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Protein cost allocation explains metabolic strategies in Escherichia coli.

Journal of biotechnology

Grigaitis P, Olivier BG, Fiedler T, Teusink B, Kummer U, Veith N.
PMID: 33309962
J Biotechnol. 2021 Feb 10;327:54-63. doi: 10.1016/j.jbiotec.2020.11.003. Epub 2020 Dec 10.

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein...

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Grigaitis P, Olivier BG, Fiedler T, et al. Protein cost allocation explains metabolic strategies in Escherichia coli. J Biotechnol. 2020;327:54-63doi: 10.1016/j.jbiotec.2020.11.003.
Grigaitis, P., Olivier, B. G., Fiedler, T., Teusink, B., Kummer, U., & Veith, N. (2021). Protein cost allocation explains metabolic strategies in Escherichia coli. Journal of biotechnology, 32754-63. https://doi.org/10.1016/j.jbiotec.2020.11.003
Grigaitis, Pranas, et al. "Protein cost allocation explains metabolic strategies in Escherichia coli." Journal of biotechnology vol. 327 (2021): 54-63. doi: https://doi.org/10.1016/j.jbiotec.2020.11.003
Grigaitis P, Olivier BG, Fiedler T, Teusink B, Kummer U, Veith N. Protein cost allocation explains metabolic strategies in Escherichia coli. J Biotechnol. 2021 Feb 10;327:54-63. doi: 10.1016/j.jbiotec.2020.11.003. Epub 2020 Dec 10. PMID: 33309962.
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Nutrient supply, cell spatial correlation and Gompertzian tumor growth.

Theory in biosciences = Theorie in den Biowissenschaften

Castorina P, Carco' D.
PMID: 33988848
Theory Biosci. 2021 Jun;140(2):197-203. doi: 10.1007/s12064-021-00344-8. Epub 2021 May 14.

Gompertzian tumor growth can be reproduced by mitosis, related to nutrient supply, with local spatial cell correlations. The global energy constraint alone does not reproduce in vivo data by the observed values of the nutrient expenditure for the cell...

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Castorina P, Carco' D. Nutrient supply, cell spatial correlation and Gompertzian tumor growth. Theory Biosci. 2021;140(2):197-203doi: 10.1007/s12064-021-00344-8.
Castorina, P., & Carco', D. (2021). Nutrient supply, cell spatial correlation and Gompertzian tumor growth. Theory in biosciences = Theorie in den Biowissenschaften, 140(2), 197-203. https://doi.org/10.1007/s12064-021-00344-8
Castorina, P, and Carco', D. "Nutrient supply, cell spatial correlation and Gompertzian tumor growth." Theory in biosciences = Theorie in den Biowissenschaften vol. 140,2 (2021): 197-203. doi: https://doi.org/10.1007/s12064-021-00344-8
Castorina P, Carco' D. Nutrient supply, cell spatial correlation and Gompertzian tumor growth. Theory Biosci. 2021 Jun;140(2):197-203. doi: 10.1007/s12064-021-00344-8. Epub 2021 May 14. PMID: 33988848; PMCID: PMC8120020.
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Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data.

Mathematical biosciences and engineering : MBE

Henscheid N.
PMID: 33378865
Math Biosci Eng. 2020 Sep 25;17(6):6531-6556. doi: 10.3934/mbe.2020341.

The use of mathematical tumor growth models coupled to noisy imaging data has been suggested as a possible component in the push towards precision medicine. We discuss the generation of population and patient-specific virtual populations in this context, providing...

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Henscheid N. Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data. Math Biosci Eng. 2020;17(6):6531-6556doi: 10.3934/mbe.2020341.
Henscheid, N. (2020). Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data. Mathematical biosciences and engineering : MBE, 17(6), 6531-6556. https://doi.org/10.3934/mbe.2020341
Henscheid, Nick. "Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data." Mathematical biosciences and engineering : MBE vol. 17,6 (2020): 6531-6556. doi: https://doi.org/10.3934/mbe.2020341
Henscheid N. Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data. Math Biosci Eng. 2020 Sep 25;17(6):6531-6556. doi: 10.3934/mbe.2020341. PMID: 33378865; PMCID: PMC7780222.
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Superlinear growth reveals the Allee effect in tumors.

