timecourse simulation to be performed -a necessary procedure to determine the distribution of the trajectories. The results of each repeat are recorded, and for each time point particle number means and standard deviations are calculated. Plots of the results can be displayed in the web interface, and all result outputs can be downloaded as a tab-separated text file. Splitting of this task is carried out using the load balancing algorithm described below.

Parallel scan

The Parameter Scan task in COPASI automatically scans through various values for one or more parameters, performing a subtask such as steady-state analysis, a timecourse simulation, or an optimization for each set of parameters. It can also be used to repeat a subtask a number of times using the same parameter values, or to sample parameter values from a random distribution. Condor-COPASI can split this task into parallel, using a non-overlapping range of parameter values for each chunk. The number of subtask repeats to perform in each parallel job is determined using the load balancing algorithm.

Due to the diverse nature of possible outputs from this task, Condor-COPASI does not produce graphical plots. Instead, a text-based output must be prepared for the task by the user, before the task is submitted. After the task has finished, a collated text file containing the output from all parallel jobs is made available to download; this file is identical to the output that would have been produced had the parameter scan run on a single machine.

Optimization repeat

Many of the optimization algorithms available in COPASI are stochastic, and will return a different solution each time they are run. Condor-COPASI can repeat an optimi-http://www.biomedcentral.com/1752-0509/6/91 zation multiple times, using the same algorithm for each repeat, and from these repeats, determine the best objective value and associated parameter set. The number of optimization repeats to be performed for each parallel job is determined using the load balancing algorithm. Once the task has finished running, a COPASI model file containing the best parameter values can be downloaded.

Alternatively, the parameter values can be displayed in the browser.

Parameter estimation repeat

The Parameter Estimation task in COPASI is able to find the best parameter set to fit experimental data. Like the optimization task, many of the algorithms available http://www.biomedcentral.com/1752-0509/6/91 are stochastic, and each time they are run will return a different solution. Condor-COPASI can repeat a parameter estimation multiple times, using the same algorithm for each repeat, determining the best solution from all repeats, using the load balancing algorithm to determine how many repeats to perform in each parallel job. Once the task has completed, a COPASI model file containing the best parameter set found by the parameter estimation can be downloaded, or the parameter values can be displayed on-screen.

Optimization with different Algorithms

Since COPASI has many optimization algorithms available, and it is not clear which one works best for each problem, a modeller often wishes to run the problem through several of them. Condor-COPASI can run an optimization several times using a different algorithm for each one. The user can select which algorithms will run, and can configure all tuning parameters of those algorithms. Each optimization is run as a separate parallel job, and after all jobs have completed, the best algorithm(s) are determined. In addition, a COPASI model file containing the best available parameter set as determined by the optimization can be downloaded, or the parameter values viewed in the browser.

Raw mode

The raw mode task is designed for advanced users, and allows one or more COPASI tasks to be repeated an arbitrary number of times. The user is able to specify all command-line arguments for the CopasiSE binary, and must specify any required input and output files. One repeat is performed per parallel job, and any output files generated must be manually collated and processed by the user. This mode extends the applications of Condor-COPASI to a number of other possibilities, however it requires the user to understand the use of COPASI through the command line interface, as well as a basic knowledge of distributed computing.

Load balancing

The Stochastic Simulation, Parallel Scan, Optimization Repeat and Parameter Estimation Repeat use cases all involve repeating a particular task multiple times, and can be run in parallel by performing a certain number of repeats per job. For these tasks, the user specifies the total number of repeats to perform, while the number of repeats to perform per job, and subsequently the total number of parallel jobs, is determined using a load balancing algorithm.

The load balancing algorithm constructs the parallel jobs such that they each run for an approximately equal length of time, t, a parameter which is set by the system administrator. The algorithm first measures how long a single repeat of the task to be performed takes to complete. The parallel jobs are then built up with an appropriate number of repeats, such that the total run time of each job is approximately equal to t. If a single run takes longer than t, the algorithm will time out, and assign only one repeat per job.

