# Presentation: 026026

## Team Science in Academia  

- Edward W. Holmes, MD
- Vice Chancellor/Dean Emeritus
- Distinguished Professor
- University of California, San Diego

## Team Science: the Need

**Studies in complex systems utilizing  multiple technologies often require teams**
**Multi-disciplinary research fosters scientific breakthroughs: multiple disciplines=multiple investigators**
**Creativity is often equated with thinking out of the box: that box is often someone else’s box**

## Team Science: Teams Vary in Size and Scope

**Brown and Goldstein**
**Genome Project**
**Bioinformatics Research Network**
**Janelia Farms**
**San Diego Consortium for Regenerative Medicine**

## Team Science: Teams Vary in Types of Partners

**CTSA centers**
**Multinational biobanks**
**Public private partnerships**

## Team Science: Where It Makes Sense

- Complex problem- no one investigator can address alone
- Urgent problem
- Large infrastructure needs
- Large budget requirements

## Team Science: the Partners

- _Collaboration is central to all team research_, therefore more than one PI
- Often multidisciplinary
- Often crosses departmental and/or institutional boundaries

## Translational Research:  Prototype of Team Science

**Broad enterprise that spans discovery of therapeutic targets to demonstration of clinical effectiveness**
**Translational research is a “team sport”**

## From Discovery to Delivery

**From Discovery to Delivery**

**Lead**

**Lead**
**Optimization**

**Drug**
**Target**

**Lead**

**Lead**
**Optimiz-**
**ation**

**Target**
**Validat-ion**

**GMP**

**Clinical**
**Trials**

**Clinical**
**Delivery**

- The Enablers

- Discovery
- Research

- High Throughput
- and Design

- Medicinal
- Chemistry

- Models: Mice Primates

- Manufacturing

- Patients

**G l o b a l**
**H e a l t h**

- Ph.D.
- MD

- Industry

- Chemist
- Translational Ph.D.
- Physician-Scientist

- Physician
- Scientist

- Clinical
- Investigator

- Clinicians

- The Human Capital

**Notes:**
In the most general form, what will LearningFramework solutions do?

## Characteristics of Translational Research That Make It Appropriate for Team Science

- Complex “model” organism
- Multiple technologies needed:
- Patient phenotyping
- Molecular phenotyping
- Advanced imaging
- Sophisticated informatics
- Multiple centers

## Team Science: the Challenges

**	****Team science can be “big” research**
- Space challenges
- Financial challenges
- Access to shared facilities

## Team Science: the Challenges

**	****Unique challenges to team science**
- Longer lead time to form teams
- Career paths for staff scientists
- Misconduct by one affects all
- Cultural differences across disciplines
- Cultural differences across institutions

## Team Science: the Challenges

**Team science brings academic challenges**
- Multi-authorship papers-first, last
- Multi-discipline papers- who to review
- Different promotion criteria across disciplines
- Shared grant funding
- Allocation of indirect cost recovery
- Institutional collaborations
- Intellectual property issues

## Considerations for Young Investigators Engaged in Team Science

- Establish in advance with collaborators what your role is to be
- Discuss with your Chair what the criteria are for success in cross disciplinary research
- Make sure your contribution to the team effort is spelled out clearly for all publications

## Considerations for Young Investigators Engaged in Team Science

- Where appropriate, assume co-PI status on NIH and other grants
- Discuss budget allocation and administrative support in advance
- Carefully choose collaborators with whom you want to work

## Considerations for Young Investigators Engaged in Team Science

- Establish time commitment for all collaborators in advance
- Work with senior PIs who agree to be your mentor and promote development of your career

## Team Science Does Not ReplaceIndividual PI Research

- 	Young investigators may want a research project that is identified solely with them

## 	Emergence of Biomedical 	Informatics

- Requirements for molecular phenotyping
- DNA, RNA arrays
- Proteomics
- Metabolomics
- Requirements for clinical phenotyping
- Collection of clinical information
- Retrieval of clinical information
- Analysis of clinical information