Education & Affiliations
Biography
As an undergraduate, Daniel P. Howsmon majored in both chemical engineering and biochemistry at Texas A&M University where he became fascinated with using computational techniques to solve problems in medicine. He then went on to earn a Ph.D. in Chemical and Biological Engineering at Rensselaer Polytechnic Institute where he developed fault detection techniques for insulin infusion pumps and identified combinations of plasma metabolites that may be informative for diagnosing autism spectrum disorder. For his postdoctoral work, he joined the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin where he modeled signal transduction pathways relevant to heart valve diseases. He joined the Department of Chemical and Biomolecular Engineering at Tulane University as an assistant professor in 2023.
Publications
29. A. Khang, Q. Nguyen, X. Feng, D. P. Howsmon, and M. S. Sacks, “Three-dimensional analysis of aortic valve interstitial cell shape and its relation to contractile behavior,” Acta Biomateriala, vol. 163, pp. 194–209, Jun. 2023. doi: 10.1016/j.actbio.2022.01.039
28. T. M. West, D. P. Howsmon, M. W. Messida, H. N. Vo, A. A. Janobas, A. B. Baker, and M. S. Sacks, “The effects of strain rate and level on aortic valve interstitial cell activation in a 3D hydrogel,” APL Bioengineering, vol. 7, no. 2, p. 026 101, 2023. doi: 10.1063/5.0138030 FEATURED ARTICLE
27. L. Bansal, E.-M. Nichols, D. P. Howsmon, J. Neisen, F. Cunningham, S. Petit-Frere, S. Ludbrook, and V. Damian, “Mathematical modeling of complement pathway dynamics for target validation and selection of drug modalities for complement therapies,” Frontiers in Pharmacology, vol. 13, p. 855 743, Apr. 2022. doi: 10.3389/fphar.2022.855743
26. A. Khang*, E. M. Lejeune*, A. Abbaspour, D. P. Howsmon, and M. S. Sacks, “On the 3D correlation between myofibroblast shape and contraction,” Journal of Biomechanical Engineering, vol. 143, no. 9, p. 094 503, Sep. 2021. doi: 10.1115/1.4050915
25. E. Castillero, D. P. Howsmon, B. V. Rego, Y. Xue, C. Camillo, S. Keeney, K. H. Driesbaugh, T. Kawashima, George, R. C. Gorman, J. H. Gorman III, M. S. Sacks, R. J. Levy, and G. Ferrari, “Altered responsiveness to TGF-β and BMP and increased CD45+ cell presence in mitral valves are unique features of ischemic mitral regurgitation,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 41, no. 6, pp. 2049–2062, Jun. 2021. doi: 10.1161/ATVBAHA.121.316111 EDITOR’S PICK
24. D. P. Howsmon and M. S. Sacks, “On valve interstital cell signaling: The link between multiscale mechanics and mechanobiology,” Cardiovascular Engineering and Technology, vol. 12, pp. 15–27, Feb. 2021. doi: 10.1007/s13239-020-00509-4
23. K. M. Kodigepalli, K. Thatcher, T. West, D. P. Howsmon, F. J. Schoen, M. S. Sacks, C. K. Breuer, and J. Lincoln, “Biology and biomechanics of heart valve extracellular matrix,” Journal of Cardiovascular Development and Disease, vol. 7, no. 4, p. 57, Dec. 2020. doi: 10.3390/jcdd7040057
22. S. Ayoub, D. P. Howsmon, C.-H. Lee, and M. S. Sacks, “On the role of predicted mitral valve interstitial cell deformation on its biosynthetic behavior,” Biomechanics and Modeling in Mechanobiology, Aug. 2020. doi: 10.1007/s10237-020-01373-w
21. D. P. Howsmon*, B. V. Rego*, E. Castillero, S. Ayoub, A. H. Khalighi, R. C. Gorman, J. H. Gorman III, G. Ferrari, and M. S. Sacks, “Mitral valve leaflet response to ischaemic mitral regurgitation: From gene expression to tissue remodeling,” Journal of the Royal Society Interface, vol. 17, no. 165, p. 20 200 098, May 2020. doi: 10.1098/rsif.2020.0098
20. D. P. Howsmon*, S. M. Quinn*, J. Hahn, and S. P. Gilbert, “Kinesin-2 heterodimerization alters catalytic properties to control entry into the processive run,” Journal of Biological Chemistry, vol. 293, no. 35, pp. 13 389–13 400, Jul. 2018. doi: 10.1074/jbc.RA118.002767