Physical review. E

Azimzade Y, Saberi AA, Gatenby RA.
PMID: 34005934
Phys Rev E. 2021 Apr;103(4):042405. doi: 10.1103/PhysRevE.103.042405.

Integrating experimental data into ecological models plays a central role in understanding biological mechanisms that drive tumor progression where such knowledge can be used to develop new therapeutic strategies. While the current studies emphasize the role of competition among...

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Azimzade Y, Saberi AA, Gatenby RA. Superlinear growth reveals the Allee effect in tumors. Phys Rev E. 2021;103(4):042405doi: 10.1103/PhysRevE.103.042405.
Azimzade, Y., Saberi, A. A., & Gatenby, R. A. (2021). Superlinear growth reveals the Allee effect in tumors. Physical review. E, 103(4), 042405. https://doi.org/10.1103/PhysRevE.103.042405
Azimzade, Youness, et al. "Superlinear growth reveals the Allee effect in tumors." Physical review. E vol. 103,4 (2021): 042405. doi: https://doi.org/10.1103/PhysRevE.103.042405
Azimzade Y, Saberi AA, Gatenby RA. Superlinear growth reveals the Allee effect in tumors. Phys Rev E. 2021 Apr;103(4):042405. doi: 10.1103/PhysRevE.103.042405. PMID: 34005934.
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Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

Akbarpour Ghazani M, Saghafian M, Jalali P, Soltani M.
PMID: 34247529
Proc Inst Mech Eng H. 2021 Nov;235(11):1335-1355. doi: 10.1177/09544119211028380. Epub 2021 Jul 10.

Uncontrolled proliferation of cells in a tissue caused by genetic mutations inside a cell is referred to as a tumor. A tumor which grows rapidly encounters a barrier when it grows to a certain size in presence of preexisting...

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Akbarpour Ghazani M, Saghafian M, Jalali P, et al. Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. Proc Inst Mech Eng H. 2021;235(11):1335-1355doi: 10.1177/09544119211028380.
Akbarpour Ghazani, M., Saghafian, M., Jalali, P., & Soltani, M. (2021). Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 235(11), 1335-1355. https://doi.org/10.1177/09544119211028380
Akbarpour Ghazani, Mehran, et al. "Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment." Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine vol. 235,11 (2021): 1335-1355. doi: https://doi.org/10.1177/09544119211028380
Akbarpour Ghazani M, Saghafian M, Jalali P, Soltani M. Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. Proc Inst Mech Eng H. 2021 Nov;235(11):1335-1355. doi: 10.1177/09544119211028380. Epub 2021 Jul 10. PMID: 34247529; PMCID: PMC8573697.
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Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model.

Annals of oncology : official journal of the European Society for Medical Oncology

Salah S, Watanabe K, Welter S, Park JS, Park JW, Zabaleta J, Ardissone F, Kim J, Riquet M, Nojiri K, Gisabella M, Kim SY, Tanaka K, Al-Haj Ali B.
PMID: 22547539
Ann Oncol. 2012 Oct;23(10):2649-2655. doi: 10.1093/annonc/mds100. Epub 2012 Apr 29.

BACKGROUND: Although resecting colorectal cancer (CRC) pulmonary metastasis is associated with long-term survival, identification of prognostic groups is needed for future randomized trials, and construction of a lung metastasectomy prognostic model (LMPM) is warranted.PATIENTS AND METHODS: We searched the...

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Salah S, Watanabe K, Welter S, et al. Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model. Ann Oncol. 2012;23(10):2649-2655doi: 10.1093/annonc/mds100.
Salah, S., Watanabe, K., Welter, S., Park, J. S., Park, J. W., Zabaleta, J., Ardissone, F., Kim, J., Riquet, M., Nojiri, K., Gisabella, M., Kim, S. Y., Tanaka, K., & Al-Haj Ali, B. (2012). Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model. Annals of oncology : official journal of the European Society for Medical Oncology, 23(10), 2649-2655. https://doi.org/10.1093/annonc/mds100
Salah, S, et al. "Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model." Annals of oncology : official journal of the European Society for Medical Oncology vol. 23,10 (2012): 2649-2655. doi: https://doi.org/10.1093/annonc/mds100
Salah S, Watanabe K, Welter S, Park JS, Park JW, Zabaleta J, Ardissone F, Kim J, Riquet M, Nojiri K, Gisabella M, Kim SY, Tanaka K, Al-Haj Ali B. Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model. Ann Oncol. 2012 Oct;23(10):2649-2655. doi: 10.1093/annonc/mds100. Epub 2012 Apr 29. PMID: 22547539.
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Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement.