It is important to choose a good value for t. If it is too small, each task will produce a large number of parallel jobs, each with few repeats. In this situation, the overhead http://www.biomedcentral.com/1752-0509/6/91 associated with submitting jobs to the Condor pool will become a significant factor, and in an extreme situation, running the job on Condor could take longer than if all jobs had been run sequentially on a single machine. However, if t is too large, then each task will produce a small number of parallel jobs, each taking a long time to complete. In this situation, the computational capacity of the Condor pool will not be fully exploited, and the benefits of running in parallel will be negated. In addition, jobs may run for too long, risking eviction from the machines they are running on -non-dedicated machines in the pool are normally only available when they would otherwise be idle, such as overnight and at weekends -meaning there is often an upper bound on the length of time a job can run for.

To determine the best value for t, we ran an optimization repeat task with t values ranging from 0.1 to 1000 minutes ( Figure 4). We found that setting t to 15 minutes gave a good trade-off between job submission overhead and gaining the benefits of running jobs in parallel. However, factors such as the number of machines in the Condor pool, and the speed of network communications between the server and machines in the pool will impact on this value. Therefore, we advise administrators to consider adjusting this value if necessary.

We also include an option for applicable tasks to override the load balancing algorithm, and to construct jobs with only one repeat per parallel job. This is useful in situations where the user knows a priori that each repeat is likely to take longer than t, saving them from having to wait for the load balancing algorithm to time out, or in situations where one wishes to make the run-time for each parallel job as short as possible.

Error handling

Condor handles various types of error -if jobs are evicted from the machine they are running on, they will automatically be re-queued and executed on an available machine. In cases where a job fails (for example, due to a malformed job specification file), the job will remain in the queue, but will be marked as 'held' . We note that while Condor supports application checkpointing for compatible software, which allows evicted jobs to resume on a different machine without loss of computation time, COPASI does not support checkpointing, so all evicted jobs must begin again from the start.

Condor-COPASI monitors the status of each submitted job -the queue of jobs is periodically polled to check for jobs marked as 'held' , and the exit status of all completed jobs is checked (a non-zero exit status indicates an error) by parsing the log files. When an error is detected, Condor-COPASI will try to determine whether it happened before COPASI was executed on the remote machine (indicating a malformed job specification file) or Figure 4 Tuning the load balancing algorithm. The load balancing algorithm uses a parameter t to determine, where possible, the ideal run time for a single parallel job. We determined that the optimal t value, minimizing the wall clock time for the task, was 15 minutes. At t <= 5 minutes, only one repeat was performed per parallel job, and the job submission overhead outweighed the benefits of running in parallel. At t >= 20 minutes, too many repeats were performed by each parallel job, and the benefits of running in parallel were reduced. Our tests were performed by running 1500 repeats of an optimization task, where each optimization had a 5 minute run time, on a Condor pool containing 2000 available executing nodes. http://www.biomedcentral.com/1752-0509/6/91 after (indicating a problem with the way the task was set up in COPASI).

After detecting an error, Condor-COPASI will email the user to notify them that the task did not complete successfully. The web interface will display the probable cause of the job failure ( Figure 5), and allow the user to download a compressed copy of all files generated by Condor-COPASI, including Condor log files, automatically generated COPASI model files, and any returned results files. Such files can assist the user in determining the cause of the failure, and allow them to manually collate any results generated if they will be useful.

Finally, Condor-COPASI will log all activity to a text file, including any errors and exceptions encountered. Examining this file can be helpful to determine the cause of a failure if it is otherwise not clear.