19. D. P. Howsmon, T. Vargason, R. A. Rubin, S. Melnyk, S. J. James, R. Frye, and J. Hahn, “Multivariate techniques enablea biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation study,” Bioengineering and Translational Medicine, vol. 3, no. 2, pp. 156–165, May 2018. doi: 10.1002/btm2.10095 TOP CITED ARTICLE 2018 – 2019
18. T. Vargason, D. P. Howsmon, and J. Hahn, “From data to diagnosis: The search for biochemical markers of autism spectrum disorder,” Chemical Engineering Progress, vol. 114, no. 5, pp. 40–45, May 2018
17. G. P. Forlenza, F. M. Cameron, T. T. Ly, D. Lam, D. P. Howsmon, N. Baysal, G. Kulina, L. Messer, P. Clinton, C. Levister, S. D. Patek, C. J. Levy, R. P. Wadwa, D. M. Maahs, B. W. Bequette, and B. A. Buckingham, “Fully closed-loop multiple model probabilistic predictive controller artificial pancreas performance in adolescents and adults in a supervised hotel setting,” Diabetes Technology & Therapeutics, vol. 20, no. 5, pp. 335–343, May 2018. doi: 10.1089/dia.2017.0424
16. D. P. Howsmon, N. Baysal, B. A. Buckingham, G. P. Forlenza, T. T. Ly, D. M. Maahs, T. Marcal, L. Towers, E. Mauritzen, S. Deshpande, L. M. Huyett, J. E. Pinsker, R. Gondhalekar, F. J. Doyle III, E. Dassau, J. Hahn, and B. W. Bequette, “Real-time detection of infusion site failures in a closed-loop artificial pancreas,” Journal of Diabetes Science and Technology, vol. 12, no. 3, May 2018. doi: 10.1177/1932296818755173
15. D. P. Howsmon, J. B. Adams, U. Kruger, E. Geis, E. Gehn, and J. Hahn, “Erythrocyte fatty acid profiles in children are not predictive of autism spectrum disorder status: A case control study,” Biomarker Research, vol. 6, p. 12, Mar. 2018. doi: 10.1186/s40364-018-0125-z
14. D.-W. Kang, Z. E. Ilhan, N. G. Isern, D. W. Hoyt, D. P. Howsmon, M. Shaffer, C. A. Lozupone, J. Hahn, J. B. Adams, and R. Krajmalnik-Brown, “Differences in fecal microbial metabolites and microbiota of children with autism spectrum disorders,” Anaerobe, vol. 49, pp. 121–131, Feb. 2018. doi: 10.1016/j.anaerobe.2017.12.007
13. D. P. Howsmon*, S. Steinmeyer*, R. C. Alaniz, J. Hahn, and A. Jayaraman, “Empirical modeling of t cell activation predicts interplay of host cytokines and bacterial indole,” Biotechnology and Bioengineering, vol. 114, no. 11, pp. 2660–2667, Nov. 2017. doi: 10.1002/bit.26371
12. F. M. Cameron, T. T. Ly, B. A. Buckingham, D. M. Maahs, G. P. Forlenza, C. J. Levy, D. Lam, P. Clinton, L. H. Messer, E. Westfall, C. Levister, Y. Y. Xie, N. Baysal, D. Howsmon, S. D. Patek, and B. W. Bequette, “Closed-loop control without meal announcement in type 1 diabetes,” Diabetes Technology & Therapeutics, vol. 19, no. 9, pp. 527–532, Aug. 2017. doi: 10.1089/dia.2017.0078
11. G. P. Forlenza*, S. Deshpande*, T. T. Ly, D. P. Howsmon, F. Cameron, N. Baysal, E. Mauritzen, T. Marcal, L. Towers, B. W. Bequette, L. M. Huyett, J. E. Pinsker, R. Gondhalekar, F. J. Doyle, D. M. Maahs, B. A. Buckingham, and E. Dassau, “Application of zone model predictive control artificial pancreas during extended use of infusion set and sensor: A randomized crossover-controlled home-use trial,” Diabetes Care, p. dc170500, Jun. 2017. doi: 10.2337/dc17-0500
10. T. Vargason, D. P. Howsmon, D. L. McGuinness, and J. Hahn, “On the use of multivariate methods for analysis of data from biological networks,” Processes, vol. 5, no. 3, p. 36, Jul. 2017. doi: 10.3390/pr5030036
9. D. P. Howsmon, U. Kruger, S. Melnyk, S. J. James, and J. Hahn, “Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation,” PLoS Computational Biology, vol. 13, no. 3, e1005385, Mar. 2017. doi: 10.1371/journal.pcbi.1005385 JOURNAL COVER
