Using Evolutionary Algorithms for Fitting High-Dimensional Models to Neuronal Data

In the study of neurosciences, and of complex biological systems in general, there is frequently a need to fit mathematical models with large numbers of parameters to highly complex datasets. Here we consider algorithms of two different classes, gradient following (GF) methods and evolutionary algorithms (EA) and examine their performance in fitting a 9-parameter model of a filter-based visual neuron to real data recorded from a sample of 107 neurons in macaque primary visual cortex (V1). Although the GF method converged very rapidly on a solution, it was highly susceptible to the effects of local minima in the error surface and produced relatively poor fits unless the initial estimates of the parameters were already very good. Conversely, although the EA required many more iterations of evaluating the model neuron’s response to a series of stimuli, it ultimately found better solutions in nearly all cases and its performance was independent of the starting parameters of the model. Thus, although the fitting process was lengthy in terms of processing time, the relative lack of human intervention in the evolutionary algorithm, and its ability ultimately to generate model fits that could be trusted as being close to optimal, made it far superior in this particular application than the gradient following methods. This is likely to be the case in many further complex systems, as are often found in neuroscience

A missense variant in IFT122 associated with a canine model of retinitis pigmentosa

Retinitis pigmentosa (RP) is a blinding eye disease affecting nearly two million people worldwide. Dogs are affected with a similar illness termed progressive retinal atrophy (PRA). Lapponian herders (LHs) are affected with several types of inherited retinal dystrophies, and variants in PRCD and BEST1 genes have been associated with generalized PRA and canine multifocal retinopathy 3 (cmr3), respectively. However, all retinal dystrophy cases in LHs are not explained by these variants, indicating additional genetic causes of disease in the breed. We collected DNA samples from 10 PRA affected LHs, with known PRCD and BEST1 variants excluded, and 34 unaffected LHs. A genome-wide association study identified a locus on CFA20 (p(raw) = 2.4 x 10(-7), p(Bonf) = 0.035), and subsequent whole-genome sequencing of an affected LH revealed a missense variant, c.3176G>A, in the intraflagellar transport 122 (IFT122) gene. The variant was also found in Finnish Lapphunds, in which its clinical relevancy needs to be studied further. The variant interrupts a highly conserved residue, p.(R1059H), in IFT122 and likely impairs its function. Variants in IFT122 have not been associated with retinal degeneration in mammals, but the loss of ift122 in zebrafish larvae impaired opsin transport and resulted in progressive photoreceptor degeneration. Our study establishes a new spontaneous dog model to study the role of IFT122 in RP biology, while the affected breed will benefit from a genetic test for a recessive condition.Peer reviewe

Multiocular defect in the Old English Sheepdog: A canine form of Stickler syndrome type II associated with a missense variant in the collagen-type gene COL11A1

Funder: Dogs Trust; funder-id: http://dx.doi.org/10.13039/501100021270Funder: Kennel Club Charitable TrustMultiocular defect has been described in different canine breeds, including the Old English Sheepdog. Affected dogs typically present with multiple and various ocular abnormalities. We carried out whole genome sequencing on an Old English Sheepdog that had been diagnosed with hereditary cataracts at the age of five and then referred to a board-certified veterinary ophthalmologist due to owner-reported visual deterioration. An ophthalmic assessment revealed that there was bilateral vitreal degeneration, macrophthalmos, and spherophakia in addition to cataracts. Follow-up consultations revealed cataract progression, retinal detachment, uveitis and secondary glaucoma. Whole genome sequence filtered variants private to the case, shared with another Old English Sheepdog genome and predicted to be deleterious were genotyped in an initial cohort of six Old English Sheepdogs (three affected by multiocular defect and three control dogs without evidence of inherited eye disease). Only one of the twenty-two variants segregated correctly with multiocular defect. The variant is a single nucleotide substitution, located in the collagen-type gene COL11A1, c.1775T&gt;C, that causes an amino acid change, p.Phe1592Ser. Genotyping of an additional 14 Old English Sheepdogs affected by multiocular defect revealed a dominant mode of inheritance with four cases heterozygous for the variant. Further genotyping of hereditary cataract-affected Old English Sheepdogs revealed segregation of the variant in eight out of nine dogs. In humans, variants in the COL11A1 gene are associated with Stickler syndrome type II, also dominantly inherited.</jats:p

RMBNToolbox: random models for biochemical networks

<p>Abstract</p> <p>Background</p> <p>There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.</p> <p>Results</p> <p>We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.</p> <p>Conclusion</p> <p>While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.</p