Performance

To illustrate the effectiveness of Condor-COPASI, we collated data from 12 months of real-world usage on our installation of Condor-COPASI; for each task submitted, we recorded the total CPU time used by all parallel jobs, the wall clock time of the task, and the number of parallel jobs used. The cumulative total of CPU time used does not include any time spent queuing for resources to become available, or any other delays caused by the Condor job submission process. It is analogous to the time it would take to run each parallel job sequentially on a single-core local machine, and therefore provides a good measure of how long the computing task would have taken to perform without using Condor-COPASI. The wall clock time represents the total waiting time between submission of the task to Condor-COPASI and its completion, and includes time where possibly no jobs were running because the Condor pool was running jobs for other users. Therefore, the total waiting time for each task is not necessarily dependent only on the nature and size of the task. The wall clock times reported come from a production system where Condor was shared with other users and are therefore indicative of typical usage. In all cases, the tasks were run on our local Condor pool of approximately 2000 execute nodes. The most common hardware configuration in the pool is an Intel Core2 Quad processor at 3GHz, with 4GB of RAM. However, we note that the pool is heterogeneous, with a number of different hardware configurations, and nodes continuously coming online and offline.

To illustrate the improvements in run time using Condor-COPASI, we calculated the speed-up factor for each task, defined as CPU time/wall clock time. The speed-up factors were plotted against the number of parallel jobs used ( Figure 6). In general, speed-up factors of between 10 0 and 10 3 are seen. For tasks except global sensitivity analysis, there is a roughly linear relationship between the number of parallel jobs used, and the speedup factor achieved. We note that the global sensitivity analysis task cannot be parallelized as efficiently as the other task types, so the highest speed-up factors for this task type were lower than other task types. Some tasks achieved a low speed-up factor (< 10 0 ); most of these tasks had a low CPU time and used few parallel jobs, so there was little benefit to running in parallel. Of particular note are two global sensitivity analysis tasks with speedup factors of around 0.1 and 0.3, with 60 parallel jobs each. For these tasks, the CPU time used was very low (around 6 minutes), while several hours were spent waiting for resources in the Condor pool.

Specific examples

To further illustrate the benefits of running tasks on Condor-COPASI, we describe 5 detailed examples of tasks that were run (summarized in Table 1), showing the extent to which they were parallelized, and the speed-up factor achieved (defined as CPU time/wall clock time).

Global sensitivity analysis -we ran a global sensitivity analysis on a model of NFκB signal transduction [19], examining the control of 27 parameters on the frequency of nuclear NFκB oscillation, using a parameter space consisting of the original parameter values ±20%. The task completed on Condor-COPASI in approximately 7 hours using 54 parallel jobs, using a cumulative total of 368 hours of computing time, achieving a speed-up factor of 53.

Global sensitivity analysis -we ran a global sensitivity analysis on a model of the MAPK signalling cascade [20], examining the control of 30 parameters on the concentration of nuclear MAPK-PP, using a parameter space consisting of the original parameter values ±50%. The task completed on Condor-COPASI in approximately 7 hours using 60 parallel jobs, using a cumulative total of 90 hours of computing time, achieving a speed-up factor of 13.

Stochastic simulation repeat -we ran 1,000,000 repeats of a stochastic time-course simulation of a threevariable calcium oscillation model [21]. Condor-COPASI completed the task in approximately 20 hours (including time taken queuing for resources and processing the resulting output files), using a cumulative total of 2,280 hours of computing time across 340 parallel jobs, achieving a speed-up factor of 114.

Scan in parallel -we used the parallel parameter scan task to perform 300,000 Monte Carlo simulations of an NFκB signal transduction model [19]. Condor-COPASI completed the task in approximately 31 hours, using 14,926 parallel jobs, using a cumulative total of 3,980 hours of computing time, achieving a speed-up factor of 128.

Scan in parallel -we used the parallel parameter scan task to perform 1,000,000 Monte Carlo simulations of a MAPK signalling cascade model [20]. Condor-COPASI completed the task in approximately 3 hours, using 1,849 parallel jobs, using a cumulative total of 429 hours of computing time, achieving a speed-up factor of 143.

Discussion

Performance

Condor-COPASI enabled us to significantly reduce the run time of many simulation and analysis tasks. In 12 months of real-world usage on our installation, we saw tasks running up to 442 times faster than if they had been run on a single computing core, with an average speed-up of 32 times. This has enabled us to perform model simulations and analyses that would otherwise not have been feasible, with some individual analysis tasks using more than a year of computing time, but completing in less than a day.

