Tata Institute of Fundamental Research
An Information Processing View of Chemical Reaction Networks
STCS Student Seminar
Speaker:
Abhishek Behera (Chennai Mathematical Institute
H1, SIPCOT IT Park
Siruseri, Kelambakkam
Chennai 603103)
Organiser:
Nikhil S Mande
Date:
Friday, 15 Apr 2016, 16:00 to 17:00
Venue:
A-201 (STCS Seminar Room)
(Scan to add to calendar)
Abstract:
One may wonder how do micro-organisms process spatially and temporally extensive information about their environment and respond in a manner that maximizes their fitness? Such statistical processing would have to be carried out via biochemical reaction pathways. So a natural model of computation to answer the above question is chemical reaction networks with appropriate dynamics (given by mass action kinetics or chemical master equation). In this model of computation we will discuss how one may:
1. (previously known result*) compute the maximum likelihood estimators for log-linear models
2. sample from the probability distribution of a graphical model
*"A Scheme for Molecular Computation of Maximum Likelihood Estimators for Log-Linear Models" - Gopalkrishnan (http://arxiv.org/pdf/1506.03172v1.pdf)
| Speaker: | Abhishek Behera (Chennai Mathematical Institute H1, SIPCOT IT Park Siruseri, Kelambakkam Chennai 603103) |
| Organiser: | Nikhil S Mande |
| Date: | Friday, 15 Apr 2016, 16:00 to 17:00 |
| Venue: | A-201 (STCS Seminar Room) |
(Scan to add to calendar)
Abstract:
One may wonder how do micro-organisms process spatially and temporally extensive information about their environment and respond in a manner that maximizes their fitness? Such statistical processing would have to be carried out via biochemical reaction pathways. So a natural model of computation to answer the above question is chemical reaction networks with appropriate dynamics (given by mass action kinetics or chemical master equation). In this model of computation we will discuss how one may:
1. (previously known result*) compute the maximum likelihood estimators for log-linear models
2. sample from the probability distribution of a graphical model
*"A Scheme for Molecular Computation of Maximum Likelihood Estimators for Log-Linear Models" - Gopalkrishnan (http://arxiv.org/pdf/1506.03172v1.pdf)
1. (previously known result*) compute the maximum likelihood estimators for log-linear models
2. sample from the probability distribution of a graphical model
*"A Scheme for Molecular Computation of Maximum Likelihood Estimators for Log-Linear Models" - Gopalkrishnan (http://arxiv.org/pdf/1506.03172v1.pdf)