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Shailesh Date, Ph.D.

Founder and Chief Executive Officer

Laboratory for Research in Complex Systems


  • Univ. of Pennsylvania School of Medicine (Postdoc in Parasitology, with David Roos & Chris Stoeckert)

  • Univ. of Texas at Austin (Ph.D. in Mol. Bio. (Computational Bio.) with Edward Marcotte)


  • National Merit Scholarship, India (1996-97)

Other appointments


Dr. Shailesh Date is Chief Executive Officer of the Laboratory for Research in Complex Systems (LRC), an independent research institute that uses rigorous quantitative approaches to solve high-value problems in the sciences and the society. He is also Associate Adjunct Professor of Epidemiology and Biostatistics at UCSF and Chief Executive Officer of Silicogenix Inc. Dr. Date’s research primarily focuses on developing computational tools (biology, chemistry) to analyze complex biological systems, pathways and multi-input processes with the goal of identifying points of intervention. His group also studies fundamental biophysical properties of living systems, including biological associations, at various scales.

    Since early in his career, Dr. Date’s goal has been to develop projects that bridge academia and industry. As a postdoctoral researcher, he published the first comprehensive interactome model of the malarial parasite Plasmodium falciparum, providing a tool to the community to initiate functional investigation of the 50%+ uncharacterized genes, as well as provide a list of potential cell-surface targets. Later at Genentech, appalled at the lack of knowledge of S. aureus behavior in human hosts (even after decades of research), he published, together with his colleagues, the first transcriptomic and proteomic map of the pathogen under true infection conditions in humans. Dr. Date has won a DARPA competition for his work on developing a statistical physics model that envisions evolution (and development of complexity) at a systems level, rather than at the level of individual genes, allowing expansion into novel biochemical, morphological and physiological dimensions. Most recently, as lead senior author, he and his group described the largest bacterium known to science – Ca. Thiomargarita magnifica. This paper highlighted the many dogmatic but incorrect assumptions in the scientific community about microbial cell sizes, cell structure and the eukaryotic-prokaryotic divide. This work was shortlisted for “breakthrough of the year” by Science magazine in 2022.

        Dr. Date obtained his Ph.D. from the University of Texas at Austin under the supervision of Dr. Edward Marcotte, with a focus on computational molecular biology. He completed his postdoctoral work at the University of Pennsylvania’s School of Medicine, where he was jointly supervised by Drs. David Roos and Chris Stoeckert. His group has been supported by the Templeton Foundation, The Moore Foundation, DARPA and other agencies/foundations.

What's your background?

I am trained in both wet lab and computational work. I wear two research hats; my work focuses on infectious diseases and public health as well as complex systems research (in biology and other areas).

What's your role at LRC?

I founded and run the LRC.

What trend, breakthrough or discovery are you most excited about?

There are many projects I am really excited about- our focus on identifying first principles of biological complexity, cognition and AI, our proposed infrastructure for pediatric health in urban areas, just to name a few. Our team members are really pushing hard towards many new breakthroughs.

Research Interests

1. Pediatric Infectious Diseases and Public Health​

I am interested pediatric infectious diseases, their spread, and the effects of "seasonal" (non life-threatening) infections on both the pediatric population and their caregivers. We hypothesize that pediatric care-giving burden is heavily underestimated, and the effects of caring for sick children have a profound effect on the workforce and the economy, particularly when considering burdens in lower socio-economic classes and traditionally disadvantaged and disfranchised groups. 

2. Origins of Complexity and Information Gain in Biological Systems

The evolution of multicellular, higher order forms from a few organic molecules in primordial soup is one of the most important, and, as yet, unanswered questions in the sciences. From a thermodynamic (biophysical) systems perspective, this is a remarkable achievement; evolution appears to have found ways to completely corral and subdue entropy, while generating incredible complexity. To better understand the mechanisms behind complexity development, we have proposed a new model – an agent-based Biophysical Model of Adaptive Evolution (BMAE) – that will focus on elucidating the role played by evolutionary processes like exaptation to spur second-order phase transitions within biological systems, particularly protein interaction networks. Through this model, we will test assumptions regarding complexity that may arise from new interactions between proteins, allowing new biochemical pathways to form, which in turn lead to physiological and morphological effects.  Additionally, we plan to undertake simple wet-lab experiments designed to test other key assumptions, such as function-uptake through exposure to novel protein domains, addition of interactors to existing interaction networks and the role of horizontal gene transfer in facilitating network enlargement. Our emphasis on examining physical phenomena makes our project quite distinct from other contemporary approaches. We are interested in using both quantitative and wet approaches to discover first principles of biological complexity, while also using synthetic biology approaches to modify and even “design” new systems if possible.

Key Publications

Morisaki JH, Smith PA, Date SV, et al. A Putative Bacterial ABC Transporter Circumvents the Essentiality of Signal Peptidase. MBio. 2016 Sep 6;7(5). pii: e00412-16.

Date SV*, Modrusan Z*, Lawrence M*, et al. Global gene expression of methicillin-resistant Staphylococcus aureus USA300 during human and mouse infection. J Infect Dis. 2014 May 15;209(10):1542-50. (* equal contributors).

Date SV* and Stoeckert CJ. Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale. Genome Res. 2006 Apr;16(4):542-9. *Corresponding author

Lee I, Date SV, Adai AT and Marcotte EM. A Probabilistic functional network of yeast genes. Science. 2004 Nov 26;306(5701):1555-8.

Adai AT, Date SV, Wieland S and Marcotte EM. LGL: Creating a Map of Protein Function with an Algorithm for Visualizing Very Large Biological Networks. JMB. 2004 Jun 25;340(1):179-90.


Date SV and Marcotte EM. Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. Nature Biotech. 2003 Sep;21(9):1055-62.



New Tools for Advancing Model Systems in Aquatic Symbiosis

Gordon and Betty Moore Foundation

Period: 9/2020 - 8/2022

Role: PI

Modeling the intersection of evolution and physical processes to explain the origin of biological complexity


Period: 7/2020 - 6/2021

Role: PI


The Boundaries of Life Initiative: Investigating the origin, evolution and bounds of life on Earth and beyond

Gordon and Betty Moore Foundation
Period: 4/2019 - 3/2022
Role: Co-PI


Boundaries of Life (Phase II)

John Templeton Foundation
Period: 9/2018 - 8/2020

Role: Co-PI and Technical Lead

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