Bioprocess and biosystems engineering

Rodriguez-Granrose D, Jones A, Loftus H, Tandeski T, Heaton W, Foley KT, Silverman L.
PMID: 33638725
Bioprocess Biosyst Eng. 2021 Jun;44(6):1301-1308. doi: 10.1007/s00449-021-02529-3. Epub 2021 Feb 27.

Modern bioprocess development employs statistically optimized design of experiments (DOE) and regression modeling to find optimal bioprocess set points. Using modeling software, such as JMP Pro, it is possible to leverage artificial neural networks (ANNs) to improve model accuracy...

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Rodriguez-Granrose D, Jones A, Loftus H, et al. Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement. Bioprocess Biosyst Eng. 2021;44(6):1301-1308doi: 10.1007/s00449-021-02529-3.
Rodriguez-Granrose, D., Jones, A., Loftus, H., Tandeski, T., Heaton, W., Foley, K. T., & Silverman, L. (2021). Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement. Bioprocess and biosystems engineering, 44(6), 1301-1308. https://doi.org/10.1007/s00449-021-02529-3
Rodriguez-Granrose, Daniel, et al. "Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement." Bioprocess and biosystems engineering vol. 44,6 (2021): 1301-1308. doi: https://doi.org/10.1007/s00449-021-02529-3
Rodriguez-Granrose D, Jones A, Loftus H, Tandeski T, Heaton W, Foley KT, Silverman L. Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement. Bioprocess Biosyst Eng. 2021 Jun;44(6):1301-1308. doi: 10.1007/s00449-021-02529-3. Epub 2021 Feb 27. PMID: 33638725; PMCID: PMC8144078.
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Defining the speciation continuum.

Evolution; international journal of organic evolution

Stankowski S, Ravinet M.
PMID: 33754340
Evolution. 2021 Jun;75(6):1256-1273. doi: 10.1111/evo.14215. Epub 2021 May 05.

A primary roadblock to our understanding of speciation is that it usually occurs over a timeframe that is too long to study from start to finish. The idea of a speciation continuum provides something of a solution to this...

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Stankowski S, Ravinet M. Defining the speciation continuum. Evolution. 2021;75(6):1256-1273doi: 10.1111/evo.14215.
Stankowski, S., & Ravinet, M. (2021). Defining the speciation continuum. Evolution; international journal of organic evolution, 75(6), 1256-1273. https://doi.org/10.1111/evo.14215
Stankowski, Sean, and Ravinet, Mark. "Defining the speciation continuum." Evolution; international journal of organic evolution vol. 75,6 (2021): 1256-1273. doi: https://doi.org/10.1111/evo.14215
Stankowski S, Ravinet M. Defining the speciation continuum. Evolution. 2021 Jun;75(6):1256-1273. doi: 10.1111/evo.14215. Epub 2021 May 05. PMID: 33754340.
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Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19.

PloS one

Browning R, Sulem D, Mengersen K, Rivoirard V, Rousseau J.
PMID: 33836020
PLoS One. 2021 Apr 09;16(4):e0250015. doi: 10.1371/journal.pone.0250015. eCollection 2021.

Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they can model incredibly complex phenomena. Traditionally Hawkes processes are a...

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Browning R, Sulem D, Mengersen K, et al. Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19. PLoS One. 2021;16(4):e0250015doi: 10.1371/journal.pone.0250015.
Browning, R., Sulem, D., Mengersen, K., Rivoirard, V., & Rousseau, J. (2021). Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19. PloS one, 16(4), e0250015. https://doi.org/10.1371/journal.pone.0250015
Browning, Raiha, et al. "Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19." PloS one vol. 16,4 (2021): e0250015. doi: https://doi.org/10.1371/journal.pone.0250015
Browning R, Sulem D, Mengersen K, Rivoirard V, Rousseau J. Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19. PLoS One. 2021 Apr 09;16(4):e0250015. doi: 10.1371/journal.pone.0250015. eCollection 2021. PMID: 33836020; PMCID: PMC8034752.
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Showing 351253 to 1077 of 1077 entries