Reductionism and Complex Systems Science: Implications for Translation Research in the Health and Behavioral Sciences

Reductionism and Complex Systems Science: Implications for Translation Research in the Health and Behavioral Sciences David G. Schlundt, Ph.D. Associate Professor of Psychology CRC Research Skills January 20, 2011

Overview • NIH party line on translation research • Problems with the party line • Reductionism in modern science • Problems with reductionism • Complex systems science as an alternative • Problems with complex systems science • Examining the obesity epidemic as a real-life exemplar • Integrating scientific approaches • Implications for basic and applied research on obesity

What is Translation Research? • Problem: basic research findings take years or decades to find their way into evidence-based practice • Problem: Landmark clinical trials take years or decades to find their way into evidence-based practice • Problem: The investment in basic research has not resulted in a corresponding improvement in health care delivery • Goal: Translate the discoveries of basic scientific research into population level gains in health

NIH Road Map • New Pathways to Discovery - unravel the complexity of biologic systems and their regulation • Research Teams of the Future – break down the barriers to interdisciplinary and transdisciplinary research • Re-engineering the Clinical Research Enterprise – bring more scientists into clinical research • Solution: Clinical Science Translation Awards (CTSA) – infrastructure to support clinical and translation research at academic institutions

The T’s of translation • T1 – from bench to bedside • Taking basic biological sciences and using them to create useful diagnostic tests, drugs, and therapies • T2 – from bedside to community • Moving clinical research findings into evidence-based practice and looking at the impact on the public’s health • These definitions: • Were created by the basic scientists who run the NIH research enterprise • Imagine a one-way flow of knowledge from basic research to improved health care • Over simplify what is a complicated problem (how to improve human health)

Problems with the T1-T2 vision • The amount of resources at the NIH continues to be disproportionately allocated for basic research • The basic scientists in charge have underestimated the difficulty and amount of time required to plan and execute translation research studies • The clinical relevance of basic research findings is overestimated • Translation research proposals are too often reviewed by basic scientists who review translation studies using their basic research framework • Much greater improvement in population health could be achieved by improving current health care delivery – based standards of care that are not implemented • Much greater improvement in population health could be achieved through health care reform

Meta Scientific Models • There are assumptions and frameworks behind the practice of science that drive the questions, the methodologies, and the development of new knowledge • Philosophical Reductionism • Offshoot of materialist philosophy • Idea that one science (biology) can be reduced to the principals of another science (chemistry) • Drive to find the most basic explanation • There is potentially a single, underlying physical science that explains everything • Methodological Reductionism • The best scientific explanations come from breaking problems into their most fundamental elements • Goal of science is to identify, isolate, and study basic causal mechanisms • Approach is to create experiments in which only one parameter is allowed to vary so that its causal effect can be isolated • Goal is to develop mechanistic explanations

Reductionism in Action • Much “basic” research follows a reductionist framework in biological and behavioral sciences • Reductionism • Leads to increasing specialization • Leads to problems being broken down into ever smaller problems • Leads to a rapidly expanding base of knowledge in which the pieces are largely disconnected from each other • Leads to new technologies and methodologies for achieving tighter and tighter control of ever smaller processes • Even when the rationale for the research is an important clinical problem (e.g., diabetes, depression, schizophrenia), the research itself ends up isolating only a small piece of the problem and studying it out of context

Reductionism Impedes Clinical Discoveries • Reductionism is not the most efficient way to improve the physical and mental health of populations of human beings • Most “breakthroughs” in basic health and neuroscience do not lead to new diagnostic or treatment approaches • The overspecialization of disciplines makes it difficult for any one scientist to pull together enough basic knowledge to create meaningful new diagnostics or interventions • Funding of basic science does not encourage interdisciplinary or transdisciplinary cooperation needed to create clinical applications

Unintended consequences of reductionism • In reductionism, causality moves one way from low order phenomenon to higher order phenomenon • Ignores the possibility of complex higher order systems exerting a causal influence on more basic lower order systems • Biogenetic determinism moves explanation of social and behavioral problems to the genes • Individual rather than social conditions or economic inequities is responsible for problems • However, the individual is not responsible, the genes are responsible • Many modern individuals have a sense of helplessness due to a naive reductionism (obesity and depression good examples) • Much effort is put towards finding new drugs that will solve social/interpersonal/emotional/economic/political problems

Alternatives to Reductionism • Holism – systems cannot be understood by taking them apart • Emergent Properties – as components associate into systems, new properties of the systems emerge which cannot be predicted from the properties of the components (e.g., hydrogen + oxygen water) • Complex systems science – systems form hierarchies of increasing complexity and exhibit adaptive behavior at each level of analysis • Homeostasis • Feedback loops • Cross-level linkages

http://necsi.org/projects/mclemens/cs_char.gif

Problems with Complex systems • Goals of science are the same (understanding, prediction, and control) but the methods are different • Requires different frameworks and methodologies which are not as well developed as experimental reductionism • Mathematical simulations • Complex statistical modeling • Nonlinear models • Multilevel models • Evaluation of real-world interventions • It becomes difficult to make reassuring cause and effect statements; Scientists are forced to live with uncertainty. • It becomes difficult to create unambiguous mechanistic explanations

Example: Obesity Epidemic • The United States and other developed countries are experiencing an epidemic of obesity • Why is this happening? • What can be done to reverse the trends? • Problem is so serious that life expectancies may begin to decline by the middle of the 21st century

Obesity Trends* Among U.S. AdultsBRFSS, 1985 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%

Obesity Trends* Among U.S. AdultsBRFSS, 1991 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%

Obesity Trends* Among U.S. AdultsBRFSS, 1997 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%

Obesity Trends* Among U.S. AdultsBRFSS, 2001 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

Obesity Trends* Among U.S. AdultsBRFSS, 2005 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

How can we explain this? • What are some possible explanations? • Is there a single cause we need to be looking for? • If there are multiple causes, how do we study them? • Are the causes additive or synergistic? • Do the causes cascade across levels of analysis (e.g., macroeconomic factors influencing individual behaviors)? • Does our framework (reductionism versus complex systems science) make a difference in how we approach these problems?

Reflections • The question is not which approach is the best approach, but which is the best for solving a specific problem • Reductionism does not automatically lead to translation research • Complex systems science may have much more translation potential • Complex systems science requires interdisciplinary research, different methodological approaches, and the abandonment of simple one-cause explanations

What characterizes “translation” research? • Addresses problems in clinical care and population health • Evidence-based (based on best science available) • Involves transfer of knowledge and or methods across disciplinary boundaries • Requires consideration of context (target is imbedded in real-world systems) • Coalitions and partnerships • Engagement of communities • Moves away from trying to find a single causal factor and towards

Familiar example of complex systems approach to improve chronic disease management

Challenges • Personalized medicine? • Matching drugs to genes • How about matching treatment to other systems that are influencing health • Family • Neighborhood • Work setting • Psychology (cognition and emotion) • Health services research? • Are there gains to be had from adopting complex systems framework? • Need viable alternatives to the clinical trial • Implementation science? • Can methods such as continuous quality improvement become scientific tools for answering questions about improving clinical care and population health • What other methods can be adapted?

Reductionism and Complex Systems Science: Implications for Translation Research in the Health and Behavioral Sciences