1. Vision for a Research Presence
Bradly Alicea

2. Computational Science1

3. Computational Science1

4. Computational Science1

5. Computational Science
* systems modeling and simulation.
* data analysis techniques.
* informatics. 1

6. Empiricism (experiment)2

7. Empiricism (experiment)2

8. Empiricism (experiment)
* molecular, developmental, and evolutionary biology.
* cognitive neuroscience.
* movement and behavior. 2

9. Theoretical Development3

10. Theoretical Development3

11. Theoretical Development3

12. Theoretical Development
* applications of complexity and biological theory
* computation of theory-building 3

13. Resources Brought to the Table
Current Resources:
Collaborative expertise (multiple areas).
* some promising avenues for future projects (complex systems and artificial life, systems biology, data science).
External
Collaborators
Department, Program,
University 4

14. Resources Brought to the Table
Current Resources:
Collaborative expertise (multiple areas).
* some promising avenues for future projects (complex systems and artificial life, systems biology, data science).
Network of methods and learning opportunities.
* infrastructure of educational, research opportunities (OpenWorm). 5

15. Goals For the Next Several Years
Build upon external collaborative opportunities with other academic labs and OpenWorm.
* interdisciplinary initiatives.
Develop funding streams.
* multi-institution grants.
Focus on further development of most promising techniques and experimental approaches.
“To have diverse research experiences
and promote interdisciplinary collaboration” 6

16. Approach to Interdisciplinarity
“To explore intellectual frontiers while learning how to produce cutting-edge research”
Research Program Theme: Exploration of the Biology – Theory – Computational interface
Student researchers interested in one aspect (e.g. biology) can gain exposure to others.
Gain unique experience in theory-building, developing quantitative models, informatics skills.
COMPUTATION
THEORY
BIOLOGY 7

17. Goals For Advancing Interdisciplinarity
Lab design: wet lab and computational lab components.
* unique opportunity to do computational biology and cognitive neurobiology at multiple scales.
Flexibility: emphasis on specialties would be contingent upon funding, current opportunities.
* example: microscopy data can be acquired via collaboration, while analysis could be in-house.
“To overcome unfair characterizations and
become methodologically versatile” 8

18. Sources of Collaboration
DevoWorm project:
* developmental biology.
* data science.
* computational modeling.
* evolution of development.
* high-resolution microscopy and image processing. 9

19. Sources of Collaborators (con’t)
OpenWorm Foundation/International Neuroinformatics Coordinating Facility:
* Neuroinformatics (via INCF).
* whole-organism computational modeling (via OpenWorm).
* Google Summer of Code (GSoC) projects.
* Open Science initiatives. 10

20. Sources of Collaborators (con’t)
BiomiLab, LLC:
* industry-oriented projects (bioinformatics and wet-lab).
* oriented towards building tools and products for cellular and molecular biology research.
* Based in Michigan, collaborators from Michigan State University and around the world. 11

21. Sources of Collaboration (con’t)
Media Neuroscience Lab (UC Santa Barbara):
* Cognitive neuroscience and virtual reality research.
previous work on brain networks and continuous cognitive dynamics.
* interests in cognitive science and statistical analysis. 12

22. Promising Project Area #1: Universal Models of Adaptation
A combination of regulatory, network, and artificial life models:
* based on issues I have dealt with in various manuscripts and talks.
* a variety of tools: soft computing, discrete systems modeling, complex systems models.
Adaptive spaces and evolutionary pathways: how do pathways, traits, and networks exhibit
an adaptable range?
* inspired by data from human performance and biological development.
2) Convolution architectures and contextual geometric spaces: how do data structures and mean field models help us understand biological and cognitive complexity? 13

23. Promising Project Area #2: Open Science Initiative
An open science system (from ideas to formalizations):
* has grown out of previous Open Science practices
(open, secondary datasets, preprint publishing).
* towards the “Future of the Scholarly Paper”: more than simply a publication .pdf
* one goal is to develop an Open Science “system” encompassing blogs, preprints, open access pubs, open data.
* another goal is to teach these practices (open data, open source) to students.
“To develop a reproducible system for developing,
debating, and disseminating research” 14

24. Open Science Practice Research Blogging
Paper profiles
Blogrolls
Open Collaboration
Radically Open Science and Data
Open Code and Documents (version control)
Open Protocols and
Notebooks
Archived Data (primary and secondary)

25. Research Management via Slack and Github
Communication and Collaboration
Datasets and Code

26. Interest in Facilitating Creation of an Interdisciplinary Program
Long-term project, depending on needs of College/University and general interest: * Data Science (Data Analysis + Modeling + Participating Departments). * Cognitive Neurobiology (Neurobiology + Cognitive Science + Biology at Multiple Scales). * Quantitative Biology (Biology at Multiple Scales + Data Analysis + Modeling). 15

27. Commitment to Innovation and Diversity
PAI program participant: University of Illinois; MSU Technologies Innovation Analyst: Michigan State University.
* mentoring skills (guiding people through process of research, education, and career).
* community building skills (finding people’s professional strengths and how they can best use them).
* commitment to intellectual, ethnic/racial, and gender diversity (people from different life experiences provide new perspectives on research questions and solutions).
* enrichment of perspective (meeting people from different backgrounds). 16

28. Laboratory Contribution Philosophy
How to manage many projects and distributed collaborations
while retaining a diverse research group (e.g. underrepresented
students).
Authorship
(code, paper, media)
Define and
Make Early
Contribution
External
Collaboration
(into the world)
Redefine
Contribution
Join Group
(via Pipeline)
Do Work
(fail AND succeed)
Mentorship
and Learning
Documention
(code, Wikis, presentation) 17