Ishanu Chattopadhyay PRO
ML Data Science Biomedicine Social Science Faculty
ishanu chattopadhyay
Asst Professsor of Medicine
ishanu@uchicago.edu
ishanu@paraknowlede.ai
Emergenet
Stamping out pandemics before the first human infection
Can we predict future mutations?
Can we define the "edge of emergence"?
Can we design countermeasures that are future proof?
Digital Twins for complex systems
Chattopadhyay, Ishanu, Kevin Wu, Jin Li, and Aaron Esser-Kahn. "Emergenet: Fast Scalable Pandemic Risk Assessment of Influenza A Strains Circulating In Non-human Hosts." (2023). Under Review in Nature
PREEMPT
curl -X POST https://us-central1-pkcsaas-01.cloudfunctions.net/emergenet_predict?api_key=7eea9f70d79c408f2b69847d6911303c -H "Content-Type: application/json" -d '{
"HA":"MKAILLVLLHTFAATSADTICVGYHANNSTDTVDTVLEKNVTVTHSVNLLEDKHNGKLCKLRGKAPLYLGKCNIAGWLLGNPECELPLTVSSWSYIVETSDSDNGTCYPGDFTNYEELREQLSSVSSFERFEMFPKESSWPNHETNKSVTAACPYAGASSFYRNLIWLVKKDDSYPMLNISYVNNKGKEVLVLWGIHHPPTEDDQKWLYKNADAYVFVGTSTYSQKFEPEIATRPRVRDQTGRMNYYWTLVKPGDKITFEATGNLVVPRYAFAMNRGSESGIIISDAPVHDCNTICQTPKGALNTSLPFQNVHPVTIGECPKYIKSTRLKMATGLRNTPSIQSRGLFGAIAGFIEGGWTGMVDGWYGYHHQNEQGSGYAADQKSTQRAVDGITNKVNSIIERMNSQFTAVGKEFSNLERRIENLNKKVDDGFLDVWTYNAELLILLENERTLDFHDSNVKNLYERVRNQLRNNAKEIGNGCFEFYHKCDNTCMESVKNGTYDYPKYSEESKLNREEIDGVKLDSTKVYQILAIYSTVASSLVVLVSLGALSFWMCSNGSL",
"NA":"MNPNQKIITIGSVSLIIATICFLMQIAILVTTVTLHFKQHDCNSSSNNQVMLCEPIIIERNKTEIVYLTNTTVEKEICPKPAEYRNWSKPQCNIAGFAPFSKDNSIRLSAGGDIWVTREPYVSCDLDKCYQFALGQGTTLNNRHSNDTVHDRTPYRTLLMNELGVPFHLGTRQVCVAWSSSSCHDGKAWLHVCITGDDNNATASFIYNGRLVDSVVSWSKNILRTQESECVCINGTCTVVMTDGSASGKADTRILFIVEGKIIHVSKLSGSAQHVEECSCYPRYPGVRCVCRDNWKGSNRPIVDINMKDYSIVSSYVCSGLVGDTPRKTDSLSSSNCLDPNNEEGDHGVKGWAFDDGDDVWMGRTINETLRLGYETFKVIKGWSKPNSKLQTNRQVIVKGGNRSGYSGIFSVEGKNCINRCFYVELIRGRREETRVWWTSNSIVVFCGTSGTYGTGSGPDGADINLMPI"
}'
cloud function emerge_predict
{"IRAT_emergence":6.599316640549309,"IRAT_impact":6.606007439884266}
E-Net
recursive forest
E-distance
a biologically informed, adaptive distance between strains
smaller distances imply a quatitatively high probability of spontaneous jump
$$J \textrm{ is the Jensen-Shannon divergence }$$
Sanov's Theorem & Pinsker's Inequality
Theorem
human strain \(x_{h}\) is "well-adapted" \(\Rightarrow Pr(x_h\rightarrow x_h) \approx 1 \)
For animal strain \(x_{a}\), \( \displaystyle \theta(x_{a},x_{h}) \approx 0 \)
Sanov's Theorem & Pinsker's Inequality
smaller \(\theta\) implies higher risk
Theorem
Show variants are high risk
animal strains isolated in humans
Influenza Risk Assessment Tool (IRAT) scoring for animal strains
Replicate IRAT scores*
*https://www.cdc.gov/flu/pandemic-resources/monitoring/irat-virus-summaries.htm
Emergenet: finding emergence risk of animal strains
CDC published 24 scores in 10 years
Emergenet time: 6 seconds
IRAT: months to compute 1 score
Emergence risk of variants
Predicted IRAT scores
All are high
some are very high
BioNorad
BioNorad
shortlist
Identifying Immunological Vulnerabilities
SARS-COV2
Would it have worked in 2009?
Next Steps
More detailed wet-lab validation (without encroaching gain-of-function guidelines)
Deploying for more viruses
Escape resistant vaccine design
By Ishanu Chattopadhyay
AI in Bio-med-social problems
ML Data Science Biomedicine Social Science Faculty