scholarly journals Chronos: a CRISPR cell population dynamics model

2021 ◽  
Author(s):  
Joshua M. Dempster ◽  
Isabella Boyle ◽  
Francisca Vazquez ◽  
David Root ◽  
Jesse S. Boehm ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Performance Metrics ◽  
Gene Knockout ◽  
Loss Of Function ◽  
Dynamics Model ◽  
Biological Experiment ◽  
Multiple Sources ◽  
Fitness Effects ◽  
Population Dynamics Model ◽  
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AbstractCRISPR loss of function screens are a powerful tool to interrogate cancer biology but are known to exhibit a number of biases and artifacts that can confound the results, such as DNA cutting toxicity, incomplete phenotype penetrance and screen quality bias. Computational methods that more faithfully model the CRISPR biological experiment could more effectively extract the biology of interest than typical current methods. Here we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of the dynamics of cell proliferation after CRISPR gene knockout. Chronos is able to exploit longitudinal CRISPR data for improved inference. Additionally, it accounts for multiple sources of bias and can effectively share information across screens when jointly analyzing large datasets such as Project Achilles and Score. We show that Chronos outperforms competing methods across a range of performance metrics in multiple types of experiments.

scholarly journals Chronos: a cell population dynamics model of CRISPR experiments that improves inference of gene fitness effects

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Joshua M. Dempster ◽  
Isabella Boyle ◽  
Francisca Vazquez ◽  
David E. Root ◽  
Jesse S. Boehm ◽  
...  
Keyword(s):  
Copy Number ◽  
Gene Knockout ◽  
Loss Of Function ◽  
Explicit Model ◽  
Dynamics Model ◽  
Fitness Effects ◽  
Population Dynamics Model ◽  
Crispr Screen ◽  
A Cell ◽  
Pan Cancer

AbstractCRISPR loss of function screens are powerful tools to interrogate biology but exhibit a number of biases and artifacts that can confound the results. Here, we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of cell proliferation dynamics after CRISPR gene knockout. We test Chronos on two pan-cancer CRISPR datasets and one longitudinal CRISPR screen. Chronos generally outperforms competitors in separation of controls and strength of biomarker associations, particularly when longitudinal data is available. Additionally, Chronos exhibits the lowest copy number and screen quality bias of evaluated methods. Chronos is available at https://github.com/broadinstitute/chronos.

scholarly journals A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

2012 ◽  
Vol 69 (11) ◽  
pp. 1881-1893 ◽  
Author(s):  
Verena M. Trenkel ◽  
Mark V. Bravington ◽  
Pascal Lorance
Keyword(s):  
Population Dynamics ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Total Mortality ◽  
Estimation Bias ◽  
Effective Sample Size ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

Independent validation of simbiosys tumorscope to predict response to neoadjuvant chemotherapy (NACT) in early breast cancer (EBC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 582-582
Author(s):  
Gong He ◽  
Frederick Howard ◽  
Tushar Pandey ◽  
Hiroyuki Abe ◽  
Rita Nanda
Keyword(s):  
Breast Cancer ◽  
Sensitivity And Specificity ◽  
Cancer Biology ◽  
Tumor Volume ◽  
Performance Metrics ◽  
External Validation ◽  
Pathologic Complete Response ◽  
Disease Free Survival ◽  
Biophysical Model ◽  
Lymph Node Status

582 Background: Despite substantial advances in the understanding of breast cancer biology, the decision to use NACT for EBC is based on tumor size, lymph node status, and subtype. Even with aggressive therapy, the majority of women will not achieve a pathologic complete response (pCR). Investigational treatment regimens, including immunotherapy, can increase pCR rates, but are associated with irreversible immune-related toxicities. Being able to accurately predict pCR could identify candidates for intensification or de-escalation of NACT, allowing for personalized medicine. SimBioSys TumorScope (TS) is a biophysical model that utilizes baseline MRI, receptor status, and planned treatment regimen to simulate response to NACT over time. TS has demonstrated accurate prediction of pCR in prior studies. Here, we describe an independent external validation of TS. Methods: We conducted a retrospective study of University of Chicago patients (pts) who received NACT for EBC from Jan 2010 - March 2020. Pts must have had a pretreatment breast MRI. Tumors were analyzed using TS by investigators who were blinded to response data. TS predicted pCR was predefined as a residual tumor volume < 0.01 cm3 or a 99.9% or greater reduction in tumor volume. Performance metrics of TS were calculated. Results: 144 tumors from 141 pts were analyzed. Average age was 52 yrs; 65% had stage II and 19% had stage III disease. Sensitivity and specificity of TS for predicting pCR were 90.4% and 92.4%, respectively. Of the 7 patients who were predicted to achieve a pCR but did not, 5 had a tumor cellularity < 5%. With a median follow-up of 4.7 yrs, the 4-yr distant disease free survival (DDFS) was 100% for patients predicted to achieve pCR, versus 81.5% for those predicted to have residual disease. Results were generally robust for all subgroups analyzed (Table). Conclusions: TS accurately predicts pCR and DDFS from baseline MRI and clinicopathologic data. Given the high sensitivity and specificity of this assay across breast cancer subtypes, TS can be used to aid in escalation/de-escalation strategies for EBC.[Table: see text]

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