Articles in peer-reviewed journals
[38] Howerton, E., Contamin, L., Mullany, L. C., Qin, M., Reich, N. G., Bents, S., ... & Lessler, J. (2023). Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nature Communications, 14(1), 7260.
[37] Bouchnita, A., and Volpert, V. (2023). Phenotype-structured model of intra-clonal heterogeneity and drug resistance in multiple myeloma. Journal of Theoretical Biology, 111652.
[36] Bouchnita, A., Yadav, K., Llored, J. P., Gurovich, A., Volpert, V. (2023). Thrombin Generation Thresholds for Coagulation Initiation under Flow. Axioms, 12(9), 873.
[35] Dampare, Francis Owusu, and Anass Bouchnita. "Equitable bivalent booster allocation strategies against emerging SARS-CoV-2 variants in US cities with large Hispanic communities: The case of El Paso County, Texas." Infectious Disease Modelling (2023).
[34] Kanté, D. S. I., Jebrane, A., Bouchnita, A., & Hakim, A. (2023). Estimating the Risk of Contracting COVID-19 in Different Settings Using a Multiscale Transmission Dynamics Model. Mathematics, 11(1), 254.
[33] Bouchnita, A., Mozokhina, A., Nony, P., Llored, J. P., & Volpert, V. (2023). Combining Computational Modelling and Machine Learning to Identify COVID-19 Patients with a High Thromboembolism Risk. Mathematics, 11(2), 289.
[32] Leon, C., Tokarev, A., Bouchnita, A., & Volpert, V. (2023). Modelling of the Innate and Adaptive Immune Response to SARS Viral Infection, Cytokine Storm and Vaccination. Vaccines, 11(1), 127.
[31] Kaucka, M., Joven Araus, A., Tesarova, M., Currie, J. D., Boström, J., Kavkova, M., ... & Adameyko, I. (2022). Altered developmental programs and oriented cell divisions lead to bulky bones during salamander limb regeneration. Nature Communications, 13(1), 6949.
[30] Bouderlique, T., Petersen, J., Faure, L., Abed-Navandi, D., Bouchnita, A., Mueller, B., ... & Adameyko, I. (2022). Surface flow for colonial integration in reef-building corals. Current Biology.
[29] Bouchnita, A., Nony, P., Llored, J. P., & Volpert, V. (2022). Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow. Mathematical Biosciences, 349, 108830.
[28] Tesařová, M., Mancini, L., Mauri, E., Aljančič, G., Năpăruş-Aljančič, M., Kostanjšek, R., ... & Kaiser, J. (2022). Living in darkness: Exploring adaptation of Proteus anguinus in 3 dimensions by X-ray imaging. GigaScience, 11.
[27] Mozokhina, A., Bouchnita, A., Volpert, V. (2021). Blood clotting decreases pulmonary circulation during the coronavirus disease. MDPI Mathematics. Accepted.
[26] Bouchnita, A., Belyaev, A., Volpert, V. (2021). Multiphase continuum modeling of thrombosis in aneurysms and recirculation zones. Physics of Fluids. Accepted.
[25] Ratto, N., Bouchnita, A., Chelle, P., Marion, M., Panteleev, M., Nechipurenko, D., ... & Volpert, V. (2021). Patient-Specific Modelling of Blood Coagulation. Bulletin of Mathematical Biology, 83(5), 1-31.
[24] Bouchnita, A., Chekroun, A., & Jebrane, A. (2021). Mathematical Modeling Predicts That Strict Social Distancing Measures Would Be Needed to Shorten the Duration of Waves of COVID-19 Infections in Vietnam. Frontiers in public health, 8, 987.
[23] Mathias, S., Coulier, A., Bouchnita, A., & Hellander, A. (2020). Impact of force function formulations on the numerical simulation of centre-based models. Bulletin of Mathematical Biology, 82(10), 1-43.
[22] Bouchnita, A., Terekhov, K., Nony, P., Vassilevski, Y., & Volpert, V. (2020). A mathematical model to quantify the effects of platelet count, shear rate, and injury size on the initiation of blood coagulation under venous flow conditions. PloS one, 15(7), e0235392.
[21] Bouchnita, A., & Jebrane, A. (2020). A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions. Chaos, Solitons & Fractals, 109941.
[20] Bouchnita, A., Jebrane, A. (2020). A multi-scale model quantifies the impact of limited movement of the population and mandatory wearing of face masks in containing the COVID-19 epidemic in Morocco. Mathematical Modelling of Natural Phenomena, 15, 31.