In an ideal situation, for most task types, the decrease in run time for running a particular task on Condor compared to running it on a single computing core should be proportional to the number of executing nodes available in the Condor pool. So, for example, if we have a task that takes 1000 minutes to run on a single core, and a Condor pool available with 1000 equally fast executing nodes, then the speed increase would be 1000-fold and the task would complete in 1 minute.

In practice, various limitations, overheads and discrepancies in the local network architecture and Condor pool mean the overall running time for a task will always be more then the theoretical minimum, and it is rarely possible to predict exactly how long a task will take to complete.

There is an overhead associated with submitting and running each parallel job -the Condor Master must add the job to the queue for resources and assign it to an appropriate executing node when one becomes available. The submitting node must then send the associated model and data files, along with a copy of the COPASI binary, over the network before job execution can start. In a situation where we have a small number of jobs to submit, each  of which will take a long time to execute, the overhead will likely not be significant. However, if we have a large number of jobs to submit (especially in the situation where there are more jobs than executing nodes available to run them), each of which will can be executed in a short amount of time, then the submission overhead will be more significant, and could become a limiting factor in the execution time for the task. In this situation, it may be preferable to construct the parallel jobs so that they each perform a certain number of repeats, increasing the job execution time while keeping the submission overhead constant, thus reducing the impact of the submission overhead. The load balancing algorithm described above attempts to find an ideal compromise between the degree to which the job is parallelized and the job submission overhead.

Another factor that can affect the execution time of our jobs is the potentially heterogeneous nature of the machines in the Condor pool -disparities in hardware specification will mean that the job execution time will vary from machine to machine. Thus, unless we specify exact hardware requirements for our jobs, their execution time will vary depending on the specification of the machine they are assigned to.

We must also consider that we may be competing for the available resources with other users. Therefore, the number of available executing nodes will depend on how many other users are using the Condor pool, and our priority within the queue relative to others.

Finally, the extent to which we can parallelize our task depends on the task type. Where the task involves repeating a subtask a certain number of times, we can parallelize up to the extent where we have one repeat per parallel job. Other task types, such as the global sensitivity analysis, can only be parallelized according to the number of parameters we are investigating. For example, an analysis on a model with 10 parameters will produce 20 parallel jobs (one maximizing optimization and one minimizing optimization for each parameter). In this situation, having more than 20 executing nodes available will not speed up overall job execution any more than having just 20.

In summary, the degree to which running a task on Condor will speed up execution compared to running the same task on a single local machine depends on the type of task being performed, and the degree to which it can be parallelized, and will also depend on a number of other factors, such as submission overheads and demand for the available resources. However, in testing, we saw vast improvements in the run-time of all task types (see Table 1).

Comparison with existing software

Unlike tools such as COPASI Web Services, Condor-COPASI works as a standalone piece of software, performing all aspects of file preparation, simulation and results processing with a user-friendly interface, requiring minimal user interaction. Other http://www.biomedcentral.com/1752-0509/6/91 tools, such as VCell and JWS Online, provide userfriendly graphical interfaces, but are less able to fully exploit the parallel nature of a distributed computing pool.

Other cluster management tools

Condor is one of a number of systems designed for cluster management and job scheduling; other systems with widespread deployment include Oracle Grid Engine (formally known as Sun Grid Engine) [22], Maui [23], and PBS [24,25]. The strengths of Condor (namely cross-platform support including Windows, Linux and OSX, its ability to utilize non-dedicated resources, and its fully opensource nature), make it an attractive choice for academic institutions, particularly those looking to utilize existing hardware. However, we recognize that many potential users may only have access to other distributed computing systems. If demand dictated, it could be possible to add support for other systems at a later date, though differences in job preparation, submission, and monitoring would make this task non-trivial. As an alternative, we note that it is possible to install Condor alongside other job schedulers; the Condor scheduler will only assign jobs to computing nodes with available computational capacity.

