CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).EU 7th Framework Programme (BIOLEDGE Contract No: 289126 to S. G. O and J. R), BBSRC (BRIC2.2 to S. G. O. and N. K. H. S.), Synthetic Biology Research Initiative Cambridge (SynBioFund to D. D., A. C. C. and J. M. L. D.

Kp1.0 : Genome-scale metabolic model for Komagataella phaffii

Kp.1.0 can also be accessible through BIOMODELS database (MODEL1703150000)

Research data supporting "A Tool for Exploiting Complex Adaptive Evolution to Optimise Protocols for Biological Experiments"

CamOptimus is a tool for applying Genetic Algorithm (GA) to solve multi-parametric optimisation problems and Symbolic Regression (SR) to obtain models using the data generated during optimisation procedure to investigate the effect of individual parameters on the system of interest. The source code for the compiled software, and the Graphical User Interface (GUI) of the application are available under free licensing (GNU General Public License v3.0). The user manual is supplied in the compressed folder. $\textbf{Important information}$: access to the files for this software has been restricted as they are out of date. The software is available on Github, where updated documentation and new releases are available. $\href{https://github.com/DuyguD}{\text{https://github.com/DuyguD}}$.The University of Cambridge Synthetic Biology Research Initiative [SynBio Fund: CamOptimus] to DD, ACC, JML

Executive functions and memory in bipolar disorder I and II: new insights from meta‐analytic results

Objective: To perform a systematic review and meta-analysis of executive functions (EF) and episodic memory in bipolar disorder (BD). Methods: A literature search was conducted on three electronic databases. Results were combined using random-effects meta-analysis. Results: A total of 126 studies (6424 patients with BDI, 702 with BDII, and 8276 controls) were included. BDI was associated with moderate to large impairments across all cognitive functions and BDII with small-tomedium impairments. Small significant differences were identified between BDI and BDII on all cognitive functions except inhibition. The Trail Making Test (TMT) (g = 0.74, 95% CI: 0.67–0.80), Hayling Test (g = 0.58, 95% CI: 0.34–0.81), Digit Span Total (g = 0.79, 95% CI: 0.57– 1.01), and Category Fluency (g = 0.59, 95% CI: 0.45–0.72) tasks were most sensitive to cognitive impairment in BDI. The TMT (g = 0.65, 95% CI: 0.50–0.80) and Category Fluency (g = 0.56, 95% CI: 0.37–0.75) were also sensitive to cognitive alterations in patients with BDII. Conclusion: BD type I was associated with more severe and widespread impairments than BDII, which showed smaller impairments on all functions except inhibition, where impairments were larger. Education and (hypo)manic symptoms should be further investigated in future studies due to their possible influence on the neuropsychological profile of BD. The instruments identified in this review should be considered for inclusion in cognitive assessment batteries in BD.Fil: Cotrena, Charles. Pontificia Universidade Católica do Rio Grande do Sul; BrasilFil: Damiani Branco, Laura. Pontificia Universidade Católica do Rio Grande do Sul; BrasilFil: Ponsoni, André. Pontificia Universidade Católica do Rio Grande do Sul; BrasilFil: Samame, Cecilia. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Milman Shansis, Flávio. Universidad de Vale do Rio dos Sinos; BrasilFil: Paz Fonseca, Rochele. Pontificia Universidade Católica do Rio Grande do Sul; Brasi

The risk factors of antenatal depression : A cross-sectional survey

Aims and objectives To investigate the prevalence of depression in the third trimester of pregnancy and identify the related demographic risk factors. Background Antenatal depression as a disabling and treatable disease has a wide-ranging impact on perinatal women and has received extensive attention from researchers. Design A cross-sectional survey was conducted at three public hospitals. Methods Demographic questionnaire was developed from the literature review, and depression was assessed using the Edinburgh Postnatal Depression Scale. A binary logistic regression model was used to assess the association between depression and demographic predictors. STROBE checklist for cross-sectional studies was applied in this paper (see Appendix S1). Result A total of 773 pregnant women participated in the study. 29.6% of participants scored more than 9 points on Edinburgh Postnatal Depression Scale. In the final logistic model, living in rural area, marital satisfaction, assisted reproductive technology, lacking of prenatal health knowledge and life events were strongly significantly associated with antenatal depression. Moreover, living in an extended family, without Medicare insurance, unemployed, working as civil servants or healthcare workers, and lower household income also predicted antenatal depression. However, education level, smoking or drinking before pregnancy was found not to be associated with antenatal depression. Conclusion Our findings suggest that the prevalence of antenatal depression was high. Satisfied with the current marital status, pregnancy without assisted reproductive technology, knowledge of perinatal care and no life events recently were considered as the protective factors for antenatal depression. Relevance to clinical practice Antenatal psychological interventions should focus on how to improve the marital satisfaction and the relationship with their family members. More attentions should be paid to the women who have had some life events recently or received assisted reproductive technology for pregnancy