[19] Bouchnita, A., Volpert, V., Koury, M. J., & Hellander, A. (2020). A multiscale model to design therapeutic strategies that overcome drug resistance to tyrosine kinase inhibitors in multiple myeloma. Mathematical Biosciences, 319, 108293.
[18] Grebennikov, D., Bouchnita, A., Volpert, V., Bessonov, N., Meyerhans, A., & Bocharov, G. (2019). Spatial lymphocyte dynamics in lymph nodes predicts the CTL frequency needed for HIV infection control. Frontiers in immunology, 10, 1213.
[17] Bouchnita, A., Hellander, S., & Hellander, A. (2019). A 3D Multiscale Model to Explore the Role of EGFR Overexpression in Tumourigenesis. Bulletin of mathematical biology, 81(7), 2323-2344.
[16] Benchaib*, M. A., Bouchnita*, A., Volpert, V., & Makhoute, A. (2019). Mathematical modeling reveals that the administration of EGF can promote the elimination of lymph node metastases by PD-1/PD-L1 blockade. Frontiers in bioengineering and biotechnology, 7. * Equal contribution
[15] Bessonov, N. M., Bocharov, G. A., Bouchnita, A., & Volpert, V. A. (2019). Hybrid models in biomedical applications. Computer research and modeling, 11(2), 287-309.
[14] Bouchnita, A., & Volpert, V. (2019). A multiscale model of platelet-fibrin thrombus growth in the flow. Computers & Fluids, 184, 10-20.
[13] Bouchnita, A., Miossec, P., Tosenberger, A., Volpert, V. (2017). Modeling of the effects of IL-17 and TNF-α on endothelial cells and thrombus growth. Comptes rendus biologies, 340(11), 456-473.
[12] Galochkina, T., Bouchnita, A., Kurbatova, P., Volpert, V. (2017). Reaction-diffusion waves of blood coagulation. Mathematical Biosciences, 288, 130-139.
[11] Bouchnita, A., Belmaati, F. E., Aboulaich, R., Koury, M. J., Volpert, V. (2017). A Hybrid Computation Model to Describe the Progression of Multiple Myeloma and Its Intra-Clonal Heterogeneity. Computation, 5(1), 16.
[10] Bouchnita, A., Bocharov, G., Meyerhans, A., Volpert, V. (2017). Towards a Multiscale Model of Acute HIV Infection. Computation, 5(1), 6.
[9] Bocharov, G., Bouchnita, A., Clairambault, J., Volpert, V. (2016). Mathematics of Pharmacokinetics and Pharmacodynamics: Diversity of Topics, Models and Methods. Mathematical Modelling of Natural Phenomena, 11(6), 1-8.
[8] Bouchnita, A., Bouzaachane, K., Galochkina, T., Kurbatova, P., Nony, P., Volpert, V. (2016). An individualized blood coagulation model to predict INR therapeutic range during warfarin treatment. Mathematical Modelling of Natural Phenomena, 11(6), 28-44.
[7] Bouchnita, A., Galochkina, T., Kurbatova, P., Nony, P., Volpert, V. (2016). Conditions of microvessel occlusion for blood coagulation in flow. International Journal for Numerical Methods in Biomedical Engineering.
[6] Bouchnita, A., Bocharov, G., Meyerhans, A., Volpert, V. (2017). Hybrid approach to model the spatial regulation of T cell responses. BMC immunology, 18(1), 29.
[5] Galochkina, T., Ouzzane, H. Bouchnita, A., Volpert, V. (2016). Traveling wave solutions in the mathematical model of blood coagulation. Applicable Analysis, 1-15.
[4] Bouchnita, A., Galochkina, T., Volpert, V. (2016). Influence of Antithrombin on the Regimes of Blood Coagulation: Insights from the Mathematical Model. Acta Biotheoretica, 1-16.
[3] Bouchnita, A., Rocca, A., Fanchon, E., Koury, M. J., Moulis, J. M., Volpert, V. (2016). Multi-scale Modelling of Erythropoiesis and Hemoglobin Production. Journal of Inorganic and Organometallic Polymers and Materials, 26(6), 1362-1379.
[2] Bouchnita, A., Eymard, N., Moyo, T. K., Koury, M. J., Volpert, V. (2016). Bone marrow infiltration by multiple myeloma causes anemia by reversible disruption of erythropoiesis. American journal of hematology, 91(4), 371–378.