We also note the growing popularity of cloud computing systems, such as Amazon's Elastic Compute Cloud (EC2), which allow users to lease any required computational capacity by the hour, without having to invest in dedicated hardware. Such systems are particularly appealing to researchers who need only occasional access to large amounts of computational power. It is currently possible to use Condor on Amazon EC2 by running one or more EC2 instances as executing nodes. However, setting up such an arrangement is difficult, requiring a detailed knowledge of network configuration, and is likely to be beyond the capacity of most casual users. Therefore, a possible future extension of Condor-COPASI could be to automatically configure and launch EC2 or other cloud computing instances to form a Condor pool, and to use this to complement, or as an alternative to, a local Condor pool.

Conclusions

Condor-COPASI enables the use of ubiquitous distributed computing by making it easy to submit systems biology simulation and analysis tasks without requiring any knowledge of programming or managing networks of workstations. The computational power available in such pools of computers can be vast, particularly in institutions such as Universities, which have thousands of computers serving as terminals for only a portion of the day. Being able to efficiently utilize such a resource can enable large-scale simulations and analyses to be performed that would otherwise require too much computing power to be feasible.

Availability and requirements

Project name: Condor-COPASI Project home page: http://code.google.com/p/condorcopasi/ Operating system: Server-side software: Linux; user interface can be accessed on any operating system through a modern web browser Programming language: Python and Django Other requirements: Python 2.6; Django 1.2; a web server with Python support such as Apache; copies of the COPASI Simulation Engine (CopasiSE) binary in 32-bit and 64-bit mode for Windows, OSX and Linux; the LXML and Matplotlib Python libraries; Condor version 7.4 or higher, a database application -either MySQL, Post-greSQL, SQLite or Oracle -with an appropriate Python wrapper. License: Artistic License 2.0 Restrictions to use by non-academics: None Full instructions on deployment, and an instruction manuals users are available on the project home page: http://code.google.com/p/condor-copasi.

Figure 2
2Stochastic simulation output screenshot. Screenshot showing showing the output of a stochastic simulation repeat task. For each of the three chemical species, particle number means and standard deviations are displayed.

Figure 3
3Enlarged stochastic simulation output plot. An enlarged version of the plot shown inFigure 2.

Figure 5
5Screenshot of failed job submission. A screenshot of a task which failed to run on Condor. Condor-COPASI is able to handle failed jobs, and can analyze log files to determine the probable cause of failure, along with suggestions on how to fix any problems. http:

Figure 6
6Speed-up Factors. The speed-up factor (CPU time/wall clock time) and number of parallel jobs used were recorded for 190 tasks submitted during 12 months of real-world usage on our Condor-COPASI installation, with a Condor pool of approximately 2000 execute nodes. The color of each marker indicates the type of task performed, and the area of each marker is proportional to the base-10 logarithm of the CPU time used.

Table 1
1Example tasks run on Condor-COPASI on a pool of 2000 nodes, showing wall clock time on Condor, total CPU time, and speed-up factor (CPU time/wall clock time)Task type
Description
Number of parallel jobs
Wall clock time
CPU time used
Speed-up factor

Global sensitivity analysis
27 parameters from NFκB signalling model [19]
54
7 hours
368 hours
53

Global sensitivity analysis
30 parameters from MAPK signalling model [20]
60
7 hours
90 hours
13

Stochastic simulation
1,000,000 repeats of calcium oscillation model [21]
340
20 hours
2,280 hours
114

Parallel scan
300,000 repeats of NFκB signalling model [19]
14,926
31 hours
3,980 hours
128

Parallel scan
100,000 repeats of MAPK signalling model [20]
1,849
3 hours
429 hours
143

© 2012Kent et al.;  licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
AcknowledgementsWe thank I. Cottam for his help with the local Condor pool and G. Konstantinidis for discussions about the use cases.Competing interestsThe authors declare that they have no competing interests.Authors' contributions EK wrote the software and prepared the first draft of the manuscript. PM and EK developed the specifications of the software. PM, EK and SH tested the software. All authors read, improved, and approved the final manuscript.FundingPfizer/BBSRC grant BB/G529859/1, NIH grant R01 GM090219, BBSRC/EPSRC grant BB/C008219/1 (MCISB), EU FP7 grant 201142 (UNICELLSYS).
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