8. T. Vargason, D. P. Howsmon, S. Melnyk, S. J. James, and J. Hahn, “Mathematical modeling of the methionine cycle and transsulfuration pathway in individuals with autism spectrum disorder,” Journal of Theoretical Biology, vol. 416, pp. 28–37, Mar. 2017. doi: 10.1016/j.jtbi.2016.12.021
7. D. P. Howsmon, F. Cameron, N. Baysal, T. T. Ly, G. P. Forlenza, D. M. Maahs, B. A. Buckingham, J. Hahn, and B. W. Bequette, “Continuous glucose monitoring enables the detection of losses in infusion set actuation (LISAs),” Sensors, vol. 17, no. 1, p. 161, Jan. 2017. doi: 10.3390/s17010161
6. J. Adams, D. P. Howsmon, U. Kruger, E. Geis, E. Gehn, V. Fimbres, E. Pollard, J. Mitchell, J. Ingram, R. Hellmers, D. Quig, and J. Hahn, “Significant association of urinary toxic metals and autism-related symptoms – A nonlinear statistical analysis with cross validation,” PLoS ONE, vol. 12, no. 1, e0169526, Jan. 2017. doi: 10.1371/journal.pone.0169526
5. B. W. Bequette, F. Cameron, N. Baysal, D. Howsmon, B. Buckingham, D. Maahs, and C. Levy, “Algorithms for a single hormone closed-loop artificial pancreas: Challenges pertinent to chemical process operations and control,” Processes, vol. 4, no. 4, p. 39, Oct. 2016. doi: 10.3390/pr4040039
4. D. P. Howsmon and J. Hahn, “Regularization techniques to overcome over-parameterization of complex biochemical reaction networks,” IEEE Life Sciences Letters, vol. 2, no. 3, pp. 31–34, Sep. 2016. doi: 10.1109/LLS.2016.2646498
3. D. Howsmon*, J. G. Zheng*, B. Zhang, J. Hahn, D. McGuinness, J. Hendler, and H. Ji, “Entity linking forbiomedical literature,” BMC Medical Informatics and Decision Making, vol. 15, S4, Suppl 1 May 2015. doi: 10.1186/1472-6947-15-S1-S4
2. D. Howsmon and B. W. Bequette, “Hypo- and hyperglycemic alarms: Devices and algorithms,” Journal of Diabetes Science and Technology, vol. 9, no. 5, pp. 1126–1137, Apr. 2015. doi: 10.1177/1932296815583507
1. C. Klemashevich, C. Wu, D. Howsmon, R. C. Alaniz, K. Lee, and A. Jayaraman, “Rational identification of diet-derived postbiotics for improving intestinal microbiota function,” Current Opinion in Biotechnology, vol. 26, pp. 85–90, Apr. 2014. doi: 10.1016/j.copbio.2013.10.006
Research
Our group uses both data-driven and mechanistic models within a process systems engineering framework to provide actionable insights into dynamic systems in biology, pharmacology, and medicine. Currently, our focus is in cardiac and fibrosis applications.
In data-rich, knowledge-poor environments, we leverage data-driven models for prediction and comparison. For example, we may not know why patients’ vital signs and hemodynamics change the way they do following specific surgeries (knowledge-poor). However, given high-frequency, high-fidelity historical data, we can develop data-driven models that compare our current patient’s trajectory to a history of patient trajectories with positive clinical outcomes (data-rich).
In knowledge-rich environments, we leverage mechanistic models for prediction and experimental design. For example, we can leverage the wealth of enzymatic, binding, and localization properties of proteins to develop mechanistic models that typically have better extrapolation properties than data-driven counterparts. Moreover, we can reuse the entire model or pieces of various models for new applications and interrogate parameters, which are physically meaningful. Process systems engineering is necessarily interdisciplinary, and we leverage collaborations with biologists, pharmacists, and clinicians to inform our research directions. Additionally, we have our own cell culture space for collecting data necessary for informing our mechanistic cell signaling research and highlighting various process systems engineering techniques.