[1] Bouchnita, A., Tosenberger, A., Volpert, V. (2016). On the regimes of blood coagulation. Applied Mathematics Letters, 51, 74-79.
Conference proceedings
[6] Mouvoh, A. C. E., Bouchnita, A., & Jebrane, A. (2020, December). A contact-structured SEIR model to assess the impact of lockdown measures on the spread of COVID-19 in Morocco’s population. In 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) (pp. 1-4). IEEE.
[5] Bouchnita, A., Belmaati, F. E., Aboulaich, R., Ellaia, R., Volpert, V. Mathematical modelling of intra-clonal heterogeneity in multiple myeloma. CARI, Hammamet, Tunisia, 2016.
[4] Bouchnita, A., Eymard, N., Moyo, T., Koury, M., Volpert, V. Normal erythropoiesis and development of multiple myeloma. ITM Web of Conferences. Vol. 5. Workshop on hybrid and multiscale modelling in cell and cell population biology, Paris, France, 2015, EDP Sciences. 2016
[3] Moyo, T. K., Bouchnita, A., Eymard, N., Volpert, V., Koury, M. J. Effects of Bone Marrow Infiltration By Multiple Myeloma on Erythropoiesis. Blood, American Society of Hematology, 2015.
[2] Bouchnita, A., Kurbatova, P., Tosenberger, A., Nony, P., Volpert, V. Numerical simulations of thrombosis development in blood flow. CMBE, Paris, France, 2015.
[1] Bouchnita, A., Eymard, N., Koury, M., Volpert, V. Initiation of erythropoiesis by BFU-E cells. In ITM Web of Conferences (Vol. 4, p. 01002). Workshop on Mathematics for Life Sciences (WMLS 2014), Sidi Bel-abbès, Algeria, 2014, EDP Sciences.
Theses
Bouchnita, A. (2017). Mathematical modelling of blood coagulation and thrombus formation under flow in normal and pathological conditions (Doctoral dissertation, Université de Lyon).
Bouchnita, A. (2014). A hybrid discrete-continuous model of multiple myeloma infiltration on erythrpoiesis (Master Eng. dissertation, Ecole Mohammadia d'Ingénieurs) in french.
Epistemology peer-reviewed papers
[3] Llored, J.P., Bouchnita, A. (2022). IA et santé: chimères, accomplissements et perspectives (AI and healthcare: chimeras, achievements and perspectives). In: Bénédicte Bévière-Boyer DD, editor. Numérique, Droit et Société (Digital, Law and Society). Editions Dalloz. 119–138
[2] Llored, J. P., & Bouchnita, A. (2022). How to establish precision medicine? Towards an alliance between artificial intelligence and conceptual modelling. Ethique et Numérique, 1(1), 154-180.
[1] Bouchnita, A., & Llored, J. P. (2021). Artificial intelligence as a tool in healthcare: limitations and perspectives. Droit, Sante et Societe, (2), 36-49.
Technical reports
[6] Bi, K., Bouchnita, A., Egbelowo, O. F., Fox, S., Lachmann, M., & Meyers, L. A. (2022). Scenario projections for the spread of SARS-CoV-2 Omicron BA. 4 and BA. 5 subvariants in the US and Texas.
[5] Bouchnita, A., Fox, S. J., Lachmann, M., Herrera-Diestra, G. G., & Meyers, L. A. (2022). Omicron scenario projections for the Austin-Round Rock MSA.
[4] Bouchnita, A., Fox, S. J., Lachmann, M., Herrera-Diestra, Gibson G., & Meyers, L. A. (2022). COVID-19 Scenario Projections: The Emergence of Omicron in the US-January 2022.
[3] Bouchnita, A., Fox, S. J., Lachmann, M., Herrera-Diestra, G. G., & Meyers, L. A. (2021). COVID-19 Scenario Projections: The Emergence of Omicron in the US.
[2] Fox, S. J., Lachmann, M., Bouchnita, A., Woody, S., Pasco, R., Johnson-Leon, M., ... & Ancel Meyers, L. COVID-19 scenario projections for Austin, Texas––August 2021.
[1] Lachmann, M., Bouchnita, A., Woody, S., Pasco, R., Johnson-Leon, M., Johnson, K., ... & Meyers, L. A. (2021). COVID-19 scenario projections for Austin, Texas––July 2021.