Skip to main content

Applying Systems Thinking to Regenerative Medicine Proceedings of a Workshop (2021) / Chapter Skim
Currently Skimming:

7 Exploring Issues of Workforce Development Related to Systems Thinking
Pages 109-120

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 109...
... • New strategies (e.g., providing support in managing student debt burdens, developing messaging to communicate the scientific importance and social impact of this work) are needed to recruit entrants into the workforce in the regenerative medicine and biopharmaceutical industries.
Read the entire page →
From page 110...
... To develop the leaders of tomorrow, scientists and engineers should advocate for systems thinking approaches, Bollenbach said, and demonstrate the value of data-rich research and development in their places of work. Speaking from his manufacturing perspective, he shared his vision of a generation of scientists and engineers who "stop saying ‘the process is the product' and start saying, ‘this process is the product, but we also know that these others are, too, because we have a robust understanding of and are well-informed by data.'" Changing Workforce Development Needs What are some of the most pressing challenges in workforce development, Bollenbach asked the panelists, particularly related to data science, AI, and computational biology?
Read the entire page →
From page 111...
... Training and Education Considerations in Workforce Development Robert Zambon, the senior director of data strategy and external innovation at Johnson & Johnson, highlighted the need to recruit data scientists, analysts, and machine learning experts to the biopharmaceutical fields. Conversely, students in biology, chemistry, and other related scientific fields should be actively recruited into data science programs while still at university, if they show an interest.
Read the entire page →
From page 112...
... Teams, Zambon suggested, should be built in a way that allows experts in different areas -- such as an expert physicist and a biologist -- to coordinate and work together toward respective goals that are similar, but not identical. When this type of collaboration is successful, the experts will often develop a common language through which they can understand each other's work, even if they are sometimes using the same term to represent completely different concepts in their respective disciplines.
Read the entire page →
From page 113...
... From an industry perspective, Zambon replied, frequent engagement can help catalyze the move toward novel approaches and using data across the board in regenerative medicine. Regular networking can also help build relationships among various actors within regulatory agencies as new regulatory programs are instituted in different agencies.
Read the entire page →
From page 114...
... This is important, he added, because while education and workforce development are very much a part of the work being done by BioFabUSA, NIIMBL, and the other manufacturing innovation institutes, their mandates are broad enough that they are often unable to dive in as deep as they would like on certain components, such as data science. Janssen and Johnson & Johnson have established data science academies that focus not only on training and developing staff, but also on 2  For more information on this NSF award to the Georgia Institute of Technology, see https://www.nsf.gov/awardsearch/showAward?
Read the entire page →
From page 115...
... Bollenbach agreed that members of leadership need to be educated about these novel approaches so that they can explain to investors why additional research and development is needed up front in product development to select sufficient and appropriate data prior to initiating clinical trials. Strategies to Strengthen the Workforce What steps can be taken within the next 3 years, Bollenbach asked, to strengthen and prepare the regenerative medicine workforce?
Read the entire page →
From page 116...
... It will be important to develop theoretical frameworks that help interpret the results of today's powerful omics tools and make it possible to understand how to use molecular data to predict emergent properties. Roy said that the discussion on potential landscapes and high probability attractor states suggests a guide for how to control manufacturing and differentiation processes.
Read the entire page →
From page 117...
... Use of Data Modeling to Reduce Data Dimension to Key Variables The fourth session explored the variety of model types and methods that can be applied within systems thinking approaches. A systems approach can help home in on those analytes and relationships that provide the most important predictive value, but identifying the most important contributors and their relationships requires analytical approaches that are designed to collapse state space.
Read the entire page →
From page 118...
... Cross-Disciplinary Training of the Workforce The sixth session explored challenges related to recruiting talented students with computational skills into the regenerative medicine space when there are other lucrative career paths available to them. Plant highlighted the discussion around building teams containing a diversity of expertise rather than seeking out individuals who can "do everything." FINAL WORDS ON MODELS AND DATA The major challenges in applying systems thinking to regenerative medicine can be placed into two broad categories, Plant said: models and data.
Read the entire page →
From page 119...
... EXPLORING ISSUES OF WORKFORCE DEVELOPMENT 119 should engage with the machine learning, AI, and data-driven modeling communities to take advantage of the synergies between these disciplines in order to make better products and provide greater benefits to patients. Additionally, clinical trials should collect the right types of data in formats that are appropriate for use from the product side, the patient side, molecular side, and other elements of the ecosystem.
Read the entire page →

Key Terms

  • workforce development
  • data science
  • machine learning
  • emergent properties
  • thinking approaches

  • common language
  • bollenbach asked
  • innovation institutes
  • challenges related
  • data modeling

  • talented students
  • help develop
  • nsf engineering
  • session explored
  • thinking approach

  • student debt
  • frequent engagement
  • biopharmaceutical fields
  • novel approaches
  • manufacturing innovation

  • career options
  • debt burdens
  • computational biology
  • sharing data
  • data strategy

  • reduce data
  • study data
  • existing workforce
  • data analytics
  • manufacturing data


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.