BEGIN:VCALENDAR VERSION:2.0 PRODID:-//jEvents 2.0 for Joomla//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VTIMEZONE TZID:America/New_York BEGIN:STANDARD DTSTART:20220117T104500 RDATE:20220313T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20221106T010000 RDATE:20230312T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20231105T010000 RDATE:20240310T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20241103T010000 RDATE:20250309T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20251102T010000 RDATE:20260308T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20261101T010000 RDATE:20270314T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20271107T010000 RDATE:20280312T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20281105T010000 RDATE:20290311T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20220313T030000 RDATE:20221106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20230312T030000 RDATE:20231105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20240310T030000 RDATE:20241103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20250309T030000 RDATE:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20260308T030000 RDATE:20261101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20270314T030000 RDATE:20271107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20280312T030000 RDATE:20281105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:9ad8b344f4f3a2192cc4242b7b959217 CATEGORIES:Mathematical Physics Seminar CREATED:20230118T093229 SUMMARY:Evolution in changing environments and driven disordered systems LOCATION:Zoom DESCRIPTION:Abstract : Biological evolution is governed by the fitness landscape, a map from the genetic sequence of an organism to its fitness.  Here fitness den otes some quantitative measure of reproductive success, such as the expecte d number of offspring. A fitness landscape depends on the organism's enviro nment, and evolution in changing environments is still poorly understood. A fter introducing the concept of fitness landscapes and their mathematical d escription, the talk will focus on a particular model of antibiotic resista nce  evolution in bacteria, where the drug concentration is an environmenta l parameter. Tradeoffs between adaptation to low and high concentration lea d to a rugged landscape with an exponentially large number of fitness peaks . With evolutionary dynamics that follow  fitness gradients, resistance evo lution under slowly changing antibiotic concentration resembles the zero te mperature dynamics of  a disordered spin system under quasistatic driving. Specifically, the set of genetic sequences that form a fitness peak at some concentration maps exactly to the metastable states in an equivalent Preis ach system, a paradigmatic model of hysteresis in random  magnets. Making u se of the conceptual tool of state transition graphs developed in the conte xt of driven disordered systems, we quantify the degree of genotypic and ph enotypic reversibility in the response of the population to antibiotic conc entration  cycling, and ask to what extent a memory of past concentration c hanges is stored in the current genetic sequence. The talk is based on join t work with Suman G. Das and Muhittin Mungan.\n X-ALT-DESC;FMTTYPE=text/html:

Abstract : Biological evolution is governed by the fitness landscape, a map from the genetic sequ ence of an organism to its fitness.  Here fitness denotes some quantit ative measure of reproductive success, such as the expected number of offsp ring. A fitness landscape depends on the organism's environment, and evolut ion in changing environments is still poorly understood. After introducing the concept of fitness landscapes and their mathematical description, the t alk will focus on a particular model of antibiotic resistance  evoluti on in bacteria, where the drug concentration is an environmental parameter. Tradeoffs between adaptation to low and high concentration lead to a rugge d landscape with an exponentially large number of fitness peaks. With evolu tionary dynamics that follow  fitness gradients, resistance evolution under slowly changing antibiotic concentration resembles the zero temperatu re dynamics of  a disordered spin system under quasistatic driving. Sp ecifically, the set of genetic sequences that form a fitness peak at some c oncentration maps exactly to the metastable states in an equivalent Preisac h system, a paradigmatic model of hysteresis in random  magnets. Makin g use of the conceptual tool of state transition graphs developed in the co ntext of driven disordered systems, we quantify the degree of genotypic and phenotypic reversibility in the response of the population to antibiotic c oncentration  cycling, and ask to what extent a memory of past concent ration changes is stored in the current genetic sequence. The talk is based on joint work with Suman G. Das and Muhittin Mungan.

CONTACT:Joachim Krug - University of Cologne DTSTAMP:20260827T051732 DTSTART;TZID=America/New_York:20230118T104500 DTEND;TZID=America/New_York:20230118T114500 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR